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Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb_adapters.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2025 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2025 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","dev_url":"https://github.com/bioexcel/biobb_adapters","home":"https://github.com/bioexcel/biobb_adapters","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"5.1.0":{"weak":["biobb_adapters >=5.1.0,<5.2.0a0"]},"5.1.1":{"weak":["biobb_adapters >=5.1.1,<5.2.0a0"]},"5.1.2":{"weak":["biobb_adapters >=5.1.2,<5.2.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_adapters/biobb_adapters-5.1.2.tar.gz","subdirs":["noarch"],"summary":"Biobb_adapters is the Biobb module collection to use the building blocks with several workflow managers.","text_prefix":false,"timestamp":1552071442,"version":"5.1.2"},"biobb_amber":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_amber  ### Introduction Biobb_amber is a BioBB category for AMBER MD package. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-amber.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-amber.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_amber","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_amber >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_amber >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_amber >=5.0.0,<6.0a0"]},"5.0.2":{"weak":["biobb_amber >=5.0.2,<6.0a0"]},"5.0.3":{"weak":["biobb_amber >=5.0.3,<6.0a0"]},"5.0.4":{"weak":["biobb_amber >=5.0.4,<6.0a0"]},"5.1.0":{"weak":["biobb_amber >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_amber >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_amber >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_amber/biobb_amber-5.2.1.tar.gz","subdirs":["linux-64","noarch","osx-64"],"summary":"Biobb_amber is a BioBB category for AMBER MD package.","text_prefix":true,"timestamp":1772710932,"version":"5.2.1"},"biobb_analysis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_analysis  ### Introduction Biobb_analysis is the Biobb module collection to perform analysis of molecular dynamics simulations. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-analysis.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-analysis.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_analysis","license":"Apache-2.0 license","post_link":true,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_analysis >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_analysis >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_analysis >=5.0.0,<6.0a0"]},"5.0.1":{"weak":["biobb_analysis >=5.0.1,<6.0a0"]},"5.1.0":{"weak":["biobb_analysis >=5.1.0,<6.0a0"]},"5.1.1":{"weak":["biobb_analysis >=5.1.1,<6.0a0"]},"5.1.2":{"weak":["biobb_analysis >=5.1.2,<6.0a0"]},"5.2.0":{"weak":["biobb_analysis >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_analysis >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_analysis/biobb_analysis-5.2.1.tar.gz","subdirs":["linux-64","noarch"],"summary":"Biobb_analysis is the Biobb module collection to perform analysis of molecular dynamics simulations.","text_prefix":true,"timestamp":1716534663,"version":"5.2.1"},"biobb_chemistry":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_chemistry  ### Introduction Biobb_chemistry is the Biobb module collection to perform chemical conversions. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-chemistry.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-chemistry.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_chemistry","license":"Apache-2.0 license","post_link":true,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_chemistry >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_chemistry >=4.2.0,<5.0a0"]},"4.2.1":{"weak":["biobb_chemistry >=4.2.1,<5.0a0"]},"5.0.0":{"weak":["biobb_chemistry >=5.0.0,<6.0a0"]},"5.0.1":{"weak":["biobb_chemistry >=5.0.1,<6.0a0"]},"5.0.2":{"weak":["biobb_chemistry >=5.0.2,<6.0a0"]},"5.0.3":{"weak":["biobb_chemistry >=5.0.3,<6.0a0"]},"5.1.0":{"weak":["biobb_chemistry >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_chemistry >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_chemistry >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_chemistry/biobb_chemistry-5.2.1.tar.gz","subdirs":["noarch"],"summary":"Biobb_chemBiobb_chemistry is the Biobb module collection to perform chemical conversions.","text_prefix":true,"timestamp":1583318551,"version":"5.2.1"},"biobb_cmip":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_cmip  ### Introduction Biobb_cmip is the Biobb module collection to compute classical molecular interaction potentials. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-cmip.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/). Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","dev_url":"https://github.com/bioexcel/biobb_cmip","doc_url":"http://biobb-cmip.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_cmip","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_cmip >=4.1.0,<5.0a0"]},"4.1.1":{"weak":["biobb_cmip >=4.1.1,<5.0a0"]},"4.2.0":{"weak":["biobb_cmip >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_cmip >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_cmip >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_cmip >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_cmip >=5.2.1,<6.0a0"]},"5.2.2":{"weak":["biobb_cmip >=5.2.2,<6.0a0"]},"5.2.3":{"weak":["biobb_cmip >=5.2.3,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_cmip/biobb_cmip-5.2.3.tar.gz","subdirs":["noarch"],"summary":"Biobb_cmip is the Biobb module collection to compute classical molecular interaction potentials.","text_prefix":true,"timestamp":1689926001,"version":"5.2.3"},"biobb_common":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"[![Documentation Status](https://readthedocs.org/projects/biobb-common/badge/?version=latest)](https://biobb-common.readthedocs.io/en/latest/?badge=latest)  # biobb_common  ### Introduction Biobb_common is the base package required to use the biobb packages. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb_common.readthedocs.io/en/latest/).   ### Copyright & Licensing ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","dev_url":"https://github.com/bioexcel/biobb_common","doc_url":"https://biobb-common.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_common","license":"Apache Software License","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_common >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_common >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_common >=5.0.0,<6.0a0"]},"5.0.1":{"weak":["biobb_common >=5.0.1,<6.0a0"]},"5.1.0":{"weak":["biobb_common >=5.1.0,<6.0a0"]},"5.1.1":{"weak":["biobb_common >=5.1.1,<6.0a0"]},"5.2.0":{"weak":["biobb_common >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_common >=5.2.1,<6.0a0"]},"5.2.2":{"weak":["biobb_common >=5.2.2,<6.0a0"]},"5.3.1":{"weak":["biobb_common >=5.3.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_common/biobb_common-5.3.1.tar.gz","subdirs":["noarch"],"summary":"Biobb_common is the base package required to use the biobb packages.","text_prefix":true,"timestamp":1534165488,"version":"5.3.1"},"biobb_cp2k":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_cp2k  ### Introduction Biobb_cp2k is a BioBB category for CP2K QM package. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-cp2k.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-cp2k.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_cp2k","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_cp2k >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_cp2k >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_cp2k >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_cp2k >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_cp2k >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_cp2k >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_cp2k/biobb_cp2k-5.2.1.tar.gz","subdirs":["noarch"],"summary":"Biobb_cp2k is a BioBB category for CP2K QM package.","text_prefix":true,"timestamp":1672143390,"version":"5.2.1"},"biobb_dna":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_dna  ### Introduction Biobb_dna is a package composed of different analyses for nucleic acid trajectories. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-dna.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-dna.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_dna","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_dna >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_dna >=4.2.0,<5.0a0"]},"4.2.1":{"weak":["biobb_dna >=4.2.1,<5.0a0"]},"4.2.2":{"weak":["biobb_dna >=4.2.2,<5.0a0"]},"4.2.4":{"weak":["biobb_dna >=4.2.4,<5.0a0"]},"5.0.0":{"weak":["biobb_dna >=5.0.0,<6.0a0"]},"5.0.1":{"weak":["biobb_dna >=5.0.1,<6.0a0"]},"5.1.0":{"weak":["biobb_dna >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_dna >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_dna >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_dna/biobb_dna-5.2.1.tar.gz","subdirs":["noarch"],"summary":"Biobb_dna is a package composed of different analyses for nucleic acid trajectories.","text_prefix":true,"timestamp":1681222290,"version":"5.2.1"},"biobb_flexdyn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_flexdyn  ### Introduction Biobb_flexdyn is a BioBB category for studies on the conformational landscape of native proteins. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-flexdyn.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","dev_url":"https://github.com/bioexcel/biobb_flexdyn","doc_url":"http://biobb-flexdyn.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_flexdyn","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_flexdyn >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_flexdyn >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_flexdyn >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_flexdyn >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_flexdyn >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_flexdyn >=5.2.1,<6.0a0"]},"5.2.2":{"weak":["biobb_flexdyn >=5.2.2,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_flexdyn/biobb_flexdyn-5.2.2.tar.gz","subdirs":["linux-64","noarch","osx-64"],"summary":"Biobb_flexdyn is a BioBB category for studies on the conformational landscape of native proteins.","text_prefix":true,"timestamp":1773077503,"version":"5.2.2"},"biobb_flexserv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_flexserv  ### Introduction Biobb_flexserv is the Biobb module collection for biomolecular flexibility studies on protein 3D structures. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-flexserv.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-flexserv.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_flexserv","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_flexserv >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_flexserv >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_flexserv >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_flexserv >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_flexserv >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_flexserv >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_flexserv/biobb_flexserv-5.2.1.tar.gz","subdirs":["linux-64","noarch","osx-64"],"summary":"Biobb_flexserv is the Biobb module collection for biomolecular flexibility studies on protein 3D structures.","text_prefix":true,"timestamp":1772717150,"version":"5.2.1"},"biobb_godmd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_godmd  ### Introduction Biobb_godmd is a BioBB category for GOdMD tool (protein conformational transitions). Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-godmd.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-godmd.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_godmd","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_godmd >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_godmd >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_godmd >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_godmd >=5.1.0,<6.0a0"]},"5.1.1":{"weak":["biobb_godmd >=5.1.1,<6.0a0"]},"5.1.2":{"weak":["biobb_godmd >=5.1.2,<6.0a0"]},"5.2.0":{"weak":["biobb_godmd >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_godmd >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_godmd/biobb_godmd-5.2.1.tar.gz","subdirs":["noarch"],"summary":"Biobb_godmd is a BioBB category for GOdMD tool (protein conformational transitions).","text_prefix":true,"timestamp":1716547979,"version":"5.2.1"},"biobb_gromacs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_gromacs  ### Introduction Biobb_gromacs is the Biobb module collection to perform molecular dynamics simulations using the GROMACS MD suite. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-gromacs.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","dev_url":"https://github.com/bioexcel/biobb_gromacs","doc_url":"http://biobb_gromacs.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_gromacs","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_gromacs >=4.1.0,<5.0a0"]},"4.1.1":{"weak":["biobb_gromacs >=4.1.1,<5.0a0"]},"4.2.0":{"weak":["biobb_gromacs >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_gromacs >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_gromacs >=5.1.0,<6.0a0"]},"5.1.1":{"weak":["biobb_gromacs >=5.1.1,<6.0a0"]},"5.1.2":{"weak":["biobb_gromacs >=5.1.2,<6.0a0"]},"5.2.0":{"weak":["biobb_gromacs >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_gromacs >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_gromacs/biobb_gromacs-5.2.1.tar.gz","subdirs":["noarch"],"summary":"biobb_gromacs is the Biobb module collection to perform molecular dynamics simulations using the GROMACS MD suite.","text_prefix":true,"timestamp":1750846709,"version":"5.2.1"},"biobb_haddock":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_haddock  ### Introduction biobb_haddock is the Biobb module collection to compute information-driven flexible protein-protein docking. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-haddock.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","dev_url":"https://github.com/bioexcel/biobb_haddock","doc_url":"http://biobb_haddock.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_haddock","license":"Apache-2.0","post_link":true,"pre_link":false,"pre_unlink":false,"run_exports":{"4.2.1":{"weak":["biobb_haddock >=4.2.1,<5.0a0"]},"5.0.0":{"weak":["biobb_haddock >=5.0.0,<6.0a0"]},"5.0.1":{"weak":["biobb_haddock >=5.0.1,<6.0a0"]},"5.1.0":{"weak":["biobb_haddock >=5.1.0,<6.0a0"]},"5.1.1":{"weak":["biobb_haddock >=5.1.1,<6.0a0"]},"5.2.0":{"weak":["biobb_haddock >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_haddock >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_haddock/biobb_haddock-5.0.0.tar.gz","subdirs":["noarch"],"summary":"biobb_haddock is the Biobb module collection to compute information-driven flexible protein-protein docking.","text_prefix":true,"timestamp":1737458118,"version":"5.2.1"},"biobb_io":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_io  ### Introduction Biobb_io is the Biobb module collection to fetch data to be consumed by the rest of the Biobb building blocks. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-io.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-io.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_io","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_io >=4.1.0,<5.0a0"]},"4.1.1":{"weak":["biobb_io >=4.1.1,<5.0a0"]},"4.2.0":{"weak":["biobb_io >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_io >=5.0.0,<6.0a0"]},"5.0.1":{"weak":["biobb_io >=5.0.1,<6.0a0"]},"5.1.0":{"weak":["biobb_io >=5.1.0,<6.0a0"]},"5.1.1":{"weak":["biobb_io >=5.1.1,<6.0a0"]},"5.2.0":{"weak":["biobb_io >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_io >=5.2.1,<6.0a0"]},"5.2.2":{"weak":["biobb_io >=5.2.2,<6.0a0"]},"5.2.3":{"weak":["biobb_io >=5.2.3,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_io/biobb_io-5.2.3.tar.gz","subdirs":["noarch"],"summary":"Biobb_io is the Biobb module collection to fetch data to be consumed by the rest of the Biobb building blocks.","text_prefix":true,"timestamp":1534502065,"version":"5.2.3"},"biobb_md":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Deprecated Package: biobb_md is no longer maintained and has been superseded by the biobb_gromacs package. Biobb_md is the Biobb module collection to perform molecular dynamics simulations. Biobb (BioExcel building blocks) packages are Python building blocks that create new layers of compatibility and interoperability over popular bioinformatics tools.","dev_url":"https://github.com/bioexcel/biobb_md","doc_url":"http://biobb_md.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_md","license":"Apache Software License","post_link":true,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/b/biobb_md/biobb_md-3.7.2.tar.gz","subdirs":["noarch"],"summary":"Deprecated Package: biobb_md is no longer maintained and has been superseded by the biobb_gromacs package. Biobb_md is the Biobb module collection to perform molecular dynamics simulations.","text_prefix":true,"timestamp":1609233356,"version":"3.7.2"},"biobb_mem":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"# biobb_mem  ### Introduction Biobb_mem is the Biobb module for membrane structure analysis. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-mem.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","dev_url":"https://github.com/bioexcel/biobb_mem","doc_url":"http://biobb-mem.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_mem","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"5.0.1":{"weak":["biobb_mem >=5.0.1,<6.0a0"]},"5.0.2":{"weak":["biobb_mem >=5.0.2,<6.0a0"]},"5.0.3":{"weak":["biobb_mem >=5.0.3,<6.0a0"]},"5.0.4":{"weak":["biobb_mem >=5.0.4,<6.0a0"]},"5.0.5":{"weak":["biobb_mem >=5.0.5,<6.0a0"]},"5.0.6":{"weak":["biobb_mem >=5.0.6,<6.0a0"]},"5.1.0":{"weak":["biobb_mem >=5.1.0,<6.0a0"]},"5.1.1":{"weak":["biobb_mem >=5.1.1,<6.0a0"]},"5.1.2":{"weak":["biobb_mem >=5.1.2,<6.0a0"]},"5.2.0":{"weak":["biobb_mem >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_mem >=5.2.1,<6.0a0"]},"5.2.2":{"weak":["biobb_mem >=5.2.2,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_mem/biobb_mem-5.0.2.tar.gz","subdirs":["linux-64","noarch","osx-64"],"summary":"Biobb_mem is the Biobb module for membrane structure analysis.","text_prefix":true,"timestamp":1736527809,"version":"5.2.2"},"biobb_ml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_ml  ### Introduction Biobb_ml is the Biobb module collection to perform machine learning predictions.  Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb_ml.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](https://mmb.irbbarcelona.org) at the [BSC](https://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","home":"https://github.com/bioexcel/biobb_ml","license":"Apache Software License","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_ml >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_ml >=4.2.0,<5.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_ml/biobb_ml-4.2.0.tar.gz","subdirs":["noarch"],"summary":"Biobb_ml is the Biobb module collection to perform machine learning predictions.","text_prefix":true,"timestamp":1603192441,"version":"4.2.0"},"biobb_model":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_model  ### Introduction Biobb_model is the Biobb module collection to check and model 3d structures, create mutations or reconstruct missing atoms. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-model.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-model.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_model","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_model >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_model >=4.2.0,<5.0a0"]},"4.2.1":{"weak":["biobb_model >=4.2.1,<5.0a0"]},"4.2.2":{"weak":["biobb_model >=4.2.2,<5.0a0"]},"4.2.3":{"weak":["biobb_model >=4.2.3,<5.0a0"]},"5.0.0":{"weak":["biobb_model >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_model >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_model >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_model >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_model/biobb_model-5.2.1.tar.gz","subdirs":["noarch"],"summary":"Biobb_model is the Biobb module collection to check and model 3d structures, create mutations or reconstruct missing atoms.","text_prefix":true,"timestamp":1673603132,"version":"5.2.1"},"biobb_morph":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_morph  ### Introduction Biobb_morph is the Biobb module collection.  Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-morph.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","dev_url":"https://github.com/bioexcel/biobb_morph","doc_url":"http://biobb_morph.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_morph","license":"Apache Software License","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"5.0.0":{"weak":["biobb_morph >=5.0.0,<6.0a0"]},"5.0.1":{"weak":["biobb_morph >=5.0.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_morph/biobb_morph-5.0.1.tar.gz","subdirs":["noarch"],"summary":"biobb_morph is the Biobb module collection to perform molecular dynamics simulations using the morph MD suite.","text_prefix":true,"timestamp":1737218911,"version":"5.0.1"},"biobb_pdb_tools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_pdb_tools  ### Introduction Biobb_pdb_tools is a swiss army knife for manipulating and editing PDB files. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-pdb-tools.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-pdb-tools.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_pdb_tools","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_pdb_tools >=4.1.0,<5.0a0"]},"4.1.1":{"weak":["biobb_pdb_tools >=4.1.1,<5.0a0"]},"4.2.0":{"weak":["biobb_pdb_tools >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_pdb_tools >=5.0.0,<6.0a0"]},"5.0.1":{"weak":["biobb_pdb_tools >=5.0.1,<6.0a0"]},"5.1.0":{"weak":["biobb_pdb_tools >=5.1.0,<6.0a0"]},"5.1.1":{"weak":["biobb_pdb_tools >=5.1.1,<6.0a0"]},"5.2.0":{"weak":["biobb_pdb_tools >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_pdb_tools >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_pdb_tools/biobb_pdb_tools-5.2.1.tar.gz","subdirs":["noarch"],"summary":"Biobb_pdb_tools is a swiss army knife for manipulating and editing PDB files.","text_prefix":true,"timestamp":1716556318,"version":"5.2.1"},"biobb_pmx":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_pmx  ### Introduction Biobb_pmx is the Biobb module collection to perform PMX (http://pmx.mpibpc.mpg.de) executions. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-pmx.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-pmx.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_pmx","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_pmx >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_pmx >=4.2.0,<5.0a0"]},"4.2.1":{"weak":["biobb_pmx >=4.2.1,<5.0a0"]},"5.0.0":{"weak":["biobb_pmx >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_pmx >=5.1.0,<6.0a0"]},"5.2.1":{"weak":["biobb_pmx >=5.2.1,<6.0a0"]},"5.2.2":{"weak":["biobb_pmx >=5.2.2,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_pmx/biobb_pmx-5.2.2.tar.gz","subdirs":["noarch"],"summary":"Biobb_pmx is the Biobb module collection to perform PMX (http://pmx.mpibpc.mpg.de) executions.","text_prefix":true,"timestamp":1603046310,"version":"5.2.2"},"biobb_pytorch":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_pytorch  ### Introduction Biobb_Pytorch is the Biobb module collection to create and train ML & DL models using the popular [PyTorch](https://pytorch.org/) Python library. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-pytorch.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","dev_url":"https://github.com/bioexcel/biobb_pytorch","doc_url":"http://biobb_pytorch.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_pytorch","license":"Apache Software License","post_link":true,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_pytorch >=4.1.0,<5.0a0"]},"4.1.1":{"weak":["biobb_pytorch >=4.1.1,<5.0a0"]},"4.1.2":{"weak":["biobb_pytorch >=4.1.2,<5.0a0"]},"4.1.3":{"weak":["biobb_pytorch >=4.1.3,<5.0a0"]},"4.2.0":{"weak":["biobb_pytorch >=4.2.0,<5.0a0"]},"4.2.1":{"weak":["biobb_pytorch >=4.2.1,<5.0a0"]},"5.0.0":{"weak":["biobb_pytorch >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_pytorch >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_pytorch >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_pytorch >=5.2.1,<6.0a0"]},"5.2.2":{"weak":["biobb_pytorch >=5.2.2,<6.0a0"]},"5.2.3":{"weak":["biobb_pytorch >=5.2.3,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_pytorch/biobb_pytorch-5.2.3.tar.gz","subdirs":["noarch"],"summary":"biobb_pytorch is the Biobb module collection to create and train ML & DL models.","text_prefix":true,"timestamp":1715082926,"version":"5.2.3"},"biobb_remote":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/bioexcel/biobb_remote","license":"Apache Software License","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/b/biobb_remote/biobb_remote-1.2.2.tar.gz","subdirs":["noarch"],"summary":"Biobb_remote is the Biobb module for remote execution via ssl.","text_prefix":true,"timestamp":1637310464,"version":"1.2.2"},"biobb_structure_checking":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Biobb_structure_checking performs a checking of the quality of a 3D structure intended to facilitate the setup of molecular dynamics simulation of protein or nucleic acids systems. Biobb_structure_checking package allows to configure the system (selection of model/chains,alternative location, addition of disulfide bonds and hydrogen atoms, side chain mutations), to detect and fix structure errors (missing side chain atoms, backbone breaks, amide assignments, incorrect chirality). It works with structures obtained from the Protein Data Bank or user provided. The Biobb_structure_checking package provides a command line utility (https://biobb-structure-checking.readthedocs.io/en/latest/command_line_usage.html) and a python API (https://biobb-structure-checking.readthedocs.io/en/latest/biobb_structure_checking.html). The latest documentation of this package can be found in our readthedocs site: http://biobb_structure_checking.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_structure_checking","license":"Apache Software License","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"3.13.4":{"weak":["biobb_structure_checking >=3.13.4,<4.0a0"]},"3.13.5":{"weak":["biobb_structure_checking >=3.13.5,<4.0a0"]},"3.15.6":{"weak":["biobb_structure_checking >=3.15.6,<4.0a0"]},"3.16.1":{"weak":["biobb_structure_checking >=3.16.1,<4.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_structure_checking/biobb_structure_checking-3.16.1.tar.gz","subdirs":["noarch"],"summary":"BioBB_structure_checking performs MDWeb structure checking set as a command line utility.","text_prefix":true,"timestamp":1590067842,"version":"3.16.1"},"biobb_structure_manager":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"[![Codacy Badge](https://api.codacy.com/project/badge/Grade/3dbc32af83244f1fba8961cfe059ae37)](https://www.codacy.com/app/jlgelpi/structure_manager?utm_source=mmb.irbbarcelona.org&amp;utm_medium=referral&amp;utm_content=gitlab/BioExcel/structure_manager&amp;utm_campaign=Badge_Grade)  ### Structure Manager package Python package to manage 3D structures. Wraps Bio.PDB with additional features.","home":"https://github.com/bioexcel/BioBB_structure_manager","license":"Apache Software License","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/b/biobb_structure_manager/biobb_structure_manager-3.0.2.tar.gz","subdirs":["noarch"],"summary":"BioBB_structure_manager is a library to efficiently load and process biomolecular 3D structures.","text_prefix":false,"timestamp":1554478018,"version":"3.0.2"},"biobb_structure_utils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"[![](https://readthedocs.org/projects/biobb-structure-utils/badge/?version=latest)](https://biobb-structure-utils.readthedocs.io/en/latest/?badge=latest) [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)  # biobb_structure_utils  Biobb_structure_utils is the Biobb module collection to modify or extract information from a PDB structure file, such as pulling out a particular model or chain, removing water molecules or ligands, or renumbering or sorting atoms or residues. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb_structure_utils.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","doc_url":"http://biobb-structure-utils.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_structure_utils","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_structure_utils >=4.1.0,<5.0a0"]},"4.2.0":{"weak":["biobb_structure_utils >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_structure_utils >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_structure_utils >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_structure_utils >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_structure_utils >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_structure_utils/biobb_structure_utils-5.2.1.tar.gz","subdirs":["noarch"],"summary":"Biobb_structure_utils is the Biobb module collection to modify or extract information from a PDB structure file.","text_prefix":true,"timestamp":1750416827,"version":"5.2.1"},"biobb_vs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# biobb_vs  ### Introduction Biobb_vs is the Biobb module collection to perform virtual screening studies. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb-vs.readthedocs.io/en/latest/).   ### Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU Horizon Europe [101093290](https://cordis.europa.eu/project/id/101093290), EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","dev_url":"https://github.com/bioexcel/biobb_vs","doc_url":"http://biobb-vs.readthedocs.io/en/latest/","home":"https://github.com/bioexcel/biobb_vs","license":"Apache-2.0 license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.1.0":{"weak":["biobb_vs >=4.1.0,<5.0a0"]},"4.1.1":{"weak":["biobb_vs >=4.1.1,<5.0a0"]},"4.1.2":{"weak":["biobb_vs >=4.1.2,<5.0a0"]},"4.2.0":{"weak":["biobb_vs >=4.2.0,<5.0a0"]},"5.0.0":{"weak":["biobb_vs >=5.0.0,<6.0a0"]},"5.1.0":{"weak":["biobb_vs >=5.1.0,<6.0a0"]},"5.2.0":{"weak":["biobb_vs >=5.2.0,<6.0a0"]},"5.2.1":{"weak":["biobb_vs >=5.2.1,<6.0a0"]}},"source_url":"https://pypi.io/packages/source/b/biobb_vs/biobb_vs-5.2.1.tar.gz","subdirs":["noarch"],"summary":"Biobb_vs is the Biobb module collection to perform virtual screening studies.","text_prefix":true,"timestamp":1694076913,"version":"5.2.1"},"biobb_wf_mutations":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"[![Documentation Status](https://readthedocs.org/projects/biobb-md/badge/?version=latest)](https://biobb-md.readthedocs.io/en/latest/?badge=latest)   # biobb_wf_mutations  ### Introduction Lysozyme + Mutations workflow built using BioBB Based on the official Gromacs tutorial: http://www.mdtutorials.com/gmx/lysozyme/01_pdb2gmx.html Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site: [latest API documentation](http://biobb_md.readthedocs.io/en/latest/).  ### Version February 2019 Release  ### Copyright & Licensing This software has been developed in the MMB group (http://mmb.irbbarcelona.org) at the BSC (http://www.bsc.es/) & IRB (https://www.irbbarcelona.org/) for the European BioExcel (http://bioexcel.eu/), funded by the European Commission (EU H2020 [675728](http://cordis.europa.eu/projects/675728)).  * (c) 2015-2019 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2019 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)  Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file [LICENSE](LICENSE) for details.  ![](https://bioexcel.eu/wp-content/uploads/2015/12/Bioexcell_logo_1080px_transp.png \"Bioexcel\")","home":"https://github.com/bioexcel/biobb_md","license":"Apache Software License","post_link":true,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/b/biobb_wf_mutations/biobb_wf_mutations-0.0.6.tar.gz","subdirs":["noarch"],"summary":"Lysozyme plus Mutations workflow built using BioBB Based on the official Gromacs tutorial: http://www.mdtutorials.com/gmx/lysozyme/01_pdb2gmx.html","text_prefix":false,"timestamp":1552297444,"version":"0.0.6"},"bioblend":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/galaxyproject/bioblend","doc_url":"https://bioblend.readthedocs.org/","home":"https://github.com/galaxyproject/bioblend","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.3.0":{"weak":["bioblend >=1.3.0,<2.0a0"]},"1.4.0":{"weak":["bioblend >=1.4.0,<2.0a0"]},"1.5.0":{"weak":["bioblend >=1.5.0,<2.0a0"]},"1.6.0":{"weak":["bioblend >=1.6.0,<2.0a0"]},"1.7.0":{"weak":["bioblend >=1.7.0,<2.0a0"]},"1.8.0":{"weak":["bioblend >=1.8.0,<2.0a0"]},"1.9.0":{"weak":["bioblend >=1.9.0,<2.0a0"]}},"source_url":"https://pypi.io/packages/source/b/bioblend/bioblend-1.9.0.tar.gz","subdirs":["linux-64","noarch","osx-64"],"summary":"A Python library for interacting with the Galaxy API.","text_prefix":true,"timestamp":0,"version":"1.9.0"},"biobloomtools":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/bcgsc/biobloom","license":"GPL-3.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"2.3.5":{"weak":["biobloomtools >=2.3.5,<3.0a0"]}},"source_url":"https://github.com/bcgsc/biobloom/releases/download/2.3.5/biobloomtools-2.3.5.tar.gz","subdirs":["linux-64","linux-aarch64"],"summary":"Building Bloom filters and using them for categorizing sequences","text_prefix":false,"timestamp":1733975424,"version":"2.3.5"},"biobox_add_taxid":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/SantaMcCloud/biobox_add_taxid","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.3":{"weak":["biobox_add_taxid >=0.3,<0.4.0a0"]},"0.4":{"weak":["biobox_add_taxid >=0.4,<0.5.0a0"]},"0.5":{"weak":["biobox_add_taxid >=0.5,<0.6.0a0"]},"0.6":{"weak":["biobox_add_taxid >=0.6,<0.7.0a0"]},"1.0":{"weak":["biobox_add_taxid >=1.0,<1.1.0a0"]},"1.1":{"weak":["biobox_add_taxid >=1.1,<1.2.0a0"]},"1.2":{"weak":["biobox_add_taxid >=1.2,<1.3.0a0"]}},"source_url":"https://github.com/SantaMcCloud/biobox_add_taxid/archive/refs/tags/release-1.2.tar.gz","subdirs":["noarch"],"summary":"CAMI amber utility script for adding the taxid output from GTDB and BAT","text_prefix":true,"timestamp":1725467173,"version":"1.2"},"bioc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/bionlplab/bioc","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"2.1":{"weak":["bioc"]}},"source_url":"https://pypi.org/packages/source/b/bioc/bioc-2.1.tar.gz","subdirs":["noarch"],"summary":"bioc - Processing BioC, Brat, and PubTator with Python.","text_prefix":false,"timestamp":1768914114,"version":"2.1"},"biocamlib":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"BiOCamLib is an OCaml foundation upon which a number of the bioinformatics tools are built, including KPop <https://github.com/PaoloRibeca/KPop>. 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Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). 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At its core, HiCPotts leverages the Potts model (Wu, 1982)\u2014a well-established graphical model\u2014to capture and quantify spatial dependencies across interaction loci arranged on a genomic lattice. By treating each interaction as a spatially correlated random variable, the Potts model enables robust segmentation of the genomic landscape into meaningful components, such as noise, true signals, and false signals. To model the influence of various genomic biases, HiCPotts employs a regression-based approach incorporating multiple covariates: Genomic distance (D): The distance between interacting loci, recognized as a fundamental driver of contact frequency. GC-content (GC): The local GC composition around the interacting loci, which can influence chromatin structure and interaction patterns. Transposable elements (TEs): The presence and abundance of repetitive elements that may shape contact probability through chromatin organization. Accessibility score (Acc): A measure of chromatin openness, informing how accessible certain genomic regions are to interaction. By embedding these covariates into a hierarchical mixture model, HiCPotts characterizes each interaction\u2019s probability of belonging to one of several latent components. The model parameters, including regression coefficients, zero-inflation parameters (for ZIP/ZINB distributions), and dispersion terms (for NB/ZINB distributions), are inferred via a MCMC sampler. This algorithm draws samples from the joint posterior distribution, allowing for flexible posterior inference on model parameters and hidden states. From these posterior samples, HiCPotts computes posterior means of regression parameters and other quantities of interest. These posterior estimates are then used to calculate the posterior probabilities that assign each interaction to a specific component. The resulting classification sheds light on the underlying structure: distinguishing genuine high-confidence interactions (signal) from background noise and potential false signals, while simultaneously quantifying the impact of genomic biases on observed interaction frequencies. In summary, HiCPotts seamlessly integrates spatial modeling, bias correction, and probabilistic classification into a unified Bayesian inference framework. It provides rich posterior summaries and interpretable, model-based assignments of interaction states, enabling researchers to better understand the interplay between genomic organization, biases, and spatial correlation in Hi-C data.","home":"https://bioconductor.org/packages/3.22/bioc/html/HiCPotts.html","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.0.0":{"weak":["bioconductor-hicpotts >=1.0.0,<1.1.0a0"]}},"source_url":["https://bioarchive.galaxyproject.org/HiCPotts_1.0.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/Archive/HiCPotts/HiCPotts_1.0.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/HiCPotts_1.0.0.tar.gz","https://depot.galaxyproject.org/software/bioconductor-hicpotts/bioconductor-hicpotts_1.0.0_src_all.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"HiCPotts: Hierarchical Modeling to Identify and Correct Genomic Biases in Hi-C","text_prefix":false,"timestamp":1772753822,"version":"1.0.0"},"bioconductor-hicrep":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Hi-C is a powerful technology for studying genome-wide chromatin interactions. 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In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.","home":"https://bioconductor.org/packages/3.22/bioc/html/MoonlightR.html","license":"GPL (>= 3)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.28.0":{"weak":["bioconductor-moonlightr >=1.28.0,<1.29.0a0"]},"1.32.0":{"weak":["bioconductor-moonlightr >=1.32.0,<1.33.0a0"]},"1.36.0":{"weak":["bioconductor-moonlightr >=1.36.0,<1.37.0a0"]}},"source_url":["https://bioarchive.galaxyproject.org/MoonlightR_1.36.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/Archive/MoonlightR/MoonlightR_1.36.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/MoonlightR_1.36.0.tar.gz","https://depot.galaxyproject.org/software/bioconductor-moonlightr/bioconductor-moonlightr_1.36.0_src_all.tar.gz"],"subdirs":["noarch"],"summary":"Identify oncogenes and tumor suppressor genes from omics data","text_prefix":false,"timestamp":1552756765,"version":"1.36.0"},"bioconductor-mops":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Identification and characterization of periodic fluctuations in time-series data.","home":"https://bioconductor.org/packages/3.11/bioc/html/MoPS.html","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://bioarchive.galaxyproject.org/MoPS_1.21.0.tar.gz","https://bioconductor.org/packages/3.11/bioc/src/contrib/MoPS_1.21.0.tar.gz","https://depot.galaxyproject.org/software/bioconductor-mops/bioconductor-mops_1.21.0_src_all.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"MoPS - Model-based Periodicity Screening","text_prefix":false,"timestamp":0,"version":"1.21.0"},"bioconductor-mosaics":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification.","home":"https://bioconductor.org/packages/3.22/bioc/html/mosaics.html","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"2.40.0":{"weak":["bioconductor-mosaics >=2.40.0,<2.41.0a0"]},"2.44.0":{"weak":["bioconductor-mosaics >=2.44.0,<2.45.0a0"]},"2.48.0":{"weak":["bioconductor-mosaics >=2.48.0,<2.49.0a0"]}},"source_url":["https://bioarchive.galaxyproject.org/mosaics_2.48.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/Archive/mosaics/mosaics_2.48.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/mosaics_2.48.0.tar.gz","https://depot.galaxyproject.org/software/bioconductor-mosaics/bioconductor-mosaics_2.48.0_src_all.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64"],"summary":"MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq)","text_prefix":false,"timestamp":1701817037,"version":"2.48.0"},"bioconductor-mosaicsexample":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Data for the mosaics package, consisting of (1) chromosome 22 ChIP and control sample data from a ChIP-seq experiment of STAT1 binding and H3K4me3 modification in MCF7 cell line from ENCODE database (HG19) and (2) chromosome 21 ChIP and control sample data from a ChIP-seq experiment of STAT1 binding, with mappability, GC content, and sequence ambiguity scores of human genome HG18.","home":"https://bioconductor.org/packages/3.22/data/experiment/html/mosaicsExample.html","license":"GPL (>= 2)","post_link":true,"pre_link":false,"pre_unlink":true,"run_exports":{"1.40.0":{"weak":["bioconductor-mosaicsexample >=1.40.0,<1.41.0a0"]},"1.44.0":{"weak":["bioconductor-mosaicsexample >=1.44.0,<1.45.0a0"]},"1.48.0":{"weak":["bioconductor-mosaicsexample >=1.48.0,<1.49.0a0"]}},"source_url":["https://bioarchive.galaxyproject.org/mosaicsExample_1.48.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/Archive/mosaicsExample/mosaicsExample_1.48.0.tar.gz","https://bioconductor.org/packages/3.22/data/experiment/src/contrib/mosaicsExample_1.48.0.tar.gz","https://depot.galaxyproject.org/software/bioconductor-mosaicsexample/bioconductor-mosaicsexample_1.48.0_src_all.tar.gz"],"subdirs":["noarch"],"summary":"Example data for the mosaics package, which implements MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data for transcription factor binding and histone modification","text_prefix":false,"timestamp":1547068136,"version":"1.48.0"},"bioconductor-mosbi":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). 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These distribution account for self-overlapping motif structures as exemplified by repeat-like or palindromic motifs, and allow to determine the p-value and fold-enrichment for a set of observed motif matches.","home":"https://bioconductor.org/packages/3.22/bioc/html/motifcounter.html","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.26.0":{"weak":["bioconductor-motifcounter >=1.26.0,<1.27.0a0"]},"1.30.0":{"weak":["bioconductor-motifcounter >=1.30.0,<1.31.0a0"]},"1.34.0":{"weak":["bioconductor-motifcounter >=1.34.0,<1.35.0a0"]}},"source_url":["https://bioarchive.galaxyproject.org/motifcounter_1.34.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/Archive/motifcounter/motifcounter_1.34.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/motifcounter_1.34.0.tar.gz","https://depot.galaxyproject.org/software/bioconductor-motifcounter/bioconductor-motifcounter_1.34.0_src_all.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"R package for analysing TFBSs in DNA sequences","text_prefix":false,"timestamp":1734647929,"version":"1.34.0"},"bioconductor-motifdb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"More than 9900 annotated position frequency matrices from 14 public sources, for multiple organisms.","home":"https://bioconductor.org/packages/3.22/bioc/html/MotifDb.html","license":"Artistic-2.0 | file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.44.0":{"weak":["bioconductor-motifdb >=1.44.0,<1.45.0a0"]},"1.48.0":{"weak":["bioconductor-motifdb >=1.48.0,<1.49.0a0"]},"1.52.0":{"weak":["bioconductor-motifdb >=1.52.0,<1.53.0a0"]}},"source_url":["https://bioarchive.galaxyproject.org/MotifDb_1.52.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/Archive/MotifDb/MotifDb_1.52.0.tar.gz","https://bioconductor.org/packages/3.22/bioc/src/contrib/MotifDb_1.52.0.tar.gz","https://depot.galaxyproject.org/software/bioconductor-motifdb/bioconductor-motifdb_1.52.0_src_all.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"An Annotated Collection of Protein-DNA Binding Sequence Motifs","text_prefix":false,"timestamp":0,"version":"1.52.0"},"bioconductor-motifmatchr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"Quickly find motif matches for many motifs and many sequences. 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After installation, users must **initialize the HEAsoft environment** by running the following commands:  ``` export HEADAS=$(ls -d \"${CONDA_PREFIX}/x86_64-pc-linux-gnu-libc\"*/ | head -n 1) source \"${HEADAS}/headas-init.sh\" export LHEAPERL=\"${CONDA_PREFIX}/bin/perl\" ```  This setup configures several environment variables required for HEAsoft, including `PATH`, `LD_LIBRARY_PATH`, `PFILES`, `PERL5LIB`, `PYTHONPATH`, and component-specific variables such as `PGPLOT_DIR`, `XANADU`, and `POW_LIBRARY`.  **Note**: `LHEAPERL` must be manually set to point to your Conda environment's Perl interpreter after sourcing `headas-init.sh`.  For mission-specific functionality (e.g., Swift, NuSTAR, IXPE), additional environment setup may be required. Refer to the HEAsoft documentation for details.  **Warning for XSPEC Users**: The `/spectral/modelData` directory (~5.9GB) is excluded from this package to reduce its size, making XSPEC unusable without it.  To enable XSPEC, follow these steps:  1. Download the HEAsoft source tarball for the same version as this package (6.35.2):    ```    wget https://heasarc.gsfc.nasa.gov/FTP/software/lheasoft/lheasoft6.35.2/heasoft-6.35.2src.tar.gz    ```    Replace `6.35.2` by the actual the package version (e.g., 6.35.2).  2. Extract the tarball:    ```    tar zxf heasoft-6.35.2src.tar.gz    ```  3. 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Use \"gambit\" instead: http://bioconda.github.io/recipes/gambit/README.html","text_prefix":true,"timestamp":1755586269,"version":"0.5.1"},"hg-color":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/pierre-morisse/HG-CoLoR","license":"GNU General Public License (GPL)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_git_url":"https://github.com/morispi/HG-CoLoR","source_url":"https://github.com/pierre-morisse/HG-CoLoR/archive/073add7bb0ba74f64c954f81338bcb208a2d72c1.zip","subdirs":["linux-64"],"summary":"HG-CoLoR (Hybrid Graph for the error Correction of Long Reads) is a hybrid method for the error correction of long reads that follows the main idea from NaS to produce corrected long reads from assemblies of related accurate short 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Resources: - Project website: https://proteins-mosaic-q.org - bio.tools: https://bio.tools/protein-mosaic-q - Updated manuscript: https://github.com/UPO-Sevilla-Fco-Javier-Lobo-Cabrera/clustering_trait_proteins/blob/main/Manuscript_and_Supplementary_Materials_v4.pdf - Preprint: https://www.biorxiv.org/content/10.1101/500025v1.full - FAIRsharing: https://fairsharing.org/8206 - SciStarter: https://scistarter.org/proteins-mosaic-q-project - EU Citizen Science: https://citizenscience.eu/project/686 - Observatorio Ciencia Ciudadana (Spain): https://ciencia-ciudadana.es/project/392 - HuggingFace Space: https://huggingface.co/spaces/ProteinsMosaicQProject/proteins-mosaic-q - Dataset: https://huggingface.co/ProteinsMosaicQ/datasets - Collaborative repository: https://proteins-mosaic-q.org/repository/","dev_url":"https://github.com/UPO-Sevilla-Fco-Javier-Lobo-Cabrera/mosaicq","doc_url":"https://proteins-mosaic-q.org","home":"https://proteins-mosaic-q.