Guoxian Yu1, Yuan Jiang1, Jun Wang1, Hao Zhang2, Haiwei Luo2. 1. College of Computer and Information Science, Southwest University, Chongqing, China. 2. School of Life Sciences and Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Abstract
Motivation: Metagenomics investigates the DNA sequences directly recovered from environmental samples. It often starts with reads assembly, which leads to contigs rather than more complete genomes. Therefore, contig binning methods are subsequently used to bin contigs into genome bins. While some clustering-based binning methods have been developed, they generally suffer from problems related to stability and robustness. Results: We introduce BMC3C, an ensemble clustering-based method, to accurately and robustly bin contigs by making use of DNA sequence Composition, Coverage across multiple samples and Codon usage. BMC3C begins by searching the proper number of clusters and repeatedly applying the k-means clustering with different initializations to cluster contigs. Next, a weight graph with each node representing a contig is derived from these clusters. If two contigs are frequently grouped into the same cluster, the weight between them is high, and otherwise low. BMC3C finally employs a graph partitioning technique to partition the weight graph into subgraphs, each corresponding to a genome bin. We conduct experiments on both simulated and real-world datasets to evaluate BMC3C, and compare it with the state-of-the-art binning tools. We show that BMC3C has an improved performance compared to these tools. To our knowledge, this is the first time that the codon usage features and ensemble clustering are used in metagenomic contig binning. Availability and implementation: The codes of BMC3C are available at http://mlda.swu.edu.cn/codes.php?name=BMC3C. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Metagenomics investigates the DNA sequences directly recovered from environmental samples. It often starts with reads assembly, which leads to contigs rather than more complete genomes. Therefore, contig binning methods are subsequently used to bin contigs into genome bins. While some clustering-based binning methods have been developed, they generally suffer from problems related to stability and robustness. Results: We introduce BMC3C, an ensemble clustering-based method, to accurately and robustly bin contigs by making use of DNA sequence Composition, Coverage across multiple samples and Codon usage. BMC3C begins by searching the proper number of clusters and repeatedly applying the k-means clustering with different initializations to cluster contigs. Next, a weight graph with each node representing a contig is derived from these clusters. If two contigs are frequently grouped into the same cluster, the weight between them is high, and otherwise low. BMC3C finally employs a graph partitioning technique to partition the weight graph into subgraphs, each corresponding to a genome bin. We conduct experiments on both simulated and real-world datasets to evaluate BMC3C, and compare it with the state-of-the-art binning tools. We show that BMC3C has an improved performance compared to these tools. To our knowledge, this is the first time that the codon usage features and ensemble clustering are used in metagenomic contig binning. Availability and implementation: The codes of BMC3C are available at http://mlda.swu.edu.cn/codes.php?name=BMC3C. Supplementary information: Supplementary data are available at Bioinformatics online.