title: GINDEL: accurate genotype calling of insertions and deletions from low coverage population sequence reads.
abstract: Insertions and deletions (indels) are important types of structural variations. Obtaining accurate genotypes of indels may facilitate further genetic study. There are a few existing methods for calling indel genotypes from sequence reads. However, none of these tools can accurately call indel genotypes for indels of all lengths, especially for low coverage sequence data. In this paper, we present GINDEL, an approach for calling genotypes of both insertions and deletions from sequence reads. GINDEL uses a machine learning approach which combines multiple features extracted from next generation sequencing data. We test our approach on both simulated and real data and compare with existing tools, including Genome STRiP, Pindel and Clever-sv. Results show that GINDEL works well for deletions larger than 50 bp on both high and low coverage data. Also, GINDEL performs well for insertion genotyping on both simulated and real data. For comparison, Genome STRiP performs less well for shorter deletions (50-200 bp) on both simulated and real sequence data from the 1000 Genomes Project. Clever-sv performs well for intermediate deletions (200-1500 bp) but is less accurate when coverage is low. Pindel only works well for high coverage data, but does not perform well at low coverage. To summarize, we show that GINDEL not only can call genotypes of insertions and deletions (both short and long) for high and low coverage population sequence data, but also is more accurate and efficient than other approaches. The program GINDEL can be downloaded at: http://sourceforge.net/p/gindel.
authors.affiliation: Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, United States of America.
authors.forename: Chong
authors.initials: C
authors.lastname: Chu
authors.affiliation: The Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors.forename: Jin
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, United States of America.
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Wu
mesh_terms: D001185:Artificial Intelligence; D005828:Genetics, Population; D005838:Genotype; D006801:Humans; D017421:Sequence Analysis