Evidence-Based Clustering of Reads and Taxonomic Analysis of Metagenomic Data

Abstract. The rapidly emerging field of metagenomics seeks to examine the genomic content of communities of organisms to understand their roles and interactions in an ecosystem. In this paper we focus on clustering methods and their application to taxonomic analysis of metagenomic data. Clustering analysis for metagenomics amounts to group similar partial sequences, such as raw sequence reads, into clusters in order to discover information about the internal structure of the considered dataset, or the relative abundance of protein families. Different methods for clustering analysis of metagenomic datasets have been proposed. Here we focus on evidence-based methods for clustering that employ knowledge extracted from proteins identified by a BLASTx search (proxygenes). We consider two clustering algorithms introduced in previous work and a new one. We discuss advantages and drawbacks of the algorithms, and use them to perform taxonomic analysis of metagenomic data. To this aim, three ...

• Type: Conference
• Year: 2009
• Where: PRIB
• Authors: Gianluigi Folino, Fabio Gori, Mike S. M. Jetten, Elena Marchiori
• Keywords: Bioinformatics, Metagenomic, Metagenomic Data, PRIB 2009, Taxonomic Analysis