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Computational discovery of gene modules and regulatory networks. Ziv Bar-Joseph et al (2003). Presented By: Dan Baluta. Agenda. Introduction Goal of Paper Methods & Results Conclusions Critique Discussion. Introduction. Interest in figuring out regulatory networks
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Computational discovery of gene modules and regulatory networks Ziv Bar-Joseph et al (2003) Presented By: Dan Baluta
Agenda • Introduction • Goal of Paper • Methods & Results • Conclusions • Critique • Discussion
Introduction • Interest in figuring out regulatory networks • Genome-wide EXPRESSION data sets • DNA-binding data (LOCATION analysis) • PROBLEMS!!!
Theory… • Integration of expression and location data ought to result in a more accurate assignment of genes to regulators, when compared to either types of data sets on their own. • Basic result of combining data is more information.
Primary Goal • Develop an algorithm that combines expression and location data to discover gene modules and regulatory networks.
Methods • GRAM Algorithm • Genetic RegulAtory Modules • As opposed to ‘GRM’ algorithm??? • GRAM Validation • Comparing results • Chromatin-IP (CHIP) experiments • MIPS category enrichment analysis • DNA binding motif analysis • Targeted data analysis using GRAM • Transcriptional regulation of rapamycin response
GRAM Algorithm Step 2: Reduce the gene sets from Step 1, by filtering out all genes from each set that do not have highly (positively) correlated expression levels. Reduced sets act as ‘seeds’ for gene ‘modules’. Step 1: search all possible (pairwise) combinations of transcript regulators. Pull out sets of genes that share binding transcriptional regulators. STRINGENT BINDING CRITERIA USED (p < .001). Expression Data Binding Data Step 3: Revisit binding data and adds genes sharing binding transcriptional regulators to gene modules from Step 2, using RELAXED BINDING CRITERIA (p < .01).
Methods • GRAM applied to binding data for 106 transcription factors and 500 expression experiments from Saccharomyces cerevisiae. List of 500 Expression Experiments
Results: GRAM Algorithm • 106 gene modules found. • Containing 655 distinct genes. • Regulated by 68 transcription factors (TFs).
Results: GRAM Algorithm (~ 35%)
Validation: Comparing Results • Picked up many more (2.5x) regulator-gene interactions than binding data alone would have predicted. • How do we know these are not all false-positives?
Validation: CHIP Experiments • Allows you to determine if a given gene actually binds to a specific TF. • Used IP experiments for Stb1 and 36 randomly chosen genes to characterize sensitivity and specificity.
Validation: CHIP Experiments • GRAM pulled out 3 TF-gene relationships that were… • A) Validated by the IP results. • B) NOT pulled out using binding data alone. • GRAM did NOT pull out TF-gene relationships that were not also validated by the IP results. • IP experiments indeed showed reduction in false negatives, and a lack of increase in false positives.
Validation: MIPS Categories • Gene modules ought to belong to same MIPS categories. • Gene modules derived using GRAM were 3X more likely to be enriched for genes in the same MIPS category than groups of genes derived from binding/location data alone.
Validation: DNA Binding Motifs • Genes linked to specific TFs ought to have the same binding motifs upstream of them as those associated with their TFs. • TRANSFAC database was used to determine whether genes in GRAM modules were more likely to be independently determined to be co-regulated vs. groups of genes from binding data alone.
Validation: DNA Binding Motifs • GRAM modules did indeed display higher percentage of genes containing the appropriate motif in the upstream region of DNA. • Further validation of GRAM algorithm.
Validation: Rapamycin Response • Rapamycin inhibits Tor kinase signaling • Mimics nutrient starvation • Selected 14 TFs and performed genome-wide location analysis on them • Ran GRAM algorithm using location data plus expression data from literature
Validation: Rapamycin Response • Found 39 Gene Modules. • 23 had significant MIPS category enrichment. • Added 192 pairs of gene-TF interactions that location data alone missed. • Generated 4 novel hypotheses.
Software Availability • Provide link to Java Application
Conclusions • GRAM provides a means of discovering putative regulatory networks that other data sets cannot detect independently. • Integrating data sets provides us with more information than is available with either set independently.
Critiques • No solid measure of sensitivity and specificity. Argue that GRAM is more sensitive, but without specificity measure, how do we know that these are not all false-positives? • Looked for positive correlations as indicative of activation. Did not look at negatively correlated expression -- potentially an important loss of information. • Software does not appear to work OOB with sample data provided.
Discussion Topics • Can this method be applied to other higher-level organisms? Should it be? • How can this model be improved to include more information? e.g. can we look at negatively correlated expression data? • Should society consider other projects, on the scale of HGP, to extract more data on organisms in a standardized and systematic way? • Pairwise data is used in many cases in biology to infer system-level interactions, which in reality are multivariate. Is using this pair-wise data wise? Is there an alternative? • Could adding multiple species sets improve our results? i.e. Use metagenes instead of genes?
