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systemPipeR: NGS workflow and report generation environment

Overview of attention for article published in BMC Bioinformatics, September 2016
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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 (86th percentile)

Mentioned by

blogs
1 blog
twitter
7 X users

Citations

dimensions_citation
169 Dimensions

Readers on

mendeley
202 Mendeley
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2 CiteULike
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Title
systemPipeR: NGS workflow and report generation environment
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1241-0
Pubmed ID
Authors

Tyler W. H. Backman, Thomas Girke

Abstract

Next-generation sequencing (NGS) has revolutionized how research is carried out in many areas of biology and medicine. However, the analysis of NGS data remains a major obstacle to the efficient utilization of the technology, as it requires complex multi-step processing of big data demanding considerable computational expertise from users. While substantial effort has been invested on the development of software dedicated to the individual analysis steps of NGS experiments, insufficient resources are currently available for integrating the individual software components within the widely used R/Bioconductor environment into automated workflows capable of running the analysis of most types of NGS applications from start-to-finish in a time-efficient and reproducible manner. To address this need, we have developed the R/Bioconductor package systemPipeR. It is an extensible environment for both building and running end-to-end analysis workflows with automated report generation for a wide range of NGS applications. Its unique features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software on local computers and computer clusters. A flexible sample annotation infrastructure efficiently handles complex sample sets and experimental designs. To simplify the analysis of widely used NGS applications, the package provides pre-configured workflows and reporting templates for RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Additional workflow templates will be provided in the future. systemPipeR accelerates the extraction of reproducible analysis results from NGS experiments. By combining the capabilities of many R/Bioconductor and command-line tools, it makes efficient use of existing software resources without limiting the user to a set of predefined methods or environments. systemPipeR is freely available for all common operating systems from Bioconductor ( http://bioconductor.org/packages/devel/systemPipeR ).

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users 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 202 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
United States 1 <1%
China 1 <1%
Italy 1 <1%
Unknown 198 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 45 22%
Student > Ph. D. Student 41 20%
Student > Doctoral Student 17 8%
Student > Bachelor 16 8%
Student > Master 14 7%
Other 32 16%
Unknown 37 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 58 29%
Agricultural and Biological Sciences 57 28%
Computer Science 13 6%
Engineering 7 3%
Medicine and Dentistry 6 3%
Other 19 9%
Unknown 42 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 24 September 2016.
All research outputs
#2,995,697
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#1,017
of 7,400 outputs
Outputs of similar age
#51,279
of 322,199 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 123 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 86% 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 322,199 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 123 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.