MOPAC: Motif-finding by Preprocessing and Agglomerative Clustering from Microarrays

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MOPAC Motif-finding by Preprocessing and
AgglomerativeClustering from Microarrays

• Thomas R. Ioerger1
• Ganesh Rajagopalan1
• Debby Siegele2

1Department of Computer Science 2Department of
Biology Texas AM University
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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 gt recovery gt exponential growth
• which genes show changes in response?

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• 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

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• 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

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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

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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

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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 gt2-fold increase
• -1 gt2-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)

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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.

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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)
• scoremean distance to all motifs not in same
  upstream region or operon
• remove if scoregt9/12 mis-matches

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Clustering

• compute similarity matrix among motifs
• repeatedly merge closest neighbors
• minimum spanning tree
• single-linkage clustering
• Stop merging when distgt3/12 mismatches
• Form consensus relax constraints on nucleotides
  at position by disjunction
• ACCATGGTATC
• ACGATGGTATT
• ACTATAGTATC
• AC(CTG)AT(AG)GTAT(TC)

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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

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Motifs Found
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Sequence Logos
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Distance to Transcription Start
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Other Forms of Validation

• Palindromicity 11/13 motifs have indexgt0.5
• 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?

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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)