Página principal > Artículos > Integrating bioinformatic resources to predict transcription factors interacting with cis-sequences conserved in co-regulated genes
Resumen: Background: Using motif detection programs it is fairly straightforward to identify conserved cis-sequences in promoters of co-regulated genes. In contrast, the identification of the transcription factors (TFs) interacting with these cis-sequences is much more elaborate. To facilitate this, we explore the possibility of using several bioinformatic and experimental approaches for TF identification. This starts with the selection of co-regulated gene sets and leads first to the prediction and then to the experimental validation of TFs interacting with cis-sequences conserved in the promoters of these co-regulated genes.Results: Using the PathoPlant database, 32 up-regulated gene groups were identified with microarray data for drought-responsive gene expression from Arabidopsis thaliana. Application of the binding site estimation suite of tools (BEST) discovered 179 conserved sequence motifs within the corresponding promoters. Using the STAMP web-server, 49 sequence motifs were classified into 7 motif families for which similarities with known cis-regulatory sequences were identified. All motifs were subjected to a footprintDB analysis to predict interacting DNA binding domains from plant TF families. Predictions were confirmed by using a yeast-one-hybrid approach to select interacting TFs belonging to the predicted TF families. TF-DNA interactions were further experimentally validated in yeast and with a Physcomitrella patens transient expression system, leading to the discovery of several novel TF-DNA interactions.Conclusions: The present work demonstrates the successful integration of several bioinformatic resources with experimental approaches to predict and validate TFs interacting with conserved sequence motifs in co-regulated genes. Idioma: Inglés DOI: 10.1186/1471-2164-15-317 Año: 2014 Publicado en: BMC Genomics 15, 1 (2014), 317 [17 pp] ISSN: 1471-2164 Factor impacto JCR: 3.986 (2014) Categ. JCR: GENETICS & HEREDITY rank: 39 / 166 = 0.235 (2014) - Q1 - T1 Categ. JCR: BIOTECHNOLOGY & APPLIED MICROBIOLOGY rank: 26 / 163 = 0.16 (2014) - Q1 - T1 Tipo y forma: Artículo (Versión definitiva)