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.3.3":{"weak":["protein-mosaic-q >=0.3.3,<0.4.0a0"]}},"source_url":"https://pypi.io/packages/source/p/protein-mosaic-q/protein_mosaic_q-0.3.3.tar.gz","subdirs":["noarch"],"summary":"Calculate the Mosaic Q descriptor, an amino acid clustering trait in protein structure","text_prefix":true,"timestamp":1779126440,"version":"0.3.3"},"proteinortho":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://gitlab.com/paulklemm_PHD/proteinortho","doc_url":"https://gitlab.com/paulklemm_PHD/proteinortho/-/blob/master/README.md","home":"https://gitlab.com/paulklemm_PHD/proteinortho","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"6.3.1":{"weak":["proteinortho >=6.3.1,<6.3.2.0a0"]},"6.3.2":{"weak":["proteinortho >=6.3.2,<6.3.3.0a0"]},"6.3.3":{"weak":["proteinortho >=6.3.3,<7.0a0"]},"6.3.4":{"weak":["proteinortho >=6.3.4,<7.0a0"]},"6.3.5":{"weak":["proteinortho >=6.3.5,<7.0a0"]},"6.3.6":{"weak":["proteinortho >=6.3.6,<7.0a0"]}},"source_url":"https://gitlab.com/paulklemm_PHD/proteinortho/-/archive/v6.3.6/proteinortho-v6.3.6.tar.gz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Proteinortho is a tool to detect orthologous genes within different species.","text_prefix":true,"timestamp":1749711988,"version":"6.3.6"},"proteinview":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/001TMF/ProteinView","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.3.0":{"weak":["proteinview >=0.3.0,<0.4.0a0"]}},"source_url":"https://github.com/001TMF/ProteinView/archive/v0.3.0.tar.gz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Terminal protein structure viewer.","text_prefix":false,"timestamp":1779465909,"version":"0.3.0"},"proteomiqon-alignmentbasedquantification":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Given an MS run in the mzLite or mzml format and a list of a list of peptides deduced by alignment., this tool iterates accross all and performs an XIC extration and quantification in similar to the PSMbasedQuantification tool. One of the drawbacks of data-dependent acquisition is the stochastic nature of peptide ion selection for MSMS fragmentation as a prerequisite for peptide identification and quantification. A way to overcome this drawback is the transfer of identified ions from one run to another using the assumption that the run is merely lacking a successful MSMS scan, but still containing the peptide itself. For each peptide ion the tools uses the scan time prediction derived using the quant based alignment tool to extract a XIC. To refine the derived scan time estimate, we then locally align the extracted XIC to the XIC of the aligned peptide using dynamic time warping. Using this scan time estimate, we use wavelet based peak detection techniques to identify all peaks present in the XIC and select the most probable peak as our target for quantification. Using parameter estimation techniques we subsequently use peak fitting to fit a set of two gaussian models to the detected peak, from whom the one with the better fit is selected. This allows us not only to report how well the signal fitted to the theoretical expected peak shape but also to obtain accurate estimates for the peak area, our estimator for peptide ion abundance.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/AlignmentBasedQuantification.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.AlignmentBasedQuantification/0.0.2","subdirs":["noarch"],"summary":"Given an MS run in the mzLite or mzml format and a list of a list of peptides deduced by alignment, this tool iterates accross all and performs an XIC extration and quantification in similar to the PSMbasedQuantification tool.","text_prefix":false,"timestamp":1678159677,"version":"0.0.2"},"proteomiqon-alignmentbasedquantstatistics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"MS-based shotgun proteomics estimates protein abundances using a proxy: peptides. Due to the acquisition method for MS2, not every peptide ion present can be identified in every run. One approach to mitigate this problem is to align information obtained from similar runs to the current run, therefore getting more quantifications. This tool scores the quantifications obtained through alignment based on peak and run properties to obtain a measurement of reliability for the alignments.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/AlignmentBasedQuantStatistics.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.AlignmentBasedQuantStatistics/0.0.3","subdirs":["noarch"],"summary":"The tool ProteomIQon.AlignmentBasedQuantStatistics scores peptide ion quantifications obtained through alignment between runs.","text_prefix":false,"timestamp":1670948724,"version":"0.0.3"},"proteomiqon-joinquantpepionswithproteins":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Results from PSMBasedQuantification contain detailed information about the quantification of every peptide, but only basic informations about the protein they originated from. This information is obtained during the ProteinInference. This tool takes the results from both tools and combines the quantification information of each peptide with the more accurate information about the corresponding protein including its q-value.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/JoinQuantPepIonsWithProteins.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.JoinQuantPepIonsWithProteins/0.0.2","subdirs":["noarch"],"summary":"The tool JoinQuantPepIonsWithProteins combines results from ProteinInference and PSMBasedQuantification.","text_prefix":false,"timestamp":1676546885,"version":"0.0.2"},"proteomiqon-labeledproteinquantification":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"After quantification and protein inference are performed, it is known which peptide originated from which protein, as well as the intensity of each peptide. The information available for each peptide now needs to be aggragated for their proteins. This tool performs the aggregation from the peptides to the protein in several steps. The first step for the labeled protein quantification is the aggregation of the differently labeled peptides. Peptides with the same sequence, modifications and charge are aggregated and the ratio between the intensity from the light and heavy version is calculated. The next two aggregation steps are optional. One of them is the aggregation based on charge state. Similarily to the first step, peptides with the same sequence and modifications, but different charge states are being aggregated. The next optional step does the same for peptides with the same sequence, but different modification. Those steps build upon each other. The last step is the aggregation of all peptides of a protein. The result of each aggregation step is given as a tab separated file. The aggregation is performed according to the given parameters for each step. If an optional aggregation is not performed, the next step takes the result from the prior aggregation. For example, if aggregation by charge and modification are skipped, the protein aggregation gets a collection of peptides, where a peptidesequence can occur with different charge states and midifications.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/LabeledProteinQuantification.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.LabeledProteinQuantification/0.0.3","subdirs":["noarch"],"summary":"The tool LabeledProteinQuantification combines the results from ProteomIQon ProteinInference and ProteomIQon PSMBasedQuantification","text_prefix":false,"timestamp":1651569651,"version":"0.0.3"},"proteomiqon-labelfreeproteinquantification":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"After quantification and protein inference are performed, it is known which peptide originated from which protein, as well as the intensity of each peptide. The information available for each peptide now needs to be aggragated for their proteins. This tool performs the aggregation from the peptides to the protein in several steps. The first two aggregation steps are optional. One of them is the aggregation based on charge state. Peptides with the same sequence and modifications, but different charge states are being aggregated. The next optional step does the same for peptides with the same sequence, but different modifications. Those steps build upon each other. The last step is the aggregation of all peptides of a protein. The result of each aggregation step is given as a tab separated file. The aggregation is performed according to the given parameters for each step. If an optional aggregation is not performed, the next step takes the result from the prior aggregation. For example, if aggregation by charge and modification are skipped, the protein aggregation is performed on previously unaggregated peptides, where a peptidesequence can occur with different charge states and modifications.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/LabelFreeProteinQuantification.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.LabelFreeProteinQuantification/0.0.3","subdirs":["noarch"],"summary":"The tool LabelFreeProteinQuantification estimates protein abundances using quantified peptide ions.","text_prefix":false,"timestamp":1676547134,"version":"0.0.3"},"proteomiqon-mzmltomzlite":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The success of modern proteomics was made possible by constant progression in the field of mass spectrometry. Over the course of the past years quite a few manufacturers of mass spectrometers have managed to establish themselfes in the field of biological research. Since aquisition and accession of mass spectra are performance critical processes, various performance optimized, but vendor specific and closed source formats have been developed to store raw MS data. This comes to the disadvantage for toolchain developers which want to provide tools for every scientist regardless of the format of their raw data.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/MzMLToMzLite.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.MzMLToMzLite/0.0.8","subdirs":["noarch"],"summary":"The tool MzMLToMzLite allows to convert mzML files to mzLite files.","text_prefix":true,"timestamp":1621004728,"version":"0.0.8"},"proteomiqon-peptidedb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"MS-based shotgun proteomics estimates protein abundances using a proxy: peptides. An established method to identify acquired MS/MS spectra is the comparison of each spectrum with peptides in a reference database. The PeptideDB tool helps to create peptide databases by in silico digestion given proteome information in the FASTA format and a set of parameters that allow the user to mimic conditions of their specific experiment. The created database stores peptide protein relationships in a SQLite database which can then be supplied to other ProteomIQon tools.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/PeptideDB.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.PeptideDB/0.0.7","subdirs":["noarch"],"summary":"The tool ProteomIQon.PeptideDB creates a peptide database in the SQLite format.","text_prefix":true,"timestamp":1621949783,"version":"0.0.7"},"proteomiqon-peptidespectrummatching":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Given raw a MS run in the mzLite or mzml format, this tool iterates accross all recorded MS/MS scans and determines the charge state of precursor ions which were selected for fragmentation. With this it is possible to query the peptide data base for every precursor ion mass +/- a tolerance (which defines the so called 'search space') and retrieve peptides that are theoretical candidates for a match. For each of the peptide candidates we create an theoretical spectrum in silico and compare it to the measured MS/MS scan.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/PeptideSpectrumMatching.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.PeptideSpectrumMatching/0.0.7","subdirs":["noarch"],"summary":"Given raw an MS run in the mzLite format, this tool iterates across all MS/MS scans, determines precursor charge states and possible peptide spectrum matches using reimplementations of SEQUEST, Andromeda and XTandem.","text_prefix":true,"timestamp":1624314034,"version":"0.0.7"},"proteomiqon-proteininference":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"MS-based shotgun proteomics estimates protein abundances using a proxy: peptides. The process of 'Protein Inference' is concerned with the mapping of identified peptides to the proteins they putatively originated from. This process is not as straightforward as one might think at a first glance on the subject, since the peptide-to-protein mapping is not necessarily a one-to-one relationship but in many cases a one-to-many relationship. This is due to the fact that many proteins share peptides with an identical sequence, e.g. two proteins originating from two different splice variants of the same gene. The ProteinInference tool relies on the concepts of protein groups and peptide evidence classes.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/ProteinInference.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.ProteinInference/0.0.7","subdirs":["noarch"],"summary":"MS-based shotgun proteomics estimates protein abundances using a proxy: peptides. The process of 'Protein Inference' is concerned with the mapping of identified peptides to the proteins they putatively originated from.","text_prefix":true,"timestamp":1621002537,"version":"0.0.7"},"proteomiqon-psmbasedquantification":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Given an MS run in the mzLite or mzml format and a list of fdr controlled peptide spectrum matches, this tool iterates accross all identified MS/MS scans and groups them by the assigned peptide ion. The scan times of each MS/MS spectrum are then weighted according to the quality of each match to build an reliable estimator for the scan time of the peptide ion in question. This scan time estimator, combined with the monoisotopic m/z, is then used to extract an ion chromatogram. Using wavelet based peak detection techniques we identify all peaks present in the XIC and select the most probable peak our target for quantification. Using parameter estimation techniques we subsequently use peak fitting to fit a set of two gaussian models to the detected peak, from whom the one with the better fit is selected. This allows us not only to report how well the signal fitted to the theoretical expected peak shape but also to obtain accurate estimates for the peak area, our estimator for peptide ion abundance. The quantification tool was designed to allow label-free quantification as well as quantification of full metabolic labeled samples. For this we use the known identity of one of the the peptide ions and calculate the m/z of the unobserved differentially labeled counterpart to extract and quantify the corresponding XIC.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/PSMBasedQuantification.