Evidence map›Paper›PMID 40225790›Full record

ArticleNAR genomics and bioinformatics2025

TRanscriptome ANalysis of StratifiEd CohorTs (TRANSECT) enables automated assessment of global gene regulation linked to disparate expression in user defined genes and gene sets.

John Toubia, Yasir Kusay, Muneeza Maqsood, Nicholas I Warnock, David M Lawrence, Cameron P Bracken, Philip A Gregory, Winnie L Kan, Luke A Selth, Simon J Conn and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NAR genomics and bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

16 authors.

John ToubiaCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.ORCID 0000-0002-5135-6504
Yasir KusayCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.
Muneeza MaqsoodCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.
Nicholas I WarnockCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.
David M LawrenceCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.
Cameron P BrackenCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.ORCID 0000-0001-7722-625X
Philip A GregoryCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.
Winnie L KanCytokine Receptor Laboratory, Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide 5000, Australia.
Luke A SelthAdelaide Medical School, The University of Adelaide, Adelaide 5000, Australia.
Simon J ConnFlinders University, College of Medicine and Public Health, Flinders Health and Medical Research Institute, Adelaide 5042, South Australia.ORCID 0000-0002-1376-4515
Angel F LopezAdelaide Medical School, The University of Adelaide, Adelaide 5000, Australia.
Susan BranfordCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.
Hamish S ScottCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.ORCID 0000-0002-5813-631X
Chung Hoow KokCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.
Gregory J GoodallCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.ORCID 0000-0003-1294-0692
Andreas W SchreiberCentre for Cancer Biology, University of South Australia and SA Pathology, Adelaide 5000, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Publicly accessible expression data produced by large consortium projects like TCGA and GTEx are increasing in number and size at an unprecedented rate. Their utility cannot be underestimated given the diversity of valuable tools widely used to interrogate these data and the many discoveries of biological and clinical significance already garnered from these datasets. However, there remain undiscovered ways to mine these rich resources and a continuing need to provide researchers with easily accessible and user-friendly applications for complex or bespoke analyses. We introduce TRanscriptome ANalysis of StratifiEd CohorTs (TRANSECT), a bioinformatics application automating the stratification and subsequent differential expression analysis of cohort data to provide further insights into gene regulation. TRANSECT works by defining two groups within a cohort based on disparate expression of a gene or a gene set and subsequently compares the groups for differences in global expression. Akin to reverse genetics minus the inherent requirement of

Indexed as

Computational BiologyGene Expression ProfilingGene Expression RegulationSoftwareTranscriptomeCohort StudiesHumans

Identifiers

PMID40225790
PMCPMC11992672

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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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 Socratic graph.