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S-MART, A Software Toolbox to Aid RNA-seq Data Analysis

Overview of attention for article published in PLOS ONE, October 2011
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Title
S-MART, A Software Toolbox to Aid RNA-seq Data Analysis
Published in
PLOS ONE, October 2011
DOI 10.1371/journal.pone.0025988
Pubmed ID
Authors

Matthias Zytnicki, Hadi Quesneville

Abstract

High-throughput sequencing is now routinely performed in many experiments. But the analysis of the millions of sequences generated, is often beyond the expertise of the wet labs who have no personnel specializing in bioinformatics. Whereas several tools are now available to map high-throughput sequencing data on a genome, few of these can extract biological knowledge from the mapped reads. We have developed a toolbox called S-MART, which handles mapped RNA-Seq data. S-MART is an intuitive and lightweight tool which performs many of the tasks usually required for the analysis of mapped RNA-Seq reads. S-MART does not require any computer science background and thus can be used by all of the biologist community through a graphical interface. S-MART can run on any personal computer, yielding results within an hour even for Gb of data for most queries. S-MART may perform the entire analysis of the mapped reads, without any need for other ad hoc scripts. With this tool, biologists can easily perform most of the analyses on their computer for their RNA-Seq data, from the mapped data to the discovery of important loci.

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Mendeley readers

The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Netherlands 1 1%
Italy 1 1%
Brazil 1 1%
Germany 1 1%
Czechia 1 1%
Sweden 1 1%
China 1 1%
New Zealand 1 1%
Other 0 0%
Unknown 70 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 43%
Student > Ph. D. Student 12 15%
Professor > Associate Professor 6 7%
Professor 6 7%
Student > Master 6 7%
Other 12 15%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 53 65%
Biochemistry, Genetics and Molecular Biology 8 10%
Computer Science 4 5%
Mathematics 2 2%
Immunology and Microbiology 2 2%
Other 6 7%
Unknown 6 7%