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Motif search

Motif search. Prof. William Stafford Noble Department of Genome Sciences Department of Computer Science and Engineering University of Washington thabangh@gmail.com. Outline. One-minute response Revision Motifs Definition and motivation Representation as a PSSM

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Motif search

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  1. Motif search Prof. William Stafford Noble Department of Genome SciencesDepartment of Computer Science and Engineering University of Washington thabangh@gmail.com

  2. Outline • One-minute response • Revision • Motifs • Definition and motivation • Representation as a PSSM • Scanning for motif occurrences • Python

  3. One-minute responses • I would appreciate a little revision on the two types of tests. • Class was not too fast today. • The new method was fine. • I understood about 50% of the Python part. • The revision was very helpful. • Need more explanation by chalk. • Today was clear. • I am OK with stats but struggling with the Python. • We need to work more on Python. • Can you explain how to control the FDR, the j* and that table of p-values? • I would like to know more about Python execution.

  4. Other comments • We need extra Python tutorials with the tutors. • Tutors should write an explanation of each program so that we understand what it does. • Emile is complaining because our code looks like yours (no main function, not modular). What is the best way to code? • Emile’s comments will not affect your marks – they are stylistic suggestions only.

  5. Revision • You have searched a database of 1000 proteins with a single query sequence. What p-value threshold should you use if you want to apply Bonferroni correction and achieve 99% confidence? • 0.01 / 1000 = 0.00001 • You have searched a database of 5000 proteins, and you observed a top-scoring p-value of 0.00002. What E-value does this correspond to? • 0.00002 * 5000 = 0.1 • How do you decide whether to control the family-wise error rate or the false discovery rate? • If the conclusion or follow-up experiment involves a single result, then control family-wise error rate; otherwise, control false discovery rate.

  6. Revision 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 ... 1 0.0000152 2 0.0001525 3 0.0057250 4 0.0017798 5 0.0025807 6 0.0057372 7 0.0080466 8 0.0104888 9 0.0111517 10 0.0113193 11 0.0121449 12 0.012389 13 0.0136114 14 0.0156425 15 0.0168226 16 0.0172086 17 0.0183523 ... 1000 0.999999 • How many p-values in this list achieve an FDR of 10%? • j = index • α = 0.1 • m = 1000

  7. Motif • Set of similar substrings, within a family of diverged sequences. Motif long DNA or protein sequence

  8. Protein motifs HAHU V.LSPADKTN..VKAAWGKVG.AHAGE..........YGAEAL.ERMFLSF..PTTKTYFPH.FDLS.HGSA HAOR M.LTDAEKKE..VTALWGKAA.GHGEE..........YGAEAL.ERLFQAF..PTTKTYFSH.FDLS.HGSA HADK V.LSAADKTN..VKGVFSKIG.GHAEE..........YGAETL.ERMFIAY..PQTKTYFPH.FDLS.HGSA HBHU VHLTPEEKSA..VTALWGKVN.VDEVG...........G.EAL.GRLLVVY..PWTQRFFES.FGDL.STPD HBOR VHLSGGEKSA..VTNLWGKVN.INELG...........G.EAL.GRLLVVY..PWTQRFFEA.FGDL.SSAG HBDK VHWTAEEKQL..ITGLWGKVNvAD.CG...........A.EAL.ARLLIVY..PWTQRFFAS.FGNL.SSPT MYHU G.LSDGEWQL..VLNVWGKVE.ADIPG..........HGQEVL.IRLFKGH..PETLEKFDK.FKHL.KSED MYOR G.LSDGEWQL..VLKVWGKVE.GDLPG..........HGQEVL.IRLFKTH..PETLEKFDK.FKGL.KTED IGLOB M.KFFAVLALCiVGAIASPLT.ADEASlvqsswkavsHNEVEIlAAVFAAY..PDIQNKFSQaFKDLASIKD GPUGNI A.LTEKQEAL..LKQSWEVLK.QNIPA..........HS.LRL.FALIIEA.APESKYVFSF.LKDSNEIPE GPYL GVLTDVQVAL..VKSSFEEFN.ANIPK...........N.THR.FFTLVLEiAPGAKDLFSF.LKGSSEVPQ GGZLB M.L.DQQTIN..IIKATVPVLkEHGVT...........ITTTF.YKNLFAK.HPEVRPLFDM.GRQ..ESLE • Protein binding site • Phosphorylation site • Structural motif

  9. Transcription from DNA to RNA

  10. Transcription factor binding Transcription factor binding sites • A transcription factor is a protein that affects transcription by binding to DNA.

