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.
Highlights
Of regulatory interactions inferred from multiple data sets has previously been demonstrated in several model organisms, and databases for literature-curated Transcription factors (TFs)-target interactions have played critical roles, e.g., RegulonDB3 for an Escherichia coli TRN4, the Yeast Proteome Database (YPD)[5] for a Saccharomyces cerevisiae TRN6, and the Regulatory Element Database for Drosophila (REDfly)[7] for a Drosophila melanogaster transcriptional regulatory networks (TRNs) construction[8]
The list of TF genes were derived from Ravasi et al.[15], which reported manually curated TF genes from several sources: i) the TRANSFAC database; ii) genes annotated by the Gene Ontology (GO) term ‘transcription factor’; iii) genes that contain the word ‘transcription’ in the Entrez description field; and iv) manually curated TF genes by Roach et al.[16]
We demonstrated that TF-target interactions of TRRUST can benchmark inferred TRNs from the computational analysis of high-throughput data
Summary
Of regulatory interactions inferred from multiple data sets has previously been demonstrated in several model organisms, and databases for literature-curated TF-target interactions have played critical roles, e.g., RegulonDB3 for an Escherichia coli TRN4, the Yeast Proteome Database (YPD)[5] for a Saccharomyces cerevisiae TRN6, and the Regulatory Element Database for Drosophila (REDfly)[7] for a Drosophila melanogaster TRN construction[8]. Several public databases for human TF-target interactions are currently available, including TFactS9, TRED10, HTRIdb[11], and ORegAnno[12]. Some of the databases include interactions inferred from high-throughput experiments, which may not be optimal for benchmarking. TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining) is a database of TF-target regulatory interactions identified via the manual curation of Medline abstracts. The current version of TRRUST contains 8,015 TF-target interactions, which to our knowledge is the largest public database of literature-curated human regulatory interactions to date. We observed that gene pairs in TRRUST are highly enriched among the top-ranked regulatory interactions inferred from high-throughput expression data, which suggests that the TRRUST data will be a useful benchmark for the computational reconstruction of human TRNs
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
