Table of ContentsDiscovery 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 |
Author: Abigail Manson McGuire |