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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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SAMSA: a comprehensive metatranscriptome analysis pipeline.

Samuel T Westreich1,2, Ian Korf1,2, David A Mills3

  • 1Department of Molecular and Cellular Biology, University of California - Davis, Davis, CA, USA.

BMC Bioinformatics
|October 1, 2016
PubMed
Summary
This summary is machine-generated.

We developed SAMSA, a new bioinformatics pipeline for analyzing microbial community activity from RNA-seq data. This open-source tool, integrated with MG-RAST, simplifies metatranscriptome analysis and establishes best practices for sequencing stool samples.

Keywords:
Best practicesBig dataMetagenomeMetatranscriptomeMicrobiomePipelineRNA-seqSoftware package

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Metatranscriptomics, the study of microbial activity via RNA sequencing, faces limitations in available analysis tools.
  • Existing methods often require restricted databases, dedicated computing infrastructure, or unvalidated metagenomic approaches.
  • There is a need for accessible, comprehensive pipelines for analyzing metatranscriptomic data.

Purpose of the Study:

  • To develop and introduce a novel bioinformatics pipeline, SAMSA (Simple Annotation of Metatranscriptomes by Sequence Analysis), for analyzing metatranscriptomic datasets.
  • To provide researchers, particularly those with limited bioinformatics experience, with an accessible tool for understanding microbial community activity.
  • To establish best practices for sequencing and analyzing stool metatranscriptomes.

Main Methods:

  • Developed SAMSA, a software package running on Metagenome-RAST (MG-RAST) servers, specifically for metatranscriptome analysis.
  • Utilized pilot and simulated fecal metatranscriptomic data to determine optimal sequencing and analysis parameters.
  • Evaluated the impact of ribosomal RNA (rRNA) depletion and read length on data accuracy.

Main Results:

  • SAMSA provides a breakdown of transcriptional activity by organism and function, identifying abundant species and significant differences between samples.
  • Optimal sequencing for stool metatranscriptomes requires long reads (>100 bp) or joined paired-end reads, with 40-50 million raw sequences per sample.
  • Ribosomal RNA depletion is recommended, but remaining rRNA sequences should be discarded; mRNA counts accurately reflect organism transcriptional activity.

Conclusions:

  • The publicly available SAMSA pipeline offers a powerful new method for gaining deeper insights into microbial community activity.
  • Recommended best practices for stool metatranscriptomes include ribodepletion, 100 bp paired-end sequencing, and a minimum of 40 million reads per sample.
  • SAMSA facilitates the detection of significant differences in organismal and functional transcriptional activity between experimental groups.