Evidence map›Paper›PMID 29650040›Full record

ArticleGenome biology2018

Gene-level differential analysis at transcript-level resolution.

Lynn Yi, Harold Pimentel, Nicolas L Bray, Lior Pachter

Abstract read
In one paragraph

Article in Genome biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
82citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

82 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. AdipocyteCells · 2026
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  7. Review
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  13. mRNA Transcript Variants Expressed in Mammalian Cells.International journal of molecular sciences · 2025
    Review
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22 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lynn YiUCLA-Caltech Medical Science Training Program, Los Angeles, CA, USA.ORCID 0000-0003-4575-0158
Harold PimentelDepartment of Genetics, Stanford University, Palo Alto, CA, USA.
Nicolas L BrayInnovative Genomics Institute, Berkeley, CA, USA. nicolas.bray@gmail.com.
Lior PachterDivision of Biology and Biological Engineering, Caltech, Pasadena, CA, USA. lpachter@caltech.edu.

Funding

UCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9M
Integration of genetic association mapping and functional data to elucidate genetic mechanisms of diseaseR01HG008140 · NHGRI · STANFORD UNIVERSITY · PI JONATHAN K PRITCHARD · 2016 to 2026
$7.3M
NIH HHS R01 HG008140NIH HHS T32 GM07616
6 · The paper itself

Abstract

Compared to RNA-sequencing transcript differential analysis, gene-level differential expression analysis is more robust and experimentally actionable. However, the use of gene counts for statistical analysis can mask transcript-level dynamics. We demonstrate that 'analysis first, aggregation second,' where the p values derived from transcript analysis are aggregated to obtain gene-level results, increase sensitivity and accuracy. The method we propose can also be applied to transcript compatibility counts obtained from pseudoalignment of reads, which circumvents the need for quantification and is fast, accurate, and model-free. The method generalizes to various levels of biology and we showcase an application to gene ontologies.

Indexed as

AnimalsGene Expression ProfilingGene OntologyMiceSequence Analysis, RNADifferential expressionFisher’s methodGene ontologyLancaster methodMeta-analysisPseudoalignmentP value aggregationRNA-seq alignmentRNA-seq quantificationRNA-sequencingŠidák correctionTranscript compatibility counts

Identifiers

PMID29650040
PMCPMC5896116

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.