Canadian Bioinformatics Workshops

1. Canadian Bioinformatics Workshops www.bioinformatics.ca

2. In collaboration with Cold Spring Harbor Laboratory & New York Genome Center

3. Contains material by Wyeth Wasserman, William Noble, Michael Hoffman, and Tim Bailey Module 6 #: Title of Module 3

4. Module 6Gene Regulation Network Analysis Michael M. Hoffman High-throughput Biology: From Sequence to Networks April 27-May 3, 2015 @michaelhoffman

5. Learning Objectives of Module At the end of the workshop, the participant will • Understand challenges in predicting transcription factor (TF) binding • Be able to identify binding sites for known TFs • Be able to discover TF binding motifs in genomic regions like ChIP-seq peaks or promotersusing Galaxy and MEME-ChIP

6. Overview Part 1: Overview of transcription Part 2: Prediction of transcription factor binding sites using binding profiles (“Discrimination”) Part 3: Detection of novel motifs (TFBS) over-represented in regulatory regions Part 4: Interrogation of sets of co-expressed genes or ChIP-seqregions to identify mediating transcription factors Part 5: Gene regulatory networks

7. Part 1Introduction to transcription in eukaryotic cells

8. Transcription over-simplified • ‌TFbinds to DNA at TF binding site • ‌TF recruits RNA polymerase II • ‌RNA polymerase IIproduces RNA RNA pol II TF UCCUAGGGUUCCGGGUUGAGGGGCUUGGUGAGUCCCAGGACACCUGUCGAGUGGAUCGAC ATTATCCATTAGCACAAGCCCGTCAGTGGCCCCATGCATAAATGTACACAGAAACAGGTGGGGAGAGAAGGGGCCAGGGTATAAAAAGGGCCCACAAGAGACCAGCTCAAGGATCCCAAGGCCCAACTCCCCGAACCACTCAGGGTCCTGTGGACAGCTCACCTAGCTG TAATAGGTAATCGTGTTCGGGCAGTCACCGGGGTACGTATTTACATGTGTCTTTGTCCACCCCTCTCTTCCCCGGTCCCATATTTTTCCCGGGTGTTCTCTGGTCGAGTTCCTAGGGTTCCGGGTTGAGGGGCTTGGTGAGTCCCAGGACACCTGTCGAGTGGATCGAC

9. Anatomy of transcriptional regulationWARNING: Terms vary widely in meaning between scientists Core promoter/initiation region (Inr) TSS Distal regulatory region Proximal regulatory region Distal regulatory region • Core promoter – Sufficient for initiation of transcription; orientation dependent • TSS – transcription start site • Often really a transcription start region • TFBS – single transcription factor binding site • Regulatory regions • Proximal/Distal – vague reference to distance from TSS • May be positive (enhancing) or negative (repressing) • Orientation independent (generally) • Modules – Sets of TFBS within a region that function together • Transcriptional unit • DNA sequence transcribed as a single polycistronic mRNA EXON EXON TFBS TFBS TFBS TFBS TFBS TATA TFBS TFBS

10. Complexity in transcription Chromatin Distal enhancer Proximal enhancer Core Promoter Distal enhancer

11. Laboratory data on regulatory regions • Promoters • RNA 5' ends (CAGE) • Epigenetic marks (ChIP-seq) • Polymerase complex (ChIP-seq) • RNA (RNA-seq) • TFBSs • TFs (ChIP-seq) • Regulatory regions • Co-activators (ChIP-seq) • Epigenetic marks (ChIP-seq) • Enhancer RNA (RNA-seq)

12. Accessing laboratory data • UCSC Genome Browser • http://genome.ucsc.edu • Gene Expression Omnibus (GEO) • http://www.ncbi.nlm.nih.gov/geo/ • ENCODE Project • http://encodeproject.org/ • Roadmap Epigenomics • http://www.roadmapepigenomics.org/ • oRegAnno • http://www.oreganno.org

13. Overview Part 1: Overview of transcription Part 2: Prediction of transcription factor binding sites using binding profiles (“Discrimination”) Part 3: Detection of novel motifs (TFBS) over-represented in regulatory regions Part 4: Interrogation of sets of co-expressed genes or ChIP-seqregions to identify mediating transcription factors Part 5: Gene regulatory networks

