Dataset Information

ITriplet, a rule-based nucleic acid sequence motif finder.

ABSTRACT: BACKGROUND:With the advent of high throughput sequencing techniques, large amounts of sequencing data are readily available for analysis. Natural biological signals are intrinsically highly variable making their complete identification a computationally challenging problem. Many attempts in using statistical or combinatorial approaches have been made with great success in the past. However, identifying highly degenerate and long (>20 nucleotides) motifs still remains an unmet challenge as high degeneracy will diminish statistical significance of biological signals and increasing motif size will cause combinatorial explosion. In this report, we present a novel rule-based method that is focused on finding degenerate and long motifs. Our proposed method, named iTriplet, avoids costly enumeration present in existing combinatorial methods and is amenable to parallel processing. RESULTS:We have conducted a comprehensive assessment on the performance and sensitivity-specificity of iTriplet in analyzing artificial and real biological sequences in various genomic regions. The results show that iTriplet is able to solve challenging cases. Furthermore we have confirmed the utility of iTriplet by showing it accurately predicts polyA-site-related motifs using a dual Luciferase reporter assay. CONCLUSION:iTriplet is a novel rule-based combinatorial or enumerative motif finding method that is able to process highly degenerate and long motifs that have resisted analysis by other methods. In addition, iTriplet is distinguished from other methods of the same family by its parallelizability, which allows it to leverage the power of today's readily available high-performance computing systems.

SUBMITTER: Ho ES

PROVIDER: S-EPMC2784457 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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iTriplet, a rule-based nucleic acid sequence motif finder.

Ho Eric S ES Jakubowski Christopher D CD Gunderson Samuel I SI

Algorithms for molecular biology : AMB 20091029

<h4>Background</h4>With the advent of high throughput sequencing techniques, large amounts of sequencing data are readily available for analysis. Natural biological signals are intrinsically highly variable making their complete identification a computationally challenging problem. Many attempts in using statistical or combinatorial approaches have been made with great success in the past. However, identifying highly degenerate and long (>20 nucleotides) motifs still remains an unmet challenge a ...[more]

PMID: 19874606

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Project description:MotivationProtein phosphorylation, driven by specific recognition of substrates by kinases and phosphatases, plays central roles in a variety of important cellular processes such as signaling and enzyme activation. Mass spectrometry enables the determination of phosphorylated peptides (and thereby proteins) in scenarios ranging from targeted in vitro studies to in vivo cell lysates under particular conditions. The characterization of commonalities among identified phosphopeptides provides insights into the specificities of the kinases involved in a study. Several algorithms have been developed to uncover linear motifs representing position-specific amino acid patterns in sets of phosphopeptides. To more fully capture the available information, reduce sensitivity to both parameter choices and natural experimental variation, and develop more precise characterizations of kinase specificities, it is necessary to determine all statistically significant motifs represented in a dataset.ResultsWe have developed MMFPh (Maximal Motif Finder for Phosphoproteomics datasets), which extends the approach of the popular phosphorylation motif software Motif-X (Schwartz and Gygi, 2005) to identify all statistically significant motifs and return the maximal ones (those not subsumed by motifs with more fixed amino acids). In tests with both synthetic and experimental data, we show that MMFPh finds important motifs missed by the greedy approach of Motif-X, while also finding more motifs that are more characteristic of the dataset relative to the background proteome. Thus MMFPh is in some sense both more sensitive and more specific in characterizing the involved kinases. We also show that MMFPh compares favorably to other recent methods for finding phosphorylation motifs. Furthermore, MMFPh is less dependent on parameter choices. We support this powerful new approach with a web interface so that it may become a useful tool for studies of kinase specificity and phosphorylation site prediction.AvailabilityA web server is at www.cs.dartmouth.edu/~cbk/.

Dataset Information

ITriplet, a rule-based nucleic acid sequence motif finder.

Publications

iTriplet, a rule-based nucleic acid sequence motif finder.

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