Kyle Jensen's MIT Ph.D. Thesis Proposal

Syntactic Pattern Discovery as a Generic Tool in Systems Biology Kyle L. Jensen 20 December 2001 Or: How I learned to stop worrying and love biology.

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Pattern Discovery ,[object Object],[object Object],introduction -> pattern discovery 0 12 13 0 2 1 7 8 9 10 integers -     characters - MSKNIVLLPGDHVGPEVVA amino acids - ATGAGCATCGATCGATCGAATCTA nucleotides - Basic Question: When are two events the same? primitive steams A B C D E F patterns: V[HDV].[ST]K  12 . . 1 . 7 TCGATCGA

A Little History ,[object Object],[object Object],[object Object],[object Object],[object Object],introduction -> syntactic pattern discovery -> a little history submedian telocentric primitives: a b c d e babcbabdacad ebabcbab RP[VI]ILDPx[DE]PT ATCATACTATACGA H…..HRD.K..N Teireisas serine kinase AlignACE yeast promoter Prosite family classifier

An Illustrative Example ,[object Object],lliw, recnac, poleved, elbi, ylbaborp, enummi, eugalp, setebaid, ylekil, otelbitpecsus, kcaj, nhoj, ylbaborpsi, llij, esnopsere, noos, retal, esnopserenummina, sire, polevedyl, recnacote, tonlliw, otenummi, otelbitpecsusylbaborpsi, sikcaj, sirecnac, polevedlli, lliwesnopsere, otylekilsi, setebaidotelbitpecsus, wnhoj, evah, alpo, sinhoj, elbirroh will, cancer, develop, ible, probably, immune, plague, diabetes, likely, susceptibleto, jack, john, isprobably, jill, eresponse, soon, later, animmuneresponse, eris, lydevelop, etocancer, willnot, immuneto, isprobablysusceptibleto, jackis, canceris, illdevelop, eresponsewill, islikelyto, susceptibletodiabetes, johnw, have, opla, johnis, horrible recnacotenummiylbaborpsikcaj • recnacotelbitpecsusylbaborpsinhoj • retalsetebaidpolevedylbaborplliwllij dabsirecnac • elbirrohsaweugalp • noosrecnacevahotylekilsikcaj • retalsetebaiddlimdepolevedllij eugalpdlimotelbitpecsusylbaborpsinhoj • wolebtonlliwesnopserenummina • retalpolevedylbaborplliwsetebaid noosrecnacpolevedylekillliwllij • eugalpdabevahlliwnhojretal • setebaidotelbitpecsussawnhoj polevedtonlliwesnopserenummina • enajnipolevedotylekilsirecnac • eugalppolevedlliwkcaj recnacotenummisienaj • setebaidotelbitpecsusebnooslliwkcaj • eugalppolevedlliwylbaborpkcaj elbirrohsirecnac • ylekiltonsiretalesnopserenummina • setebaidotelbitpecsussinhoj recnacpolevedylekilnooslliwnhoj • ylekilebtonlliwsetebaid • tceffenaevahtonlliwrecnac eugalpotenummisillij • elbirroheblliwesnopsereht jackisprobablyimmunetocancer • johnisprobablysusceptibletocancer • marywillprobablydevelopdiabeteslater cancerisbad • plaguewashorrible • jackislikelytohavecancersoon • marydevelopedmilddiabeteslater johnisprobablysusceptibletomildplague • animmuneresponsewillnotbelow • diabeteswillprobablydeveloplater marywilllikelydevelopcancersoon • laterjohnwillhavebadplague • maryisprobablysusceptibletocancer animmuneresponseislikelytodevelopsoon • jackisprobablyimmunetoplague • johnwassusceptibletodiabetes animmuneresponsewillnotdevelop • cancerislikelytodevelopinjane • jackwilldevelopplague • janeisimmunetocancer jackwillsoonbesusceptibletodiabetes • jackprobablywilldevelopplague • cancerishorrible animmuneresponselaterisnotlikely • johnissusceptibletodiabetes • johnwillsoonlikelydevelopcancer diabeteswillnotbelikely • cancerwillnothaveaneffect • maryisimmunetoplague • therebsponsewillbehorrible introduction -> syntactic pattern discovery -> a quick example Given sequences: Strings with 4+ chars occurring 3+ times: … things that occur many times… … find important features… … but, what is “important”… How do we know these are important? John is probably susceptible to cancer.

