ArticleGigaScience2017
Lightning-fast genome variant detection with GROM.
Article in GigaScience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Recent Dominant Transposition Events Affect Gene Regulatory Regions, but Not Coding Sequences, in Polar and Brown Bear Genomes.Current issues in molecular biology · 2026Article
- Review
- Leveraging Whole-Genome Resequencing to Uncover Genetic Diversity and Promote Conservation Strategies for Ruminants in Asia.Animals : an open access journal from MDPI · 2025Review
- VISTA: an integrated framework for structural variant discovery.Briefings in bioinformatics · 2024Article
- A comprehensive benchmarking of WGS-based deletion structural variant callers.Briefings in bioinformatics · 2022Article
- Chasing breeding footprints through structural variations in Cucumis melo and wild relatives.G3 (Bethesda, Md.) · 2021Article
- Four novel mutations of FAM20A in amelogenesis imperfecta type IG and review of literature for its genotype and phenotype spectra.Molecular genetics and genomics : MGG · 2020Article
- Structural variant identification and characterization.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2020Article
- Structural variants in 3000 rice genomes.Genome research · 2019Article
- ARIADNA: machine learning method for ancient DNA variant discovery.DNA research : an international journal for rapid publication of reports on genes and genomes · 2018Article
- Lightning-fast genome variant detection with GROM.GigaScience · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.