Dataset Information

Combinatorial analysis for sequence and spatial motif discovery in short sequence fragments.

ABSTRACT: Motifs are overrepresented sequence or spatial patterns appearing in proteins. They often play important roles in maintaining protein stability and in facilitating protein function. When motifs are located in short sequence fragments, as in transmembrane domains that are only 6-20 residues in length, and when there is only very limited data, it is difficult to identify motifs. In this study, we introduce combinatorial models based on permutation for assessing statistically significant sequence and spatial patterns in short sequences. We show that our method can uncover previously unknown sequence and spatial motifs in beta-barrel membrane proteins and that our method outperforms existing methods in detecting statistically significant motifs in this data set. Last, we discuss implications of motif analysis for problems involving short sequences in other families of proteins.

SUBMITTER: Jackups R

PROVIDER: S-EPMC3417775 | biostudies-literature | 2010 Jul-Sep

REPOSITORIES: biostudies-literature

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Combinatorial analysis for sequence and spatial motif discovery in short sequence fragments.

Jackups Ronald R Liang Jie J

IEEE/ACM transactions on computational biology and bioinformatics 20100701 3

Motifs are overrepresented sequence or spatial patterns appearing in proteins. They often play important roles in maintaining protein stability and in facilitating protein function. When motifs are located in short sequence fragments, as in transmembrane domains that are only 6-20 residues in length, and when there is only very limited data, it is difficult to identify motifs. In this study, we introduce combinatorial models based on permutation for assessing statistically significant sequence a ...[more]

PMID: 20671322

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Project description:BackgroundTraditional methods for computational motif discovery often suffer from poor performance. In particular, methods that search for sequence matches to known binding motifs tend to predict many non-functional binding sites because they fail to take into consideration the biological state of the cell. In recent years, genome-wide studies have generated a lot of data that has the potential to improve our ability to identify functional motifs and binding sites, such as information about chromatin accessibility and epigenetic states in different cell types. However, it is not always trivial to make use of this data in combination with existing motif discovery tools, especially for researchers who are not skilled in bioinformatics programming.ResultsHere we present MotifLab, a general workbench for analysing regulatory sequence regions and discovering transcription factor binding sites and cis-regulatory modules. MotifLab supports comprehensive motif discovery and analysis by allowing users to integrate several popular motif discovery tools as well as different kinds of additional information, including phylogenetic conservation, epigenetic marks, DNase hypersensitive sites, ChIP-Seq data, positional binding preferences of transcription factors, transcription factor interactions and gene expression. MotifLab offers several data-processing operations that can be used to create, manipulate and analyse data objects, and complete analysis workflows can be constructed and automatically executed within MotifLab, including graphical presentation of the results.ConclusionsWe have developed MotifLab as a flexible workbench for motif analysis in a genomic context. The flexibility and effectiveness of this workbench has been demonstrated on selected test cases, in particular two previously published benchmark data sets for single motifs and modules, and a realistic example of genes responding to treatment with forskolin. MotifLab is freely available at http://www.motiflab.org.

Dataset Information

Combinatorial analysis for sequence and spatial motif discovery in short sequence fragments.

Publications

Combinatorial analysis for sequence and spatial motif discovery in short sequence fragments.

Similar Datasets

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