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MedGen 505 Gene Regulation Bioinformatics Wyeth W. Wasserman

MedGen 505 Gene Regulation Bioinformatics Wyeth W. Wasserman. www.cisreg.ca. Overview. TFBS Prediction with Motif Models Improving Specificity of Predictions Analysis of Sets of Co-Expressed and Co-Regulated Genes.

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MedGen 505 Gene Regulation Bioinformatics Wyeth W. Wasserman

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  1. MedGen 505 Gene Regulation Bioinformatics Wyeth W. Wasserman www.cisreg.ca

  2. Overview • TFBS Prediction with Motif Models • Improving Specificity of Predictions • Analysis of Sets of Co-Expressed and Co-Regulated Genes

  3. Transcription Factor Binding Sites(over-simplified for pedagogical purposes) URF Pol-II URE TATA

  4. Teaching a computer to find TFBS…

  5. Laboratory Discovery of TFBS ACTIVITY LUCIFERASE LUCIFERASE LUCIFERASE LUCIFERASE LUCIFERASE LUCIFERASE LUCIFERASE

  6. A matrix describing a a set of sites A 14 16 4 0 1 19 20 1 4 13 4 4 13 12 3 C 3 0 0 0 0 0 0 0 7 3 1 0 3 1 12 G 4 3 17 0 0 2 0 0 9 1 3 0 5 2 2 T 0 2 0 21 20 0 1 20 1 4 13 17 0 6 4 Representing Binding Sites for a TF Set of binding sites AAGTTAATGA CAGTTAATAA GAGTTAAACA CAGTTAATTA GAGTTAATAA CAGTTATTCA GAGTTAATAA CAGTTAATCA AGATTAAAGA AAGTTAACGA AGGTTAACGA ATGTTGATGA AAGTTAATGA AAGTTAACGA AAATTAATGA GAGTTAATGA AAGTTAATCA AAGTTGATGA AAATTAATGA ATGTTAATGA AAGTAAATGA AAGTTAATGA AAGTTAATGA AAATTAATGA AAGTTAATGA AAGTTAATGA AAGTTAATGA AAGTTAATGA • A single site • AAGTTAATGA • A set of sites represented as a consensus • VDRTWRWWSHD (IUPAC degenerate DNA)

  7. TGCTG = 0.9 PFMs to PWMs Add the following features to the model: 1. Correcting for the base frequencies in DNA 2. Weighting for the confidence (depth) in the pattern 3. Convert to log-scale probability for easy arithmetic wmatrix fmatrix A 1.6 -1.7 -0.2 -1.7 -1.7 C -1.7 0.5 0.5 1.3 -1.7 G -1.7 1.0 -0.2 -1.7 1.3 T -1.7 -1.7 -0.2 -0.2 -0.2 A 5 0 1 0 0 C 0 2 2 4 0 G 0 3 1 0 4 T 0 0 1 1 1 f(b,i)+ s(n) Log() p(b)

  8. Performance of Profiles • 95% of predicted sites bound in vitro (Tronche 1997) • MyoD binding sites predicted about once every 600 bp (Fickett 1995) • The Futility Conjuncture • Nearly 100% of predicted transcription factor binding sites have no function in vivo

  9. JASPAR AN OPEN-ACCESS DATABASE OF TF BINDING PROFILES

  10. PROBLEM: Too many spurious predictions Actin, alpha cardiac

  11. I.9 Terms • Specificity – The portion of predictions that are correct • Sensitivity – The portion of “positives” that are detected • The detection of TFBS is limited by terrible specificity. Why?

  12. Method#1Phylogenetic Footprinting 70,000,000 years of evolution reveals most regulatory regions

  13. Phylogenetic Footprinting FoxC2 100% 80% 60% 40% 20% 0%

  14. Phylogenetic Footprinting to Identify Functional Segments % Identity 200 bp Window Start Position (human sequence) Actin gene compared between human and mouse with DPB.

  15. Human Mouse Phylogenetic Footprinting Dramatically Reduces Spurious Hits Actin, alpha cardiac

  16. Performance: Human vs. Mouse • Testing set: 40 experimentally defined sites in 15 well studied genes (Replicated with 100+ site set) • 75-90% of defined sites detected with conservation filter, while only 11-16% of total predictions retained SELECTIVITY SENSITIVITY

  17. ConSite (www.cisreg.ca) NEW: Ortholog Sequence Retrieval Service

  18. Emerging Issues • Multiple sequence comparisons • Incorporate phylogenetic trees • Visualization • Analysis of closely related species • Phylogenetic shadowing • Genome rearrangements • Inversion compatible alignment algorithm • Higher order models of TFBS

  19. I.18 OnLine Resources for Phylogenetic Footprinting • Linked to TFBS • ConSite • rVISTA • Alignments • Blastz • Lagan • Avid • ORCA • Visualization • Sockeye • Vista Browser • PipMaker