Teiresias Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],introduction -> teiresias -> teiresias overview density = 9/19 Example Output: 6/15/2 patterns AFGLYEPC......L HQ.G.ET[ST]NS L.....A....SLKII.KA LFPCFY wildcard density = 6/6

Teiresias Example ,[object Object],>protein 0 MSKNIVLLPGDHVGPEVVAEAVKVLEAVSSAIGVKFNFSKHLIGGASIDAYGVPLSDEALEAAKK >protein 1 MSKQILVLPGDGIGPEIMAEAVKVLELANDRFQLGFELAEDVIGGAAIDKHGVP >protein 2 MKFLILLFNILCLFPVLAADNHGVGPQGASGVDPITFDINSNQTGPAFLT Take away point: Given sequences, Teiresias finds possibly important patterns in them. introduction -> teiresias -> teiresias example All patterns with at least 5 characters, density 5/8, and support 2 TEIRESIAS 5/8/2 pattern GPE..AEAVKVLE IGGA.ID..GVP MSK.I..LPGD..GPE A.D.HGV location (0,13) (1,13) (0,42) (1,42) (0,00) (1,00) (1,46) (2,17)

Biological Sequences ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],proposed problems -> biological sequences

Proposed Problems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],proposed problems -> biological sequences -> proposed problems

Expression and Physiology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],proposed problems -> expression and physiology -> motivation

Association Discovery Example ,[object Object],[object Object],proposed problems -> expression and physiological data -> association discovery 63 1 145 233 1 2 150 0 3 0 6 0 67 1 160 286 0 2 108 1 2 3 3 2 67 1 120 229 0 2 129 1 2 2 7 1 37 1 130 250 0 0 187 0 3 0 3 0 41 0 130 204 0 2 172 0 1 0 3 0 Patients with type 2 EKG anomaly, with positive fluoroscopy results and high blood pressure are likely to have more than one critically clogged artery. age sex blood pres. pain type choles. blood sug. ekg exercise ekg depress. fluoroscopy +’s ekg anomaly #>50% clogged Find conserved motifs in the rows

Proposed Problems ,[object Object],[object Object],proposed problems -> expression and physiological data physiological 1 2 3 4 A B C D samples 1 16 10 15 5 26 45 65 45 16 7 54 14 9 8 23 0 -2 7 9 2 3 4 -1 1 5 5 -2 -2 3 -1 2 Example associations: “ Genes 1 and 4 are associated with pathway  ” or “ Up-regulation of genes {4,6,10,…} gives rise to phenotype  ” gene expression 1 2 3 4 A B C D How does the genome relate to the “physiome”? Are there any recurring motifs? … biological significance?

Motivation ,[object Object],[object Object],sequence alignments work to date -> aa scoring matrices -> motivation sequence KSDFKJSDTLK ASLD KJFSLD D SLKDJFSKL SKDJFKD KSJDLKL SLKDJLKSJDL LKJDLKSJDKS database scoring matrix KSDFSDTLK ASLDKJFSLDD SLKDJFSKL LKD KSJDLKL SLKDJLKSJDL LKJDLJDKS KSDFSDD ASLDKJF SLKDJFS LKDFJDK KSJDLKL SLKDJLK LKJDLJD KSDFSDTLK ASLDKJFSLDD SLKDJFSKL LKD KSJDLKL SLKDJLKSJDL LKJDLJDKS But what do we mean by similar?

