ArticlePloS one2014
A note on an exon-based strategy to identify differentially expressed genes in RNA-seq experiments.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Article
- Sensitive period-regulating genetic pathways and exposure to adversity shape risk for depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Article
- Exon-level estimates improve the detection of differentially expressed genes in RNA-seq studies.RNA biology · 2021Article
- Profiling RNA-Seq at multiple resolutions markedly increases the number of causal eQTLs in autoimmune disease.PLoS genetics · 2017Article
- The exon quantification pipeline (EQP): a comprehensive approach to the quantification of gene, exon and junction expression from RNA-seq data.Nucleic acids research · 2016Article
- RNA-Seq Count Data Modelling by Grey Relational Analysis and Nonparametric Gaussian Process.PloS one · 2016Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA-sequencing (RNA-seq) has rapidly become the method of choice in many genome-wide transcriptomic studies. To meet the high expectations posed by this technology, powerful computational techniques are needed to translate the measurements into biological and biomedical understanding. A number of statistical procedures have already been developed to identify differentially expressed genes between distinct sample groups. With these methods statistical testing is typically performed after the data has been summarized at the gene level. As an alternative strategy, developed with the aim to improve the results, we demonstrate a method in which statistical testing at the exon level is performed prior to the summary of the results at the gene level. Using publicly available RNA-seq datasets as case studies, we illustrate how this exon-based strategy can improve the performance of the widely used differential expression software packages as compared to the conventional gene-based strategy. In particular, we show how it enables robust detection of moderate but systematic changes that are missed when relying on single gene-level summary counts only.
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Registered trials
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