  20. Method#2Discrimination of Regulatory Modules TFs do NOT act in isolation

  21. Layers of Complexity in Metazoan Transcription

  22. Diverse and non-uniform use of terms: Partial glossary for tutorial Promoter Region • Promoter – Sufficient to support the initiation of transcription; orientation dependent; includes TSS • Regulatory Regions • Proximal – adjacent to promoter • Distal – some distance away from promoter (vague) • May be positive (enhancing) or negative (repressing) • TSS – transcription start site • TFBS – single transcription factor binding site • Modules – Sets of TFBS that function together Distal Regulatory Region Proximal Regulatory Region Distal R.R. EXON EXON TFBS TFBS TFBS TFBS TFBS TATA TFBS TFBS TSS

  23. Detecting Clusters of TF Binding Sites • Trained Methods • Sufficient examples of real clusters to establish weights on the relative importance of each TF • Statistical Over-Representation of Combinations • Binding profiles available for a set of biologically motivated TFs

  24. Training for the detection of liver cis-regulatory modules (CRMs)

  25. Models for Liver TFs… HNF3 HNF1 HNF4 C/EBP

  26. Logistic Regression Analysis * a1 * a2 * a3 * a4 Optimize a vector to maximize the distance between output values for positive and negative training data. Output value is: elogit p(x)= 1 + elogit S “logit”

  27. Performance of the Liver Model • Performance • Sensitivity: 60% of known CRMs detected • Specificity: 1 prediction/35,000bp • Limitations • Applies to genes expressed late in hepatocyte differentiation • Requires 10-15 genes in positive training set • This model doesn’t account for multiple sites for the same TF • New methods from several groups address this limit

  28. UGT1A1 Wildtype Other Liver Module Model Score “Window” Position in Sequence

  29. Making better predictions • Profiles make far too many false predictions to have predictive value in isolation • Phylogenetic footprinting eliminates ~90% of false predictions • Algorithms for detection of clusters of binding sites perform better, especially when possible to create train on known examples for the target context

  30. Method#3 Higher Order Models Position-position dependence

  31. T C G A First-order: A A m:th-order: The chance of drawing base x is dependant on the identity of the previous m bases Probabilistic Methods for Pattern Discovery(7) What is a higher-order background model? p(A)=0.29, p(C)=0.21, p(G)=0.21, p(T)=0.29 Zero-order:

  32. Linking co-expressed genes to candidate transcription factors

  33. Deciphering Regulation of Co-Expressed Genes

  34. Automated sequence retrieval from EnsEMBL Set of co-expressed genes PhylogeneticFootprinting ORCA Putative mediating transcription factors Statistical significance of binding sites Detection of transcription factor binding sites oPOSSUM Procedure

  35. Statistical Methods for Identifying Over-represented TFBS • Z scores • Based on the number of occurrences of the TFBS relative to background • Normalized for sequence length • Simple binomial distribution model • Fisher exact probability scores • Based on the number of genes containing the TFBS relative to background • Hypergeometric probability distribution

  36. The oPOSSUM Database • Orthologous genes:  8468 • Promoter pairs:  6911 • Promoters with TFBS:  6758 • Total # of TFBS predictions:  1638293 • Overall failure rate:  20.2%

  37. Validation using Reference Gene Sets TFs with experimentally-verified sites in the reference sets.

  38. Application to Microarray Data Sets • NF-кB inhibition microarray study

  39. C-Myc SAGE Data • c-Myc transcription factor dimerizes with the Max protein • Key regulator of cell proliferation, differentiation and apoptosis • Menssen and Hermeking identified 216 different SAGE tags corresponding to unique mRNAs that were induced after adenoviral expression of c-Myc in HUVEC cells • They then went on to confirm the induction of 53 genes using microarray analysis and RT-PCR

  40. C-Fos Microarray Experiment • In a study examining the role of transcriptional repression in oncogenesis, Ordway et al. compared the gene expression profiles of fibroblasts transformed by c-fos to the parental 208F rat fibroblast cell line • We mapped the list of 252 induced Affymetrix Rat Genome U34A GeneChip sequences to 136 human orthologs

  41. oPOSSUM Server

  42. http://www.cisreg.ca/cgi-bin/oPOSSUM/opossum INPUT A LIST OF CO-EXPRESSED GENES

  43. SELECT YOUR TFBS PROFILES

  44. SELECT: • CONSERVATION • PSSM MATCH THRESHOLD • PROMOTER REGION • STATISTICAL MEASURE

  45. de novo Discovery of TF Binding Sites

  46. Pattern Discovery

  47. de novo Pattern Discovery • Exhaustive • e.g. YMF (Sinha & Tompa) • Generalization: Identify over-represented oligomers in comparison of “+” and “-” (or complete) promoter collections • Monte Carlo/Gibbs Sampling • e.g. AnnSpec (Workman & Stormo) • Generalization: Identify strong patterns in “+” promoter collection vs. background model of expected sequence characteristics

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