HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies.
Genome Res
; 27(5): 801-812, 2017 05.
Article
em En
| MEDLINE
| ID: mdl-27940952
ABSTRACT
Many tools have been developed for haplotype assembly-the reconstruction of individual haplotypes using reads mapped to a reference genome sequence. Due to increasing interest in obtaining haplotype-resolved human genomes, a range of new sequencing protocols and technologies have been developed to enable the reconstruction of whole-genome haplotypes. However, existing computational methods designed to handle specific technologies do not scale well on data from different protocols. We describe a new algorithm, HapCUT2, that extends our previous method (HapCUT) to handle multiple sequencing technologies. Using simulations and whole-genome sequencing (WGS) data from multiple different data types-dilution pool sequencing, linked-read sequencing, single molecule real-time (SMRT) sequencing, and proximity ligation (Hi-C) sequencing-we show that HapCUT2 rapidly assembles haplotypes with best-in-class accuracy for all data types. In particular, HapCUT2 scales well for high sequencing coverage and rapidly assembled haplotypes for two long-read WGS data sets on which other methods struggled. Further, HapCUT2 directly models Hi-C specific error modalities, resulting in significant improvements in error rates compared to HapCUT, the only other method that could assemble haplotypes from Hi-C data. Using HapCUT2, haplotype assembly from a 90× coverage whole-genome Hi-C data set yielded high-resolution haplotypes (78.6% of variants phased in a single block) with high pairwise phasing accuracy (â¼98% across chromosomes). Our results demonstrate that HapCUT2 is a robust tool for haplotype assembly applicable to data from diverse sequencing technologies.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Haplótipos
/
Software
/
Análise de Sequência de DNA
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Mapeamento de Sequências Contíguas
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Genômica
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article