  11. Why identify motifs? • In proteins • Identify functionally important regions of a protein family • Find similarities to known proteins • In DNA • Discover how genes are regulated

  12. Representing motifs as PSSMs Convert these 9 6-letter sequences into a PSSM. Use uniform background probabilities (A=0.25, C=0.25, G=0.25, T=0.25) and a pseudocount weight of 1. AAGTGT TAATGT AATTGT AATTGA ATCTGT AATTGT TGTTGT AAATGA TTTTGT A 6 6 2 0 0 2 C 0 0 1 0 0 0 G 0 1 1 0 9 0 T 3 2 5 9 0 7 A 6.25 6.25 2.25 0.25 0.25 2.25 C 0.25 0.25 1.25 0.25 0.25 0.25 G 0.25 1.25 1.25 0.25 9.25 0.25 T 3.25 2.25 5.25 9.25 0.25 7.25 A 0.625 0.625 0.225 0.025 0.025 0.225 C 0.025 0.025 0.125 0.025 0.025 0.025 G 0.025 0.125 0.125 0.025 0.925 0.025 T 0.325 0.225 0.525 0.925 0.025 0.725 A 2.5 2.5 0.9 0.1 0.1 1 C 0.1 0.1 0.5 0.1 0.1 0 G 0.1 0.5 0.5 0.1 3.7 0 T 1.3 0.9 2.1 3.7 0.1 3 A 1.32 1.32 -0.15 -3.32 -3.32 -0.15 C -3.32 -3.32 -1.00 -3.32 -3.32 -3.32 G -3.32 -1.00 -1.00 -3.32 1.89 -3.32 T 0.38 -0.15 1.07 1.89 -3.32 1.54

  13. Scanning for motif occurrences • Given: • a long DNA sequence, and TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAG • a DNA motif represented as a PSSM • Find: • occurrences of the motif in the sequence A 1.32 1.32 -0.15 -3.32 -3.32 -0.15 C -3.32 -3.32 -1.00 -3.32 -3.32 -3.32 G -3.32 -1.00 -1.00 -3.32 1.89 -3.32 T 0.38 -0.15 1.07 1.89 -3.32 1.54

  14. Scanning for motif occurrences A 1.32 1.32 -0.15 -3.32 -3.32 -0.15 C -3.32 -3.32 -1.00 -3.32 -3.32 -3.32 G -3.32 -1.00 -1.00 -3.32 1.89 -3.32 T 0.38 -0.15 1.07 1.89 -3.32 1.54 0.38 + 1.32 – 0.15 + 1.89 + 1.89 + 1.54 = 6.87 • TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAG

  15. Scanning for motif occurrences A 1.32 1.32 -0.15 -3.32 -3.32 -0.15 C -3.32 -3.32 -1.00 -3.32 -3.32 -3.32 G -3.32 -1.00 -1.00 -3.32 1.89 -3.32 T 0.38 -0.15 1.07 1.89 -3.32 1.54 1.32 + 1.32 + 1.07 – 3.32 – 3.32 + 1.54 = -1.39 • TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAG

  16. CTCF • One of the most important transcription factors in human cells. • Responsible both for turning genes on and for maintaining 3D structure of the DNA.

  17. Searching human chromosome 21 with the CTCF motif

  18. Significance of scores A 1.32 1.32 -0.15 -3.32 -3.32 -0.15 C -3.32 -3.32 -1.00 -3.32 -3.32 -3.32 G -3.32 -1.00 -1.00 -3.32 1.89 -3.32 T 0.38 -0.15 1.07 1.89 -3.32 1.54 Motif scanning algorithm 6.30 Low score = not a motif occurrence High score = motif occurrence How high is high enough? TTGACCAGCAGGGGGCGCCG

  19. Two way to assess significance • Empirical • Randomly generate data according to the null hypothesis. • Use the resulting score distribution to estimate p-values. • Exact • Mathematically calculate all possible scores • Use the resulting score distribution to estimate p-values.