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.PSMBasedQuantification/0.0.9","subdirs":["noarch"],"summary":"The quantification tool was designed to allow label-free quantification as well as quantification of full metabolic labeled samples.","text_prefix":true,"timestamp":1676547382,"version":"0.0.9"},"proteomiqon-psmstatistics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"To measure the similarity of in silico generated spectra and measured MS/MS scans we use our own implementations of three established search enginge scores: SEQUEST, Andromeda and XTandem. Additionally, we also record quality control parameters such as the mass difference between the precursor ion and the theoretically calulated mass or the uniquness of each score in comparison to 'competing' peptides within the search space. The PSMStatistics tool utilizes semi supervised machine learning techniques to integrate search engine scores as well as the mentioned quality scores into one single consensus score. Since the search space is extended by so called decoys - reversed counterparts of peptides within the search space - we can estimate the distribution of 'true negatives' and calculate local (PEP values) and global (Q values) false discovery rates at each consensus score. The reported peptides at user defined local and global FDR cutoffs can then be used as inputs for any downstream analysis be it ProteinInference or PSMBasedQuantification.","dev_url":"https://github.com/CSBiology/ProteomIQon","doc_url":"https://csbiology.github.io/ProteomIQon/tools/PSMStatistics.html","home":"https://csbiology.github.io/ProteomIQon/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://www.nuget.org/api/v2/package/ProteomIQon.PSMStatistics/0.0.8","subdirs":["noarch"],"summary":"The PSMStatistics tool utilizes semi supervised machine learning techniques to integrate search engine scores as well as the mentioned quality scores into one single consensus score.","text_prefix":true,"timestamp":1624319429,"version":"0.0.8"},"proteowizard":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://proteowizard.sourceforge.net","license":"Apache 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tool that infers posterior SNP effect sizes under continuous shrinkage (CS) priors using GWAS summary statistics and an external LD reference panel.","text_prefix":false,"timestamp":1649428256,"version":"1.1.0"},"prsedm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sethsh7/PRSedm","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.3.0":{"weak":["prsedm >=1.3.0,<2.0a0"]}},"source_url":"https://pypi.org/packages/source/p/prsedm/prsedm-1.3.0.tar.gz","subdirs":["noarch"],"summary":"Polygenic risk score toolkit for 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Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. 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It also offers convenient web-access to public databases, and enables testing null models of macroevolution using corrected test statistics.  Trees of class \"phylo\" (from 'ape' package) can be converted easily. Implements methods described in Bortolussi et al. (2005) <doi:10.1093/bioinformatics/bti798> and Maliet et al. 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It is useful with the Rscript front-end and facilitates turning an R script into an executable script.","text_prefix":false,"timestamp":0,"version":"0.4"},"r-argumentcheck":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/nutterb/ArgumentCheck","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/ArgumentCheck_0.10.2.tar.gz","https://cran.r-project.org/src/contrib/Archive/ArgumentCheck/ArgumentCheck_0.10.2.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"The typical process of checking arguments in functions is iterative.  In this process, an error may be returned and the user may fix it only to receive another error on a different argument.  'ArgumentCheck' facilitates a more helpful way to perform argument checks allowing the programmer to run all of the checks and then return all of the errors and warnings in a single message.","text_prefix":false,"timestamp":0,"version":"0.10.2"},"r-aroma.affymetrix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.aroma-project.org/, https://github.com/HenrikBengtsson/aroma.affymetrix","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"3.2.2":{"weak":["r-aroma.affymetrix >=3.2.2,<3.3.0a0"]},"3.2.3":{"weak":["r-aroma.affymetrix >=3.2.3,<3.3.0a0"]}},"source_url":["https://cran.r-project.org/src/contrib/Archive/aroma.affymetrix/aroma.affymetrix_3.2.3.tar.gz","https://cran.r-project.org/src/contrib/aroma.affymetrix_3.2.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system.  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Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets).  In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit.  This speeds up the read substantially.  This package is supporting the Aroma framework and should not be used elsewhere.)","text_prefix":false,"timestamp":0,"version":"0.6.0"},"r-aroma.core":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/HenrikBengtsson/aroma.core","home":"https://www.aroma-project.org/","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"3.3.1":{"weak":["r-aroma.core >=3.3.1,<4.0a0"]},"3.3.2":{"weak":["r-aroma.core >=3.3.2,<4.0a0"]}},"source_url":["https://cran.r-project.org/src/contrib/Archive/aroma.core/aroma.core_3.3.2.tar.gz","https://cran.r-project.org/src/contrib/aroma.core_3.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Core methods and classes used by higher-level 'aroma.*' packages part of the Aroma Project, e.g. 'aroma.affymetrix' and 'aroma.cn'.","text_prefix":false,"timestamp":0,"version":"3.3.2"},"r-ash":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/ash/index.html","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/ash_1.0-15.tar.gz","http://cran.r-project.org/src/contrib/Archive/ash/ash_1.0-15.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"David Scott's ASH routines ported from S-PLUS to R.","text_prefix":false,"timestamp":0,"version":"1.0_15"},"r-asics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ASICS","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/ASICS_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/Archive/ASICS/ASICS_1.0.1.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"With a set of pure metabolite spectra, ASICS quantifies  metabolites concentration in a complex spectrum. 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The md5 algorithm by Ron Rivest is specified in RFC 1321, the sha-1  and sha-256 algorithms are specified in FIPS-180-1 and FIPS-180-2,  and the crc32 algorithm is described in  ftp://ftp.rocksoft.com/cliens/rocksoft/papers/crc_v3.txt. . For md5, sha-1, sha-256 and aes, this package uses small standalone implementations that were provided by Christophe Devine. For crc32, code from the zlib library is used. For sha-512, an implementation by Aaron D. Gifford is used. For xxhash, the implementation by Yann Collet is used. For murmurhash, an implementation by Shane Day is used. . Please note that this package is not meant to be deployed for  cryptographic purposes for which more comprehensive (and widely  tested) libraries such as OpenSSL should be used.","text_prefix":false,"timestamp":0,"version":"0.6.12"},"r-dimsum":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/lehner-lab/DiMSum","home":"https://github.com/lehner-lab/DiMSum","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.3.1":{"weak":["r-dimsum >=1.3.1,<2.0a0"]},"1.3.2":{"weak":["r-dimsum >=1.3.2,<2.0a0"]},"1.4":{"weak":["r-dimsum >=1.4,<2.0a0"]}},"source_url":"https://github.com/lehner-lab/DiMSum/archive/v1.4.tar.gz","subdirs":["noarch"],"summary":"An error model and pipeline for analyzing deep mutational scanning (DMS) data and diagnosing common experimental pathologies","text_prefix":false,"timestamp":1601054982,"version":"1.4"},"r-dinamic.duo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DiNAMIC.Duo","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.0.3":{"weak":["r-dinamic.duo >=1.0.3,<2.0a0"]},"1.0.4":{"weak":["r-dinamic.duo >=1.0.4,<2.0a0"]}},"source_url":["https://cran.r-project.org/src/contrib/Archive/DiNAMIC.Duo/DiNAMIC.Duo_1.0.4.tar.gz","https://cran.r-project.org/src/contrib/DiNAMIC.Duo_1.0.4.tar.gz"],"subdirs":["noarch"],"summary":"In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. Here we use cyclic shift permutations to identify recurrent copy number alterations in a single cohort or recurrent copy number differences in two cohorts based on a common set of genomic markers. Additional functionality is provided to perform downstream analyses, including the creation of summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al. (2011) <doi:10.1093/bioinformatics/btq717> and leverages the theory developed in Walter et al. (2015) <doi:10.1093/biomet/asv046>. A manuscript based on DiNAMIC.Duo is currently under development.","text_prefix":false,"timestamp":1734764670,"version":"1.0.4"},"r-diptest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=diptest","license":"GPL (>=2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/diptest_0.75-7.tar.gz","http://cran.r-project.org/src/contrib/Archive/diptest/diptest_0.75-7.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Compute Hartigan's dip test statistic for unimodality / multimodality and provide a test with simulation based p-values, where the original public code has been corrected.","text_prefix":false,"timestamp":0,"version":"0.75_7"},"r-disco":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=disco","license":"GPL (>= 2.0)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/disco/disco_0.6.tar.gz","https://cran.r-project.org/src/contrib/disco_0.6.tar.gz"],"subdirs":["noarch"],"summary":"Concordance and discordance of homologous gene regulation allows comparing reaction to stimuli in different organisms,  for example human patients and animal models of a disease. The package contains functions to calculate discordance and concordance score for homologous gene pairs, identify concordantly or discordantly regulated transcriptional modules and visualize the results. It is intended for analysis of transcriptional data.","text_prefix":false,"timestamp":1552782481,"version":"0.6"},"r-discriminer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.gastonsanchez.com","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/DiscriMiner_0.1-29.tar.gz","http://cran.r-project.org/src/contrib/Archive/DiscriMiner/DiscriMiner_0.1-29.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Functions for Discriminant Analysis and Classification purposes covering various methods such as descriptive, geometric, linear, quadratic, PLS, as well as qualitative discriminant analyses","text_prefix":false,"timestamp":0,"version":"0.1_29"},"r-disprose":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=disprose","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/disprose/disprose_0.1.6.tar.gz","https://cran.r-project.org/src/contrib/disprose_0.1.6.tar.gz"],"subdirs":["noarch"],"summary":"Set of tools for molecular probes selection and design of a microarray, e.g. the assessment of physical and chemical properties, blast performance, selection according to sensitivity and selectivity. Methods used in package are described in: Lorenz R., Stephan H.B., H\u00f6ner zu Siederdissen C. et al. (2011) <doi:10.1186/1748-7188-6-26>; Camacho C., Coulouris G., Avagyan V. et al. (2009) <doi:10.1186/1471-2105-10-421>.","text_prefix":false,"timestamp":1733932823,"version":"0.1.6"},"r-dndscv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Description: This package contains functions for studying selection on coding sequences using a Poisson implementation of dN/dS. A Poisson model of dN/dS facilitates the study of selection beyond traditional codon models, including complex context-dependent mutation effects and selection on nonsense and splice site mutations. This model is best suited for resequencing studies, with very low density of mutations per base pair. The model was initially developed for cancer genome sequencing studies, and specific functions are provided to perform driver gene discovery using the dNdScv method on human cancer genomic data.","doc_url":"https://htmlpreview.github.io/?http://github.com/im3sanger/dndscv/blob/master/vignettes/dNdScv.html","home":"https://github.com/im3sanger/dndscv","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.1.0":{"weak":["r-dndscv >=0.1.0,<0.2.0a0"]}},"source_url":"https://github.com/im3sanger/dndscv/archive/refs/tags/0.1.0.tar.gz","subdirs":["noarch"],"summary":"dN/dS methods to quantify selection in cancer and somatic evolution.","text_prefix":false,"timestamp":1746702349,"version":"0.1.0"},"r-dnet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://dnet.r-forge.r-project.org, https://github.com/hfang-bristol/dnet","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dnet/dnet_1.1.7.tar.gz","https://cran.r-project.org/src/contrib/dnet_1.1.7.tar.gz"],"subdirs":["noarch"],"summary":"The focus of the dnet by Fang and Gough (2014) <doi:10.1186/s13073-014-0064-8> is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including: human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.","text_prefix":false,"timestamp":1579798488,"version":"1.1.7"},"r-docopt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/docopt/docopt.R","license":"MIT + file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/docopt_0.4.3.3.tar.gz","http://cran.r-project.org/src/contrib/Archive/docopt/docopt_0.4.3.3.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Define a command-line interface by just giving it a description in the specific format.","text_prefix":false,"timestamp":0,"version":"0.4.3.3"},"r-dorng":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://renozao.github.io/doRNG","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/doRNG_1.6.tar.gz","http://cran.r-project.org/src/contrib/Archive/doRNG/doRNG_1.6.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"This package provides functions to perform reproducible parallel foreach loops, using independent random streams as generated by L'Ecuyer's combined multiple-recursive generator [L'Ecuyer (1999)]. It enables to easily convert standard %dopar% loops into fully reproducible loops, independently of the number of workers, the task scheduling strategy, or the chosen parallel environment and associated foreach backend.","text_prefix":false,"timestamp":0,"version":"1.6"},"r-downloader":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.rstudio.com/web/packages/downloader/index.html","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://cran.rstudio.com/src/contrib/downloader_0.4.tar.gz","subdirs":["linux-64","osx-64"],"summary":"Provides a wrapper for the download.file function, making it possible to download files over HTTPS on Windows, Mac OS X, and other Unix-like platforms. The RCurl package provides this functionality (and much more) but can be difficult to install because it must be compiled with external dependencies. This package has no external dependencies, so it is much easier to install.","text_prefix":false,"timestamp":0,"version":"0.0.4"},"r-dowser":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://dowser.readthedocs.io","license":"AGPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dowser/dowser_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/dowser_1.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a set of functions for inferring, visualizing, and analyzing B cell phylogenetic trees. Provides methods to 1) reconstruct unmutated ancestral sequences, 2) build B cell phylogenetic trees using multiple methods, 3) visualize trees with metadata at the tips, 4) reconstruct intermediate sequences, 5) detect biased ancestor-descendant relationships among metadata types Workflow examples available at documentation site (see URL). Citations: Hoehn et al (2020) <doi:10.1101/2020.05.30.124446>, Hoehn et al (2021) <doi:10.1101/2021.01.06.425648>.","text_prefix":false,"timestamp":1642806670,"version":"1.2.0"},"r-dpeak":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://dongjunchung.github.io/dpeak/","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_git_url":"https://github.com/dongjunchung/dpeak.git","subdirs":["linux-64","osx-64"],"summary":"This package provides functions for fitting dPeak, a statistical framework to deconvolve ChIP-seq peaks.","text_prefix":false,"timestamp":1734870239,"version":"2.0.1"},"r-dplyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/hadley/dplyr","license":"MIT + file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/dplyr_0.5.0.tar.gz","http://cran.r-project.org/src/contrib/Archive/dplyr/dplyr_0.5.0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"A fast, consistent tool for working with data frame like objects, both in memory and out of memory.","text_prefix":false,"timestamp":0,"version":"0.5.0"},"r-drc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.r-project.org, http://www.bioassay.dk","license":"GPL-2 | file LICENCE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/drc_3.0-1.tar.gz","https://cran.r-project.org/src/contrib/Archive/drc/drc_3.0-1.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.","text_prefix":false,"timestamp":0,"version":"3.0_1"},"r-dsb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"This lightweight R package provides a method for normalizing and denoising protein expression data from droplet based single cell experiments. Raw protein Unique Molecular Index (UMI) counts from sequencing DNA-conjugated antibody derived tags (ADT) in droplets (e.g. 