TY - JOUR
T1 - Lightning-fast genome variant detection with GROM
AU - Smith, Sean D.
AU - Kawash, Joseph K.
AU - Grigoriev, Andrey
N1 - Publisher Copyright: © The Author 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Copy number variants
KW - GROM
KW - Indels
KW - SNVs
KW - Structural variants
KW - Variant detection
KW - Whole genome sequencing
UR - http://www.scopus.com/inward/record.url?scp=85042528987&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042528987&partnerID=8YFLogxK
U2 - 10.1093/GIGASCIENCE/GIX091
DO - 10.1093/GIGASCIENCE/GIX091
M3 - Comment/debate
C2 - 29048532
SN - 2047-217X
VL - 6
SP - 1
EP - 7
JO - GigaScience
JF - GigaScience
IS - 10
ER -