14. Part 2 Prediction of TF binding sites Teaching a computer to find TFBS…

15. Set of sites, represented as aposition frequency matrix (PFM) 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 Logo – A graphical representation of frequency matrix. Y-axis is information content , which reflects the strength of the pattern in each column of the matrix 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 • Single site • AAGTTAATGA • Set of sites, represented as a consensus • VDRTWRWWSHD (IUPAC degenerate DNA)

16. TGCTG = 0.9 Conversion of PFMsto position specific scoring matrices (PSSM) Add the following features to the matrix profile: Correct for nucleotide frequencies in genome Weight for the confidence (depth) in the pattern Convert to log-scale probability for easy arithmetic PSSM PFM 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

17. Relative scores A [-0.2284 0.4368 -1.5 -1.5 -1.5 0.4368 -1.5 -1.5 -0.2284 0.4368 ] C [-0.2284 -0.2284 -1.5 -1.5 1.5128 -1.5 -0.2284 -1.5 -0.2284 -1.5 ] G [ 1.23481.23482.12222.1222 0.4368 1.23481.51281.74571.7457 -1.5 ] T [ 0.4368 -0.2284 -1.5 -1.5 -0.2284 0.4368 0.4368 0.4368 -1.5 1.7457 ] Max_score = 15.2 (sum of highest column scores) A [-0.2284 0.4368 -1.5 -1.5 -1.5 0.4368 -1.5 -1.5 -0.2284 0.4368 ] C [-0.2284 -0.2284 -1.5 -1.5 1.5128 -1.5 -0.2284 -1.5 -0.2284 -1.5 ] G [ 1.2348 1.2348 2.1222 2.1222 0.4368 1.2348 1.5128 1.7457 1.7457 -1.5 ] T [ 0.4368 -0.2284-1.5 -1.5 -0.2284 0.4368 0.4368 0.4368 -1.5 1.7457 ] Min_score = -10.3 (sum of lowest column scores) Detecting binding sites in a single sequence Raw scores Sp1 ACCCTCCCCAGGGGCGGGGGGCGGTGGCCAGGACGGTAGCTCC A [-0.2284 0.4368 -1.5 -1.5 -1.5 0.4368 -1.5 -1.5 -0.2284 0.4368 ] C [-0.2284 -0.2284 -1.5 -1.5 1.5128 -1.5 -0.2284 -1.5 -0.2284 -1.5 ] G [ 1.2348 1.2348 2.1222 2.1222 0.4368 1.2348 1.5128 1.7457 1.7457 -1.5 ] T [ 0.4368 -0.2284 -1.5 -1.5 -0.2284 0.4368 0.4368 0.4368 -1.5 1.7457 ] Abs_score = 13.4 (sum of column scores) Empirical p-value Scores 0.3 0.2 0.1 0.0 Area to right of value Area under entire curve Frequency 0.0 0.2 0.4 0.6 0.8 1.0 Relative score

18. JASPAR: An open-access databaseof TF binding profiles http://jaspar.genereg.net

19. The Good… • Tronche (1997) tested 50 predicted HNF1 TFBS using an in vitro binding test and found that 96% of the predicted sites were bound! • Stormo and Fields (1998) found in detailed biochemical studies that the best weight matrices produce scores highly correlated with in vitro binding energy BINDING ENERGY PSSM SCORE

20. …the Bad… • Fickett (1995) found that a profile for the MyoDTF made predictions at a rate of 1 per ~500bp of human DNA sequence • This corresponds to an average of 20 sites / gene (assuming 10,000 bp as average gene size)

21. …and the Ugly! Human Cardiac a-Actin gene analyzed with a set of profiles (each line represents a TFBS prediction) Futility conjuncture: TFBS predictions are almost always wrong Red boxes are protein coding exons - TFBS predictions excluded in this analysis

22. PPV THRESHOLD More stringency doesn’t help • Counter to intuition, the ratio of true positives to predictions fails to improve for “stringent” thresholds • For most predictive models this ratio would increase • Why? • True binding sites are defined by properties not incorporated into the profile scores - above some threshold all sites could be bound if present in the right setting