'CITE-seq') have substantial measurement noise. Our experiments and computational modeling revealed two major components of this noise: 1) protein-specific noise originating from ambient, unbound antibody encapsulated in droplets that can be accurately inferred via the expected protein counts detected in empty droplets, and 2) droplet/cell-specific noise revealed via the shared variance component associated with isotype antibody controls and background protein counts in each cell. This package normalizes and removes both of these sources of noise from raw protein data derived from methods such as 'CITE-seq', 'REAP-seq', 'ASAP-seq', 'TEA-seq', 'proteogenomic' data from the Mission Bio platform, etc. See the vignette for tutorials on how to integrate dsb with 'Seurat' and 'Bioconductor' and how to use dsb in 'Python'. Please see our paper Mul\u00e8 M.P., Martins A.J., and Tsang J.S. Nature Communications 2022 <https://www.nature.com/articles/s41467-022-29356-8> for more details on the method.","home":"https://github.com/niaid/dsb","license":"CC0 | file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.0.3":{"weak":["r-dsb >=1.0.3,<2.0a0"]},"1.0.4":{"weak":["r-dsb >=1.0.4,<2.0a0"]},"2.0.0":{"weak":["r-dsb >=2.0.0,<3.0a0"]},"2.0.1":{"weak":["r-dsb >=2.0.1,<3.0a0"]}},"source_url":["https://cran.r-project.org/src/contrib/Archive/dsb/dsb_2.0.1.tar.gz","https://cran.r-project.org/src/contrib/dsb_2.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Normalizing and denoising protein expression data from droplet-based single cell profiling","text_prefix":false,"timestamp":1743628334,"version":"2.0.1"},"r-dt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://rstudio.github.io/DT","license":"GPL-3 | file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"http://cran.r-project.org/src/contrib/DT_0.2.tar.gz","subdirs":["linux-64","osx-64"],"summary":"Data objects in R can be rendered as HTML tables using the JavaScript library 'DataTables' (typically via R Markdown or Shiny). The 'DataTables' library has been included in this R package. The package name 'DT' is an abbreviation of 'DataTables'.","text_prefix":false,"timestamp":0,"version":"0.2"},"r-dunn.test":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/dunn.test_1.3.1.tar.gz","http://cran.r-project.org/src/contrib/Archive/dunn.test/dunn.test_1.3.1.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. 'dunn.test' accounts for tied ranks.","text_prefix":false,"timestamp":0,"version":"1.3.1"},"r-dwls":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/omnideconv/DWLS","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://github.com/omnideconv/DWLS/archive/refs/tags/v1.0.tar.gz"],"subdirs":["noarch"],"summary":"Deconvolution of bulk mRNA data using single-cell RNAseq to provide cell type specific signatures","text_prefix":false,"timestamp":1735278685,"version":"1.0"},"r-dynamictreecut":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/BranchCutting/","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/dynamicTreeCut_1.63-1.tar.gz","http://cran.r-project.org/src/contrib/Archive/dynamicTreeCut/dynamicTreeCut_1.63-1.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Contains methods for detection of clusters in hierarchical clustering dendrograms.","text_prefix":false,"timestamp":0,"version":"1.63_1"},"r-e1071":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/e1071/index.html","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/e1071_1.6-8.tar.gz","http://cran.r-project.org/src/contrib/Archive/e1071/e1071_1.6-8.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, ...","text_prefix":false,"timestamp":0,"version":"1.6_8"},"r-eacon":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gustaveroussy/EaCoN","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/gustaveroussy/EaCoN/archive/refs/tags/0.3.6.tar.gz","subdirs":["noarch"],"summary":"Easy Copy Number. EaCoN aims to be an all-packed in, user-friendly solution to perform relative or absolute copy-number analysis for multiple sources of data, with three different segmenters available (and corresponding three copy-number modelization methods)","text_prefix":false,"timestamp":1603992570,"version":"0.3.6"},"r-easydifferentialgenecoexpression":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/davidechicco/easyDifferentialGeneCoexpression","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.4":{"weak":["r-easydifferentialgenecoexpression >=1.4,<2.0a0"]}},"source_url":["https://cran.r-project.org/src/contrib/Archive/easyDifferentialGeneCoexpression/easyDifferentialGeneCoexpression_1.4.tar.gz","https://cran.r-project.org/src/contrib/easyDifferentialGeneCoexpression_1.4.tar.gz"],"subdirs":["noarch"],"summary":"A function that reads in the GEO code of a list of probesets or gene symbols, a gene expression dataset GEO accession code, the name of the dataset feature discriminating the two conditions for the differential coexpression, and the values of the two different conditions for the differential coexpression, and returns the significant pairs of genes/probesets with highest differential coexpression (p-value < 0.005). If the input gene list is made of gene symbols, this package associates the probesets to these gene symbols, if found.  Platforms available: GPL80, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293,  GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>.","text_prefix":false,"timestamp":1689525738,"version":"1.4"},"r-easylift":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/caleblareau/easyLift","home":"https://github.com/caleblareau/easyLift","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.2.1":{"weak":["r-easylift >=0.2.1,<0.3.0a0"]}},"source_url":"https://github.com/caleblareau/easyLift/archive/43590e059828027e6f1fa057484b239b028da5fd.zip","subdirs":["noarch"],"summary":"A convenience package for converting between popular mouse & human builds.","text_prefix":false,"timestamp":1735250970,"version":"0.2.1"},"r-easypar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/caravagn/easypar","home":"https://caravagnalab.github.io/easypar/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.0.0":{"weak":["r-easypar >=1.0.0,<1.1.0a0"]}},"source_url":["https://github.com/caravagnalab/easypar/archive/refs/tags/v1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"The easypar package makes it easy to implement parallel computations in R. To use this package, you need to have a function that carries out your desired computation. easypar will take care of the burden of turning that function into a runnable parallel piece of code, offering a soilution based on the foreach and doParallel paradigms for parallel computing, or generating array jobs for the popular LSF workload for distributed high performance computing.","text_prefix":false,"timestamp":1745320991,"version":"1.0.0"},"r-ebimetagenomics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ebimetagenomics","license":"LGPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ebimetagenomics/ebimetagenomics_0.6.tar.gz","https://cran.r-project.org/src/contrib/ebimetagenomics_0.6.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Functions for querying the EBI Metagenomics Portal <https://www.ebi.ac.uk/metagenomics/>. 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This package makes it easy to use system TrueType fonts and with PDF or PostScript output files, and with bitmap output files in Windows. extrafont can also be used with fonts packaged specifically to be used with, such as the fontcm package, which has Computer Modern PostScript fonts with math symbols. 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The eztune function uses a genetic algorithm or Hooke-Jeeves optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through validation error, cross validation, or resubstitution. The function eztune.cv will compute a cross validated error rate. 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The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis.","text_prefix":false,"timestamp":0,"version":"1.38"},"r-fail":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mllg/fail","license":"BSD_3_clause + file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/fail_1.3.tar.gz","http://cran.r-project.org/src/contrib/Archive/fail/fail_1.3.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"More comfortable interface to work with R data or source files in a key-value fashion.","text_prefix":false,"timestamp":0,"version":"1.3"},"r-fastbaps":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/gtonkinhill/fastbaps","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/gtonkinhill/fastbaps/archive/v1.0.8.tar.gz","subdirs":["linux-64","noarch","osx-64"],"summary":"A fast approximation to a Dirichlet Process Mixture model (DPM) for clustering genetic data","text_prefix":false,"timestamp":1645714890,"version":"1.0.8"},"r-fastcluster":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://danifold.net/fastcluster.html","license":"FreeBSD | GPL-2 | file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/fastcluster_1.1.20.tar.gz","http://cran.r-project.org/src/contrib/Archive/fastcluster/fastcluster_1.1.20.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"This is a two-in-one package which provides interfaces to both R and Python. It implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: linkage() in the SciPy package 'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the 'flashClust' package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide. For information on how to install the Python files, see the file INSTALL in the source distribution.","text_prefix":false,"timestamp":0,"version":"1.1.20"},"r-fastica":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fastICA","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/fastICA_1.2-0.tar.gz","https://cran.r-project.org/src/contrib/Archive/fastICA/fastICA_1.2-0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Implementation of FastICA algorithm to perform Independent Component Analysis (ICA) and Projection Pursuit.","text_prefix":false,"timestamp":0,"version":"1.2_0"},"r-fastmatch":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.rforge.net/fastmatch","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/fastmatch_1.1-0.tar.gz","https://cran.r-project.org/src/contrib/Archive/fastmatch/fastmatch_1.1-0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Package providing a fast match() replacement for cases that require repeated look-ups. It is slightly faster that R's built-in match() function on first match against a table, but extremely fast on any subsequent lookup as it keeps the hash table in memory.","text_prefix":false,"timestamp":0,"version":"1.1_0"},"r-fastqcr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.sthda.com/english/rpkgs/fastqcr/","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fastqcr/fastqcr_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/fastqcr_0.1.2.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data.   It produces, for each sample, an html report and a compressed file containing the raw data.  If you have hundreds of samples, you are not going to open up each 'HTML' page.  You need some way of looking at these data in aggregate.  'fastqcr' Provides helper functions to easily parse, aggregate and analyze  'FastQC' reports for large numbers of samples. It provides a convenient solution for building  a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations.","text_prefix":false,"timestamp":1559518540,"version":"0.1.2"},"r-fateid":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=FateID","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/FateID/FateID_0.1.8.tar.gz","https://cran.r-project.org/src/contrib/FateID_0.1.8.tar.gz","https://depot.galaxyproject.org/software/r-fateid/r-fateid_0.1.8_src_all.tar.gz"],"subdirs":["linux-64","noarch"],"summary":"Application of 'FateID' allows computation and visualization of cell fate bias for multi-lineage single cell transcriptome data. Herman, J.S., Sagar, Gr\u00fcn D. (2017) <DOI:10.1038/nmeth.4662>.","text_prefix":false,"timestamp":1567460068,"version":"0.1.8"},"r-fda":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.functionaldata.org","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/fda_2.4.4.tar.gz","http://cran.r-project.org/src/contrib/Archive/fda/fda_2.4.4.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer.  They were ported from earlier versions in Matlab and S-PLUS.  An introduction appears in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009) Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book.  Matlab versions of the code and sample analyses are no longer distributed through CRAN, as they were when the book was published.  For those, ftp from http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/ There you find a set of .zip files containing the functions and sample analyses, as well as two .txt files giving instructions for installation and some additional information. The changes from Version 2.4.1 are fixes of bugs in density.fd and removal of functions create.polynomial.basis, polynompen, and  polynomial. These were deleted because the monomial basis does the same thing and because there were errors in the code.","text_prefix":false,"timestamp":0,"version":"2.4.4"},"r-ff":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://ff.r-forge.r-project.org/","license":"GPL-2 | file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/ff_2.2-13.tar.gz","https://cran.r-project.org/src/contrib/Archive/ff/ff_2.2-13.tar.gz"],"subdirs":["linux-64"],"summary":"The ff package provides data structures that are stored on disk but behave (almost) as if they were in RAM by transparently  mapping only a section (pagesize) in main memory - the effective  virtual memory consumption per ff object. ff supports R''s standard  atomic data types ''double'', ''logical'', ''raw'' and ''integer'' and  non-standard atomic types boolean (1 bit), quad (2 bit unsigned),  nibble (4 bit unsigned), byte (1 byte signed with NAs), ubyte (1 byte  unsigned), short (2 byte signed with NAs), ushort (2 byte unsigned),  single (4 byte float with NAs). For example ''quad'' allows efficient  storage of genomic data as an ''A'',''T'',''G'',''C'' factor. The unsigned  types support ''circular'' arithmetic. There is also support for  close-to-atomic types ''factor'', ''ordered'', ''POSIXct'', ''Date'' and  custom close-to-atomic types.  ff not only has native C-support for vectors, matrices and arrays  with flexible dimorder (major column-order, major row-order and  generalizations for arrays). There is also a ffdf class not unlike  data.frames and import/export filters for csv files. ff objects store raw data in binary flat files in native encoding, and complement this with metadata stored in R as physical and virtual attributes. ff objects have well-defined hybrid copying semantics,  which gives rise to certain performance improvements through  virtualization. ff objects can be stored and reopened across R  sessions. ff files can be shared by multiple ff R objects  (using different data en/de-coding schemes) in the same process  or from multiple R processes to exploit parallelism. A wide choice of  finalizer options allows to work with ''permanent'' files as well as  creating/removing ''temporary'' ff files completely transparent to the  user. On certain OS/Filesystem combinations, creating the ff files works without notable delay thanks to using sparse file allocation. Several access optimization techniques such as Hybrid Index  Preprocessing and Virtualization are implemented to achieve good  performance even with large datasets, for example virtual matrix  transpose without touching a single byte on disk. Further, to reduce  disk I/O, ''logicals'' and non-standard data types get stored native and  compact on binary flat files i.e. logicals take up exactly 2 bits to  represent TRUE, FALSE and NA.  