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Measure transcript integrity using RNA-seq data

Overview of attention for article published in BMC Bioinformatics, February 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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

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18 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

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110 Dimensions

Readers on

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239 Mendeley
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Title
Measure transcript integrity using RNA-seq data
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0922-z
Pubmed ID
Authors

Liguo Wang, Jinfu Nie, Hugues Sicotte, Ying Li, Jeanette E. Eckel-Passow, Surendra Dasari, Peter T. Vedell, Poulami Barman, Liewei Wang, Richard Weinshiboum, Jin Jen, Haojie Huang, Manish Kohli, Jean-Pierre A. Kocher

Abstract

Stored biological samples with pathology information and medical records are invaluable resources for translational medical research. However, RNAs extracted from the archived clinical tissues are often substantially degraded. RNA degradation distorts the RNA-seq read coverage in a gene-specific manner, and has profound influences on whole-genome gene expression profiling. We developed the transcript integrity number (TIN) to measure RNA degradation. When applied to 3 independent RNA-seq datasets, we demonstrated TIN is a reliable and sensitive measure of the RNA degradation at both transcript and sample level. Through comparing 10 prostate cancer clinical samples with lower RNA integrity to 10 samples with higher RNA quality, we demonstrated that calibrating gene expression counts with TIN scores could effectively neutralize RNA degradation effects by reducing false positives and recovering biologically meaningful pathways. When further evaluating the performance of TIN correction using spike-in transcripts in RNA-seq data generated from the Sequencing Quality Control consortium, we found TIN adjustment had better control of false positives and false negatives (sensitivity = 0.89, specificity = 0.91, accuracy = 0.90), as compared to gene expression analysis results without TIN correction (sensitivity = 0.98, specificity = 0.50, accuracy = 0.86). TIN is a reliable measurement of RNA integrity and a valuable approach used to neutralize in vitro RNA degradation effect and improve differential gene expression analysis.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 239 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Argentina 1 <1%
Finland 1 <1%
Sweden 1 <1%
Denmark 1 <1%
China 1 <1%
Spain 1 <1%
Italy 1 <1%
Unknown 228 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 66 28%
Researcher 52 22%
Student > Master 24 10%
Student > Bachelor 17 7%
Student > Postgraduate 17 7%
Other 33 14%
Unknown 30 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 79 33%
Agricultural and Biological Sciences 62 26%
Medicine and Dentistry 16 7%
Computer Science 15 6%
Neuroscience 12 5%
Other 18 8%
Unknown 37 15%

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 04 November 2016.
All research outputs
#2,171,096
of 16,638,522 outputs
Outputs from BMC Bioinformatics
#854
of 5,984 outputs
Outputs of similar age
#50,913
of 347,277 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 7 outputs
Altmetric has tracked 16,638,522 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,984 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 85% 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 347,277 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 85% 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 5 of them.