Deciphering Metatranscriptomic Data

  • Evguenia KopylovaEmail author
  • Laurent Noé
  • Corinne Da Silva
  • Jean-Frédéric Berthelot
  • Adriana Alberti
  • Jean-Marc Aury
  • Hélène Touzet
Part of the Methods in Molecular Biology book series (MIMB, volume 1269)


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.

Key words

Metatranscriptomics High-throughput sequencing 16S rRNA phylogenetic analysis 



This research was supported by the French National Agency for Research (grant ANR-2010-COSI-004) and the French National Sequencing Center (Genoscope).


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Evguenia Kopylova
    • 1
    • 2
    Email author
  • Laurent Noé
    • 1
    • 2
  • Corinne Da Silva
    • 3
  • Jean-Frédéric Berthelot
    • 2
  • Adriana Alberti
    • 3
  • Jean-Marc Aury
    • 3
  • Hélène Touzet
    • 1
    • 2
  1. 1.LIFL, UMR CNRS 8022Lille 1 UniversityVilleneuve d’AscqFrance
  2. 2.Inria Lille Nord-EuropeVilleneuve d’AscqFrance
  3. 3.Genoscope—National Sequencing CenterEvryFrance

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