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SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis

Overview of attention for article published in BMC Bioinformatics, February 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
60 tweeters
facebook
1 Facebook page
wikipedia
1 Wikipedia page

Citations

dimensions_citation
35 Dimensions

Readers on

mendeley
142 Mendeley
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Title
SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0923-y
Pubmed ID
Authors

Benjamin K. Johnson, Matthew B. Scholz, Tracy K. Teal, Robert B. Abramovitch

Abstract

Many tools exist in the analysis of bacterial RNA sequencing (RNA-seq) transcriptional profiling experiments to identify differentially expressed genes between experimental conditions. Generally, the workflow includes quality control of reads, mapping to a reference, counting transcript abundance, and statistical tests for differentially expressed genes. In spite of the numerous tools developed for each component of an RNA-seq analysis workflow, easy-to-use bacterially oriented workflow applications to combine multiple tools and automate the process are lacking. With many tools to choose from for each step, the task of identifying a specific tool, adapting the input/output options to the specific use-case, and integrating the tools into a coherent analysis pipeline is not a trivial endeavor, particularly for microbiologists with limited bioinformatics experience. To make bacterial RNA-seq data analysis more accessible, we developed a Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis (SPARTA). SPARTA is a reference-based bacterial RNA-seq analysis workflow application for single-end Illumina reads. SPARTA is turnkey software that simplifies the process of analyzing RNA-seq data sets, making bacterial RNA-seq analysis a routine process that can be undertaken on a personal computer or in the classroom. The easy-to-install, complete workflow processes whole transcriptome shotgun sequencing data files by trimming reads and removing adapters, mapping reads to a reference, counting gene features, calculating differential gene expression, and, importantly, checking for potential batch effects within the data set. SPARTA outputs quality analysis reports, gene feature counts and differential gene expression tables and scatterplots. SPARTA provides an easy-to-use bacterial RNA-seq transcriptional profiling workflow to identify differentially expressed genes between experimental conditions. This software will enable microbiologists with limited bioinformatics experience to analyze their data and integrate next generation sequencing (NGS) technologies into the classroom. The SPARTA software and tutorial are available at sparta.readthedocs.org.

Twitter Demographics

The data shown below were collected from the profiles of 60 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
France 1 <1%
Germany 1 <1%
Luxembourg 1 <1%
Unknown 135 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 27%
Student > Ph. D. Student 31 22%
Student > Master 21 15%
Student > Bachelor 13 9%
Student > Doctoral Student 9 6%
Other 13 9%
Unknown 17 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 45 32%
Biochemistry, Genetics and Molecular Biology 43 30%
Immunology and Microbiology 12 8%
Engineering 4 3%
Computer Science 4 3%
Other 11 8%
Unknown 23 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 36. 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 03 September 2021.
All research outputs
#902,611
of 21,944,157 outputs
Outputs from BMC Bioinformatics
#83
of 7,070 outputs
Outputs of similar age
#18,224
of 377,755 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 7 outputs
Altmetric has tracked 21,944,157 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,070 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 particularly well, scoring higher than 98% 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 377,755 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them