COMMET: comparing and combining multiple metagenomic datasets

Nicolas Maillet 1 Guillaume Collet 2 Thomas Vannier 3 Dominique Lavenier 1 Pierre Peterlongo 1, *
* Auteur correspondant
1 GenScale - Scalable, Optimized and Parallel Algorithms for Genomics
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
2 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
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 metage-nomic 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 compar-isons 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.
Type de document :
Communication dans un congrès
IEEE BIBM 2014, Nov 2014, Belfast, United Kingdom. 2014
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Nicolas Maillet, Guillaume Collet, Thomas Vannier, Dominique Lavenier, Pierre Peterlongo. COMMET: comparing and combining multiple metagenomic datasets. IEEE BIBM 2014, Nov 2014, Belfast, United Kingdom. 2014. 〈hal-01080050〉

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