TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions

Abstract

Transcription factors (TFs) are major trans-acting factors in transcriptional regulation. Therefore, elucidating TF-target interactions is a key step toward understanding the regulatory circuitry underlying complex traits such as human diseases. We previously published a reference TF-target interaction database for humans-TRRUST (Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining)-which was constructed using sentence-based text mining, followed by manual curation. Here, we present TRRUST v2 (www.grnpedia.org/trrust) with a significant improvement from the previous version, including a significantly increased size of the database consisting of 8444 regulatory interactions for 800 TFs in humans. More importantly, TRRUST v2 also contains a database for TF-target interactions in mice, including 6552 TF-target interactions for 828 mouse TFs. TRRUST v2 is also substantially more comprehensive and less biased than other TF-target interaction databases. We also improved the web interface, which now enables prioritization of key TFs for a physiological condition depicted by a set of user-input transcriptional responsive genes. With the significant expansion in the database size and inclusion of the new web tool for TF prioritization, we believe that TRRUST v2 will be a versatile database for the study of the transcriptional regulation involved in human diseases.

Figure 1.

(A) An overall workflow of sentence-based text mining for mouse TF–target regulatory interactions from research articles. (B) Proportion of true-positive sentences for each bin of 5000 candidate sentences rank-ordered by scores based on the difference between the frequencies of each word based on positive gold-standard sentences and that based on negative gold-standard sentences.

Figure 2.

Comparison of TRRUST v2 with other databases of TF–target interactions. (A) Radar plots depicting five database-related parameters: TF, the number of transcription factors (TFs); TG, the number of target genes; Interaction, the number of TF–target interactions; MoR, the number of TF–target interactions with mode of regulation information; PMID, the number of PubMed articles to support the given database. The maximum value of each parameter is shown in parentheses, and scales were normalized by the maximum value. (B) The proportion of the interaction involving the top 10 TFs with the highest number of interactions in humans and mice. (C) Gini index that measures the degree of dispersion of interactions across TFs in humans and mice.

Figure 3.

Screenshots of the search result pages for human BRCA1 gene (A) and for mouse Brca1 gene (B). Screenshots of the search result page for candidate key transcription factors (TFs) based on 33 genes responsive to siRNA knockdown of ESR1 gene (C) and based on 100 differentially expressed genes in lung tumor samples (D). In the case of lung tumor samples, only 97 of the 100 user-input genes are valid genes, which are annotated by CCDS.