A framework for space-efficient read clustering in metagenomic samples

Improving metagenomic binning results with overlapped bins using assembly graphs

Background

Metagenomic sequencing allows us to study the structure, diversity and ecology in microbial communities without the necessity of obtaining pure cultures. In many metagenomics studies, the reads obtained from metagenomics sequencing are first assembled into longer contigs and these contigs are then binned into clusters of contigs where contigs in a cluster are expected to come from the same species. As different species may share common sequences in their genomes, one assembled contig may belong to multiple species. However, existing tools for binning contigs only support non-overlapped binning, i.e., each contig is assigned to at most one bin (species).

Results

In this paper, we introduce GraphBin2 which refines the binning results obtained from existing tools and, more importantly, is able to assign contigs to multiple bins. GraphBin2 uses the connectivity and coverage information from assembly graphs to adjust existing binning results on contigs and to infer contigs shared by multiple species. Experimental results on both simulated and real datasets demonstrate that GraphBin2 not only improves binning results of existing tools but also supports to assign contigs to multiple bins.

Conclusion

GraphBin2 incorporates the coverage information into the assembly graph to refine the binning results obtained from existing binning tools. GraphBin2 also enables the detection of contigs that may belong to multiple species. We show that GraphBin2 outperforms its predecessor GraphBin on both simulated and real datasets. GraphBin2 is freely available at https://github.com/Vini2/GraphBin2.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13015-021-00185-6.

De Novo Clustering of Long-Read Transcriptome Data Using a Greedy, Quality Value-Based Algorithm

Long-read sequencing of transcripts with Pacific Biosciences (PacBio) Iso-Seq and Oxford Nanopore Technologies has proven to be central to the study of complex isoform landscapes in many organisms. However, current de novo transcript reconstruction algorithms from long-read data are limited, leaving the potential of these technologies unfulfilled. A common bottleneck is the dearth of scalable and accurate algorithms for clustering long reads according to their gene family of origin. To address this challenge, we develop isONclust, a clustering algorithm that is greedy (to scale) and makes use of quality values (to handle variable error rates). We test isONclust on three simulated and five biological data sets, across a breadth of organisms, technologies, and read depths. Our results demonstrate that isONclust is a substantial improvement over previous approaches, both in terms of overall accuracy and/or scalability to large data sets.