Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data

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Documentation for package ‘wavClusteR’ version 2.11.0

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wavClusteR-package A comprehensive pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non- experimental sources by a non-parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq).
annotateClusters Annotate clusters with respect to transcript features
estimateFDR Estimate False Discovery Rate within the relative substitution frequency support by integrating PAR-CLIP data and RNA-Seq data
exportClusters Export clusters as BED track
exportCoverage Export coverage as BigWig track
exportHighConfSub Export high-confidence substitutions as BED track
exportSequences Export cluster sequences for motif search analysis
filterClusters Merge clusters and compute all relevant cluster statistics
fitMixtureModel Fit a non-parametric mixture model from all identified substitutions
getAllSub Identify all substitutions observed across genomic positions exhibiting a specified minimum coverage
getClusters Identify clusters containing high-confidence substitutions and resolve boundaries at high resolution
getExpInterval Identify the interval of relative substitution frequencies dominated by experimental induction.
getHighConfSub Classify substitutions based on identified RSF interval and return high confidence transitions
getMetaCoverage Compute and plot distribution of average coverage or relative log-odds as metagene profile using identified clusters
getMetaGene Compute and plot metagene profile using identified clusters
getMetaTSS Compute and plot read densities in genomic regions around transcription start sites
model Components of the non-parametric mixture moodel fitted on Ago2 PAR-CLIP data
plotSizeDistribution Plot the distribution of cluster sizes
plotStatistics Pairs plot visualization of clusters statistics
plotSubstitutions Barplot visualization of the number of genomic positions exhibiting a given substitution and, if model provided, additional diagnostic plots.
readSortedBam Load a sorted BAM file
wavClusteR A comprehensive pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non- experimental sources by a non-parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq).