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reString: an open-source Python software to perform automatic functional enrichment retrieval, results aggregation and data visualization

delete2021-12-06
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OA
AI
S
Stefano Manzini
M
Marco Busnelli
A
A. Colombo
E
Elsa Franchi
P
Pasquale Grossano
G
Giulia Chiesa *
DOI:10.1038/s41598-021-02528-0delete
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摘要

摘要

En 中文
Functional enrichment analysis is an analytical method to extract biological insights from gene expression data, popularized by the ever-growing application of high-throughput techniques. Typically, expression profiles are generated for hundreds to thousands of genes/proteins from samples belonging to two experimental groups, and after ad-hoc statistical tests, researchers are left with lists of statistically significant entities, possibly lacking any unifying biological theme. Functional enrichment tackles the problem of putting overall gene expression changes into a broader biological context, based on pre-existing knowledge bases of reference: database collections of known expression regulation, relationships and molecular interactions. STRING is among the most popular tools, providing both protein-protein interaction networks and functional enrichment analysis for any given set of identifiers. For complex experimental designs, manually retrieving, interpreting, analyzing and abridging functional enrichment results is a daunting task, usually performed by hand by the average wet-biology researcher. We have developed reString, a cross-platform software that seamlessly retrieves from STRING functional enrichments from multiple user-supplied gene sets, with just a few clicks, without any need for specific bioinformatics skills. Further, it aggregates all findings into human-readable table summaries, with built-in features to easily produce user-customizable publication-grade clustermaps and bubble plots. Herein, we outline a complete reString protocol, showcasing its features on a real use-case.
Keyword:
GENETICALLY-MODIFIED MICE
WEB SERVER
EXPRESSION
METABOLISM
KNOWLEDGE
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Scientific Reports
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3.9
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机构

I
IRCCS Ca Granda Ospedale Maggiore Policlinico
学者数:
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论文数: 1.1W
被引数: 12
U
University of Milan
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论文数: 3.9W
被引数: 5.0W
引用论文

引用论文

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