Overlap graph-based generation of haplotigs for diploids and polyploids

Abstract

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. We present POLYTE (POLYploid genome fitTEr) as a new approach to de novo generation of haplotigs for diploid and polyploid genomes. 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. POLYTE is freely available as part of the HaploConduct package at https://github.com/HaploConduct/HaploConduct, implemented in Python and C++.

StrainXpress: strain aware metagenome assembly from short reads

“…Seen from a larger perspective, combining the ideas raised in prior work [3,4] makes perfect sense; apparently, this insight had passed unnoticed so far. Certainly, a major reason is that [3] had been explicitly designed for fixed ploidy settings, which is rather the opposite of what is needed in metagenome assembly, where numbers and abundances of species and strains are unknown at the beginning. The crucial insight is to realize that the strategy suggested in [3] also works for metagenomes, if presented with well arranged, pre-processed portions of the raw read data.…”

“…For step (2), we adopt an OG based, ploidy aware assembly strategy suggested earlier [3,POLYTE]. Here, it serves as a generic template for the conquer stage of StrainXpress.…”

“…The decisive adaptation is to replace the FM index based computation of approximate overlaps as originally proposed [3] with minimizer bases schemes, as implemented by Minimap2 [19]. For that, the key insight has been to realize that Minimap2, although not primarily meant to deal with short reads, achieves improvements over the FM index based overlaps implemented in [3]. This 4/20 drastic improvements in terms of computational expenses, without incurring losses in terms of the quality of the OGs on which strain aware metagenome assembly is based.…”

“…The approach specializes in virus genomes, which means that genomes are only of short length, whereas the coverage of the individual genomes is large. Subsequently, it was shown how to construct OGs for genomes spanning several millions of nucleotides, which enabled to assemble genomes of higher, but fixed ploidy in a ploidy-aware manner [3]. Further, an OG based approach was suggested that clusters raw metagenome sequencing reads into species specific groups without the need for a reference genome [4].…”

Estimating the time since admixture from phased and unphased molecular data

“…Eagle (Loh et al, 2016b), Beagle (Browning & Browning, 2007) or ShapeIt (O'Connell et al, 2016)), others allow de novo haplotype aware assembly (e.g. POLYTE (Baaijens & Schönhuth, 2019)). Further improvements include the usage of third‐generation sequencing (Ebler et al, 2019; Kronenberg et al, 2021; Tangherloni et al, 2019; Tourdot & Zhang, 2021).…”