UNDR ROVER - a fast and accurate variant caller for targeted DNA sequencing

BMC Bioinformatics

Software

Roger Li¹  Bernard J. Pope²  Tú Nguyen-Dumont³  Daniel J. Park⁴  Edmund Lau⁵  Peter Georgeson⁵ 

[1]Department of Computing and Information Systems, The University of Melbourne, 3010, Melbourne, Victoria, Australia

[2]Department of Computing and Information Systems, The University of Melbourne, 3010, Melbourne, Victoria, Australia

[3]Victorian Life Sciences Computation Initiative, The University of Melbourne, 3053, Melbourne, Victoria, Australia

[4]Genetic Epidemiology Laboratory, School of Biomedical Sciences, Medical Building, The University of Melbourne, 3010, Melbourne, Victoria, Australia

[5]Genetic Epidemiology Laboratory, School of Biomedical Sciences, Medical Building, The University of Melbourne, 3010, Melbourne, Victoria, Australia

[6]Victorian Life Sciences Computation Initiative, The University of Melbourne, 3053, Melbourne, Victoria, Australia

[7]Victorian Life Sciences Computation Initiative, The University of Melbourne, 3053, Melbourne, Victoria, Australia

关键词: PCR-MPS;    Hi-Plex;    ROVER;    Targeted sequencing;    Massively parallel sequencing;    Variant calling;   

DOI  :  10.1186/s12859-016-1014-9

 received in 2015-07-31, accepted in 2016-04-06,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundPreviously, we described ROVER, a DNA variant caller which identifies genetic variants from PCR-targeted massively parallel sequencing (MPS) datasets generated by the Hi-Plex protocol. ROVER permits stringent filtering of sequencing chemistry-induced errors by requiring reported variants to appear in both reads of overlapping pairs above certain thresholds of occurrence. ROVER was developed in tandem with Hi-Plex and has been used successfully to screen for genetic mutations in the breast cancer predisposition gene PALB2.ROVER is applied to MPS data in BAM format and, therefore, relies on sequence reads being mapped to a reference genome. In this paper, we describe an improvement to ROVER, called UNDR ROVER (Unmapped primer-Directed ROVER), which accepts MPS data in FASTQ format, avoiding the need for a computationally expensive mapping stage. It does so by taking advantage of the location-specific nature of PCR-targeted MPS data.ResultsThe UNDR ROVER algorithm achieves the same stringent variant calling as its predecessor with a significant runtime performance improvement. In one indicative sequencing experiment, UNDR ROVER (in its fastest mode) required 8-fold less sequential computation time than the ROVER pipeline and 13-fold less sequential computation time than a variant calling pipeline based on the popular GATK tool.UNDR ROVER is implemented in Python and runs on all popular POSIX-like operating systems (Linux, OS X). It requires as input a tab-delimited format file containing primer sequence information, a FASTA format file containing the reference genome sequence, and paired FASTQ files containing sequence reads. Primer sequences at the 5′ end of reads associate read-pairs with their targeted amplicon and, thus, their expected corresponding coordinates in the reference genome. The primer-intervening sequence of each read is compared against the reference sequence from the same location and variants are identified using the same algorithm as ROVER. Specifically, for a variant to be ‘called’ it must appear at the same location in both of the overlapping reads above user-defined thresholds of minimum number of reads and proportion of reads.ConclusionsUNDR ROVER provides the same rapid and accurate genetic variant calling as its predecessor with greatly reduced computational costs.

【 授权许可】

CC BY   
© Park et al. 2016