Combinatorial motif analysis and hypothesis generation on a genomic scale

Motivation: Computer-assisted methods are essential for the analysis of bio sequences. 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 mo tifs and to predict the activity of combinations of those motifs. Approach: Our research begins with a new motif-finding method, using multip le objective functions and an improved stochastic iterative sampling strate gy. 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 a nd 14 artificial families of sequences demonstrated the effectiveness of th e new motif-finding method Motif combination and classification approaches were used in the analysis of a sample DNA array data set derived from genom e-wide gene expression analysis.