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BART: bioinformatics array research tool

Overview of attention for article published in BMC Bioinformatics, August 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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Title
BART: bioinformatics array research tool
Published in
BMC Bioinformatics, August 2018
DOI 10.1186/s12859-018-2308-x
Pubmed ID
Authors

Maria Luisa Amaral, Galina A. Erikson, Maxim N. Shokhirev

Abstract

Microarray experiments comprise more than half of all series in the Gene Expression Omnibus (GEO). However, downloading and analyzing raw or semi-processed microarray data from GEO is not intuitive and requires manual error-prone analysis and a bioinformatics background. This is due to a lack of standardization in array platform fabrication as well as the lack of a simple interactive tool for clustering, plotting, differential expression testing, and testing for functional enrichment. We introduce the Bioinformatics Array Research Tool (BART), an R Shiny web application that automates the microarray download and analysis process across diverse microarray platforms. It provides an intuitive interface, automatically downloads and parses data from GEO, suggests groupings of samples for differential expression testing, performs batch effect correction, outputs quality control plots, converts probe IDs, generates full lists of differentially expressed genes, and performs functional enrichment analysis. We show that BART enables a more comprehensive analysis of a wider range of microarray datasets on GEO by comparing it to four leading online microarray analysis tools. BART allows a scientist with no bioinformatics background to extract knowledge from their own microarray data or microarray experiments available from GEO. BART is functional on more microarray experiments and provides more comprehensive analyses than extant microarray analysis tools. BART is hosted on bart.salk.edu , includes a user tutorial, and is available for download from https://bitbucket.org/Luisa_amaral/bart .

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 21%
Student > Bachelor 8 15%
Researcher 8 15%
Student > Master 6 11%
Student > Doctoral Student 3 6%
Other 6 11%
Unknown 11 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 26%
Agricultural and Biological Sciences 10 19%
Computer Science 5 9%
Neuroscience 2 4%
Social Sciences 2 4%
Other 6 11%
Unknown 14 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 15 August 2018.
All research outputs
#3,815,738
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#1,377
of 7,418 outputs
Outputs of similar age
#72,649
of 332,229 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 96 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,418 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 81% 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 332,229 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 78% of its contemporaries.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.