A binning tool to reconstruct viral haplotypes from assembled contigs
Related Research Unit(s)
|Journal / Publication||BMC Bioinformatics|
|Online published||4 Nov 2019|
|Publication status||Published - 2019|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85074546599&origin=recordpage|
Results: We developed a contig binning tool, VirBin, which clusters contigs into different groups so that each grouprepresents a haplotype. Commonly used features based on sequence composition and contig coverage cannoteffectively distinguish viral haplotypes because of their high sequence similarity and heterogeneous sequencingcoverage for RNA viruses. VirBin applied prototype-based clustering to cluster regions that are more likely to containmutations specific to a haplotype. The tool was tested on multiple simulated sequencing data with differenthaplotype abundance distributions and contig sizes, and also on mock quasispecies sequencing data. The benchmarkresults with other contig binning tools demonstrated the superior sensitivity and precision of VirBin in contig binningfor viral haplotype reconstruction.
Conclusions: In this work, we presented VirBin, a new contig binning tool for distinguishing contigs from differentviral haplotypes with high sequence similarity. It competes favorably with other tools on viral contig binning. Thesource codes are available at: https://github.com/chjiao/VirBin.
- RNA viral haplotype, K-means clustering, Contig binning
A binning tool to reconstruct viral haplotypes from assembled contigs. / Chen, Jiao; Shang, Jiayu; Wang, Jianrong; Sun, Yanni.In: BMC Bioinformatics, Vol. 20, 544, 2019.