Discovery of Regulatory Elements is Improved by a Discriminatory Approach

Abstract

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.

NanoCAGE: A High-Resolution Technique to Discover and Interrogate Cell Transcriptomes

“…Conversely, for many organisms, a full-length sequence of the rDNA repeated unit is not available, and the percentage of reads matching rRNA will not be an accurate measurement of the efficiency of template-switching. The hallmark of CAGE (Carninci, 2010; Kodzius et al, 2006; Valen et al, 2009) is the addition of extra guanosines at the 5′ end of the tags, but for nanoCAGE these guanosines become part of the 5′ linker, and most of them are removed as linker sequence. Nevertheless, mismatches at the 5′ end of the tags are expected to be G in most of the cases.…”

“…For samples where ~50 µg of RNA is available, the original CAGE method (Kodzius et al, 2006) may be preferable as it is free from PCR bias. CAGE libraries can be used for different types of analysis (Carninci, 2010), for instance promoter discovery (in particular when the available transcript annotation for a tissue or a species is rudimentary), differential expression analysis (Valen et al, 2009), inference of transcription factor binding site (Vitezic et al, 2010) or gene networks (Suzuki et al, 2009). For samples where enough RNA is available, we recommend the preparation of technical replicates if the analysis is centered on the use of the expression levels.…”

GRISOTTO: A greedy approach to improve combinatorial algorithms for motif discovery with prior knowledge

“…The most effective PSP's have been built in a discriminative way by taking into account not only the sequence-sets that were bounded by some profile TF, but also sequence-sets that were not bounded. This is accordant to the evidence that the discovery of regulatory elements is improved by employing discriminative approaches [12]. A PSP is built in pre-processing time and then used to bias the optimization procedure towards real motifs.…”

Challenges rising from learning motif evaluation functions using genetic programming

“…This is based on the idea of negative samples [26], which is suggested to alleviate the "pattern drowning" problem. If a pattern occurs also frequently in the promoters regulated by other transcription factors, which means it is unlikely to be the TFBS for the current transcription factor, then its bgn will be high.…”