A prominent challenge in computational biology is to distinguish microbial haplotypes -- closely related organisms with highly similar genomes -- due to small genomic differences that can cause significant phenotypic variations. Current genome assembly tools struggle with distinguishing these haplotypes, especially for long-read sequencing data with high error rates, such as PacBio or Oxford Nanopore Technology (ONT) reads. While existing methods work well for either viral or bacterial haplotypes, they often fail with low-abundance haplotypes and are computationally intensive.

This work by Faure, Lavenier, and Flot [1] introduces a new tool -- HairSplitter -- that offers a solution for both viral and bacterial haplotype separation, even with error-prone long reads. It does this by efficiently calling variants, clustering reads into haplotypes, creating new separated contigs, and resolving the assembly graph. A key advantage of HairSplitter is that it is entirely parameter-free and does not require prior knowledge of the organism's ploidy. HairSplitter is designed to handle both metaviromes and bacterial metagenomes, offering a more versatile and efficient solution than existing tools, like stRainy [2], Strainberry [3], and hifiasm-meta [4].

References

[1] Roland Faure, Dominique Lavenier, Jean-François Flot (2024) HairSplitter: haplotype assembly from long, noisy reads. bioRxiv, ver.3 peer-reviewed and recommended by PCI Math Comp Biol https://doi.org/10.1101/2024.02.13.580067

[2] Kazantseva E, A Donmez, M Pop, and M Kolmogorov (2023). stRainy: assembly-based metagenomic strain phasing using long reads. Bioinformatics. https://doi.org/10.1101/2023.01.31.526521

[3] Vicedomini R, C Quince, AE Darling, and R Chikhi (2021). Strainberry: automated strain separation in low complexity metagenomes using long reads. Nature Communications, 12, 4485. ISSN: 2041-1723. https://doi.org/10.1038/s41467-021-24515-9

[4] Feng X, H Cheng, D Portik, and H Li (2022). Metagenome assembly of high-fidelity long reads with hifiasm-meta. Nature Methods, 19, 1–4. https://doi.org/10.1038/s41592-022-01478-3