Beyond basic access functions, the ff package also provides  compatibility functions that facilitate writing code for ff and ram  objects and support for batch processing on ff objects (e.g. as.ram,  as.ff, ffapply). ff interfaces closely with functionality from package  ''bit'': chunked looping, fast bit operations and coercions between  different objects that can store subscript information (''bit'',  ''bitwhich'', ff ''boolean'', ri range index, hi hybrid index). This allows to work interactively with selections of large datasets and quickly  modify selection criteria.  Further high-performance enhancements can be made available upon request.","text_prefix":false,"timestamp":0,"version":"2.2_13"},"r-fftwtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/krahim/fftwtools","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/fftwtools_0.9-7.tar.gz","http://cran.r-project.org/src/contrib/Archive/fftwtools/fftwtools_0.9-7.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Provides a wrapper for several FFTW functions. This package provides access to the two-dimensional FFT, the multivariate FFT, and the one-dimensional real to complex FFT using FFTW3. The package includes the functions fftw and mvfftw which are designed to mimic the functionality of the R functions fft and mvfft. The FFT functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.","text_prefix":false,"timestamp":0,"version":"0.9.7"},"r-fgwas":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/wzhy2000/fGWAS","license":"GNU GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/wzhy2000/fGWAS/archive/v0.3.6.tar.gz","subdirs":["linux-64","noarch","osx-64"],"summary":"GWAS tools for longitudinal genetic traits based on fGWAS statistical model.","text_prefix":false,"timestamp":1545599819,"version":"0.3.6"},"r-fields":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.image.ucar.edu/fields","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fields/fields_8.10.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"For curve, surface and function fitting with an emphasis on splines, spatial data and spatial statistics. The major methods include cubic, and thin plate splines, Kriging and compact covariances for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter).  A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview.  The fields source code is deliberately commented and provides useful explanations of numerical details in addition to the manual pages. The commented source code can be viewed by expanding the package source code \"tarball\" (ending in tar.gz) and looking in the R subdirectory. Please cite fields along with its DOI in your publications!","text_prefix":false,"timestamp":0,"version":"8.10"},"r-findpython":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/trevorld/findpython","license":"MIT + file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/findpython_1.0.1.tar.gz","http://cran.r-project.org/src/contrib/Archive/findpython/findpython_1.0.1.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Package designed to find an acceptable python binary.","text_prefix":false,"timestamp":0,"version":"1.0.1"},"r-firebrowser":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/mariodeng/FirebrowseR","home":"https://github.com/mariodeng/FirebrowseR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/mariodeng/FirebrowseR/releases/download/1.1.35/FirebrowseR_1.1.35.tar.gz","subdirs":["noarch"],"summary":"An R client for broads firehose pipeline, providing TCGA data sets.","text_prefix":false,"timestamp":1733954471,"version":"1.1.35"},"r-fitdistrplus":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://riskassessment.r-forge.r-project.org","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/Archive/fitdistrplus/fitdistrplus_1.0-6.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Extends the fitdistr function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available.","text_prefix":false,"timestamp":0,"version":"1.0_6"},"r-flanders":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"flanders is an R package designed to seamlessly convert finemapping output files from the nf-flanders pipeline into a unified AnnData object and facilitate colocalization analysis. 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Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability.","text_prefix":false,"timestamp":0,"version":"1.3_4"},"r-flexmix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=flexmix","license":"GPL (>=2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/flexmix_2.3-14.tar.gz","http://cran.r-project.org/src/contrib/Archive/flexmix/flexmix_2.3-14.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"A general framework for finite mixtures of regression models using the EM algorithm is implemented. The package provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.","text_prefix":false,"timestamp":0,"version":"2.3_14"},"r-floral":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://vdblab.github.io/FLORAL/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.4.0":{"weak":["r-floral >=0.4.0,<0.5.0a0"]},"0.5.0":{"weak":["r-floral >=0.5.0,<0.6.0a0"]},"0.6.0":{"weak":["r-floral >=0.6.0,<0.7.0a0"]},"0.7.0":{"weak":["r-floral >=0.7.0,<0.8.0a0"]}},"source_url":"https://cran.r-project.org/src/contrib/FLORAL_0.7.0.tar.gz","subdirs":["noarch"],"summary":"Log-ratio Lasso regression for continuous, binary, and survival outcomes with (longitudinal) compositional features. See Fei and others (2024) <doi:10.1016/j.crmeth.2024.100899>.","text_prefix":false,"timestamp":1744730521,"version":"0.7.0"},"r-flowr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sahilseth/flowr","license":"MIT + file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/flowr_0.9.10.tar.gz","http://cran.r-project.org/src/contrib/Archive/flowr/flowr_0.9.10.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"This framework allows you to design and implement complex pipelines, and deploy them on your institution's computing cluster. This has been built keeping in mind the needs of bioinformatics workflows. However, it is easily extendable to any field where a series of steps (shell commands) are to be executed in a (work)flow.","text_prefix":false,"timestamp":0,"version":"0.9.10"},"r-fmsb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://minato.sip21c.org/msb/","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fmsb/fmsb_0.6.3.tar.gz","https://cran.r-project.org/src/contrib/fmsb_0.6.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Several utility functions for the book entitled  \"Practices of Medical and Health Data Analysis using R\" (Pearson Education Japan, 2007) with Japanese demographic data and some demographic analysis related functions.","text_prefix":false,"timestamp":1539717917,"version":"0.6.3"},"r-fnn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=FNN","license":"GPL (>= 2.1)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/FNN_1.1.tar.gz","http://cran.r-project.org/src/contrib/Archive/FNN/FNN_1.1.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications including KNN classification, regression and information measures are implemented.","text_prefix":false,"timestamp":0,"version":"1.1"},"r-fpc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fpc","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/fpc_2.1-10.tar.gz","http://cran.r-project.org/src/contrib/Archive/fpc/fpc_2.1-10.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther's prediction strength, Fang and Wang's bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures.","text_prefix":false,"timestamp":0,"version":"2.1_10"},"r-freerange":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/acidgenomics/freerange","home":"https://freerange.acidgenomics.com/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/acidgenomics/freerange/archive/v0.2.8.tar.gz","subdirs":["linux-64","noarch","osx-64"],"summary":"Generate and manipulate genomic ranges.","text_prefix":false,"timestamp":1559209844,"version":"0.2.8"},"r-funr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sahilseth/funr","license":"MIT + file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/funr_0.2.0.tar.gz","http://cran.r-project.org/src/contrib/Archive/funr/funr_0.2.0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"A small utility which wraps Rscript and provides access to all R functions from the shell.","text_prefix":false,"timestamp":0,"version":"0.2.0"},"r-funrar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Rekyt/funrar","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/funrar/funrar_1.2.2.tar.gz","https://cran.r-project.org/src/contrib/funrar_1.2.2.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Computes functional rarity indices as proposed by Violle et al. (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed using both regional and local information. Functional Rarity combines both the functional aspect of rarity as well as the extent aspect of rarity. 'funrar' is presented in Greni\u00e9 et al. (2017) <doi:10.1111/ddi.12629>.","text_prefix":false,"timestamp":1546708346,"version":"1.2.2"},"r-futile.logger":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"license":"LGPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/futile.logger_1.4.1.tar.gz","http://cran.r-project.org/src/contrib/Archive/futile.logger/futile.logger_1.4.1.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Provides a simple yet powerful logging utility. Based loosely on log4j, futile.logger takes advantage of R idioms to make logging a convenient and easy to use replacement for cat and print statements.","text_prefix":false,"timestamp":0,"version":"1.4.1"},"r-futile.options":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"license":"LGPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/futile.options_1.0.0.tar.gz","http://cran.r-project.org/src/contrib/Archive/futile.options/futile.options_1.0.0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"A scoped options management framework","text_prefix":false,"timestamp":0,"version":"1.0.0"},"r-future":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/future","license":"LGPL (>= 2.1)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/Archive/future/future_1.2.0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"A Future API for R is provided. In programming, a future is an abstraction for a value that may be available at some point in the future. The state of a future can either be unresolved or resolved. As soon as it is resolved, the value is available. Futures are useful constructs in for instance concurrent evaluation, e.g. parallel processing and distributed processing on compute clusters. The purpose of this package is to provide a lightweight interface for using futures in R. Functions 'future()' and 'value()' exist for creating futures and requesting their values, e.g. 'f <- future({ mandelbrot(-0.75, 0, side=3) })' and 'v <- value(f)'. The 'resolved()' function can be used to check if a future is resolved or not. An infix assignment operator '%<-%' exists for creating futures whose values are accessible by the assigned variables (as promises), e.g. 'v %<-% { mandelbrot(-0.75, 0, side=3) }'. This package implements synchronous \"lazy\" and \"eager\" futures, and asynchronous \"multicore\", \"multisession\" and ad hoc \"cluster\" futures. Globals variables and functions are automatically identified and exported. Required packages are attached in external R sessions whenever needed. All types of futures are designed to behave the same such that the exact same code work regardless of futures used or number of cores, background sessions or cluster nodes available. Additional types of futures are provided by other packages enhancing this package.","text_prefix":false,"timestamp":0,"version":"1.2.0"},"r-gam":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gam","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/gam_1.14-4.tar.gz","https://cran.r-project.org/src/contrib/Archive/gam/gam_1.14-4.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Functions for fitting and working with generalized additive models, as described in chapter 7 of \"Statistical Models in S\" (Chambers and Hastie (eds), 1991), and \"Generalized Additive Models\" (Hastie and Tibshirani, 1990).","text_prefix":false,"timestamp":0,"version":"1.14_4"},"r-gamlss":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.gamlss.org/","license":"GPL-2 | GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/Archive/gamlss/gamlss_5.0-0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Functions for fitting, displaying and checking GAMLSS Models.","text_prefix":false,"timestamp":0,"version":"5.0_0"},"r-gamlss.data":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.gamlss.org/","license":"GPL-2 | GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/gamlss.data_5.0-0.tar.gz","http://cran.r-project.org/src/contrib/Archive/gamlss.data/gamlss.data_5.0-0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Data for GAMLSS models.","text_prefix":false,"timestamp":0,"version":"5.0_0"},"r-gamlss.dist":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.gamlss.org/","license":"GPL-2 | GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/gamlss.dist_5.0-0.tar.gz","http://cran.r-project.org/src/contrib/Archive/gamlss.dist/gamlss.dist_5.0-0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"The different distributions used for the response variables in GAMLSS modelling.","text_prefix":false,"timestamp":0,"version":"5.0_0"},"r-gap":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://jinghuazhao.github.io","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gap/gap_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/gap_1.2.1.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"It is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates.","text_prefix":false,"timestamp":1545553611,"version":"1.2.1"},"r-garnett":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/cole-trapnell-lab/garnett","home":"https://cole-trapnell-lab.github.io/garnett/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/cole-trapnell-lab/garnett/archive/0.2.8.tar.gz","subdirs":["noarch"],"summary":"Bioconda-installable version of Garnett cell classification tool.","text_prefix":false,"timestamp":1569938107,"version":"0.2.8"},"r-gbm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://code.google.com/p/gradientboostedmodels/","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/gbm_2.1.3.tar.gz","https://cran.r-project.org/src/contrib/Archive/gbm/gbm_2.1.3.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).","text_prefix":false,"timestamp":0,"version":"2.1.3"},"r-gchromvar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/caleblareau/gchromVAR","home":"https://caleblareau.github.io/gchromVAR/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.3.2":{"weak":["r-gchromvar >=0.3.2,<0.4.0a0"]}},"source_url":"https://github.com/caleblareau/gchromVAR/archive/e4f33cad4115160ee4bdf16fd625c2fcd0bf3910.zip","subdirs":["noarch"],"summary":"R package for computing cell-type specific GWAS enrichments from Finemapping data and quantitative epigenomic data.","text_prefix":false,"timestamp":1735749188,"version":"0.3.2"},"r-gdtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gdtools","license":"GPL-3 | file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/gdtools_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/Archive/gdtools/gdtools_0.1.4.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Useful tools for writing vector graphics devices.","text_prefix":false,"timestamp":0,"version":"0.1.4"},"r-geiger":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.webpages.uidaho.edu/~lukeh/software.html","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/geiger/geiger_2.0.6.2.tar.gz","https://cran.r-project.org/src/contrib/geiger_2.0.6.2.