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Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
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High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomic studies aim at comparing either abundance levels or the transcriptome composition between given conditions, and as a first step, the sequencing reads must be used as the basis for abundance quantification of transcriptomic features of interest, such as genes or transcripts. Several different quantification approaches have been proposed, ranging from simple counting of reads that overlap given genomic regions to more complex estimation of underlying transcript abundances. In this paper, we show that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of performance and interpretability. We also illustrate that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets. Finally, we provide an R package ( tximport ) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.
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PSAP variants are associated with lysosomal storage disorders and Parkinson's Disease.
“Given the screen phenotypes of PSAP, which were unexpected based on its known functions, and the association of PSAP variants not only with lysosomal storage disorders60–62 but also Parkinson’s Disease63, we decided to further investigate the underlying mechanisms of PSAP in neuronal redox regulation.”
From Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis (2021) · cites this paper as [62]
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Research works printed but not linked: 6
- C Soneson, 2015, Data set 1 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences., F1000Research., 10.5256/f1000research.7563.d109328 · DOI
- C Soneson, 2015, Data set 2 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences., F1000Research., 10.5256/f1000research.7563.d109329 · DOI
- C Soneson, 2015, Data set 3 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences., F1000Research., 10.5256/f1000research.7563.d109330 · DOI
- C Soneson, 2015, Data set 4 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences., F1000Research., 10.5256/f1000research.7563.d109331 · DOI
- C Soneson, 2015, Data set 5 in: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences., F1000Research., 10.5256/f1000research.7563.d109332 · DOI
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Metadata
- Authors
- Soneson, C., Love, M. I., & Robinson, M. D.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- Harvard University (US)
- Corresponding author
- Soneson, Charlotte
- Love, Michael I.
- Robinson, Mark D.
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2015-12-30
- Volume
- 4
- Pages
- 1521
- ISSN
- 2046-1402 (ISSN-L)
- PMC
- PMC4712774
- OpenAlex
- W2197124664
- Cited by
- 3,929
- References
- 30
- Citations by year
- 2026: 349 · 2025: 478 · 2024: 594 · 2023: 520 · 2022: 552 · 2021: 574 · 2020: 365 · 2019: 288 · 2018: 147 · 2017: 47 · 2016: 15
- FWCI
- 30.23
- Type
- journal article
- Language
- en
- Update notices
- new_version (2016-02-29)
- Open access
- gold
- doi.org
- Locations
- F1000Research (publishedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- European Commission (305626)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (143883)
- National Institutes of Health
- National Cancer Institute (T32CA009337)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- RNA-seq, differential expression analysis, isoform usage, false discovery rate
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