Deciphering metatranscriptomic data

Evguenia Kopylova 1, 2, 3 Laurent Noé 2, 1 Corinne Da Silva 4, 5 Jean-Frédéric Berthelot 2, 1 Adriana A. Alberti 4, 5 Jean-Marc Aury 4, 5 Helene Touzet 2, 1
1 BONSAI - Bioinformatics and Sequence Analysis
Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189, CNRS - Centre National de la Recherche Scientifique
Abstract : Metatranscriptomic data contributes another piece of the puzzle to understanding the phylogenetic structure and function of a community of organisms. High-quality total RNA is a bountiful mixture of ribosomal, transfer, messenger and other noncoding RNAs, where each family of RNA is vital to answering questions concerning the hidden microbial world. Software tools designed for deciphering metatranscriptomic data fall under two main categories: the first is to reassemble millions of short nucleotide fragments produced by high-throughput sequencing technologies into the original full-length transcriptomes for all organisms within a sample, and the second is to taxonomically classify the organisms and determine their individual functional roles within a community. Species identification is mainly established using the ribosomal RNA genes, whereas the behavior and functionality of a community is revealed by the messenger RNA of the expressed genes. Numerous chemical and computational methods exist to separate families of RNA prior to conducting further downstream analyses, primarily suitable for isolating mRNA or rRNA from a total RNA sample. In this chapter, we demonstrate a computational technique for filtering rRNA from total RNA using the software SortMeRNA. Additionally, we propose a post-processing pipeline using the latest software tools to conduct further studies on the filtered data, including the reconstruction of mRNA transcripts for functional analyses and phylogenetic classification of a community using the ribosomal RNA.
Type de document :
Chapitre d'ouvrage
Methods in Molecular Biology, 1269, Springer, pp.279-291, 2015, RNA Bioinformatics, 978-1-4939-2290-1. <10.1007/978-1-4939-2291-8_17>
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https://hal.inria.fr/hal-01104015
Contributeur : Laurent Noé <>
Soumis le : jeudi 15 janvier 2015 - 19:02:39
Dernière modification le : samedi 18 février 2017 - 01:14:25

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Evguenia Kopylova, Laurent Noé, Corinne Da Silva, Jean-Frédéric Berthelot, Adriana A. Alberti, et al.. Deciphering metatranscriptomic data. Methods in Molecular Biology, 1269, Springer, pp.279-291, 2015, RNA Bioinformatics, 978-1-4939-2290-1. <10.1007/978-1-4939-2291-8_17>. <hal-01104015>

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