rGREAT: an R/bioconductor package for functional enrichment on genomic regions.
rGREAT: an R/bioconductor package for functional enrichment on genomic regions.
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DOI：
10.1093/bioinformatics/btac745
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发表时间：
2023-01-01
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影响因子：
--
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GREAT（Genomic Regions Enrichment of Annotations Tool）是一种广泛使用的基因组区域功能富集工具。然而，作为一个在线工具，它具有过时的注释数据，支持的生物体和基因集集合数量少，并且无法为用户扩展的局限性。在这里，我们开发了一个新的R/Bioconductorpackage名为rGREAT，它在本地实现了GREAT算法。rGREAT默认支持超过600种生物和大量的基因集集合，以及用户自备的基因集和生物。此外，它还实现了一种处理背景区域的通用方法。软件包rGREAT可从Bioconductor项目免费获得：https://bioconductor.org/packages/rGREAT/。开发版本可在https://github.com/jokergoo/rGREAT上获得。从Ensembl BioMart检索的600多种生物体的基因本体基因集呈现在R包BioMartGOGeneSets中，其可在https://github.com/jokergoo/BioMartGOGeneSets获得。 补充数据可在Bioinformatics在线获得。
GREAT (Genomic Regions Enrichment of Annotations Tool) is a widely used tool for functional enrichment on genomic regions. However, as an online tool, it has limitations of outdated annotation data, small numbers of supported organisms and gene set collections, and not being extensible for users. Here, we developed a new R/Bioconductorpackage named rGREAT which implements the GREAT algorithm locally. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions. The package rGREAT is freely available from the Bioconductor project: https://bioconductor.org/packages/rGREAT/. The development version is available at https://github.com/jokergoo/rGREAT. Gene Ontology gene sets for more than 600 organisms retrieved from Ensembl BioMart are presented in an R package BioMartGOGeneSets which is available at https://github.com/jokergoo/BioMartGOGeneSets. Supplementary data are available at Bioinformatics online.
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影响因子：
5.8
作者：
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者：
Mesirov, Jill P.
DOI：
10.1093/bioinformatics/btv612
发表时间：
2016-02-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Sheffield NC;Bock C
通讯作者：
Bock C
影响因子：
14.9
作者：
Frankish A;Diekhans M;Jungreis I;Lagarde J;Loveland JE;Mudge JM;Sisu C;Wright JC;Armstrong J;Barnes I;Berry A;Bignell A;Boix C;Carbonell Sala S;Cunningham F;Di Domenico T;Donaldson S;Fiddes IT;García Girón C;Gonzalez JM;Grego T;Hardy M;Hourlier T;Howe KL;Hunt T;Izuogu OG;Johnson R;Martin FJ;Martínez L;Mohanan S;Muir P;Navarro FCP;Parker A;Pei B;Pozo F;Riera FC;Ruffier M;Schmitt BM;Stapleton E;Suner MM;Sycheva I;Uszczynska-Ratajczak B;Wolf MY;Xu J;Yang YT;Yates A;Zerbino D;Zhang Y;Choudhary JS;Gerstein M;Guigó R;Hubbard TJP;Kellis M;Paten B;Tress ML;Flicek P
通讯作者：
Flicek P
影响因子：
3
作者：
Domanska, Diana;Kanduri, Chakravarthi;Sandve, Geir Kjetil
通讯作者：
Sandve, Geir Kjetil
影响因子：
46.9
作者：
McLean CY;Bristor D;Hiller M;Clarke SL;Schaar BT;Lowe CB;Wenger AM;Bejerano G
通讯作者：
Bejerano G