This article is part of the supplement: Proceedings of the Tenth Annual Research in Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics

Open Access Proceedings

Compareads: comparing huge metagenomic experiments

Nicolas Maillet1*, Claire Lemaitre1, Rayan Chikhi2, Dominique Lavenier1 and Pierre Peterlongo1*

Author affiliations

1 INRIA Rennes - Bretagne Atlantique/IRISA, EPI GenScale, Rennes, France

2 ENS Cachan/IRISA, EPI GenScale, Rennes, France

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 19):S10  doi:10.1186/1471-2105-13-S19-S10

Published: 19 December 2012



Nowadays, metagenomic sample analyses are mainly achieved by comparing them with a priori knowledge stored in data banks. While powerful, such approaches do not allow to exploit unknown and/or "unculturable" species, for instance estimated at 99% for Bacteria.


This work introduces Compareads, a de novo comparative metagenomic approach that returns the reads that are similar between two possibly metagenomic datasets generated by High Throughput Sequencers. One originality of this work consists in its ability to deal with huge datasets. The second main contribution presented in this paper is the design of a probabilistic data structure based on Bloom filters enabling to index millions of reads with a limited memory footprint and a controlled error rate.


We show that Compareads enables to retrieve biological information while being able to scale to huge datasets. Its time and memory features make Compareads usable on read sets each composed of more than 100 million Illumina reads in a few hours and consuming 4 GB of memory, and thus usable on today's personal computers.


Using a new data structure, Compareads is a practical solution for comparing de novo huge metagenomic samples. Compareads is released under the CeCILL license and can be freely downloaded from webcite.