Chapter title |
Deciphering metatranscriptomic data.
|
---|---|
Chapter number | 17 |
Book title |
RNA Bioinformatics
|
Published in |
Methods in molecular biology, December 2014
|
DOI | 10.1007/978-1-4939-2291-8_17 |
Pubmed ID | |
Book ISBNs |
978-1-4939-2290-1, 978-1-4939-2291-8
|
Authors |
Evguenia Kopylova, Laurent Noé, Corinne Da Silva, Jean-Frédéric Berthelot, Adriana Alberti, Jean-Marc Aury, Hélène Touzet |
Editors |
Ernesto Picardi |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 9% |
Canada | 1 | 9% |
Unknown | 9 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 3 | 27% |
Researcher | 2 | 18% |
Student > Ph. D. Student | 1 | 9% |
Student > Doctoral Student | 1 | 9% |
Professor | 1 | 9% |
Other | 1 | 9% |
Unknown | 2 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 4 | 36% |
Agricultural and Biological Sciences | 3 | 27% |
Computer Science | 1 | 9% |
Earth and Planetary Sciences | 1 | 9% |
Unknown | 2 | 18% |