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Finding sequence motifs in PBM data Workshop Project

Finding sequence motifs in PBM data Workshop Project. Yaron Orenstein October 2010. Outline. 1. Some background again… 2. The project. 1. Background. Slides with Ron Shamir and Chaim Linhart. Gene: from DNA to protein. Pre-mRNA. Mature mRNA. DNA. protein. transcription. splicing.

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Finding sequence motifs in PBM data Workshop Project

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  1. Finding sequence motifs in PBM data Workshop Project Yaron Orenstein October 2010

  2. Outline 1. Some background again… 2. The project

  3. 1. Background Slides with Ron Shamir and Chaim Linhart

  4. Gene: from DNA to protein Pre-mRNA Mature mRNA DNA protein transcription splicing translation

  5. DNA • DNA: a “string” over the alphabet of 4 bases (nucleotides): { A, C, G, T } • Resides in chromosomes • Complementary strands: A-T ; C-G • Forward/sense strand: AACTTGCG • Reverse-complement/anti-sense strand: TTGAACGC • Directional: from 5’ to 3’: • (upstream) AACTTGCGATACTCCTA (downstream) 5’ end 3’ end

  6. Gene structure (eukaryotes) Promoter DNA Coding strand Transcription start site (TSS) Transcription (RNA polymerase) Pre-mRNA Intron Exon Exon Splicing (spliceosome) 5’ UTR 3’ UTR Mature mRNA Stop codon Start codon Coding region Translation (ribosome) Protein

  7. Translation • Codon - a triplet of bases, codes a specific amino acid (except the stop codons); many-to-1 relation • Stop codons - signal termination of the protein synthesis process http://ntri.tamuk.edu/cell/ribosomes.html

  8. Genome sequences • Many genomes have been sequences, including those of viruses, microbes, plants and animals. • Human: • 23 pairs of chromosomes • 3+ Gbps (bps = base pairs) , only ~3% are genes • ~25,000 genes • Yeast: • 16 chromosomes • 20 Mbps • 6,500 genes

  9. Regulation of Expression • Each cell contains an identical copy of the whole genome - but utilizes only a subset of the genes to perform diverse, unique tasks • Most genes are highly regulated – their expression is limited to specific tissues, developmental stages, physiological condition • Main regulatory mechanism – transcriptional regulation

  10. TF TF 5’ 3’ Gene BS BS Transcriptional regulation • Transcription is regulated primarily by transcription factors (TFs) – proteins that bind to DNA subsequences, called binding sites (BSs) • TFBSs are located mainly (not always!) in the gene’s promoter – the DNA sequence upstream the gene’s transcription start site (TSS) • BSs of a particular TF share a common pattern, or motif • Some TFs operate together – TF modules TSS

  11. TFBS motif models AC CG ACT T • Consensus (“degenerate”) string: gene 1 gene 2 AACTGT gene 3 CACTGT gene 4 CACTCT gene 5 CACTGT gene 6 gene 7 gene 8 gene 9 AACTGT gene 10 • Statistical models… • Motif logo representation

  12. Human G2+M cell-cycle genes:The CHR – NF-Y module CDCA3(trigger of mitotic entry 1) CTCAGCCAATAGGGTCAGGGCAGGGGGCGTGGCGGGAAGTTTGAAACT -18 CDCA8(cell division cycle associated 8) TTGTGATTGGATGTTGTGGGA…[25bp]…TGACTGTGGAGTTTGAATTGG +23 CDC2(cell division control protein 2 homolog) CTCTGATTGGCTGCTTTGAAAGTCTACGGGCTACCCGATTGGTGAATCCGGGGCCCTTTAGCGCGGTGAGTTTGAAACTGCT 0 CDC42EP4 (cdc42 effector protein 4) GCTTTCAGTTTGAACCGAGGA…[25bp]…CGACGGCCATTGGCTGCTGC -110 CCNB1(G2/mitotic-specific cyclin B1) AGCCGCCAATGGGAAGGGAG…[30bp]…AGCAGTGCGGGGTTTAAATCT +45 CCNB2(G2/mitotic-specific cyclin B2) TTCAGCCAATGAGAGT…[15bp]…GTGTTGGCCAATGAGAAC…[15bp]…GGGCCGCCCAATGGGGCGCAAGCGACGCGGTATTTGAATCCTGGA +10 BS’s are short, non-specific, hiding in both strands and at various locations along the promoters TFs: NF-Y , CHR

  13. Protein Binding MicroarraysBerger et al, Nat. Biotech 2006 Generate an array of double-stranded DNA with all possible k-mers Detect TF binding to specific k-mers 13

  14. PBM (2) 14

  15. PBM - implementation Use 60-mers (Agilent): 25nt constant primer + 35nt variable region De Bruijn seq of all 10-mers (410 long) split into 35nt long fragments with 9nt overlap ~40K probes For each 8-mer, combine signals from all probes that contain it (or differ in 1nt) to obtain its bindingscore 15

  16. The computational challenge • Input: PBM data (sequences and binding scores) of one TF. • Goal: Find a motif (PWM) that is the binding site of that TF. • Intuition: sequences that match the motif (on one of the two possible strands!) are expected to have high binding scores.

