TRRUST: a reference database of human transcriptional regulatory interactions

By Heonjong Han, Hongseok Shim, Donghyun Shin, Jung Eun Shim, Yunhee Ko, Junha Shin, Hanhae Kim, A-Ra Cho, Eiru Kim, Tak Lee and 11 more. Scientific Reports, 2015

435 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/22797dc5179171f9b61476a9499a80ea20b37276

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Rui Kuang
2. Doron Lancet
3. Wei Zhang
4. Jeongsik Yong
5. Insuk Lee
6. Jeremy Chien

Citations in the corpus

• GeneHancer: genome-wide integration of enhancers and target genes in GeneCards (2017, result): “The robustness analyses performed included the examination of co-expression between TFs and candidate target genes, both in cases of known promoter-target gene pairs and for text-mined TF-target gene pairs (77), yielding supportive results.”
• TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions (2017, methodology): “We therefore prioritized the sentences using methods similar to those described in our previous work (1).”
• Network-based machine learning and graph theory algorithms for precision oncology (2017, background): “16, 17 A transcriptional regulatory network is a directed graph in which the edges connect a regulator to its targets.”