Syntactic Pattern Discovery as a Generic Tool in Systems Biology Kyle L. Jensen 20 December 2001 Or: How I learned to stop...
Outline <ul><li>Introductio n </li></ul><ul><ul><li>Pattern Discovery </li></ul></ul><ul><ul><li>Teireisas </li></ul></ul>...
Part I:   Introduction
Pattern Discovery <ul><li>Decision-Theoretic </li></ul><ul><li>Syntactic </li></ul>introduction -> pattern discovery 0 12 ...
A Little History <ul><li>Formal language theory  </li></ul><ul><ul><li>pattern recognition </li></ul></ul><ul><li>Biologic...
An Illustrative Example <ul><li>Patterns in sequences </li></ul>lliw, recnac, poleved, elbi, ylbaborp, enummi, eugalp, set...
Teiresias Overview <ul><li>Finds patterns in primitive streams  </li></ul><ul><ul><li>L/W/K patterns </li></ul></ul><ul><u...
Teiresias Example <ul><li>Finding protein motifs </li></ul>>protein 0 MSKNIVLLPGDHVGPEVVAEAVKVLEAVSSAIGVKFNFSKHLIGGASIDAYG...
Part II:   Proposed Problems
Biological Sequences <ul><li>Motivation </li></ul><ul><ul><li>Protein and DNA sequences </li></ul></ul><ul><ul><li>Lots  o...
Proposed Problems <ul><li>Amino acid scoring matrix design </li></ul><ul><ul><li>Model protein evolution using conserved m...
Expression and Physiology <ul><li>Motivation </li></ul><ul><ul><li>Creating associations: simple observations of complex b...
Association Discovery Example <ul><li>Heart disease clinical data </li></ul><ul><ul><li>Cleveland study of 500 patients </...
Proposed Problems <ul><li>Linking expression and phenotype </li></ul><ul><ul><li>Association discovery </li></ul></ul>prop...
Part III:   Work To Date
Motivation <ul><li>The sequence alignment problem </li></ul><ul><ul><li>Given a protein sequence, find similar proteins in...
Scoring Matrix Basics <ul><li>Describe how we should align proteins </li></ul><ul><ul><li>Matrix specifies a score for ali...
Protein Evolution <ul><li>A  simple  model of evolution </li></ul>work to date -> aa scoring matrices -> protein evolution...
Discovering Patterns <ul><li>Example: four ATP-associated proteins </li></ul>>sp•Q07698•ABCA_AERSA ABC transporter protein...
Patterns to Matrix <ul><li>Counting pairs of amino acids </li></ul>work to date -> aa scoring matrices -> patterns to matr...
Patterns to Matrix <ul><li>Make a Log-of-odds matrix </li></ul>Take away point: The evolutionary information contained in ...
Basic Idea TEIRESIAS MATRIX ENGINE Take away point: Given a set of sequences, we use Teiresias to discover important patte...
Example Results <ul><li>Isocitrate dehydrogenase family </li></ul><ul><ul><li>100 sequences from Prosite PS00470 </li></ul...
Current Work <ul><li>Applying to Bio-Dictionary </li></ul><ul><ul><li>full SWISS-PROT/TrEMBL </li></ul></ul><ul><li>“ Twea...
Acknowledgements <ul><li>Dr. Isidore Rigoutsos </li></ul><ul><li>Prof. Greg Stephanopoulos </li></ul>Group members: Mike, ...
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Kyle Jensen's MIT Ph.D. Thesis Proposal

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This is the presentation I gave for my thesis proposal, sometime in 2001. Obviously, almost all of these ideas failed miserably!

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Kyle Jensen's MIT Ph.D. Thesis Proposal