  17. 2. The project

  18. General goals • Research - Learn about known solutions - Trial and error with training data • Develop software from A-Z: • Design • Implementation (Optimization) • Execution & analysis of test data • A taste of bioinformatics • Have fun • Get credit…

  19. The computational task • Given a set of PBM data of different TFs. • Find the binding site motif in PWM format of each TF. • Main challenges: • Performance (time, memory) • Accuracy

  20. Input File with 41,923 lines, each containing a probe sequence of length 35 and binding intensity. <sequence 35bp> \t <intensity> \n

  21. Input (II) • For the training data, an additional PWM file will be supplied for each PBM data set. A: <freq1> <freq2> … <freq10> C: <freq1> … <freq10> G: … T: … • Separated by \t and \n. • All lines must contain same number of frequencies (10 is just an example).

  22. Input (III) You will be given: • 10 training sets (PBM data + PWM) • 4 test sets (PBM data). You have to provide the PWM. • In the final project presentation, you will be given an online test set (PBM data) and your software will be applied to it.

  23. Output • A PWM file describing the binding site found in the given PBM file. • The PWM in motif logo format (i.e. displayed on the screen). The file logo.zip contains a java package with the code that will easily display your motif. bits = 2 - entropy

  24. Output (II) • Show graphically how well your motif predicts the binding intensity. • One example (note it’s not PWM):

  25. Ranking 8-mers • One possible way to start: rank the 8-mers in some way. Scores for example: 1. Signal average. 2. Signal median. • You can think of other scores that incorporate more information, e.g. position in probe sequence. • This is just an example. You can think of other ways to start.

  26. Alignment procedure • Then, you can align the significant 8-mers. • You may take into account the relative score. • Don’t forget about the reverse complement! • Example: Cebpb TF

  27. Enrichment scores • To test how good your motif is, you can use an enrichment score. • An enrichment score tests how good the motif distinguishes between high-ranking probes and the rest of the probes.

  28. Hypergeometric probability

  29. Hypergeometric enrichment score • Let B and T (TB) denote the BG and target sets, respectively, and let b and t denote the subset of probes from the BG and target set, respectively, that contain at least one occurrence of the motif.

  30. Hypergeometric score (2) • The HG enrichment score computes the probability of observing at least |t| target sequences with a motif occurrence, under the null hypothesis that the probes in the target set were drawn randomly, independently, and without replacement from the BG set. • Code is provided in math.zip

  31. Wilcoxon-Mann-Whitney (WMW) enrichment score • Foreground probes are all those containing a match, background are all the others. • B and F are the sizes of background and foreground, respectively. • ρB and ρF are the sums of the background and foreground ranks. • Read more in supplementary info (Berger06).

  32. Deciding the length of the motif • Another challenge is to decide the length of the motif. • Most binding site are 6-12 bp long. • You should consider the information each position contains and decide on the length accordingly.

  33. Scoring your PWM • One way to score your motif is by ranking the probe sequences according to a match score. • You may use the given code for match score. • Compare the ranking of the probes you got to the ranking according to binding intensities. There are different correlation score for that.

  34. Match Score between PWMs • Already implemented for you: • Euclidian Distance: • Pearson Correlation Coefficient • KL Divergence

  35. Implementation • Java (Eclipse) ; Linux (Other languages are possible, but will not participate in bonus). • Input: one single argument PBM filename • Output: PWM file, PWM presented in logo and graphical presentation of PWM matching distribution among probes. • Packages for motif logo and statistical scores will be supplied • Time performance will be measured • Reasonable documentation • Separate packages for data-structures, scores, GUI, I/O, etc.

  36. Submission • Printed design document. • Printed code – for comments and remarks. • Printed results document – for each test set PWM logo + how good your result in terms of correlation to the probes ranks. • 4 PWM files, e.g. Test_1.pwm (submitted by email). • Executable for the online test.

  37. Grade • 20% for the design • 30% for the implementation (20% for modularity, clarity, documentation, 10% for efficiency) • 30% for the performance and experimental results (20% for the accuracy on the 4 test queries and 10% for the accuracy on the online test query) • 20% for the final report and presentation • 10% bonus to the group with the most accurate results • 10% bonus for the group with the fastest implementation

  38. Bonus grading • Accuracy will be determined using the provided code that compares two PWMs. • We will take the average of runs on several different PBM data sets. • Running time will be measured in java implementation, and the average will be taken.

  39. Schedule • First progress report 23/11 • Design document 21/12 • Final presentation 16/2 • We shall meet with each group on each of these dates – mark your calendars! • Schedule can be made earlier if you are ready. • You are always welcome to meet us. Contact us by email.

  40. Design document • Due in week 12 (21/12). • 3-5 pages (Word), Hebrew/English • Briefly describe main goal, input and output of program • Describe main data structures, algorithms, and scores. • Meet with me before submission.

  41. Reference • Berger MF, Philippakis AA, Quershi AM, He FS, EstepIII PW, Bulyk ML. Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nature biotechnology. 2006;338:1429-1435. Very important! Read: the_brain.bwh.harvard.edu/UPBMseqn/suppl_methods.doc • Chen X, Hughes TR, Morris Q. RankMotif++: a motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors. Bioinformatics. 2007 Jul 1;23(13):i72-79.

  42. Fin

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