23. Section 2What have we learned? • PSSMs accurately reflect in vitro binding properties of DNA binding proteins • Suitable binding sites occur at a rate far too frequent to reflect in vivo function • Bioinformatics methods that use PSSMs for binding site studies must incorporate additional information to enhance specificity • Unfiltered predictions are too noisy for most applications • Organisms with short regulatory sequences are less problematic (e.g. yeast and E.coli)

24. Overview Part 1: Overview of transcription Part 2: Prediction of transcription factor binding sites using binding profiles (“Discrimination”) Part 3: Detection of novel motifs (TFBS) over-represented in regulatory regions Part 4: Interrogation of sets of co-expressed genes or ChIP-seqregions to identify mediating transcription factors Part 5: Gene regulatory networks

25. Part 3:de novo Discovery of TF Binding Sites

26. Motif discovery problem • Given sequences • Find motif seq. 1 seq. 2 seq. 3 seq. 1 seq. 2 seq. 3 IGRGGFGEVY at position 515 LGEGCFGQVV at position 430 VGSGGFGQVY at position 682

27. Motif discovery problem • Given: • a sequence or family of sequences. • Find: • the number of motifs • the width of each motif • the locations of motif occurrences

28. Why is this hard? • Input sequences are long (thousands or millions of residues). • Motif may be subtle • Instances are short. • Instances are only slightly similar. ? ?

29. TCTCTCTCCACGGCTAATTAGGTGATCATGAAAAAATGAAAAATTCATGAGAAAAGAGTCAGACATCGAAACATACATTCTCTCTCCACGGCTAATTAGGTGATCATGAAAAAATGAAAAATTCATGAGAAAAGAGTCAGACATCGAAACATACAT …HIS7 …ARO4 ATGGCAGAATCACTTTAAAACGTGGCCCCACCCGCTGCACCCTGTGCATTTTGTACGTTACTGCGAAATGACTCAACG …ILV6 CACATCCAACGAATCACCTCACCGTTATCGTGACTCACTTTCTTTCGCATCGCCGAAGTGCCATAAAAAATATTTTTT TGCGAACAAAAGAGTCATTACAACGAGGAAATAGAAGAAAATGAAAAATTTTCGACAAAATGTATAGTCATTTCTATC …THR4 …ARO1 ACAAAGGTACCTTCCTGGCCAATCTCACAGATTTAATATAGTAAATTGTCATGCATATGACTCATCCCGAACATGAAA ATTGATTGACTCATTTTCCTCTGACTACTACCAGTTCAAAATGTTAGAGAAAAATAGAAAAGCAGAAAAAATAAATAA …HOM2 GGCGCCACAGTCCGCGTTTGGTTATCCGGCTGACTCATTCTGACTCTTTTTTGGAAAGTGTGGCATGTGCTTCACACA …PRO3 TFBS motif discovery example We are given a set of promoters from co-regulated genes.

