MOPAC: Motif-finding by Preprocessing and Agglomerative Clustering from Microarrays Thomas R. Ioerger 1 Ganesh Rajagopalan 1 Debby Siegele 2 1 Department.
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MOPAC: Motif-finding by Preprocessing and Agglomerative Clustering from Microarrays Thomas R. Ioerger 1 Ganesh Rajagopalan 1 Debby Siegele 2 1 Department of Computer Science 2 Department of Biology Texas A&M University
Analyzing Gene Expression Patterns DNA microarrays ~4000 genes E. coli, ~6000 genes for yeast Compare expression levels between conditions Example: starvation response in E. coli –starve cells for nutrient sources –reintroduce => recovery => exponential growth –which genes show changes in response?
types of response: –up-regulation –down-regulation –transient response (spike) –(arbitrary temporal patterns) Problem: can cluster genes based on response pattern, but then what? –not all genes in cluster are regulated the same way
Couple with genomic analysis –search for common motifs in up-stream regions –subsets of co-regulated genes within clusters Assumptions: 1. regulation occurs by interaction of transcription factors with small motifs (~10-20bp) within several hundred bp of transcription start site 2. among many motifs, the ones of interest will be common to some genes in a cluster, but not found in any genes outside (with different responses) 3. the motif does not have to be shared by all genes in the cluster, only a subset
Related Work Many algorithms exist for motif finding –assume cluster (gene set) is already defined –word/string analysis models –probabilistic models Gibbs sampling (AlignACE, MotifSampler) Expectation Maximization (MEME) HMMs –graph algorithms (e.g. clique) Pevzner and Sze –what if motif only appears in a subset of genes? count as parameter in MotifSampler, MEME
Overview Our Approach 1. Definition of regulation patterns 2. Extraction of upstream sequences (for up-reg) 3. Define control set (genes with no change) 4. Make a list of all 12-mers in upstream regions 5. Find motifs that occur (more than once) in up- regulated set, but not at all in control set 6. Group the motifs using clustering, form consensus of patterns
Define Regulation Patterns measured at 0, 5, and 15min after recovery discrete representation of changes in expression levels relative to exp. growth phase conditions +1: >2-fold increase -1: >2-fold decrease 0: otherwise (no significant change) up-regulation patterns: (0,1,1) (0,1,0) (0,0,1) (-1,1,1) (-1,1,0) (-1,0,1) define control set: (0,0,0) (1,1,1) (-1,-1,-1)
Extraction of Upstream Sequences nominally, 600bp upstream of translation start site (i.e. ORF; not transcription start) If gene is a member of an operon: –take 300bp upstream of gene –plus 300bp upstream of translation start of first gene in operon databases: K12 sequence: GOLD –operon relationships: E. coli Linkage Map (Berlyn et al.) use reverse complement if transcribed in rev.
Pre-processing extract all 12-mers (overlapping) from upstream regions of up-regulated genes note: better than DFS remove those that appear in the control set remove those that are dissimilar to everything else (“de-noising”) –score=mean distance to all motifs not in same upstream region or operon –remove if score>~9/12 mis-matches
Clustering compute similarity matrix among motifs repeatedly merge closest neighbors –minimum spanning tree –single-linkage clustering Stop merging when dist>3/12 mismatches Form consensus: relax constraints on nucleotides at position by disjunction –ACCATGGTATC –ACGATGGTATT –ACTATAGTATC –AC(CTG)AT(AG)GTAT(TC)
Experiments Starvation of E. coli for glucose in medium 3 time-points: starved (0min), 5min, 15min Data collected in Siegele lab up-regulated: 22 genes control set: 1361 genes
Other Forms of Validation Palindromicity: 11/13 motifs have index>0.5 TRANSFAC database: –e.g. motif 2 matches pattern for MetJ-MetF site –a number of other hits for known transcription factors biological verification awaits... –role in regulation pathway for starvation response?
Conclusions Augment cluster-analysis of expression patterns with motif analysis Efficient method for generating candidates –from 12-mers in upstream regions Efficient method for screening them –empirically, against a control set, rather than probabilistic background model Advantage: Pattern does not have to be in all the genes in a set Challenges: defining appropriate upstream regions and the right control set (as filter)