Oct 20, 2017

Lightning-fast genome variant detection with GROM

GigaScience
Sean D SmithAndrey Grigoriev

Abstract

Current human whole genome sequencing projects produce massive amounts of data, often creating significant computational challenges. Different approaches have been developed for each type of genome variant and method of its detection, necessitating users to run multiple algorithms to find variants. We present Genome Rearrangement OmniMapper (GROM), a novel comprehensive variant detection algorithm accepting aligned read files as input and finding SNVs, indels, structural variants (SVs), and copy number variants (CNVs). We show that GROM outperforms state-of-the-art methods on 7 validated benchmarks using 2 whole genome sequencing (WGS) data sets. Additionally, GROM boasts lightning-fast run times, analyzing a 50× WGS human data set (NA12878) on commonly available computer hardware in 11 minutes, more than an order of magnitude (up to 72 times) faster than tools detecting a similar range of variants. Addressing the needs of big data analysis, GROM combines in 1 algorithm SNV, indel, SV, and CNV detection, providing superior speed, sensitivity, and precision. GROM is also able to detect CNVs, SNVs, and indels in non-paired-read WGS libraries, as well as SNVs and indels in whole exome or RNA sequencing data sets.

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Mentioned in this Paper

Sequence Determinations, RNA
Synaptic Vesicles
Whole Exome Sequencing
Computer Programs and Programming
CNV Protocol
Analysis
cDNA Library
Genetic Polymorphism
Genome Sequencing
Contingent Negative Variation

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