1 / 16

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

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

tommy
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MOPAC: Motif-finding by Preprocessing and AgglomerativeClustering from Microarrays Thomas R. Ioerger1 Ganesh Rajagopalan1 Debby Siegele2 1Department of Computer Science 2Department of Biology Texas A&M University

  2. 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?

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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)

  8. 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.

  9. 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

  10. 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)

  11. 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

  12. Motifs Found

  13. Sequence Logos

  14. Distance to Transcription Start

  15. 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?

  16. 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)

More Related