RESUMO
Basic knowledge of transcriptional regulation is needed to understand the mechanisms governing biological processes, i.e., nitrogen fixation by Rhizobiales bacteria in symbiosis with leguminous plants. The RhizoBindingSites database is a computer-assisted framework providing motif-gene-associated conserved sequences potentially implicated in transcriptional regulation in nine symbiotic species. A dyad analysis algorithm was used to deduce motifs in the upstream regulatory region of orthologous genes, and only motifs also located in the gene seed promoter with a p-value of 1e-4 were accepted. A genomic scan analysis of the upstoream sequences with these motifs was performed. These predicted binding sites were categorized according to low, medium and high homology between the matrix and the upstream regulatory sequence. On average, 62.7% of the genes had a motif, accounting for 80.44% of the genes per genome, with 19613 matrices (a matrix is a representation of a motif). The RhizoBindingSites database provides motif and gene information, motif conservation in the order Rhizobiales, matrices, motif logos, regulatory networks constructed from theoretical or experimental data, a criterion for selecting motifs and a guide for users. The RhizoBindingSites database is freely available online at rhizobindingsites.ccg.unam.mx.
RESUMO
Position-specific scoring matrices (PSSMs) are routinely used to predict transcription factor (TF)-binding sites in genome sequences. However, their reliability to predict novel binding sites can be far from optimum, due to the use of a small number of training sites or the inappropriate choice of parameters when building the matrix or when scanning sequences with it. Measures of matrix quality such as E-value and information content rely on theoretical models, and may fail in the context of full genome sequences. We propose a method, implemented in the program 'matrix-quality', that combines theoretical and empirical score distributions to assess reliability of PSSMs for predicting TF-binding sites. We applied 'matrix-quality' to estimate the predictive capacity of matrices for bacterial, yeast and mouse TFs. The evaluation of matrices from RegulonDB revealed some poorly predictive motifs, and allowed us to quantify the improvements obtained by applying multi-genome motif discovery. Interestingly, the method reveals differences between global and specific regulators. It also highlights the enrichment of binding sites in sequence sets obtained from high-throughput ChIP-chip (bacterial and yeast TFs), and ChIP-seq and experiments (mouse TFs). The method presented here has many applications, including: selecting reliable motifs before scanning sequences; improving motif collections in TFs databases; evaluating motifs discovered using high-throughput data sets.