![]() ![]() Therefore, we need to expand the size of our current set of gold-standard regulatory interactions for the more effective reconstruction of human TRNs, which is our main motivation for developing an extensive database of literature-curated human regulatory interactions. If we count only literature-curated interactions from those databases, then the size of the gold-standard data set may not be large enough to fairly evaluate TRNs. However, some of the databases include interactions inferred from high-throughput experiments, which may not be optimal for benchmarking. Several public databases for human TF-target interactions are currently available, including TFactS 9, TRED 10, HTRIdb 11 and ORegAnno 12. The reconstruction of TRNs via the integration 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., RegulonDB 3 for an Escherichia coli TRN 4, the Yeast Proteome Database (YPD) 5 for a Saccharomyces cerevisiae TRN 6 and the Regulatory Element Database for Drosophila (REDfly) 7 for a Drosophila melanogaster TRN construction 8. The integrative approach of TRN modelling requires gold-standard TF-target interactions to benchmark inferred regulatory interactions from different data sets. Thus, by integrating TF-target interactions inferred from a wide variety of cellular contexts, we may effectively reconstruct genome-scale TRNs. Expression data have advantages over DNA-binding data in the coverage of diverse cellular contexts, which reveal disparate sets of regulatory interactions. TF-target regulatory interactions also can be inferred from high-throughput gene expression data using a wide variety of computational algorithms 2. ![]() Interactions between TFs and CREs of target genes are generally detected by DNA-binding experiments such as chromatin immunoprecipitation (ChIP), which is often followed by microarray analysis (ChIP-chip) or deep-sequencing analysis (ChIP-seq). ![]() However, genome-scale regulatory circuit models remain inadequate due to the intrinsic complexities of human transcriptional regulatory programs as well as technical limitations in mapping regulatory interactions. The reverse engineering of transcriptional regulatory networks (TRNs) by inferring interactions between TFs and target genes has been a key challenge in understanding the genetic regulation of complex human phenotypes. The human genome is estimated to encode approximately 2,000 TFs 1, which operate programs that change cellular states by binding to proxy or distal cis-regulatory elements (CREs) for a set of target genes. Transcription factors (TFs) are major molecules that control the transcriptional activity of genes. 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. 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. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. Here we present a database of literature-curated human TF-target interactions, TRRUST ( transcriptional regulatory relationships unravelled by sentence-based text-mining, ), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. 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. ![]() The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. ![]()
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