↓ Skip to main content

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
Altmetric Badge

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 (94th percentile)

Mentioned by

blogs
1 blog
twitter
48 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
213 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
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
Authors

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

Abstract

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 48 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 213 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%
Denmark 1 <1%
Australia 1 <1%
Canada 1 <1%
China 1 <1%
Ukraine 1 <1%
Other 2 <1%
Unknown 195 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 68 32%
Student > Ph. D. Student 57 27%
Student > Master 30 14%
Other 12 6%
Student > Bachelor 9 4%
Other 26 12%
Unknown 11 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 82 38%
Biochemistry, Genetics and Molecular Biology 72 34%
Computer Science 16 8%
Immunology and Microbiology 4 2%
Medicine and Dentistry 4 2%
Other 15 7%
Unknown 20 9%

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.
All research outputs
#662,793
of 15,823,937 outputs
Outputs from Genome Biology (Online Edition)
#612
of 3,400 outputs
Outputs of similar age
#17,631
of 344,195 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 2 outputs
Altmetric has tracked 15,823,937 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 3,400 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.4. This one has done well, scoring higher than 82% 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 344,195 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 94% of its contemporaries.
We're also able to compare this research output to 2 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