Biology subjects

Baaijens, J.

Publications and source records attributed to Baaijens, J..

3 recordsLinked to original sources

bioRxiv2019

Viral quasispecies reconstruction via contig abundance estimation in variation graphs

The goal of strain-aware genome assembly is to reconstruct all individual haplotypes from a mixed sample at the strain level and to provide abundance estimates for the strains. Given that the use of a reference genome can introduce significant biases, de novo approaches are most suitable for this task. So far, reference-genome-independent assemblers have been shown to reconstruct haplotypes for mixed samples of limited complexity and genomes not exceeding 10000 bp in length. Here, we present VG-Flow, a de novo approach that enables full-length haplotype reconstruction from pre-assembled contigs of complex mixed samples. Our method increases contiguity of the input assembly and, at the same time, it performs haplotype abundance estimation. VG-Flow is the first approach to require polynomial, and not exponential runtime in terms of the underlying graphs. Since runtime increases only linearly in the length of the genomes in practice, it enables the reconstruction also of genomes that are longer by orders of magnitude, thereby establishing the first de novo solution to strain-aware full-length genome assembly applicable to bacterial sized genomes. VG-Flow is based on the flow variation graph as a novel concept that both captures all diversity present in the sample and enables to cast the central contig abundance estimation problem as a flow-like, polynomial time solvable optimization problem. As a consequence, we are in position to compute maximal-length haplotypes in terms of decomposing the resulting flow efficiently using a greedy algorithm, and obtain accurate frequency estimates for the reconstructed haplotypes through linear programming techniques. Benchmarking experiments show that our method outperforms state-of-the-art approaches on mixed samples from short genomes in terms of assembly accuracy as well as abundance estimation. Experiments on longer, bacterial sized genomes demonstrate that VG-Flow is the only current approach that can reconstruct full-length haplotypes from mixed samples at the strain level in human-affordable runtime.

bioinformatics↗

bioRxiv2018

Overlap graph-based generation of haplotigs for diploids and polyploids

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++.

bioinformatics↗

bioRxiv2018

Full-length de novo viral quasispecies assembly through variation graph construction

MotivationViruses populate their hosts as a viral quasispecies: a collection of genetically related mutant strains. Viral quasispecies assembly refers to reconstructing the strain-specific haplotypes from read data, and predicting their relative abundances within the mix of strains, an important step for various treatment-related reasons. Reference-genome-independent (\"de novo\") approaches have yielded benefits over reference-guided approaches, because reference-induced biases can become overwhelming when dealing with divergent strains. While being very accurate, extant de novo methods only yield rather short contigs. It remains to reconstruct full-length haplotypes together with their abundances from such contigs.\n\nMethodWe first construct a variation graph, a recently popular, suitable structure for arranging and integrating several related genomes, from the short input contigs, without making use of a reference genome. To obtain paths through the variation graph that reflect the original haplotypes, we solve a minimization problem that yields a selection of maximal-length paths that is optimal in terms of being compatible with the read coverages computed for the nodes of the variation graph. We output the resulting selection of maximal length paths as the haplotypes, together with their abundances.\n\nResultsBenchmarking experiments on challenging simulated data sets show significant improvements in assembly contiguity compared to the input contigs, while preserving low error rates. As a consequence, our method outperforms all state-of-the-art viral quasispecies assemblers that aim at the construction of full-length haplotypes, in terms of various relevant assembly measures. Our tool, Virus-VG, is publicly available at https://bitbucket.org/jbaaijens/virus-vg.

bioinformatics↗