  1. 1. 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.
  2. 2. Outline <ul><li>Introductio n </li></ul><ul><ul><li>Pattern Discovery </li></ul></ul><ul><ul><li>Teireisas </li></ul></ul><ul><li>Proposed Problems </li></ul><ul><ul><li>Biological Sequences </li></ul></ul><ul><ul><li>Gene Expression and Physiological Data </li></ul></ul><ul><li>Work to Date </li></ul><ul><ul><li>Protein Evolution and Scoring Matrices </li></ul></ul>
  3. 3. Part I: Introduction
  4. 4. Pattern Discovery <ul><li>Decision-Theoretic </li></ul><ul><li>Syntactic </li></ul>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
  5. 5. A Little History <ul><li>Formal language theory </li></ul><ul><ul><li>pattern recognition </li></ul></ul><ul><li>Biological sequence analysis </li></ul><ul><ul><li>Teiresias, Blocks, Emotif, AlignACE, Prosite… </li></ul></ul><ul><ul><li>Discovery: functional, structural, classification </li></ul></ul>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
  6. 6. An Illustrative Example <ul><li>Patterns in sequences </li></ul>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.
  7. 7. Teiresias Overview <ul><li>Finds patterns in primitive streams </li></ul><ul><ul><li>L/W/K patterns </li></ul></ul><ul><ul><ul><li>L = minimum number of primitives in pattern </li></ul></ul></ul><ul><ul><ul><li>L/W = minimum density ( % non-wildcards ) </li></ul></ul></ul><ul><ul><ul><li>K = number of times a pattern occurs </li></ul></ul></ul>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
  8. 8. Teiresias Example <ul><li>Finding protein motifs </li></ul>>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)
  9. 9. Part II: Proposed Problems
  10. 10. Biological Sequences <ul><li>Motivation </li></ul><ul><ul><li>Protein and DNA sequences </li></ul></ul><ul><ul><li>Lots of data </li></ul></ul><ul><ul><ul><li>GenBank > 10 7 sequences, 10 10 nt </li></ul></ul></ul><ul><ul><ul><li>Swiss-Prot/TrEMBL nrdb 600,000 proteins </li></ul></ul></ul><ul><ul><li>Natural language metaphor </li></ul></ul><ul><li>Many interesting problems </li></ul><ul><ul><li>sequence-structure, molecular evolution, splicing, gene-finding, alignment </li></ul></ul>proposed problems -> biological sequences
  11. 11. Proposed Problems <ul><li>Amino acid scoring matrix design </li></ul><ul><ul><li>Model protein evolution using conserved motifs in protein databases. </li></ul></ul><ul><ul><li>Use this model of evolution to design scoring matrices for homology detection and sequence alignment. </li></ul></ul><ul><li>Oligonucleotide probe design </li></ul><ul><ul><li>Predict hybridization kinetics from pattern based homology </li></ul></ul><ul><ul><li>Use these prediction to choose optimal oligonucleotide probes for DNA mircoarrays </li></ul></ul>proposed problems -> biological sequences -> proposed problems
  12. 12. Expression and Physiology <ul><li>Motivation </li></ul><ul><ul><li>Creating associations: simple observations of complex biological systems </li></ul></ul><ul><ul><li>Indicators for further research </li></ul></ul><ul><li>Association Discovery </li></ul><ul><ul><li>Event streams are all the same length </li></ul></ul><ul><ul><li>Patterns cannot be shifted </li></ul></ul><ul><ul><li>Multiple associations possible, unlike clustering </li></ul></ul><ul><ul><li>Sensitive to local similarity and global </li></ul></ul>proposed problems -> expression and physiology -> motivation
  13. 13. Association Discovery Example <ul><li>Heart disease clinical data </li></ul><ul><ul><li>Cleveland study of 500 patients </li></ul></ul>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
  14. 14. Proposed Problems <ul><li>Linking expression and phenotype </li></ul><ul><ul><li>Association discovery </li></ul></ul>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?
  15. 15. Part III: Work To Date
  16. 16. Motivation <ul><li>The sequence alignment problem </li></ul><ul><ul><li>Given a protein sequence, find similar proteins in a database. </li></ul></ul>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?
  17. 17. Scoring Matrix Basics <ul><li>Describe how we should align proteins </li></ul><ul><ul><li>Matrix specifies a score for aligning each pair of amino acids </li></ul></ul>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
  18. 18. Protein Evolution <ul><li>A simple model of evolution </li></ul>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
  19. 19. Discovering Patterns <ul><li>Example: four ATP-associated proteins </li></ul>>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
  20. 20. Patterns to Matrix <ul><li>Counting pairs of amino acids </li></ul>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
  21. 21. Patterns to Matrix <ul><li>Make a Log-of-odds matrix </li></ul>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
  22. 22. 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
  23. 23. Example Results <ul><li>Isocitrate dehydrogenase family </li></ul><ul><ul><li>100 sequences from Prosite PS00470 </li></ul></ul>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)
  24. 24. Current Work <ul><li>Applying to Bio-Dictionary </li></ul><ul><ul><li>full SWISS-PROT/TrEMBL </li></ul></ul><ul><li>“ Tweaking” </li></ul><ul><ul><li>Which pattern classes are evolutionarily meaningful? </li></ul></ul><ul><ul><li>Different “PAM-distance” matrices </li></ul></ul><ul><li>More testing </li></ul>work to date -> aa scoring matrices -> current work … and the oligo probes…
  25. 25. Acknowledgements <ul><li>Dr. Isidore Rigoutsos </li></ul><ul><li>Prof. Greg Stephanopoulos </li></ul>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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