TRRUST: a reference database of human transcriptional regulatory interactions.
TRRUST: a reference database of human transcriptional regulatory interactions.
复制标题
DOI：
10.1038/srep11432
复制
发表时间：
2015-06-12
影响因子：
4.6
通讯作者：
Lee I
中科院分区：
文献类型：
--
作者：
Han H;Shim H;Shin D;Shim JE;Ko Y;Shin J;Kim H;Cho A;Kim E;Lee T;Kim H;Kim K;Yang S;Bae D;Yun A;Kim S;Kim CY;Cho HJ;Kang B;Shin S;Lee I
转录调控网络（TRN）的重建是人类遗传学中的一个长期挑战。已经开发了许多计算方法来从高通量数据推断人类转录因子（TF）与靶基因之间的调控相互作用，并且其性能评估需要金标准相互作用。在这里，我们提出了一个文献策划的人类TF-靶标相互作用的数据库，TRRUST（基于文本挖掘的转录调控关系，http：//www.grnpedia.org/trrust），目前包含748个TF基因和1,975个非TF基因之间的8,015个相互作用。一个基于文本挖掘的方法，采用了有效的手动策展监管互动约20万Medline摘要。据我们所知，TRRUST是迄今为止最大的文献策划的人类TF-靶标相互作用的公开数据库。TRRUST还具有几个有用的特征：i）关于调节模式的信息; ii）针对查询TF的靶模块性的测试; iii）针对查询靶的TF协同性的测试; iv）关于查询TF的协同TF的推断;以及v）对与查询TF相关联的通路和疾病进行优先级排序。我们观察到TRRUST中TF-靶对的高度富集，用于从高通量数据推断的得分最高的相互作用，这表明TRRUST为人类TRN的计算重建提供了可靠的基准。
The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. Numerous computational methods have been developed to infer regulatory interactions between human transcriptional factors (TFs) and target genes from high-throughput data, and their performance evaluation requires gold-standard interactions. Here we present a database of literature-curated human TF-target interactions, TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining, http://www.grnpedia.org/trrust), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. TRRUST also has several useful features: i) information about the mode-of-regulation; ii) tests for target modularity of a query TF; iii) tests for TF cooperativity of a query target; iv) inferences about cooperating TFs of a query TF; and v) prioritizing associated pathways and diseases with a query TF. We observed high enrichment of TF-target pairs in TRRUST for top-scored interactions inferred from high-throughput data, which suggests that TRRUST provides a reliable benchmark for the computational reconstruction of human TRNs.
登录
查看更多内容
影响因子：
4.3
作者：
Beyer A;Workman C;Hollunder J;Radke D;Möller U;Wilhelm T;Ideker T
通讯作者：
Ideker T
影响因子：
14.9
作者：
Salgado H;Peralta-Gil M;Gama-Castro S;Santos-Zavaleta A;Muñiz-Rascado L;García-Sotelo JS;Weiss V;Solano-Lira H;Martínez-Flores I;Medina-Rivera A;Salgado-Osorio G;Alquicira-Hernández S;Alquicira-Hernández K;López-Fuentes A;Porrón-Sotelo L;Huerta AM;Bonavides-Martínez C;Balderas-Martínez YI;Pannier L;Olvera M;Labastida A;Jiménez-Jacinto V;Vega-Alvarado L;Del Moral-Chávez V;Hernández-Alvarez A;Morett E;Collado-Vides J
通讯作者：
Collado-Vides J
影响因子：
14.9
作者：
Keshava Prasad TS;Goel R;Kandasamy K;Keerthikumar S;Kumar S;Mathivanan S;Telikicherla D;Raju R;Shafreen B;Venugopal A;Balakrishnan L;Marimuthu A;Banerjee S;Somanathan DS;Sebastian A;Rani S;Ray S;Harrys Kishore CJ;Kanth S;Ahmed M;Kashyap MK;Mohmood R;Ramachandra YL;Krishna V;Rahiman BA;Mohan S;Ranganathan P;Ramabadran S;Chaerkady R;Pandey A
通讯作者：
Pandey A
影响因子：
4.4
作者：
Bovolenta LA;Acencio ML;Lemke N
通讯作者：
Lemke N
影响因子：
14.9
作者：
Gallo SM;Gerrard DT;Miner D;Simich M;Des Soye B;Bergman CM;Halfon MS
通讯作者：
Halfon MS