30. 5’- TCTCTCTCCACGGCTAATTAGGTGATCATGAAAAAATGAAAAATTCATGAGAAAAGAGTCAGACATCGAAACATACAT 5’- TCTCTCTCCACGGCTAATTAGGTGATCATGAAAAAATGAAAAATTCATGAGAAAAGAGTCAGACATCGAAACATACAT …HIS7 …HIS7 …ARO4 …ARO4 5’- ATGGCAGAATCACTTTAAAACGTGGCCCCACCCGCTGCACCCTGTGCATTTTGTACGTTACTGCGAAATGACTCAACG 5’- ATGGCAGAATCACTTTAAAACGTGGCCCCACCCGCTGCACCCTGTGCATTTTGTACGTTACTGCGAAATGACTCAACG …ILV6 …ILV6 5’- CACATCCAACGAATCACCTCACCGTTATCGTGACTCACTTTCTTTCGCATCGCCGAAGTGCCATAAAAAATATTTTTT 5’- CACATCCAACGAATCACCTCACCGTTATCGTGACTCACTTTCTTTCGCATCGCCGAAGTGCCATAAAAAATATTTTTT 5’- TGCGAACAAAAGAGTCATTACAACGAGGAAATAGAAGAAAATGAAAAATTTTCGACAAAATGTATAGTCATTTCTATC 5’- TGCGAACAAAAGAGTCATTACAACGAGGAAATAGAAGAAAATGAAAAATTTTCGACAAAATGTATAGTCATTTCTATC …THR4 …THR4 …ARO1 …ARO1 5’- ACAAAGGTACCTTCCTGGCCAATCTCACAGATTTAATATAGTAAATTGTCATGCATATGACTCATCCCGAACATGAAA 5’- ACAAAGGTACCTTCCTGGCCAATCTCACAGATTTAATATAGTAAATTGTCATGCATATGACTCATCCCGAACATGAAA 5’- ATTGATTGACTCATTTTCCTCTGACTACTACCAGTTCAAAATGTTAGAGAAAAATAGAAAAGCAGAAAAAATAAATAA 5’- ATTGATTGACTCATTTTCCTCTGACTACTACCAGTTCAAAATGTTAGAGAAAAATAGAAAAGCAGAAAAAATAAATAA …HOM2 …HOM2 5’- GGCGCCACAGTCCGCGTTTGGTTATCCGGCTGACTCATTCTGACTCTTTTTTGGAAAGTGTGGCATGTGCTTCACACA 5’- GGCGCCACAGTCCGCGTTTGGTTATCCGGCTGACTCATTCTGACTCTTTTTTGGAAAGTGTGGCATGTGCTTCACACA …PRO3 …PRO3 TFBS motif discovery example An unknown transcription factor binds to positions unknown to us, on either DNA strand.

31. 5’- TCTCTCTCCACGGCTAATTAGGTGATCATGAAAAAATGAAAAATTCATGAGAAAAGAGTCAGACATCGAAACATACAT …HIS7 …ARO4 5’- ATGGCAGAATCACTTTAAAACGTGGCCCCACCCGCTGCACCCTGTGCATTTTGTACGTTACTGCGAAATGACTCAACG …ILV6 5’- CACATCCAACGAATCACCTCACCGTTATCGTGACTCACTTTCTTTCGCATCGCCGAAGTGCCATAAAAAATATTTTTT 5’- TGCGAACAAAAGAGTCATTACAACGAGGAAATAGAAGAAAATGAAAAATTTTCGACAAAATGTATAGTCATTTCTATC …THR4 …ARO1 5’- ACAAAGGTACCTTCCTGGCCAATCTCACAGATTTAATATAGTAAATTGTCATGCATATGACTCATCCCGAACATGAAA 5’- ATTGATTGACTCATTTTCCTCTGACTACTACCAGTTCAAAATGTTAGAGAAAAATAGAAAAGCAGAAAAAATAAATAA …HOM2 5’- GGCGCCACAGTCCGCGTTTGGTTATCCGGCTGACTCATTCTGACTCTTTTTTGGAAAGTGTGGCATGTGCTTCACACA …PRO3 TFBS motif discovery example The DNA binding motif of the transcription factor can be described by a position-specific scoring matrix (PSSM).

32. 5’- TCTCTCTCCACGGCTAATTAGGTGATCATGAAAAAATGAAAAATTCATGAGAAAAGAGTCAGACATCGAAACATACAT …HIS7 …ARO4 5’- ATGGCAGAATCACTTTAAAACGTGGCCCCACCCGCTGCACCCTGTGCATTTTGTACGTTACTGCGAAATGACTCAACG …ILV6 5’- CACATCCAACGAATCACCTCACCGTTATCGTGACTCACTTTCTTTCGCATCGCCGAAGTGCCATAAAAAATATTTTTT 5’- TGCGAACAAAAGAGTCATTACAACGAGGAAATAGAAGAAAATGAAAAATTTTCGACAAAATGTATAGTCATTTCTATC …THR4 …ARO1 5’- ACAAAGGTACCTTCCTGGCCAATCTCACAGATTTAATATAGTAAATTGTCATGCATATGACTCATCCCGAACATGAAA 5’- ATTGATTGACTCATTTTCCTCTGACTACTACCAGTTCAAAATGTTAGAGAAAAATAGAAAAGCAGAAAAAATAAATAA …HOM2 5’- GGCGCCACAGTCCGCGTTTGGTTATCCGGCTGACTCATTCTGACTCTTTTTTGGAAAGTGTGGCATGTGCTTCACACA …PRO3 TFBS motif discovery example The sequence motif discovery problem is to discover the sites (or the motif) given just the sequences.

