OMMA enables population-scale analysis of complex genomic features and phylogenomic relationships from nanochannel-based optical maps. Issue 7 (9th July 2019)

• Record Type: Journal Article
• Authors: Leung, Alden King-Yung; Liu, Melissa Chun-Jiao; Li, Le; Lai, Yvonne Yuk-Yin; Chu, Catherine; Kwok, Pui-Yan; Ho, Pak-Leung; Yip, Kevin Y; Chan, Ting-Fung
• Is Part Of: GigaScience. Volume 8:Issue 7(2019)
• Publication Date: 2019-07-09
• Subjects: optical mapping -- comparative genomics -- structural variation -- copy number variation -- haplotypes -- single-molecule analysis; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giz079
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Background: Optical mapping is an emerging technology that complements sequencing-based methods in genome analysis. It is widely used in improving genome assemblies and detecting structural variations by providing information over much longer (up to 1 Mb) reads. Current standards in optical mapping analysis involve assembling optical maps into contigs and aligning them to a reference, which is limited to pairwise comparison and becomes bias-prone when analyzing multiple samples. Findings: We present a new method, OMMA, that extends optical mapping to the study of complex genomic features by simultaneously interrogating optical maps across many samples in a reference-independent manner. OMMA captures and characterizes complex genomic features, e.g., multiple haplotypes, copy number variations, and subtelomeric structures when applied to 154 human samples across the 26 populations sequenced in the 1000 Genomes Project. For small genomes such as pathogenic bacteria, OMMA accurately reconstructs the phylogenomic relationships and identifies functional elements across 21 Acinetobacter baumannii strains. Conclusions: With the increasing data throughput of optical mapping system, the use of this technology in comparative genome analysis across many samples will become feasible. OMMA is a timely solution that can address such computational need. The OMMA software is available at https://github.com/TF-Chan-Lab/OMTools .