RT Journal
A1 Albers, Cornelis A.
A1 Lunter, Gerton
A1 MacArthur, Daniel G.
A1 McVean, Gilean
A1 Ouwehand, Willem H.
A1 Durbin, Richard
T1 Dindel: Accurate indel calls from short-read data
JF Genome Research 
JO Genome Research 
YR 2011 
FD June 01 
VO 21 
IS 6 
SP 961 
OP 973 
DO 10.1101/gr.112326.110 
UL http://genome.cshlp.org/content/21/6/961.abstract 
AB 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.