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Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage

Overview of attention for article published in Genome Biology (Online Edition), January 2016
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)

Mentioned by

1 blog
47 tweeters
1 Facebook page
1 Google+ user


94 Dimensions

Readers on

253 Mendeley
3 CiteULike
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Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage
Published in
Genome Biology (Online Edition), January 2016
DOI 10.1186/s13059-015-0862-3
Pubmed ID

Charlotte Soneson, Katarina L. Matthes, Malgorzata Nowicka, Charity W. Law, Mark D. Robinson


RNA-seq has been a boon to the quantitative analysis of transcriptomes. A notable application is the detection of changes in transcript usage between experimental conditions. For example, discovery of pathological alternative splicing may allow the development of new treatments or better management of patients. From an analysis perspective, there are several ways to approach RNA-seq data to unravel differential transcript usage, such as annotation-based exon-level counting, differential analysis of the percentage spliced in, or quantitative analysis of assembled transcripts. The goal of this research is to compare and contrast current state-of-the-art methods, and to suggest improvements to commonly used work flows. We assess the performance of representative work flows using synthetic data and explore the effect of using non-standard counting bin definitions as input to DEXSeq, a state-of-the-art inference engine. Although the canonical counting provided the best results overall, several non-canonical approaches were as good or better in specific aspects and most counting approaches outperformed the evaluated event- and assembly-based methods. We show that an incomplete annotation catalog can have a detrimental effect on the ability to detect differential transcript usage in transcriptomes with few isoforms per gene and that isoform-level prefiltering can considerably improve false discovery rate control. Count-based methods generally perform well in the detection of differential transcript usage. Controlling the false discovery rate at the imposed threshold is difficult, particularly in complex organisms, but can be improved by prefiltering the annotation catalog.

Twitter Demographics

The data shown below were collected from the profiles of 47 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 253 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
Germany 2 <1%
Brazil 2 <1%
Spain 2 <1%
Canada 1 <1%
Argentina 1 <1%
Ukraine 1 <1%
China 1 <1%
Australia 1 <1%
Other 2 <1%
Unknown 235 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 76 30%
Student > Ph. D. Student 61 24%
Student > Master 39 15%
Student > Bachelor 14 6%
Other 14 6%
Other 30 12%
Unknown 19 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 91 36%
Agricultural and Biological Sciences 87 34%
Computer Science 19 8%
Immunology and Microbiology 4 2%
Medicine and Dentistry 4 2%
Other 18 7%
Unknown 30 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 33. 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 12 June 2020.
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Outputs from Genome Biology (Online Edition)
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Outputs of similar age from Genome Biology (Online Edition)
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Altmetric has tracked 17,944,974 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 3,680 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.7. This one has done well, scoring higher than 81% of its peers.
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