Reconstruction of Microbial Haplotypes by Integration of Statistical and Physical Linkage in Scaffolding. (6th February 2021)

• Record Type: Journal Article
• Authors: Cao, Chen; He, Jingni; Mak, Lauren; Perera, Deshan; Kwok, Devin; Wang, Jia; Li, Minghao; Mourier, Tobias; Gavriliuc, Stefan; Greenberg, Matthew; Morrissy, A Sorana; Sycuro, Laura K; Yang, Guang; Jeffares, Daniel C; Long, Quan
• Editors: Heyer, Evelyne
• Is Part Of: Molecular biology and evolution. Volume 38:Number 6(2021)
• Page Start: 2660
• Page End: 2672
• Publication Date: 2021-02-06
• Subjects: haplotype reconstruction -- within-host evolution -- linkage disequilibrium -- regularization; Molecular biology -- Periodicals; Molecular evolution -- Periodicals; Evolution, Molecular -- Periodicals; Molecular Biology -- Periodicals; 572.8
• DOI: 10.1093/molbev/msab037
• Languages: English
• ISSNs: 0737-4038

Abstract

Abstract: DNA sequencing technologies provide unprecedented opportunities to analyze within-host evolution of microorganism populations. Often, within-host populations are analyzed via pooled sequencing of the population, which contains multiple individuals or "haplotypes." However, current next-generation sequencing instruments, in conjunction with single-molecule barcoded linked-reads, cannot distinguish long haplotypes directly. Computational reconstruction of haplotypes from pooled sequencing has been attempted in virology, bacterial genomics, metagenomics, and human genetics, using algorithms based on either cross-host genetic sharing or within-host genomic reads. Here, we describe PoolHapX, a flexible computational approach that integrates information from both genetic sharing and genomic sequencing. We demonstrated that PoolHapX outperforms state-of-the-art tools tailored to specific organismal systems, and is robust to within-host evolution. Importantly, together with barcoded linked-reads, PoolHapX can infer whole-chromosome-scale haplotypes from 50 pools each containing 12 different haplotypes. By analyzing real data, we uncovered dynamic variations in the evolutionary processes of within-patient HIV populations previously unobserved in single position-based analysis.