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Methods for fitting macroevolutionary models to phylogenetic trees.","text_prefix":false,"timestamp":1565699114,"version":"2.0.6.2"},"r-genabel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.genabel.org","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/GenABEL_1.8-0.tar.gz","https://cran.r-project.org/src/contrib/Archive/GenABEL/GenABEL_1.8-0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"A package for genome-wide association analysis between quantitative or binary traits and single-nucleotide polymorphisms (SNPs).","text_prefix":false,"timestamp":0,"version":"1.8_0"},"r-genabel.data":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.genabel.org, http://forum.genabel.org, http://genabel.r-forge.r-project.org/","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/GenABEL.data_1.0.0.tar.gz","http://cran.r-project.org/src/contrib/Archive/GenABEL.data/GenABEL.data_1.0.0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"GenABEL.data package consists of a data set used by GenABEL functions","text_prefix":false,"timestamp":0,"version":"1.0.0"},"r-geneexpressionfromgeo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/davidechicco/geneExpressionFromGEO","home":"https://github.com/davidechicco/geneExpressionFromGEO","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.2":{"weak":["r-geneexpressionfromgeo >=1.2,<2.0a0"]},"1.3":{"weak":["r-geneexpressionfromgeo >=1.3,<2.0a0"]}},"source_url":["https://cran.r-project.org/src/contrib/Archive/geneExpressionFromGEO/geneExpressionFromGEO_1.3.tar.gz","https://cran.r-project.org/src/contrib/geneExpressionFromGEO_1.3.tar.gz"],"subdirs":["noarch"],"summary":"A function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optionally) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293,  GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>. More information can be found in the following manuscript: Davide Chicco, \"geneExpressionFromGEO: an R package to facilitate data reading from Gene Expression Omnibus (GEO)\". 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This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.","text_prefix":false,"timestamp":0,"version":"1.1.0"},"r-mkmisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.stamats.de/","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"2.0":{"weak":["r-mkmisc >=2.0,<3.0a0"]}},"source_url":["https://cran.r-project.org/src/contrib/Archive/MKmisc/MKmisc_2.0.tar.gz","https://cran.r-project.org/src/contrib/MKmisc_2.0.tar.gz"],"subdirs":["noarch"],"summary":"Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices.","text_prefix":false,"timestamp":1622658334,"version":"2.0"},"r-mlgt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://personalpages.manchester.ac.uk/staff/David.Gerrard/","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/mlgt_0.16.tar.gz","https://cran.r-project.org/src/contrib/Archive/mlgt/mlgt_0.16.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Processing and analysis of high throughput (Roche 454) sequences generated from multiple loci and multiple biological samples. 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'speaq' offers the possibility of raw spectra alignment and quantitation but also an analysis based on features whereby the spectra are converted to peaks which are then grouped and turned into features. These features can be processed with any number of statistical tools either included in 'speaq' or available elsewhere on CRAN. More detail can be found in Vu et al. (2011) <doi:10.1186/1471-2105-12-405> and Beirnaert et al. 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This package allows the user to downloads NCBI data dumps and create a local database for fast and local taxonomic assignment.","text_prefix":false,"timestamp":1619199680,"version":"0.7.1"},"r-tcga2stat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.liuzlab.org/TCGA2STAT/","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/TCGA2STAT/TCGA2STAT_1.2.tar.gz","https://cran.r-project.org/src/contrib/TCGA2STAT_1.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Automatically downloads and processes TCGA genomics and clinical data into a format convenient for statistical analyses in the R environment.","text_prefix":false,"timestamp":1546725272,"version":"1.2"},"r-tcr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://imminfo.github.io/tcr/","license":"Apache License 2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tcR/tcR_2.3.2.tar.gz","https://cran.r-project.org/src/contrib/tcR_2.3.2.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Platform for the advanced analysis of T cell receptor and Immunoglobulin repertoires data and visualisation of the analysis results.","text_prefix":false,"timestamp":1546609117,"version":"2.3.2"},"r-teddy":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/YeehanXiao/Teddy","home":"https://github.com/YeehanXiao/Teddy","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.0.3":{"weak":["r-teddy >=1.0.3,<2.0a0"]},"1.1.0":{"weak":["r-teddy >=1.1.0,<2.0a0"]},"1.1.1":{"weak":["r-teddy >=1.1.1,<2.0a0"]},"1.1.8":{"weak":["r-teddy >=1.1.8,<2.0a0"]}},"source_url":"https://github.com/YeehanXiao/Teddy/archive/refs/tags/v1.1.8.tar.gz","subdirs":["linux-64","osx-64"],"summary":"Analysis of TE-dependent gene isoforms.","text_prefix":false,"timestamp":1779222053,"version":"1.1.8"},"r-testthat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/hadley/testthat","license":"MIT + file LICENSE","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/testthat_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/Archive/testthat/testthat_1.0.2.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"A unit testing system designed to be fun, flexible and easy to set up.","text_prefix":false,"timestamp":0,"version":"1.0.2"},"r-tfmpvalue":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ge11232002/TFMPvalue","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/TFMPvalue_0.0.6.tar.gz","http://cran.r-project.org/src/contrib/Archive/TFMPvalue/TFMPvalue_0.0.6.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"In putative Transcription Factor Binding Sites (TFBSs)  identification from sequence/alignments, we are interested in the significance of certain match score. 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RRMScorer has several features to either calculate the binding score for a specific RRM and RNA sequences, for a set of RRM sequences in a FASTA file, or to explore which are the best RNA binders according to our scoring method.  RRMScorer has been developed by Bio2Byte within the RNAct project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement No. 813239.  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[![GitHub release](https://img.shields.io/github/release/t-neumann/slamdunk.svg)](https://github.com/t-neumann/slamdunk/releases/latest) [![Travis CI](https://img.shields.io/travis/t-neumann/slamdunk.svg)](https://travis-ci.org/t-neumann/slamdunk)  [![Docker Pulls](https://img.shields.io/docker/pulls/tobneu/slamdunk.svg)](https://hub.docker.com/r/tobneu/slamdunk) [![Docker Automated build](https://img.shields.io/docker/automated/tobneu/slamdunk.svg)](https://hub.docker.com/r/tobneu/slamdunk/builds/)  [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat-square)](http://bioconda.github.io/recipes/slamdunk/README.html) [![Anaconda build](https://anaconda.org/bioconda/slamdunk/badges/version.svg )](https://anaconda.org/bioconda/slamdunk) [![Anaconda downloads](https://anaconda.org/bioconda/slamdunk/badges/downloads.svg )](https://anaconda.org/bioconda/slamdunk)  [![PyPI release](https://img.shields.io/pypi/v/slamdunk.svg)](https://pypi.python.org/pypi/slamdunk) ![Github Stars](https://img.shields.io/github/stars/t-neumann/slamdunk.svg?style=social&label=Star)  -----  ### Slamdunk documentation  http://t-neumann.github.io/slamdunk  ### Please cite  Neumann, T., Herzog, V. 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Please download all configuration files under the 'data' directory from the [Github page](https://github.com/t2diabetesgenes/t1dgrs2) and adjust paths in 't1dgrs2_setttings.yml' accordingly.  ### Authors * Diane P Fraser ([email](mailto:d.p.fraser@exeter.ac.uk)) * Seth A Sharp ([email](mailto:ssharp@stanford.edu)) * Ankit M Arni ([email](mailto:a.m.arni@exeter.ac.uk)) * Richard A Oram ([email](mailto:r.oram@exeter.ac.uk)) * Michael N Weedon ([email](mailto:m.n.weedon@exeter.ac.uk)) * Kashyap A Patel ([email](mailto:k.a.patel@exeter.ac.uk))  ### References 1. Oram RA, Patel K, Hill A, et al. (2016) A Type 1 Diabetes Genetic Risk Score Can Aid Discrimination Between Type 1 and Type 2 Diabetes in Young Adults. Diabetes Care 39(3): 337-344. [10.2337/dc15-1111](https://doi.org/10.2337/dc15-1111). 2. Patel KA, Oram RA, Flanagan SE, et al. (2016) Type 1 Diabetes Genetic Risk Score: A Novel Tool to Discriminate Monogenic and Type 1 Diabetes. Diabetes 65(7): 2094-2099. [10.2337/db15-1690](https://doi.org/10.2337/db15-1690). 3. Sharp SA, Rich SS, Wood AR, et al. (2019) Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis. Diabetes Care 42(2): 200-207. 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see https://genome.ucsc.edu/license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"466":{"weak":["ucsc-bedgraphpack >=466"]},"469":{"weak":["ucsc-bedgraphpack >=469"]},"482":{"weak":["ucsc-bedgraphpack >=482"]}},"source_url":"https://hgdownload.cse.ucsc.edu/admin/exe/userApps.archive/userApps.v482.src.tgz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Pack together adjacent records representing same value.","text_prefix":false,"timestamp":1751014940,"version":"482"},"ucsc-bedgraphtobigwig":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ucscGenomeBrowser/kent","doc_url":"https://github.com/ucscGenomeBrowser/kent/blob/v482_base/README","home":"https://hgdownload.cse.ucsc.edu/admin/exe","license":"Varies; see https://genome.ucsc.edu/license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"455":{"weak":["ucsc-bedgraphtobigwig >=455"]},"469":{"weak":["ucsc-bedgraphtobigwig >=469"]},"472":{"weak":["ucsc-bedgraphtobigwig >=472"]},"481":{"weak":["ucsc-bedgraphtobigwig >=481"]},"482":{"weak":["ucsc-bedgraphtobigwig >=482"]}},"source_url":"https://hgdownload.cse.ucsc.edu/admin/exe/userApps.archive/userApps.v482.src.tgz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Convert a bedGraph file to bigWig format.","text_prefix":false,"timestamp":1750539887,"version":"482"},"ucsc-bedintersect":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ucscGenomeBrowser/kent","doc_url":"https://github.com/ucscGenomeBrowser/kent/blob/v482_base/README","home":"https://hgdownload.cse.ucsc.edu/admin/exe","license":"Varies; 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see http://genome.ucsc.edu/license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"http://hgdownload.cse.ucsc.edu/admin/exe/userApps.archive/userApps.v377.src.tgz","subdirs":["linux-64","osx-64"],"summary":"given a bed file and tab file where each have a column with matching values: first get the value of column0, the offset and line length from inTabFile. 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What passes.","text_prefix":false,"timestamp":1751230742,"version":"482"},"ucsc-netsplit":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ucscGenomeBrowser/kent","doc_url":"https://github.com/ucscGenomeBrowser/kent/blob/v482_base/README","home":"https://hgdownload.cse.ucsc.edu/admin/exe","license":"Varies; see https://genome.ucsc.edu/license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"469":{"weak":["ucsc-netsplit >=469"]},"482":{"weak":["ucsc-netsplit >=482"]}},"source_url":"https://hgdownload.cse.ucsc.edu/admin/exe/userApps.archive/userApps.v482.src.tgz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Split a genome net file into chromosome net files.","text_prefix":false,"timestamp":1751237637,"version":"482"},"ucsc-netsyntenic":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ucscGenomeBrowser/kent","doc_url":"https://github.com/ucscGenomeBrowser/kent/blob/v482_base/README","home":"https://hgdownload.cse.ucsc.edu/admin/exe","license":"Varies; 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see https://genome.ucsc.edu/license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"455":{"weak":["ucsc-pslsortacc >=455"]},"482":{"weak":["ucsc-pslsortacc >=482"]}},"source_url":"https://hgdownload.cse.ucsc.edu/admin/exe/userApps.archive/userApps.v482.src.tgz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Remove chain-breaking alignments from chains that break nested chains.","text_prefix":false,"timestamp":1750904607,"version":"482"},"ucsc-pslsplicejunctions":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ucscGenomeBrowser/kent","doc_url":"https://github.com/ucscGenomeBrowser/kent/blob/v482_base/README","home":"https://hgdownload.cse.ucsc.edu/admin/exe","license":"Varies; see https://genome.ucsc.edu/license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"482":{"weak":["ucsc-pslsplicejunctions >=482"]}},"source_url":"https://hgdownload.cse.ucsc.edu/admin/exe/userApps.archive/userApps.v482.src.tgz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Extract splice junctions from a PSL file.","text_prefix":false,"timestamp":1750944521,"version":"482"},"ucsc-pslsplitontarget":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ucscGenomeBrowser/kent","doc_url":"https://github.com/ucscGenomeBrowser/kent/blob/v482_base/README","home":"https://hgdownload.cse.ucsc.edu/admin/exe","license":"Varies; 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see https://genome.ucsc.edu/license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"469":{"weak":["ucsc-qacagplift >=469"]},"482":{"weak":["ucsc-qacagplift >=482"]}},"source_url":"https://hgdownload.cse.ucsc.edu/admin/exe/userApps.archive/userApps.v482.src.tgz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Use AGP to combine per-scaffold qac into per-chrom qac.","text_prefix":false,"timestamp":1751232110,"version":"482"},"ucsc-qactoqa":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ucscGenomeBrowser/kent","doc_url":"https://github.com/ucscGenomeBrowser/kent/blob/v482_base/README","home":"https://hgdownload.cse.ucsc.edu/admin/exe","license":"Varies; see https://genome.ucsc.edu/license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"469":{"weak":["ucsc-qactoqa >=469"]},"482":{"weak":["ucsc-qactoqa >=482"]}},"source_url":"https://hgdownload.cse.ucsc.edu/admin/exe/userApps.archive/userApps.v482.src.tgz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Convert from compressed to uncompressed.","text_prefix":false,"timestamp":1751231035,"version":"482"},"ucsc-qactowig":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ucscGenomeBrowser/kent","doc_url":"https://github.com/ucscGenomeBrowser/kent/blob/v482_base/README","home":"https://hgdownload.cse.ucsc.edu/admin/exe","license":"Varies; 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see https://genome.ucsc.edu/license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"469":{"weak":["ucsc-randomlines >=469"]},"482":{"weak":["ucsc-randomlines >=482"]}},"source_url":"https://hgdownload.cse.ucsc.edu/admin/exe/userApps.archive/userApps.v482.src.tgz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Pick out random lines from file.","text_prefix":false,"timestamp":1751233622,"version":"482"},"ucsc-rasqlquery":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ucscGenomeBrowser/kent","doc_url":"https://github.com/ucscGenomeBrowser/kent/blob/v482_base/README","home":"https://hgdownload.cse.ucsc.edu/admin/exe","license":"Varies; see https://genome.ucsc.edu/license","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"469":{"weak":["ucsc-rasqlquery >=469"]},"482":{"weak":["ucsc-rasqlquery >=482"]}},"source_url":"https://hgdownload.cse.ucsc.edu/admin/exe/userApps.archive/userApps.v482.src.tgz","subdirs":["linux-64","osx-64"],"summary":"Do a SQL-like query on a RA file.","text_prefix":false,"timestamp":1529447773,"version":"482"},"ucsc-ratolines":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ucscGenomeBrowser/kent","doc_url":"https://github.com/ucscGenomeBrowser/kent/blob/v482_base/README","home":"https://hgdownload.cse.ucsc.edu/admin/exe","license":"Varies; 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