Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F16%3A00093926" target="_blank" >RIV/00216224:14110/16:00093926 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1038/ncomms12846" target="_blank" >http://dx.doi.org/10.1038/ncomms12846</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/ncomms12846" target="_blank" >10.1038/ncomms12846</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd

Popis výsledku v původním jazyce

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository.

Název v anglickém jazyce

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd

Popis výsledku anglicky

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

EE - Mikrobiologie, virologie

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Nature Communications

ISSN

2041-1723

e-ISSN

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Svazek periodika

7

Číslo periodika v rámci svazku

12846

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

000385363000014

EID výsledku v databázi Scopus

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