↓ Skip to main content

RNA Bioinformatics

Overview of attention for book
Cover of 'RNA Bioinformatics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Free Energy Minimization to Predict RNA Secondary Structures and Computational RNA Design
  3. Altmetric Badge
    Chapter 2 RNA Secondary Structure Prediction from Multi-Aligned Sequences
  4. Altmetric Badge
    Chapter 3 A Simple Protocol for the Inference of RNA Global Pairwise Alignments
  5. Altmetric Badge
    Chapter 4 De Novo Secondary Structure Motif Discovery Using RNAProfile
  6. Altmetric Badge
    Chapter 5 Drawing and Editing the Secondary Structure(s) of RNA
  7. Altmetric Badge
    Chapter 6 Modeling and Predicting RNA Three-Dimensional Structures
  8. Altmetric Badge
    Chapter 7 Fast Prediction of RNA–RNA Interaction Using Heuristic Algorithm
  9. Altmetric Badge
    Chapter 8 Quality Control of RNA-Seq Experiments.
  10. Altmetric Badge
    Chapter 9 Accurate Mapping of RNA-Seq Data.
  11. Altmetric Badge
    Chapter 10 Quantifying Entire Transcriptomes by Aligned RNA-Seq Data
  12. Altmetric Badge
    Chapter 11 Transcriptome Assembly and Alternative Splicing Analysis
  13. Altmetric Badge
    Chapter 12 Detection of post-transcriptional RNA editing events.
  14. Altmetric Badge
    Chapter 13 Prediction of miRNA Targets
  15. Altmetric Badge
    Chapter 14 Using Deep Sequencing Data for Identification of Editing Sites in Mature miRNAs
  16. Altmetric Badge
    Chapter 15 NGS-Trex: An Automatic Analysis Workflow for RNA-Seq Data
  17. Altmetric Badge
    Chapter 16 e-DNA Meta-Barcoding: From NGS Raw Data to Taxonomic Profiling.
  18. Altmetric Badge
    Chapter 17 Deciphering metatranscriptomic data.
  19. Altmetric Badge
    Chapter 18 RIP-Seq Data Analysis to Determine RNA–Protein Associations
  20. Altmetric Badge
    Chapter 19 The ViennaRNA Web Services.
  21. Altmetric Badge
    Chapter 20 Exploring the RNA Editing Potential of RNA-Seq Data by ExpEdit
  22. Altmetric Badge
    Chapter 21 A Guideline for the Annotation of UTR Regulatory Elements in the UTRsite Collection
  23. Altmetric Badge
    Chapter 22 Rfam: Annotating Families of Non-Coding RNA Sequences
  24. Altmetric Badge
    Chapter 23 ASPicDB: A Database Web Tool for Alternative Splicing Analysis
  25. Altmetric Badge
    Chapter 24 Analysis of Alternative Splicing Events in Custom Gene Datasets by AStalavista.
  26. Altmetric Badge
    Chapter 25 Computational Design of Artificial RNA Molecules for Gene Regulation
Attention for Chapter 17: Deciphering metatranscriptomic data.
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

blogs
1 blog
twitter
1 X user

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
11 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 September 2015.
All research outputs
#3,876,341
of 23,498,099 outputs
Outputs from Methods in molecular biology
#970
of 13,368 outputs
Outputs of similar age
#54,013
of 356,912 outputs
Outputs of similar age from Methods in molecular biology
#56
of 992 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,368 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 356,912 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 992 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.