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Related Concept Videos

RNA-seq03:21

RNA-seq

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 microarray-based...

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Unbiased Deep Sequencing of RNA Viruses from Clinical Samples
09:36

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

Mining RNA-seq data for infections and contaminations.

Thomas Bonfert1, Gergely Csaba, Ralf Zimmer

  • 1Institute for Informatics, Ludwig-Maximilians-Universität München, Munich, Germany.

Plos One
|September 11, 2013
PubMed
Summary

ContextMap software easily screens RNA sequencing (RNA-seq) data for microbial and viral infections. This tool enhances transcriptomic analysis by identifying contaminants and analyzing gene expression in infecting agents.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • RNA sequencing (RNA-seq) offers nucleotide-resolution transcriptomics, including for host-infecting viruses and microbes.
  • Standard mapping approaches often overlook non-host expression and struggle with genome redundancies and gaps.

Purpose of the Study:

  • To introduce ContextMap, a novel mapping software for detecting microbial and viral contaminations and infections in RNA-seq data.
  • To assess the performance of ContextMap against existing metagenomics tools.

Main Methods:

  • Developed and applied ContextMap for screening RNA-seq reads for contaminants and infections.
  • Utilized mapping-derived statistics to evaluate species/strain identification confidence and misidentifications.
  • Compared ContextMap's runtime and mapping capabilities with GASiC, GRAMMy, and MEGAN4.

Main Results:

  • ContextMap efficiently screens RNA-seq data for infections and contaminations.
  • The software provides mapping confidence, assesses species/strain similarities, and identifies misalignments.
  • ContextMap significantly outperformed GASiC and GRAMMy in runtime and offered superior read mapping resolution compared to MEGAN4.

Conclusions:

  • Routinely mining RNA-seq experiments for microbial and viral infections is crucial.
  • ContextMap enables the analysis of infecting agent gene expression, offering new insights into infection processes and tumorigenesis.