HaploDMF: viral haplotype reconstruction from long reads via deep matrix factorization

Abstract

Motivation Lacking strict proofreading mechanisms, many RNA viruses can generate progeny with slightly changed genomes. Being able to characterize highly similar genomes (i.e. haplotypes) in one virus population helps study the viruses’ evolution and their interactions with the host/other microbes. High-throughput sequencing data has become the major source for characterizing viral populations. However, the inherent limitation on read length by next-generation sequencing (NGS) makes complete haplotype reconstruction difficult. Results In this work, we present a new tool named HaploDMF that can construct complete haplotypes using third-generation sequencing (TGS) data. HaploDMF utilizes a deep matrix factorization model with an adapted loss function to learn latent features from aligned reads automatically. The latent features are then used to cluster reads of the same haplotype. Unlike existing tools whose performance can be affected by the overlap size between reads, HaploDMF is able to achieve highly robust performance on data with different coverage, haplotype number, and error rates. In particular, it can generate more complete haplotypes even when the sequencing coverage drops in the middle. We benchmark HaploDMF against the state-of-the-art tools on simulated and real sequencing TGS data on different viruses. The results show that HaploDMF competes favorably against all others. Availability The source code and the documentation of HaploDMF are available at https://github.com/dhcai21/HaploDMF. Supplementary information Supplementary data are available at Bioinformatics online.

HairSplitter: haplotype assembly from long, noisy reads

“…In addition of HairSplitter, we chose to evaluate the software stRainy (Kazantseva et al, 2023) and Strainberry (Vicedomini et al, 2021), which have been introduced specifically as bacterial strain separation methods, hifiasm-meta (X Feng et al, 2022), which is the most popular assembler for direct HiFi assembly, Strainline (X Luo et al, 2022) and HaploDMF (Cai et al, 2022), which have been introduced as viral strain separation methods and finally iGDA (Z Feng et al, 2021), which can perform both.…”

“…In the context of bacterial strain separation, Vicedomini et al, 2021 showed that mainstream assemblers such as metaFlye (Kolmogorov et al, 2020) and Canu (Koren et al, 2017) failed to distinguish close bacterial haplotypes and proposed a new tool, called Strainberry, to reconstruct strains. In the context of viral strain separation, Strainline (X Luo et al, 2022) and HaploDMF (Cai et al, 2022) were presented to tackle specifically the viral haplotype reconstruction problem and need very high depth of sequencing to work. The method iGDA (Z Feng et al, 2021) was proposed as a general approach to phase minor variants while handling high error rates and could theoretically assemble both bacterial and viral haplotypes.…”

Easing genomic surveillance: A comprehensive performance evaluation of long-read assemblers across multi-strain mixture data of HIV-1 and Other pathogenic viruses for constructing a user-friendly bioinformatic pipeline

“…This study aimed to assess the accuracy, performance, and computational efficacy of seven candidate long-read assemblers, categorized into two approaches for haplotype reconstruction: (1) de novo long-read assemblers, which include Canu, 47 Goldrush, 48 MetaFlye, 42 and Strainline, 28 and (2) reference-based long-read assemblers, which comprise HaploDMF, 43 iGDA, 44 and RVHaplo. 45 The assembly software, including Canu, MetaFlye, and iGDA, was installed using the Micromamba package manager, which utilized recipes from the conda-forge (https://anaconda.org/conda-forge) and bioconda channels (https://anaconda.org/bioconda).…”

“…28,42 HaploDMF, iGDA, and RVHaplo are reference-based assemblers designed for multi-strain mixtures. [43][44][45] iGDA and RVHaplo employ similar clustering approaches, while HaploDMF uses deep matrix factorization for contig extension. 43,46 Despite numerous assembly software options tailored for SMS data, 28,[42][43][44][45]47,48 selecting the best tool for haplotype reconstruction and HIV-1 quasispecies analysis is challenging due to a lack of systematic studies.…”

“…[43][44][45] iGDA and RVHaplo employ similar clustering approaches, while HaploDMF uses deep matrix factorization for contig extension. 43,46 Despite numerous assembly software options tailored for SMS data, 28,[42][43][44][45]47,48 selecting the best tool for haplotype reconstruction and HIV-1 quasispecies analysis is challenging due to a lack of systematic studies. Furthermore, the absence of standardized benchmarking across different software and computing environments complicates the selection process, especially with limited computational resources.…”