Reference: Hu YJ, et al. (2000) Combinatorial motif analysis and hypothesis generation on a genomic scale. Bioinformatics 16(3):222-32

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Abstract


Motivation: Computer-assisted methods are essential for the analysis of biosequences. Gene activity is regulated in part by the binding of regulatory molecules (transcription factors) to combinations of short motifs. The goal of our analysis is the development of algorithms to identify regulatory motifs and to predict the activity of combinations of those motifs.

Approach: Our research begins with a new motif-finding method, using multiple objective functions and an improved stochastic iterative sampling strategy. Combinatorial motif analysis is accomplished by constructive induction that analyzes potential motif combinations. The hypothesis is generated by applying standard inductive learning algorithms.

Results: Tests using 10 previously identified regulons from budding yeast and 14 artificial families of sequences demonstrated the effectiveness of the new motif-finding method. Motif combination and classification approaches were used in the analysis of a sample DNA array data set derived from genome-wide gene expression analysis.

Availability: Programs will be available as executable files upon request.

Contact: yhu@ics.uci.eduor yhu@cse.ttu.edu.tw

Reference Type
Journal Article | Research Support, Non-U.S. Gov't
Authors
Hu YJ, Sandmeyer S, McLaughlin C, Kibler D
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Gene Ontology Annotations


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Gene/Complex Qualifier Gene Ontology Term Aspect Annotation Extension Evidence Method Source Assigned On Reference

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Gene Phenotype Experiment Type Mutant Information Strain Background Chemical Details Reference

Disease Annotations


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Gene Disease Ontology Term Qualifier Evidence Method Source Assigned On Reference

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Post-translational Modifications


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Site Modification Modifier Reference

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Interactor Interactor Allele Assay Annotation Action Phenotype SGA score P-value Source Reference

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Gene Species Gene ID Strain background Direction Details Source Reference