33. Gibbs sampling

34. Alternating approach • Guess an initial weight matrix • Use weight matrix to predict instances in the input sequences • Use instances to predict a weight matrix • Repeat 2 & 3 until satisfied.

35. Initialization • Randomly guess an instance si from each of t input sequences {S1, ..., St}. sequence 1 ACAGTGT TTAGACC GTGACCA ACCCAGG CAGGTTT sequence 2 sequence 3 sequence 4 sequence 5

36. Gibbs sampler • Initially: randomly guess an instance si from each of t input sequences {S1, ..., St}. • Steps 2 & 3 (search): • Throw away an instance si: remaining (t - 1)instances define weight matrix. • Weight matrix defines instance probability at each position of input string Si • Pick new si according to probability distribution • Return highest-scoring motif seen

37. Sampler step illustration: ACAGTGT TAGGCGT ACACCGT ??????? CAGGTTT A C G T ACAGTGT TAGGCGT ACACCGT ACGCCGT CAGGTTT sequence 4 11% ACGCCGT:20% ACGGCGT:52%

38. TOMTOM predicts which protein(s) may bind a DNA motif Query motif Alignment to Matching Motif TOMTOM Motif Databases • TOMTOM compares the query motif against all motifs in databases of known motifs (e.g., Transfac). • TOMTOM reports all statistically significant matches.

39. Overview Part 1: Overview of transcription Part 2: Prediction of transcription factor binding sites using binding profiles (“Discrimination”) Part 3: Detection of novel motifs (TFBS) over-represented in regulatory regions Part 4: Interrogation of sets of co-expressed genes or ChIP-seqregions to identify mediating transcription factors Part 5: Gene regulatory networks

40. Part 4: Inferring regulating TFs for sets of co-expressed genes

41. Co-Expressed Negative Controls Deciphering regulation of co-expressed genes

42. TFBS enrichment • Akin to methods for GO term enrichment/over-representation analysis, we seek to determine if a set of co-expressed genes contains an over-abundance of predicted binding sites for a known TF

43. Foreground Foreground More Total TFBS Background Background Two examples of TFBS enrichment More Genes with TFBS

44. Statistical methods for identifying enriched TFBS • Binomial test (Z scores) • Based on the number of occurrences of the TFBS relative to background • Normalized for sequence length • Simple binomial distribution model • Fisher’s exact test probability scores • Based on the number of genes containing the TFBS relative to background • Hypergeometric probability distribution

45. 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 oPOSSUMprocedure

46. Validation using reference gene sets TFs with experimentally-verified sites in the reference sets.

47. oPOSSUM Server

48. Structurally-related TFs with Indistinguishable TFBS • Most structurally related TFs bind to highly similar DNA motifs • Zn-finger family is a big exception

49. EXAMPLEEts Family • How to pick which one? • At this stage there are TF catalogs coming that will be coupled to characteristics. • Candidate gene prioritization software can be used (if not tightly coupled to chromosomal region) such as TOPPGENE • Etv1 • Etv2 • Etv3 • Etv3l • Etv4 • Etv5 • Etv6 • Fev • Fli1 • Gabpa • LOC100 • LOC100 • factor) • LOC634494 • Sfpi1 • Spdef • Spib • Spic • EG232974 • EG432800 • Ehf • Elf1 • Elf2 • Elf3 • Elf4 • Elf5 • Elk1 • Elk3 • Elk4 • Erf • Erg • Ets1 • Ets2

50. Overview Part 1: Overview of transcription Part 2: Prediction of transcription factor binding sites using binding profiles (“Discrimination”) Part 3: Detection of novel motifs (TFBS) over-represented in regulatory regions Part 4: Interrogation of sets of co-expressed genes or ChIP-seqregions to identify mediating transcription factors Part 5: Gene regulatory networks