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Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems

Overview of attention for article published in BMC Bioinformatics, December 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#12 of 7,089)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
1 blog
twitter
131 tweeters
facebook
3 Facebook pages
wikipedia
2 Wikipedia pages
googleplus
1 Google+ user

Citations

dimensions_citation
83 Dimensions

Readers on

mendeley
288 Mendeley
citeulike
2 CiteULike
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Title
Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0364-4
Pubmed ID
Authors

Ruolin Liu, Ann E Loraine, Julie A Dickerson

Abstract

BackgroundAlternative Splicing (AS) as a post-transcription regulation mechanism is an important application of RNA-seq studies in eukaryotes. A number of software and computational methods have been developed for detecting AS. Most of the methods, however, are designed and tested on animal data, such as human and mouse. Plants genes differ from those of animals in many ways, e.g., the average intron size and preferred AS types. These differences may require different computational approaches and raise questions about their effectiveness on plant data. The goal of this paper is to benchmark existing computational differential splicing (or transcription) detection methods so that biologists can choose the most suitable tools to accomplish their goals.ResultsThis study compares the eight popular public available software packages for differential splicing analysis using both simulated and real Arabidopsis thaliana RNA-seq data. All software are freely available. The study examines the effect of varying AS ratio, read depth, dispersion pattern, AS types, sample sizes and the influence of annotation. Using a real data, the study looks at the consistences between the packages and verifies a subset of the detected AS events using PCR studies.ConclusionsNo single method performs the best in all situations. The accuracy of annotation has a major impact on which method should be chosen for AS analysis. DEXSeq performs well in the simulated data when the AS signal is relative strong and annotation is accurate. Cufflinks achieve a better tradeoff between precision and recall and turns out to be the best one when incomplete annotation is provided. Some methods perform inconsistently for different AS types. Complex AS events that combine several simple AS events impose problems for most methods, especially for MATS. MATS stands out in the analysis of real RNA-seq data when all the AS events being evaluated are simple AS events.

Twitter Demographics

The data shown below were collected from the profiles of 131 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 288 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
Germany 3 1%
Japan 2 <1%
United Kingdom 2 <1%
Sweden 2 <1%
Brazil 1 <1%
France 1 <1%
Switzerland 1 <1%
Malaysia 1 <1%
Other 3 1%
Unknown 266 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 87 30%
Researcher 66 23%
Student > Master 46 16%
Student > Bachelor 18 6%
Student > Doctoral Student 15 5%
Other 38 13%
Unknown 18 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 162 56%
Biochemistry, Genetics and Molecular Biology 57 20%
Computer Science 16 6%
Medicine and Dentistry 8 3%
Engineering 6 2%
Other 15 5%
Unknown 24 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 87. 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 31 December 2019.
All research outputs
#391,785
of 22,053,184 outputs
Outputs from BMC Bioinformatics
#12
of 7,089 outputs
Outputs of similar age
#4,738
of 307,066 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 468 outputs
Altmetric has tracked 22,053,184 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,089 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 particularly well, scoring higher than 99% 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 307,066 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 98% of its contemporaries.
We're also able to compare this research output to 468 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 99% of its contemporaries.