HTRIdb: an open-access database for experimentally verified human transcriptional regulation interactions

Autor(es)

Bovolenta, Luiz A. ^[1] ; Acencio, Marcio L. ^[1] ; Lemke, Ney ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Paulista, UNESP, Inst Biociencias Botucatu, Dept Fis & Biofis, BR-18618970 Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: BMC Genomics; v. 13, AUG 17 2012.
• Citações Web of Science: 111

Resumo

Background: The modeling of interactions among transcription factors (TFs) and their respective target genes (TGs) into transcriptional regulatory networks is important for the complete understanding of regulation of biological processes. In the case of experimentally verified human TF-TG interactions, there is no database at present that explicitly provides such information even though many databases containing human TF-TG interaction data have been available. In an effort to provide researchers with a repository of experimentally verified human TF-TG interactions from which such interactions can be directly extracted, we present here the Human Transcriptional Regulation Interactions database (HTRIdb). Description: The HTRIdb is an open-access database that can be searched via a user-friendly web interface and the retrieved TF-TG interactions data and the associated protein-protein interactions can be downloaded or interactively visualized as a network through the web version of the popular Cytoscape visualization tool, the Cytoscape Web. Moreover, users can improve the database quality by uploading their own interactions and indicating inconsistencies in the data. So far, HTRIdb has been populated with 284 TFs that regulate 18302 genes, totaling 51871 TF-TG interactions. HTRIdb is freely available at http://www.lbbc.ibb.unesp.br/htri. Conclusions: HTRIdb is a powerful user-friendly tool from which human experimentally validated TF-TG interactions can be easily extracted and used to construct transcriptional regulation interaction networks enabling researchers to decipher the regulation of biological processes. (AU)

• Processo FAPESP: 09/10382-2 - Aprendizado de máquina em biologia molecular de sistemas
• Beneficiário: Ney Lemke
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 10/20684-3 - Utilização de aprendizado de máquina em redes biológicas para previsão e determinação de regras para emergência de fenótipos de interesse
• Beneficiário: Marcio Luis Acencio
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado