TRRUST: A reference database of human transcriptional regulatory interactions

Heonjong Han, Hongseok Shim, Donghyun Shin, Jung Eun Shim, Yunhee Ko, Junha Shin, Hanhae Kim, Ara Cho, Eiru Kim, Tak Lee, Hyojin Kim, Kyungsoo Kim, Sunmo Yang, Dasom Bae, Ayoung Yun, Sunphil Kim, Chan Yeong Kim, Hyeon Jin Cho, Byunghee Kang, Susie ShinIn suk Lee

Research output: Contribution to journalArticle

100 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number11432
JournalScientific reports
Volume5
DOIs
Publication statusPublished - 2015 Jun 12

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Databases
Data Mining
Gene Regulatory Networks
Genes
Benchmarking
Medical Genetics
Gold

All Science Journal Classification (ASJC) codes

  • General

Cite this

Han, Heonjong ; Shim, Hongseok ; Shin, Donghyun ; Shim, Jung Eun ; Ko, Yunhee ; Shin, Junha ; Kim, Hanhae ; Cho, Ara ; Kim, Eiru ; Lee, Tak ; Kim, Hyojin ; Kim, Kyungsoo ; Yang, Sunmo ; Bae, Dasom ; Yun, Ayoung ; Kim, Sunphil ; Kim, Chan Yeong ; Cho, Hyeon Jin ; Kang, Byunghee ; Shin, Susie ; Lee, In suk. / TRRUST : A reference database of human transcriptional regulatory interactions. In: Scientific reports. 2015 ; Vol. 5.
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abstract = "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.",
author = "Heonjong Han and Hongseok Shim and Donghyun Shin and Shim, {Jung Eun} and Yunhee Ko and Junha Shin and Hanhae Kim and Ara Cho and Eiru Kim and Tak Lee and Hyojin Kim and Kyungsoo Kim and Sunmo Yang and Dasom Bae and Ayoung Yun and Sunphil Kim and Kim, {Chan Yeong} and Cho, {Hyeon Jin} and Byunghee Kang and Susie Shin and Lee, {In suk}",
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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, IS 2015, 'TRRUST: A reference database of human transcriptional regulatory interactions', Scientific reports, vol. 5, 11432. https://doi.org/10.1038/srep11432

TRRUST : A reference database of human transcriptional regulatory interactions. / Han, Heonjong; Shim, Hongseok; Shin, Donghyun; Shim, Jung Eun; Ko, Yunhee; Shin, Junha; Kim, Hanhae; Cho, Ara; Kim, Eiru; Lee, Tak; Kim, Hyojin; Kim, Kyungsoo; Yang, Sunmo; Bae, Dasom; Yun, Ayoung; Kim, Sunphil; Kim, Chan Yeong; Cho, Hyeon Jin; Kang, Byunghee; Shin, Susie; Lee, In suk.

In: Scientific reports, Vol. 5, 11432, 12.06.2015.

Research output: Contribution to journalArticle

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AU - Han, Heonjong

AU - Shim, Hongseok

AU - Shin, Donghyun

AU - Shim, Jung Eun

AU - Ko, Yunhee

AU - Shin, Junha

AU - Kim, Hanhae

AU - Cho, Ara

AU - Kim, Eiru

AU - Lee, Tak

AU - Kim, Hyojin

AU - Kim, Kyungsoo

AU - Yang, Sunmo

AU - Bae, Dasom

AU - Yun, Ayoung

AU - Kim, Sunphil

AU - Kim, Chan Yeong

AU - Cho, Hyeon Jin

AU - Kang, Byunghee

AU - Shin, Susie

AU - Lee, In suk

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N2 - 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.

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