Dindel: accurate indel calls from short-read data.

By Cornelis A. Albers, Gerton A. Lunter, Daniel G. MacArthur, G. McVean, Willem H. Ouwehand, Richard Durbin. Genome Research, 2011

455 citations recorded by the source corpus.

Small insertions and deletions (indels) are a common and functionally important type of sequence polymorphism. Most of the focus of studies of sequence variation is on single nucleotide variants (SNVs) and large structural variants. In principle, high-throughput sequencing studies should allow identification of indels just as SNVs. However, inference of indels from next-generation sequence data is challenging, and so far methods for identifying indels lag behind methods for calling SNVs in terms of sensitivity and specificity. We propose a Bayesian method to call indels from short-read sequence data in individuals and populations by realigning reads to candidate haplotypes that represent alternative sequence to the reference. The candidate haplotypes are formed by combining candidate indels and SNVs identified by the read mapper, while allowing for known sequence variants or candidates from other methods to be included. In our probabilistic realignment model we account for base-calling errors, mapping errors, and also, importantly, for increased sequencing error indel rates in long homopolymer runs. We show that our method is sensitive and achieves low false discovery rates on simulated and real data sets, although challenges remain. The algorithm is implemented in the program Dindel, which has been used in the 1000 Genomes Project call sets.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d7b02543a0536923fcd660217c6d7f821adcd30d

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Citations in the corpus

Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications (2014, background): “First, mapping-based callers often focus on a single variant type 1–4 , leading to errors around indels and larger variants 2,15 .”
Haplotype-based variant detection from short-read sequencing (2012, unknown): “, 2012] or locally [Albers et al., 2011].”
Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism (2013, unknown): “, 2009), whereas insertion-deletions were predicted using local realignment with Dindel (Albers et al., 2011).”
Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing (2012, background): “Because indels generally aremore challenging to call accurately than SNPs (78),”
Scaling accurate genetic variant discovery to tens of thousands of samples (2017, background): “The first generation of variant calling algorithms scanned short read data mapped to a reference sequence to identify mismatches [1, 6, 15, 17, 22].”
Toward better understanding of artifacts in variant calling from high-coverage samples (2014, background): “We were also unware that realignment of INDELs in LCRs remains a great challenge even after the many existing efforts in this direction (Homer and Nelson, 2010; Li, 2011a; Albers et al., 2011; Narzisi et al., 2014).”