  20. CTCF empirical null distribution

  21. Poor precision in the tail

  22. Converting scores to p-values • Linearly rescale the matrix values to the range [0,100] and integerize. A -2.3 1.7 1.1 0.1 C 1.2 -0.3 0.4 -1.0 G -3.0 2.0 0.5 0.8 T 4.0 0.0 -2.1 1.5 A 10 67 59 44 C 60 39 49 29 G 0 71 50 54 T 100 43 13 64

  23. Converting scores to p-values • Find the smallest value. • Subtract that value from every entry in the matrix. • All entries are now non-negative. A -2.3 1.7 1.1 0.1 C 1.2 -0.3 0.4 -1.0 G -3.0 2.0 0.5 0.8 T 4.0 0.0 -2.1 1.5 A 0.7 4.7 4.1 3.1 C 4.2 2.7 3.4 2.0 G 0.0 5.0 3.5 3.8 T 7.0 3.0 0.9 4.5

  24. Converting scores to p-values • Find the largest value. • Divide 100 by that value. • Multiply through by the result. • All entries are now between 0 and 100. A 0.7 4.7 4.1 3.1 C 4.2 2.7 3.4 2.0 G 0.0 5.0 3.5 3.8 T 7.0 3.0 0.9 4.5 A 10.00 67.14 58.57 44.29 C 60.00 38.57 48.57 28.57 G 0.00 71.43 50.00 54.29 T 100.00 42.86 12.85 64.29 100 / 7 = 14.2857

  25. Converting scores to p-values • Round to the nearest integer. A 10.00 67.14 58.57 44.29 C 60.00 38.57 48.57 28.57 G 0.00 71.43 50.00 54.29 T 100.00 42.86 12.85 64.29 A 10 67 59 44 C 60 39 49 29 G 0 71 50 54 T 100 43 13 64

  26. Converting scores to p-values 0 1 2 3 4 … 400 • Say that your motif has N rows. Create a matrix that has N rows and 100N columns. • The entry in row i, column j is the number of different sequences of length i that can have a score of j. A 10 67 59 44 C 60 39 49 29 G 0 71 50 54 T 100 43 13 64

  27. Converting scores to p-values 0 1 2 3 4 … 10 60 100 400 • For each value in the first column of your motif, put a 1 in the corresponding entry in the first row of the matrix. • There are only 4 possible sequences of length 1. A 10 67 59 44 C 60 39 49 29 G 0 71 50 54 T 100 43 13 64 1 1 1 1

  28. Converting scores to p-values 0 1 2 3 4 … 10 60 77 100 400 • For each value x in the second column of your motif, consider each value y in the zth column of the first row of the matrix. • Add y to the x+zth column of the matrix. A 10 67 59 44 C 60 39 49 29 G 0 71 50 54 T 100 43 13 64 1 1 1 1 1

  29. Converting scores to p-values 0 1 2 3 4 … 10 60 77 100 400 • For each value x in the second column of your motif, consider each value y in the zth column of the first row of the matrix. • Add y to the x+zth column of the matrix. • What values will go in row 2? • 10+67, 10+39, 10+71, 10+43, 60+67, …, 100+43 • These 16 values correspond to all 16 strings of length 2. A 10 67 59 44 C 60 39 49 29 G 0 71 50 54 T 100 43 13 64 1 1 1 1 1

  30. Converting scores to p-values 0 1 2 3 4 … 10 60 77 100 400 • In the end, the bottom row contains the scores for all possible sequences of length N. • Use these scores to compute a p-value. A 10 67 59 44 C 60 39 49 29 G 0 71 50 54 T 100 43 13 64 1 1 1 1 1

  31. The probability of observing a score >4 is the area under the curve to the right of 4. This probability is called a p-value. p-value = Pr(data|null) Computing a p-value

  32. Sample problem #1 • Given: • a file containing a length-n DNA sequence, and • a file containing a DNA motif represented as a PSSM of length n. • Return: • the score of the motif versus the sequence A 1.32 1.32 -0.15 -3.32 -3.32 -0.15 C -3.32 -3.32 -1.00 -3.32 -3.32 -3.32 G -3.32 -1.00 -1.00 -3.32 1.89 -3.32 T 0.38 -0.15 1.07 1.89 -3.32 1.54

  33. Sample problem #2 • Given: • a file containing a DNA sequence, and • a file containing a DNA motif represented as a PSSM. • Return: • For each position that scores greater than 0, print the position, the score and the matching sequence A 1.32 1.32 -0.15 -3.32 -3.32 -0.15 C -3.32 -3.32 -1.00 -3.32 -3.32 -3.32 G -3.32 -1.00 -1.00 -3.32 1.89 -3.32 T 0.38 -0.15 1.07 1.89 -3.32 1.54

  34. Sample problem #3 • Modify the previous program to print the same results, but in sorted order, with the greatest score first.

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