MOTIVATION: The field of metagenomics has provided valuable insights into the structure, diversity and ecology within microbial communities. One key step in metagenomics analysis is to assemble reads into longer contigs which are then binned into groups of contigs that belong to different species present in the metagenomic sample. Binning of contigs plays an important role in metagenomics and most available binning algorithms bin contigs using genomic features such as oligonucleotide/k-mer composition and contig coverage. As metagenomic contigs are derived from the assembly process, they are output from the underlying assembly graph which contains valuable connectivity information between contigs that can be used for binning. RESULTS: We propose GraphBin, a new binning method that makes use of the assembly graph and applies a label propagation algorithm to refine the binning result of existing tools. We show that GraphBin can make use of the assembly graphs constructed from both the de Bruijn graph and the overlap-layout-consensus approach. Moreover, we demonstrate improved experimental results from GraphBin in terms of identifying mis-binned contigs and binning of contigs discarded by existing binning tools. To the best of our knowledge, this is the first time that the information from the assembly graph has been used in a tool for the binning of metagenomic contigs. AVAILABILITY AND IMPLEMENTATION: The source code of GraphBin is available at https://github.com/Vini2/GraphBin. CONTACT: vijini.mallawaarachchi@anu.edu.au or yu.lin@anu.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The field of metagenomics has provided valuable insights into the structure, diversity and ecology within microbial communities. One key step in metagenomics analysis is to assemble reads into longer contigs which are then binned into groups of contigs that belong to different species present in the metagenomic sample. Binning of contigs plays an important role in metagenomics and most available binning algorithms bin contigs using genomic features such as oligonucleotide/k-mer composition and contig coverage. As metagenomic contigs are derived from the assembly process, they are output from the underlying assembly graph which contains valuable connectivity information between contigs that can be used for binning. RESULTS: We propose GraphBin, a new binning method that makes use of the assembly graph and applies a label propagation algorithm to refine the binning result of existing tools. We show that GraphBin can make use of the assembly graphs constructed from both the de Bruijn graph and the overlap-layout-consensus approach. Moreover, we demonstrate improved experimental results from GraphBin in terms of identifying mis-binned contigs and binning of contigs discarded by existing binning tools. To the best of our knowledge, this is the first time that the information from the assembly graph has been used in a tool for the binning of metagenomic contigs. AVAILABILITY AND IMPLEMENTATION: The source code of GraphBin is available at https://github.com/Vini2/GraphBin. CONTACT: vijini.mallawaarachchi@anu.edu.au or yu.lin@anu.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Elizabeth G Wilbanks; Hugo Doré; Meredith H Ashby; Cheryl Heiner; Richard J Roberts; Jonathan A Eisen Journal: ISME J Date: 2022-04-22 Impact factor: 11.217
Authors: Mindia A S Haryono; Ying Yu Law; Krithika Arumugam; Larry C-W Liew; Thi Quynh Ngoc Nguyen; Daniela I Drautz-Moses; Stephan C Schuster; Stefan Wuertz; Rohan B H Williams Journal: Front Microbiol Date: 2022-06-13 Impact factor: 6.064
Authors: Corinna Breusing; Maximilian Genetti; Shelbi L Russell; Russell B Corbett-Detig; Roxanne A Beinart Journal: Proc Natl Acad Sci U S A Date: 2022-03-29 Impact factor: 12.779