Commet: comparing and combining multiple metagenomic datasets

TL;DR

Commet is a scalable method for comparing multiple large metagenomic datasets directly from raw reads, enabling similarity analysis, clustering, and visualization without assembly.

Contribution

It introduces an efficient indexing and bit vector-based approach for large-scale metagenomic dataset comparison and clustering, overcoming previous scalability limitations.

Findings

Enables all-against-all comparison of large metagenomic datasets

Provides a compressed representation of read files for efficient analysis

Facilitates visualization of dataset similarities through clustering

Abstract

Metagenomics offers a way to analyze biotopes at the genomic level and to reach functional and taxonomical conclusions. The bio-analyzes of large metagenomic projects face critical limitations: complex metagenomes cannot be assembled and the taxonomical or functional annotations are much smaller than the real biological diversity. This motivated the development of de novo metagenomic read comparison approaches to extract information contained in metagenomic datasets. However, these new approaches do not scale up large metagenomic projects, or generate an important number of large intermediate and result files. We introduce Commet ("COmpare Multiple METagenomes"), a method that provides similarity overview between all datasets of large metagenomic projects. Directly from non-assembled reads, all against all comparisons are performed through an efficient indexing strategy. Then, results are stored as bit vectors, a compressed representation of read files, that can be used to further combine read subsets by common logical operations. Finally, Commet computes a clusterization of metagenomic datasets, which is visualized by dendrogram and heatmaps. Availability: http://github.com/pierrepeterlongo/commet