Scoring Matrix Basics ,[object Object],[object Object],RKISWMEIYTGEKSTKVYGQDVWLPAETLDLIREYRVAIKGPLTTPVGGGIRSLNVALRQ ::: :.:.: :::.:. : .. ::: :::....::.:.:::::::::::: :::::.:: RKIEWLEVYAGEKATQMYDSETWLPEETLNILQEYKVSIKGPLTTPVGGGMSSLNVAIRQ For detecting homology the matrix should capture evolutionary processes. … but how do we describe evolution? Highest score is the “best” alignment. alignment work to date -> aa scoring matrices -> scoring matrix basics score for K-Q alignment A R N D C M E G H I L K Q A R N K C Q E 5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1 – 1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7 5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1 – 1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7 5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1 – 1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7 – 4 –6 –7 –3 –4 –6 –7 –3 scoring matrix

Protein Evolution ,[object Object],work to date -> aa scoring matrices -> protein evolution ILHLV G PN G A GK S TL LARMA ancestral protein IVTLI G AN G A GK S TL LMTLC MAFLT G HS G A GK S TL LKLIC VVVII G PS G S GK S TL VRCIN NIMVV G PS G S GK S TL LRCIN VTAFI G PS G C GK T TL LRTFN MAFLT G HS G A GK S P L LKLIC VVVII G PS V S GK S TL VRCIN … use syntactic pattern discovery to find these conserved motifs. not functional The distribution of amino acids in the changing positions describes the evolutionary process… G .. G . GK . TL active site NIMVV G QS G L GK S TL INTLF descendant proteins

Discovering Patterns ,[object Object],>sp•Q07698•ABCA_AERSA ABC transporter protein MSEPVLAVSGVNKSFPIYRSPWQALWHALNPKADVKVFQALRDIELTVYRGETIGIV GHNGAGKSTLLQLITGVMQPDCGQITRTGRVVGLLELGSGFNPEFTGRENIFFNGAI LGMSQREMDDRLERILSFAAIGDFIDQPVKNYSSGMMVRLAFSVIINTDPDVLIIDE ALAVGDDAFQRKCYARLKQLQSQGVTILLVSHAAGSVIELCDRAVLLDRGEVLLQGE PKAVVHNYHKLLHMEGDERARFRYHLRQTGRGDSYISDESTSEPKIKSAPGILSVDL QPQSTVWYESKGAVLSDVHIESF >sp•Q02856•ABCX_ANTSP Probable ATP•dependent transporter MNNRILLNIKNLDVTIGETQILNSLNLSIKPGEIHAIMGKNGSGKSTLAKVIAGHPSYKI TNGQILFENQDVTEIEPEDRSHLGIFLAFQYPVEIPGVTNADFLRIAYNAKRAFDNKEEL DPLSFFSFIENKISNIDLNSTFLSRNVNEGFSGGEKKKNEILQMSLLNSKLAILDETDSG LDIDALKTIAKQINSLKTQENSIILITHYQRLLDYIKPDYIHVMQKGEIIYTGGSDTAMK LEKYGYDYLNK ATP binding motif G..G.GK[ST]TL was “discovered” in 2500 sequences in SWISS-PROT/TrEMBL. … how do we construct the scoring matrix? >sp•P07655•PSTB_ECOLI ATP•BINDING PROTEIN PSTB MSMVETAPSKIQVRNLNFYYGKFHALKNINLDIAKNQVTAFIGPSGCGKSTLLRTFNKMFELYPEQRAEGEILLDGDNILTNSQDIALLRAKVGMVFQKPTPFPMSIYDNIAFGVRLFEKLSRADMDERVQWALTKAALWNETKDKLHQSGYSLSGGQQQRLCIARGIAIRPEVLLLDEPCSALDPISTGRIEELITELKQDYTVVIVTHNMQQAARCSDHTAFMYLGELIEFSNTDDLFTKPAKKQTEDYITGRYG >sp•P10346•GLNQ_ECOLI ATP•BINDING PROTEIN GLNQ GPTQVLHNIDLNIAQGEVVVIIGPSGSGKSTLLRCINKLEEITSGDLIVDGLKVNDPKVDERLIRQEAGMVFQQFYLFPHLTALENVMFGPLRVRGANKEEAKLARELLAKVGLAERAHHYPSELSGGQQQRVAIARALAVKPKMMLFDEPTSALDPELRHEVLKVMQDLAEEGMTMVIVTHEIGFAEKVASRLIFIDKGRIAEDGNPQVLIKNPPSQRLQEFLQHVS Given a database, we can use Teiresias to find the conserved motifs… work to date -> aa scoring matrices -> discovering motifs ATP binding signature

