Motivation: Metagenomics is a recent field of biology that studies microbial communities by analyzing their genomic content directly sequenced from the environment. A metagenomic dataset consists of many short DNA or RNA fragments called reads. One interesting problem in metagenomic data analysis is the discovery of the taxonomic composition of a given dataset. A simple method for this task, called the Lowest Common Ancestor (LCA), is employed in state-of-the-art computational tools for metagenomic data analysis of very short reads (about 100 bp). However LCA has two main drawbacks: it possibly assigns many reads to high taxonomic ranks and it discards a high number of reads.
Results: We present MTR, a new method for tackling these drawbacks using clustering at Multiple Taxonomic Ranks. Unlike LCA, which processes the reads one-by-one, MTR exploits information shared by reads. Specifically, MTR consists of two main phases. First, for each taxonomic rank, a collection of potential clusters of reads is generated, and each potential cluster is associated to a taxon at that rank. Next, a small number of clusters is selected at each rank using a combinatorial optimization algorithm. The effectiveness of the resulting method is tested on a large number of simulated and real-life metagenomes. Results of experiments show that MTR improves on LCA by discarding a significantly smaller number of reads and by assigning much more reads at lower taxonomic ranks. Moreover, MTR provides a more faithful taxonomic characterization of the metagenome population distribution.
Availability: Matlab and C++ source codes of the method available at http://cs.ru.nl/gori/software/MTR.tar.gz.