Supporting data for "Lightning-fast genome variant detection with GROM"

Dataset type: Software
Data released on September 22, 2017

Grigoriev A; Kawash JK; Smith SD (2017): Supporting data for "Lightning-fast genome variant detection with GROM" GigaScience Database.


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 GROM (Genome Rearrangement OmniMapper), 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 seven validated benchmarks using two whole genome sequencing (WGS) datasets. Additionally, GROM boasts lightning fast run times, analyzing a 50x WGS human dataset (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 one algorithm SNV, indel, SV, and CNV detection providing superior speed, sensitivity, and precision.

Additional details

Read the peer-reviewed publication(s):

Smith, S. D., Kawash, J. K., & Grigoriev, A. (2017). Lightning-fast genome variant detection with GROM. GigaScience, 6(10). doi:10.1093/gigascience/gix091

Additional information:

File NameSample IDData TypeFile FormatSizeRelease Date 
SoftwareUNKNOWN2.85 MB2017-09-13
SoftwareUNKNOWN808.18 KB2017-09-13
Tabular DataTEXT12.42 MB2017-09-13
Tabular DataTEXT12.42 MB2017-09-13
otherTEXT17.67 KB2017-09-13
SoftwareUNKNOWN0.46 KB2017-09-13
Genome sequenceFASTA2.63 MB2017-09-13
ReadmeUNKNOWN10.35 KB2017-09-13
ReadmeTEXT1.84 KB2017-09-13
SoftwareTAR4.68 MB2017-09-13
Displaying 1-10 of 14 File(s).
Funding body Awardee Award ID Comments
National Science Foundation A. Grigoriev DBI-1458202
Date Action
September 20, 2017 Dataset publish
October 17, 2017 Manuscript Link added : 10.1093/gigascience/gix091