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Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data

Overview of attention for article published in Genome Biology (Online Edition), July 2018
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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 (91st percentile)

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1 blog
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37 tweeters

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17 Dimensions

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119 Mendeley
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Title
Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data
Published in
Genome Biology (Online Edition), July 2018
DOI 10.1186/s13059-018-1466-5
Pubmed ID
Authors

Alemu Takele Assefa, Katrijn De Paepe, Celine Everaert, Pieter Mestdagh, Olivier Thas, Jo Vandesompele

Abstract

Long non-coding RNAs (lncRNAs) are typically expressed at low levels and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 25 pipelines for testing DE in RNA-seq data is comprehensively evaluated, with a particular focus on lncRNAs and low-abundance mRNAs. Fifteen performance metrics are used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets. Gene expression data are simulated using non-parametric procedures in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, results for mRNA and lncRNA were tracked separately. All the pipelines exhibit inferior performance for lncRNAs compared to mRNAs across all simulated scenarios and benchmark RNA-seq datasets. The substandard performance of DE tools for lncRNAs applies also to low-abundance mRNAs. No single tool uniformly outperformed the others. Variability, number of samples, and fraction of DE genes markedly influenced DE tool performance. Overall, linear modeling with empirical Bayes moderation (limma) and a non-parametric approach (SAMSeq) showed good control of the false discovery rate and reasonable sensitivity. Of note, for achieving a sensitivity of at least 50%, more than 80 samples are required when studying expression levels in realistic settings such as in clinical cancer research. About half of the methods showed a substantial excess of false discoveries, making these methods unreliable for DE analysis and jeopardizing reproducible science. The detailed results of our study can be consulted through a user-friendly web application, giving guidance on selection of the optimal DE tool ( http://statapps.ugent.be/tools/AppDGE/ ).

Twitter Demographics

The data shown below were collected from the profiles of 37 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 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 119 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 28%
Student > Ph. D. Student 28 24%
Student > Master 12 10%
Student > Bachelor 10 8%
Student > Doctoral Student 7 6%
Other 13 11%
Unknown 16 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 48 40%
Agricultural and Biological Sciences 32 27%
Medicine and Dentistry 4 3%
Computer Science 3 3%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Other 7 6%
Unknown 23 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 May 2020.
All research outputs
#836,199
of 16,123,692 outputs
Outputs from Genome Biology (Online Edition)
#800
of 3,434 outputs
Outputs of similar age
#25,115
of 280,128 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 16,123,692 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 3,434 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.9. This one has done well, scoring higher than 76% of its peers.
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