R: Find novel alleles from repertoire sequencing data

findNovelAlleles {tigger} | R Documentation

## Find novel alleles from repertoire sequencing data

### Description

`findNovelAlleles` analyzes mutation patterns in sequences thought to align to each germline allele in order to determine which positions might be polymorphic.

### Usage

    findNovelAlleles(
      data,
      germline_db,
      v_call = "v_call",
      j_call = "j_call",
      seq = "sequence_alignment",
      junction = "junction",
      junction_length = "junction_length",
      germline_min = 200,
      min_seqs = 50,
      auto_mutrange = TRUE,
      mut_range = 1:10,
      pos_range = 1:312,
      y_intercept = 0.125,
      alpha = 0.05,
      j_max = 0.15,
      min_frac = 0.75,
      nproc = 1
    )

### Arguments

`data` | `data.frame` containing repertoire data. See details.
`germline_db` | vector of named nucleotide germline sequences matching the V calls in `data`. These should be the gapped reference germlines used to make the V calls.
`v_call` | name of the column in `data` with V allele calls. Default is `v_call`.
`j_call` | name of the column in `data` with J allele calls. Default is `j_call`.
`seq` | name of the column in `data` with the aligned, IMGT-numbered, V(D)J nucleotide sequence. Default is `sequence_alignment`.
`junction` | Junction region nucleotide sequence, which includes the CDR3 and the two flanking conserved codons. Default is `junction`.
`junction_length` | Number of junction nucleotides in the junction sequence. Default is `junction_length`.
`germline_min` | the minimum number of sequences that must have a particular germline allele call for the allele to be analyzed.
`min_seqs` | minimum number of total sequences (within the desired mutational range and nucleotide range) required for the samples to be considered.
`auto_mutrange` | if `TRUE`, the algorithm will attempt to determine the appropriate mutation range automatically using the mutation count of the most common sequence assigned to each allele analyzed.
`mut_range` | range of mutations that samples may carry and be considered by the algorithm.
`pos_range` | range of IMGT-numbered positions that should be considered by the algorithm.
`y_intercept` | y-intercept threshold above which positions should be considered potentially polymorphic.
`alpha` | alpha value used for determining whether the fit y-intercept is greater than the `y_intercept` threshold.
`j_max` | maximum fraction of sequences perfectly aligning to a potential novel allele that are allowed to utilize to a particular combination of junction length and J gene. The closer to 1, the less strict the filter for J gene diversity will be.
`min_frac` | minimum fraction of sequences that must have usable nucleotides in a given position for that position to considered.
`nproc` | number of processors to use.

### Details

The TIgGER allele-finding algorithm, briefly, works as follows: Mutations are determined through comparison to the provided germline. Mutation frequency at each *position* is determined as a function of *sequence-wide* mutation counts. Polymorphic positions exhibit a high mutation frequency despite sequence-wide mutation count. False positive of potential novel alleles resulting from clonally-related sequences are guarded against by ensuring that sequences perfectly matching the potential novel allele utilize a wide range of combinations of J gene and junction length.

### Value

A `data.frame` with a row for each known allele analyzed. Besides metadata on the the parameters used in the search, each row will have either a note as to where the polymorphism-finding algorithm exited or a nucleotide sequence for the predicted novel allele, along with columns providing additional evidence.

The output contains the following columns:

- `germline_call`: The input (uncorrected) V call.

- `note`: Comments regarding the inferrence.

- `polymorphism_call`: The novel allele call.

- `nt_substitutions`: Mutations identified in the novel allele, relative to the reference germline (`germline_call`)

- `novel_imgt`: The novel allele sequence.

- `novel_imgt_count`: The number of times the sequence `novel_imgt` is found in the input data. Considers the subsequence of `novel_imgt` in the `pos_range`.

- `novel_imgt_unique_j`: Number of distinct J calls associated to `novel_imgt` in the input data. Considers the subsequence of `novel_imgt` in the `pos_range`.

- `novel_imgt_unique_cdr3`: Number of distinct CDR3 sequences associated with `novel_imgt` in the input data. Considers the subsequence of `novel_imgt` in the `pos_range`.

- `perfect_match_count`: Final number of sequences retained to call the new allele. These are unique sequences that have V segments that perfectly match the predicted germline in the `pos_range`.

- `perfect_match_freq`: `perfect_match_count / germline_call_count`

- `germline_call_count`: The number of sequences with the `germline_call` in the input data that were initially considered for the analysis.

- `germline_call_freq`: The fraction of sequences with the `germline_call` in the input data initially considered for the analysis.

- `germline_imgt`: Germline sequence for `germline_call`.

- `germline_imgt_count`: The number of times the `germline_imgt` sequence is found in the input data.

- `mut_min`: Minimum mutation considered by the algorithm.

- `mut_max`: Maximum mutation considered by the algorithm.

- `mut_pass_count`: Number of sequences in the mutation range.

- `pos_min`: First position of the sequence considered by the algorithm (IMGT numbering).

- `pos_max`: Last position of the sequence considered by the algorithm (IMGT numbering).

- `y_intercept`: The y-intercept above which positions were considered potentially polymorphic.

- `y_intercept_pass`: Number of positions that pass the `y_intercept` threshold.

- `snp_pass`: Number of sequences that pass the `y_intercept` threshold and are within the desired nucleotide range (`min_seqs`).

- `unmutated_count`: Number of unmutated sequences.

- `unmutated_freq`: Number of unmutated sequences over `germline_imgt_count`.

- `unmutated_snp_j_gene_length_count`: Number of distinct combinations of SNP, J gene, and junction length.

- `snp_min_seqs_j_max_pass`: Number of SNPs that pass both the `min_seqs` and `j_max` thresholds.

- `alpha`: Significance threshold to be used when constructing the confidence interval for the y-intercept.

- `min_seqs`: Input `min_seqs`. The minimum number of total sequences (within the desired mutational range and nucleotide range) required for the samples to be considered.

- `j_max`: Input `j_max`. The maximum fraction of sequences perfectly aligning to a potential novel allele that are allowed to utilize to a particular combination of junction length and J gene.

- `min_frac`: Input `min_frac`. The minimum fraction of sequences that must have usable nucleotides in a given position for that position to be considered.

The following comments can appear in the `note` column:

- Novel allele found: A novel allele was detected.

- Plurality sequence too rare: No sequence is frequent enough to pass the J test (`j_max`).

- A J-junction combination is too prevalent: Not enough J diversity (`j_max`).

- No positions pass y-intercept test: No positions above `y_intercept`.

- Insufficient sequences in desired mutational range: `mut_range` and `pos_range`.

- Not enough sequences: Not enough sequences in the desired mutational range and nucleotide range (`min_seqs`).

- No unmutated versions of novel allele found: All observed variants of the allele are mutated.

### See Also

selectNovel to filter the results to show only novel alleles. plotNovel to visualize the data supporting any novel alleles hypothesized to be present in the data and inferGenotype and inferGenotypeBayesian to determine if the novel alleles are frequent enought to be included in the subject's genotype.

### Examples

    # Note: In this example, with SampleGermlineIGHV,
    # which contains reference germlines retrieved on August 2014,
    # TIgGER finds the allele IGHV1-8*02_G234T. This allele
    # was added to IMGT as IGHV1-8*03 on March 28, 2018.

    # Find novel alleles and return relevant data
    novel <- findNovelAlleles(AIRRDb, SampleGermlineIGHV)
    selectNovel(novel)

[Package tigger version 1.0.0 Index]