A Monte Carlo-based framework enhances the discovery and interpretation of regulatory sequence motifs

Abstract

BackgroundDiscovery of functionally significant short, statistically overrepresented subsequence patterns (motifs) in a set of sequences is a challenging problem in bioinformatics. Oftentimes, not all sequences in the set contain a motif. These non-motif-containing sequences complicate the algorithmic discovery of motifs. Filtering the non-motif-containing sequences from the larger set of sequences while simultaneously determining the identity of the motif is, therefore, desirable and a non-trivial problem in motif discovery research.ResultsWe describe MotifCatcher, a framework that extends the sensitivity of existing motif-finding tools by employing random sampling to effectively remove non-motif-containing sequences from the motif search. We developed two implementations of our algorithm; each built around a commonly used motif-finding tool, and applied our algorithm to three diverse chromatin immunoprecipitation (ChIP) data sets. In each case, the motif finder with the MotifCatcher extension demonstrated improved sensitivity over the motif finder alone. Our approach organizes candidate functionally significant discovered motifs into a tree, which allowed us to make additional insights. In all cases, we were able to support our findings with experimental work from the literature.ConclusionsOur framework demonstrates that additional processing at the sequence entry level can significantly improve the performance of existing motif-finding tools. For each biological data set tested, we were able to propose novel biological hypotheses supported by experimental work from the literature. Specifically, in Escherichia coli, we suggested binding site motifs for 6 non-traditional LexA protein binding sites; in Saccharomyces cerevisiae, we hypothesize 2 disparate mechanisms for novel binding sites of the Cse4p protein; and in Halobacterium sp. NRC-1, we discoverd subtle differences in a general transcription factor (GTF) binding site motif across several data sets. We suggest that small differences in our discovered motif could confer specificity for one or more homologous GTF proteins. We offer a free implementation of the MotifCatcher software package at http://www.bme.ucdavis.edu/facciotti/resources_data/software/.

Internal RNAs overlapping coding sequences can drive the production of alternative proteins in archaea

“…The detected intraRNA expression is 4-fold up-regulated in stationary phase relative to exponential phase and 2-fold up-regulated in gas vesicles release phase relative to exponential phase, meanwhile htr15 is down-regulated >2-fold at the same time-points. The existence of ChIP-seq binding sites for transcription factors TfbB and TfbG [48] within 10 bp upstream of the iTSS and tiling microarray data showing temporal modulation consistent with our findings validates the intraRNA expression pattern (Fig. S10).…”

“…In spite of being the best characterized archaea from the gene regulatory network point of view [25,47], there is only a small set of transcription factor (TfbB, TfbD, and TfbG) binding sites mapped using the ChIP-seq technology [48], which have the resolution necessary to investigate TF control of intraRNAs. Only 9 intraRNAs with valid intraORFs are immediately downstream (<50 bp) to these available TF binding sites and none of them share the same TF with their cognate gene (Table S8).…”

“…Secondary structure prediction with different methods did not show evidence of structures that would generate dRNA-seq false positives (data not shown). Also, it is possible to identify: (i) the general BRE-TATAbox-PPE-TSS layout, as established for TFB binding in H. salinarum [48], (ii) a pyrimidine at position −1 from the iTSS, and (iii) the presence of a SD-like sequence located 20 nt upstream of the first available ATG start codon (Fig. S11).…”

Identification of an archaeal mercury regulon by chromatin immunoprecipitation

“…Putative merR binding sequences were obtained by analysing 150 bp centred on each Piquepredicted peak centre. These sequences were input into MotifCatcher (Seitzer et al, 2012) running MEME (Bailey & Elkan, 1994) with the following parameters: minimum motif width, 9; maximum motif width, 20; reverse complement, no; random seeds, 100; seed size, 4 (all other parameters were unchanged from the default settings). MotifCatcher recovered two independent putative binding motifs that were each subsequently scanned against the S. solfataricus 98/2 genome using MAST (Bailey & Gribskov, 1998) and visualized in the Gaggle Genome Browser (Bare et al, 2010).…”

“…In order to assess the nature of MerR target sites across the S. solfataricus 98/2 genome, sequences associated with the curated ChIP-Seq peaks were analysed using MotifCatcher, a Monte Carlo-based motif searching algorithm (Seitzer et al, 2012). The informatics analysis identified the motif WDKRGMGMAHAWGAAT (MC-Motif 1, Fig.…”