1. Motif search
GENOME 559: Introduction to Statistical and Computational Genomics
Prof. William Stafford Noble

2. One-minute response
Would like to keep format of ½ theory and ½ python. x5
I agree that we don’t need to go over sample problems at beginning of next class.
The stats went from very basic to showing a difficult proof. Explanation of the proof was kind of a waste of time.
Deriving Bonferroni made sense conceptually but might have helped with more concrete example.

3. One-minute response: positives
Thank you for giving lots of time to ask questions.
I really understood the statistics part very well. Thank you!
I like that we spent more time on programming.
Python section was very useful / do-able / easy to follow.
It was nice to get the separate reference sheet.

4. One-minute response: pacing
I thought pace for both were good today. x2
Good pacing today. x2
Loved the pace of both parts.
Class was really good today. Good pacing, good exercises.
I liked the pacing for the class.
Good pace and contents overall. x2
Theory seemed to move slowly today.
I think we could go a little faster, like introducing if and for loops in the same class.
I don’t think we should go slower with Python.
Going slower through practice problems helped, as well as working through the code.

5. One-minute response: other
My biggest problem is I keep forgetting to put commas in my print statements.
The second problem was difficult for me to get without help.
Bonus problems for class exercises could be fun.
I could use more sample problems for the theory side.
Wish we spent more time on probability. It seems like we are doing really small chunks.
I would prefer to use a larger % of class time for Python.
Perhaps going more in depth on why you used certain parts of code would help with homework.

6. Outline Motifs – definition and motivation Motif databases
Scanning with a PSSM

7. What is a motif?
Set of similar substrings, within a family of diverged sequences.
Motif
long DNA or protein sequence

8. Protein motifs Protein binding site Phosphorylation site
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 factor binding site motifs
Transcription factor binding sites

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

11. xxxxxxxxxxx.xxxxxxxxx.xxxxx..........xxxxxx.xxxxxxx.xxxxxxxxxx.xxxxxxxxx
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.PDIQNKFSQFaGKDLASIKD
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
xxxxx.xxxxxxxxxxxxx..xxxxxxxxxxxxxxx..xxxxxxx.xxxxxxx...xxxxxxxxxxxxxxxx
HAHU QVKGH.GKKVADA.LTN......AVA.HVDDMPNA...LSALS.D.LHAHKL....RVDPVNF.KLLSHCLL
HAOR QIKAH.GKKVADA.L.S......TAAGHFDDMDSA...LSALS.D.LHAHKL....RVDPVNF.KLLAHCIL
HADK QIKAH.GKKVAAA.LVE......AVN.HVDDIAGA...LSKLS.D.LHAQKL....RVDPVNF.KFLGHCFL
HBHU AVMGNpKVKAHGK.KVLGA..FSDGLAHLDNLKGT...FATLS.E.LHCDKL....HVDPENF.RL.LGNVL
HBOR AVMGNpKVKAHGA.KVLTS..FGDALKNLDDLKGT...FAKLS.E.LHCDKL....HVDPENFNRL..GNVL
HBDK AILGNpMVRAHGK.KVLTS..FGDAVKNLDNIKNT...FAQLS.E.LHCDKL....HVDPENF.RL.LGDIL
MYHU EMKASeDLKKHGA.TVL......TALGGILKKKGHH..EAEIKPL.AQSHATK...HKIPVKYLEFISECII
MYOR EMKASaDLKKHGG.TVL......TALGNILKKKGQH..EAELKPL.AQSHATK...HKISIKFLEYISEAII
IGLOB T.GA...FATHATRIVSFLseVIALSGNTSNAAAV...NSLVSKL.GDDHKA....R.GVSAA.QF..GEFR
GPUGNI NNPK...LKAHAAVIFKTI...CESATELRQKGHAVwdNNTLKRL.GSIHLK....N.KITDP.HF.EVMKG
GPYL NNPD...LQAHAG.KVFKL..TYEAAIQLEVNGAVAs.DATLKSL.GSVHVS....K.GVVDA.HF.PVVKE
GGZLB Q......PKALAM.TVL......AAAQNIENLPAIL..PAVKKIAvKHCQAGVaaaH.YPIVGQEL.LGAIK
xxxxxxxxx.xxxxxxxxx.xxxxxxxxxxxxxxxxxxxxxxx..x
HAHU VT.LAA.H..LPAEFTPA..VHASLDKFLASV.STVLTS..KY..R
HAOR VV.LAR.H..CPGEFTPS..AHAAMDKFLSKV.ATVLTS..KY..R
HADK VV.VAI.H..HPAALTPE..VHASLDKFMCAV.GAVLTA..KY..R
HBHU VCVLAH.H..FGKEFTPP..VQAAYQKVVAGV.ANALAH..KY..H
HBOR IVVLAR.H..FSKDFSPE..VQAAWQKLVSGV.AHALGH..KY..H
HBDK IIVLAA.H..FTKDFTPE..CQAAWQKLVRVV.AHALAR..KY..H
MYHU QV.LQSKHPgDFGADAQGA.MNKALELFRKDM.ASNYKELGFQ..G
MYOR HV.LQSKHSaDFGADAQAA.MGKALELFRNDM.AAKYKEFGFQ..G
IGLOB TA.LVA.Y..LQANVSWGDnVAAAWNKA.LDN.TFAIVV..PR..L
GPUGNI ALLGTIKEA.IKENWSDE..MGQAWTEAYNQLVATIKAE..MK..E
GPYL AILKTIKEV.VGDKWSEE..LNTAWTIAYDELAIIIKKE..MKdaA
GGZLB EVLGDAAT..DDILDAWGK.AYGVIADVFIQVEADLYAQ..AV..E
Globin motifs

12. Splice site motif in logo format
weblogo.berkeley.edu

16. Position-specific scoring matrix
This PSSM assigns the sequence NMFWAFGH a score of = 12. A -1 -2 R 5 1 -3 N 6 D C Q E 2 G H 8 I -4 L K M F P S T W Y 3 V

17. Representing a motif as a PSSM
Convert these 9 6-letter sequences into a PSSM.
In practice, we add a small pseudocount, convert to frequencies, and take the log.
AAGTGT
TAATGT
AATTGT
AATTGA
ATCTGT
TGTTGT
AAATGA
TTTTGT A C G T

18. Representing motifs as PSSMs
Add a pseudocount.
Convert to frequencies.
Take the logarithm.
AAGTGT
TAATGT
AATTGT
AATTGA
ATCTGT
TGTTGT
AAATGA
TTTTGT A C G T A C G T A C G T A C G T

19. 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 C G T

20. Scanning for motif occurrences
– = 6.87
TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAG

21. Scanning for motif occurrences
– 3.32 – = -1.39
TAATGTTTGTGCTGGTTTTTGTGGCATCGGGCGAGAATAGCGCGTGGTGTGAAAG

22. Summary
DNA and protein motifs represent functionally or structurally important sequence elements.
Motifs are typically represented using position-specific scoring matrices.
A PSSM can be used to scan a given DNA or protein sequence to search for occurrences of the motif.