Evidence map›Paper›PMID 36587217›Full record

ArticleBMC bioinformatics2022

PhenoExam: gene set analyses through integration of different phenotype databases.

Alejandro Cisterna, Aurora González-Vidal, Daniel Ruiz, Jordi Ortiz, Alicia Gómez-Pascual, Zhongbo Chen, Mike Nalls, Faraz Faghri, John Hardy, Irene Díez and 4 more

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.3field-weighted citation impact, top 43% of its field
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

5 citing papers in PubMed, 5 citations in OpenAlex.

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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

14 authors at 7 institutions in 5 countries.

Alejandro CisternaDepartamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain.
Aurora González-VidalDepartamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain.
Daniel RuizDepartamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain.
Jordi OrtizDepartamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain.
Alicia Gómez-PascualDepartamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain.
Zhongbo ChenDepartment of Neurodegenerative Disease, UCL, Institute of Neurology, London, UK.
Mike NallsData Tecnica International LLC, Glen Echo, MD, USA.
Faraz FaghriData Tecnica International LLC, Glen Echo, MD, USA.
John HardyDepartment of Neurodegenerative Disease, UCL, Institute of Neurology, London, UK.
Irene DíezNIMGenetics Genómica y Medicina S.L, Madrid, Spain.
Paolo MaiettaNIMGenetics Genómica y Medicina S.L, Madrid, Spain.
Sara ÁlvarezNIMGenetics Genómica y Medicina S.L, Madrid, Spain.
Mina RytenDepartment of Neurodegenerative Disease, UCL, Institute of Neurology, London, UK.
Juan A BotíaDepartamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain. juanbot@um.es.ORCID http://orcid.org/0000-0002-6992-598X
Universidad de Murcia · ESInstituto de Medicina Genómica · ESAlzheimer's Association · USGreat Ormond Street Hospital · GBHong Kong University of Science and Technology · HKMRC Prion Unit · GBUniversity College London · GB

Funding

Fundación Séneca 00007/COVI/20Fundación Séneca 20762/FPI/18Fundación Séneca 21230/PD/19Medical Research Council G0701075Medical Research Council G0901254Medical Research Council MR/N008324/1Medical Research Council MR/N026004/Medical Research Council MR/N026004/1Wellcome Trust 202903/Z/16/Z
6 · The paper itself

Abstract

backgroundGene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases.

resultsPhenoExam generates sensitive and accurate phenotype enrichment analyses. It is also effective in segregating gene sets or Mendelian diseases with very similar phenotypes. We tested the tool with two similar diseases (Parkinson and dystonia), to show phenotype-level similarities but also potentially interesting differences. Moreover, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy.

conclusionsWe developed PhenoExam, a freely available R package and Web application, which performs phenotype enrichment and disease enrichment analysis on gene set G, measures statistically significant phenotype similarities between pairs of gene sets G and G' and detects statistically significant exclusive phenotypes or disease terms, across different databases. We proved with simulations and real cases that it is useful to distinguish between gene sets or diseases with very similar phenotypes. Github R package URL is https://github.com/alexcis95/PhenoExam . Shiny App URL is https://alejandrocisterna.shinyapps.io/phenoexamweb/ .

Indexed as

Computational BiologySoftwareDatabases, FactualDatabases, GeneticPhenotypeEpilepsyPhenotype Enrichment ToolShiny

Identifiers

PMID36587217
PMCPMC9805686
OpenAlexW4313396096

What Socratic 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 Socratic graph.