Table of Contents
Discovery of DNA regulatory motifs
Outline
Overview
Regulon Prediction
Methods for predicting regulons
mRNA expression data
GeneChip expression analysis
Cluster analysis of mRNA expression data
Representation of expression data
Identifying prevalent expression patterns (gene clusters)
Cluster contents
From expression patterns to DNA sequence patterns
Predicting regulons from conserved operons
Predicting the E. coli PurR regulon from conserved operons
Predicting the PurR regulon by piecing together smaller operons
Using protein fusions to predict protein-protein interactions:
Examples of protein fusions
Using protein phylogenetic profiles to infer functional coupling
Constructing phylogenetic profiles
Clustering phylogenetic profiles
Metabolic pathways
Known regulons in other organisms
Motif-finding algorithms
Gibbs sampler algorithm (AlignACE)
AlignACE Example: Input Data Set
AlignACE Example: Initial Seeding
MAP score
AlignACE Example: Sampling
AlignACE Example: Column Sampling
AlignACE Example: The Best Motif
AlignACE Example: Iterative Masking
AlignACE Example: The Next Motif
AlignACE Example: Final Results
Differences between AlignACE and original Gibbs sampler algorithm
Indices for evaluating the significance of motifs
Indices used to evaluate motifs found by AlignACE:
Searching for additional motif instances in the entire genome sequence
Group Specificity Score (Sgroup)
Site Specificity Score (Ssite)
Site specificity score vs. Group specificity score
Ssite can discriminate real regulatory motifs
Positional Bias
Comparisons of motifs
Clustering motifs by similarity
Palindromicity
AlignACE applications
Conservation of DNA regulatory motifs and discovery of new motifs in microbial genomes
Outline
Motif-finding in genomes with operons
Prediction of upstream regions for motif finding
Methods for predicting regulons
Analysis of motif conservation
E. coli regulons in other organisms
Pooling upstream sequences from closely related organisms to find conserved motifs
Conservation of E. coli motifs in 17 microbial genomes
A different motif in another organism can signify:
Different mechanisms for regulating carbon metabolism
Divergence of binding site residues in LexA
New motifs upstream of anaerobic regulons in A. fulgidus
Regulons predicted from conserved operons in other organisms
Predicting the E. coli PurR regulon from conserved operons
Predicting known regulons from conserved operons
New motif predicted to regulate ferrous ion transport in A. fulgidus, M. thermoautotrophicum, and P. horokoshii
Regulons predicted from functional groups (metabolic pathways)
Motifs from AlignACE runs in 17 bacterial genomes
New motif predicted to regulate methane metabolism and folate biosynthesis in M. thermoautotrophicum
Summary: motif finding in bacteria
Applications of AlignACE in S. cerevisiae
Computational identification of cis-regulatory elements associated with groups of functionally related genes in S. cerevisiae
S. cerevisiae AlignACE test set
Most specific motifs (ranked by Sgroup)
Most positionally biased motifs
Negative Controls
Positive Controls
Motif finding in gene groups with similar temporal expression profiles in S. cerevisiae cell cycle
Conclusions
Acknowledgments
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