r-cran-tigger - 0.3.1-1 main

Summary: Infers the V genotype of an individual from immunoglobulin (Ig)
repertoire-sequencing (Rep-Seq) data, including detection of any novel
alleles. This information is then used to correct existing V allele calls
from among the sample sequences.
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High-throughput sequencing of B cell immunoglobulin receptors is
providing unprecedented insight into adaptive immunity. A key step in
analyzing these data involves assignment of the germline V, D and J gene
segment alleles that comprise each immunoglobulin sequence by matching
them against a database of known V(D)J alleles. However, this process
will fail for sequences that utilize previously undetected alleles,
whose frequency in the population is unclear.
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TIgGER is a computational method that significantly improves V(D)J
allele assignments by first determining the complete set of gene segments
carried by an individual (including novel alleles) from V(D)J-rearrange
sequences. TIgGER can then infer a subject’s genotype from these
sequences, and use this genotype to correct the initial V(D)J allele
assignments.
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The application of TIgGER continues to identify a surprisingly high
frequency of novel alleles in humans, highlighting the critical need
for this approach. TIgGER, however, can and has been used with data
from other species.
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Core Abilities:
* Detecting novel alleles
* Inferring a subject’s genotype
* Correcting preliminary allele calls
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Required Input
* A table of sequences from a single individual, with columns containing
the following:
- V(D)J-rearranged nucleotide sequence (in IMGT-gapped format)
- Preliminary V allele calls
- Preliminary J allele calls
- Length of the junction region
* Germline Ig sequences in IMGT-gapped fasta format (e.g., as those
downloaded from IMGT/GENE-DB)
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The former can be created through the use of IMGT/HighV-QUEST and
Change-O.