Discovery of Regulatory Elements is Improved by a Discriminatory Approach

The Bioinformatics Centre, Department of Biology and the Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark.
PLoS Computational Biology (Impact Factor: 4.62). 11/2009; 5(11):e1000562. DOI: 10.1371/journal.pcbi.1000562
Source: PubMed


A major goal in post-genome biology is the complete mapping of the gene regulatory networks for every organism. Identification of regulatory elements is a prerequisite for realizing this ambitious goal. A common problem is finding regulatory patterns in promoters of a group of co-expressed genes, but contemporary methods are challenged by the size and diversity of regulatory regions in higher metazoans. Two key issues are the small amount of information contained in a pattern compared to the large promoter regions and the repetitive characteristics of genomic DNA, which both lead to "pattern drowning". We present a new computational method for identifying transcription factor binding sites in promoters using a discriminatory approach with a large negative set encompassing a significant sample of the promoters from the relevant genome. The sequences are described by a probabilistic model and the most discriminatory motifs are identified by maximizing the probability of the sets given the motif model and prior probabilities of motif occurrences in both sets. Due to the large number of promoters in the negative set, an enhanced suffix array is used to improve speed and performance. Using our method, we demonstrate higher accuracy than the best of contemporary methods, high robustness when extending the length of the input sequences and a strong correlation between our objective function and the correct solution. Using a large background set of real promoters instead of a simplified model leads to higher discriminatory power and markedly reduces the need for repeat masking; a common pre-processing step for other pattern finders.

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    • "Most use a fixed set of sequences and identify motifs that are overrepresented in this set compared to a Markov chain background model (Gibbs Sampler [13], MEME [14], and Weeder [15]). Other methods do discriminative analysis, where the goal is to identify motifs that are over-represented in a positive set compared to a negative or background set of sequences (DEME [16] and [17]). However often we are dealing with transcriptome-wide measurements of gene expression, and a priori it is difficult to set a natural cut-off that defines the positive (or negative) set. "
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