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Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq

Overview of attention for article published in BMC Bioinformatics, January 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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1 blog
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41 X users
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2 Facebook pages

Citations

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60 Dimensions

Readers on

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309 Mendeley
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4 CiteULike
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Title
Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq
Published in
BMC Bioinformatics, January 2017
DOI 10.1186/s12859-016-1457-z
Pubmed ID
Authors

Claire R. Williams, Alyssa Baccarella, Jay Z. Parrish, Charles C. Kim

Abstract

RNA-Seq has supplanted microarrays as the preferred method of transcriptome-wide identification of differentially expressed genes. However, RNA-Seq analysis is still rapidly evolving, with a large number of tools available for each of the three major processing steps: read alignment, expression modeling, and identification of differentially expressed genes. Although some studies have benchmarked these tools against gold standard gene expression sets, few have evaluated their performance in concert with one another. Additionally, there is a general lack of testing of such tools on real-world, physiologically relevant datasets, which often possess qualities not reflected in tightly controlled reference RNA samples or synthetic datasets. Here, we evaluate 219 combinatorial implementations of the most commonly used analysis tools for their impact on differential gene expression analysis by RNA-Seq. A test dataset was generated using highly purified human classical and nonclassical monocyte subsets from a clinical cohort, allowing us to evaluate the performance of 495 unique workflows, when accounting for differences in expression units and gene- versus transcript-level estimation. We find that the choice of methodologies leads to wide variation in the number of genes called significant, as well as in performance as gauged by precision and recall, calculated by comparing our RNA-Seq results to those from four previously published microarray and BeadChip analyses of the same cell populations. The method of differential gene expression identification exhibited the strongest impact on performance, with smaller impacts from the choice of read aligner and expression modeler. Many workflows were found to exhibit similar overall performance, but with differences in their calibration, with some biased toward higher precision and others toward higher recall. There is significant heterogeneity in the performance of RNA-Seq workflows to identify differentially expressed genes. Among the higher performing workflows, different workflows exhibit a precision/recall tradeoff, and the ultimate choice of workflow should take into consideration how the results will be used in subsequent applications. Our analyses highlight the performance characteristics of these workflows, and the data generated in this study could also serve as a useful resource for future development of software for RNA-Seq analysis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 41 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 309 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 5 2%
Spain 3 <1%
United States 2 <1%
Israel 1 <1%
Finland 1 <1%
Czechia 1 <1%
Sweden 1 <1%
Denmark 1 <1%
Germany 1 <1%
Other 0 0%
Unknown 293 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 88 28%
Student > Ph. D. Student 62 20%
Student > Master 35 11%
Student > Bachelor 28 9%
Other 13 4%
Other 40 13%
Unknown 43 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 97 31%
Biochemistry, Genetics and Molecular Biology 89 29%
Computer Science 21 7%
Medicine and Dentistry 12 4%
Neuroscience 8 3%
Other 32 10%
Unknown 50 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 30. 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 14 April 2017.
All research outputs
#1,235,087
of 24,598,501 outputs
Outputs from BMC Bioinformatics
#137
of 7,559 outputs
Outputs of similar age
#26,638
of 427,201 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 143 outputs
Altmetric has tracked 24,598,501 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,559 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 427,201 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 93% of its contemporaries.
We're also able to compare this research output to 143 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.