Patterns to Matrix ,[object Object],work to date -> aa scoring matrices -> patterns to matrix Example Pattern: L..F.L..CI...L IINSSLWWIIKGPILISI L VN F I L FI CI IRI L VQKLRPPDIG Seq A • LTLITRVGLA L SL F C L LL CI LTF L LVRPIQGSRTTIHLHLCICLFVG Seq B • IKTPILVSI L RN F I L FI CI IRI L VQKLHSPDVGHNE Seq C • How many AA pairs are there at each position? pairs 1 – VS 1 – VR 1 • SR pairs 1 – FF 2 • LF Count AA pairs for all patterns and construct a table of pair counts. A R N D C M E G H I L K Q A R N K C Q E 34 23 43 56 78 32 12 54 76 43 23 21 11 12 54 76 43 23 21 11 12 54 76 43 23 21 23 43 56 78 32 12 54 76 43 23 21 76 43 76 43 23 21 76 43 23 21 76 43 23 21 45 67 87 76 43 23 21 12 39 05 37 29 04 23 90 76 43 23 21 76 43 23 21 87 76 43 22 54 23 54 23 12 64 76 45 AA pair frequency table

Patterns to Matrix ,[object Object],Take away point: The evolutionary information contained in the patterns is stored in terms of the scoring matrix. work to date -> aa scoring matrices -> patterns to matrix odds that a AA pair does not occur by chance probability of seeing AA pair in our patterns probability of seeing AA pair by chance = A R N D C M E G H I L K Q A R N K C Q E 34 23 43 56 78 32 12 54 76 43 23 21 11 12 54 76 43 23 21 11 12 54 76 43 23 21 23 43 56 78 32 12 54 76 43 23 21 76 43 76 43 23 21 76 43 23 21 76 43 23 21 45 67 87 76 43 23 21 12 39 05 37 29 04 23 90 76 43 23 21 76 43 23 21 87 76 43 22 54 23 54 23 12 64 76 45 AA pair frequency table A R N D C M E G H I L K Q A R N K C Q E 5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1 – 1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7 5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1 – 1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7 5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1 – 1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7 – 4 6 –7 –3 –4 –6 –7 –3 AA log•of•odds scoring matrix MATH positive values mean these pairs are more prevalent in our patterns than by chance… … and negative values are less prevalent

Basic Idea TEIRESIAS MATRIX ENGINE Take away point: Given a set of sequences, we use Teiresias to discover important patterns and construct a scoring matrix which captures the way these patterns are evolving. BDSUM: B io- D ictionary AA Su bstitution M atrices work to date -> aa scoring matrices -> basic idea KSDFKJSDTLK ASLD KJFSLD D SLKDJFSKL SKDJFKD KSJDLKL SLKDJLKSJDL LKJDLKSJDKS database HQ.G.ET..STNS RP..K.TSTP.NS L.S.DF.SLKS.DKIS V...EG.A..YPDVEL A..YPDVEL.NS EG.A K.T patterns scoring matrix

Example Results ,[object Object],[object Object],Experiment: Using each sequence from the family, try to detect the other 99 sequences in the Swiss-Prot/TrEMBL database. work to date -> aa scoring matrices -> example results 100 0 0 Results: BDSUM(PS00470) win loss tie BLOSUM62(PS00470) BLOSUM62(Prosite) 30 17 53 BDSUM(PS00470) 47 9 44 BLOSUM50(Prosite) BDSUM(PS00470)

Current Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],work to date -> aa scoring matrices -> current work … and the oligo probes…

Acknowledgements ,[object Object],[object Object],Group members: Mike, Maciek, Bill, Daehee, Jatin, Vipin, Maria, Javier, Maria, Matt, Gary, Saliya, Juan, Angelo, Chris, Dan, Giovanna, Joanne, Hyun-Tae, Patrick, Kyongbum…

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