Journal Article

Theoretical and empirical quality assessment of transcription factor-binding motifs

Alejandra Medina-Rivera, Cei Abreu-Goodger, Morgane Thomas-Chollier, Heladia Salgado, Julio Collado-Vides and Jacques van Helden

in Nucleic Acids Research

Volume 39, issue 3, pages 808-824
Published in print February 2011 | ISSN: 0305-1048
Published online October 2010 | e-ISSN: 1362-4962 | DOI: https://dx.doi.org/10.1093/nar/gkq710

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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.

Journal Article.  8715 words.  Illustrated.

Subjects: Chemistry ; Biochemistry ; Bioinformatics and Computational Biology ; Genetics and Genomics ; Molecular and Cell Biology

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