Overlap graph-based generation of haplotigs for diploids and polyploids
Bioinformatics , Volume 35 - Issue 21 p. 4281- 4289
Motivation: Haplotype-aware genome assembly plays an important role in genetics, medicine and various other disciplines, yet generation of haplotype-resolved de novo assemblies remains a major challenge. Beyond distinguishing between errors and true sequential variants, one needs to assign the true variants to the different genome copies. Recent work has pointed out that the enormous quantities of traditional NGS read data have been greatly underexploited in terms of haplotig computation so far, which reflects that methodology for reference independent haplotig computation has not yet reached maturity.
Results: We present POLYploid genome fitTEr (POLYTE) as a new approach to de novo generation of haplotigs for diploid and polyploid genomes of known ploidy. Our method follows an iterative scheme where in each iteration reads or contigs are joined, based on their interplay in terms of an underlying haplotype-aware overlap graph. Along the iterations, contigs grow while preserving their haplotype identity. Benchmarking experiments on both real and simulated data demonstrate that POLYTE establishes new standards in terms of error-free reconstruction of haplotype-specific sequence. As a consequence, POLYTE outperforms state-of-the-art approaches in various relevant aspects, where advantages become particularly distinct in polyploid settings.
Availability and implementation: POLYTE is freely available as part of the HaploConduct package at https://github.com/HaploConduct/HaploConduct, implemented in Python and C++.
Supplementary information: Supplementary data are available at Bioinformatics online.
|Statistical Models for Structural Genetic Variants in the Genome of the Netherlands|
|Organisation||Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands|
Baaijens, J.A, & Schönhuth, A. (2019). Overlap graph-based generation of haplotigs for diploids and polyploids. Bioinformatics, 35(21), 4281–4289. doi:10.1093/bioinformatics/btz255