Gemoda
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Gemoda Presentation Transcript

  • 1. A generic motif discovery algorithm for diverse biomolecular data Kyle Jensen Gregory Stephanopoulos Department of Chemical Engineering Massachusetts Institute of Technology
  • 2. Motif discovery is the automated search for similar regions in streams of data
    • Un-sequential data
      • No “ordering”
    • Sequential data
      • A natural ordering of the data
        • Nucleotide and amino acid sequences
        • 3. Stock prices, protein structures
    MLRQGIAAQKKSFATLAAEQLLPKKYGGRYTVTLIPGDGVGKEVTDSVVKIFENENIPIDWETIDISGLENTENVQRAVESLKRNKVGLKGIWHTPADQTGHGSLNVALRKQLDIFANVALFKSIPGVKTRLNNIDMVIIRENTEGEYSGLEHESVPGVVESLKIMTRAKSERIARFAFDFALKNNRKSVCAVHKANIMKLGDGLFRNTVNEIGANEYPELDVKNIIVDNASMQAVAKPHQFDVLVTPNLYGSILGNIGSALIGGPGLVPGANFGREYAVFEPGSRHVGLDIKGQNVANPTAMILSSTLMLRHLGLNAYADRISKATYDVISEGKSTTRDIGGSASMLRQGIAAQKKSFATLAAEQLLPKKYGGRYTVTLIPGDGVGKEVTDSVVKIFENENIPIDWETIDISGLENTENVQRAVESLKRNKVGLKGIWHTPADQTGHGSLNVALRKQLDIFANVALFKSIPGVKTRLNNIDMVIIRENTEGEYSGLEHESVPGVVESLKIMTRAKSERIARFAFDFA A motif is just a collection of mutually similar regions in the data stream
  • 4. There are two classes of motif discovery tools commonly used for sequence analysis
    • “Exhaustive” regular-expression based tools
      • Teiresias
      • 5. Pratt
    • “Descriptive” position weight matrix-based tools
      • Gibbs sampler
      • 6. MEME
      • 7. Consensus
    TGCTGTATATACTCACAGCA AACTGTATATACACCCAGGG TACTGTATGAGCATACAGTA ACCTGAATGAATATACAGTA TACTGTACATCCATACAGTA TACTGTATATTCATTCAGGT AACTGTTTTTTTATCCAGTA ATCTGTATATATACCCAGCT TACTGTATATAAAAACAGTA CT[AT].[GT]....A..CAG
  • 8. “Gemoda” was designed to be exhaustive and have descriptive power
    • Gemoda exhaustively returns maximal motifs
      • Uses convolution of Teiresias
        • Way of “stiching together” smaller patterns combinatorially
    • Gets descriptiveness from similarity metric
      • Generic, context dependent definition of similarity
    MLRQGIAAQKKSFATLAAEQLLPKKYGGRYTVTLIPGDGVGKEVTDSVVKIFENENIPIDWETIDISGLENTENVQRAVESLKRNKVGLKGIWHTPADQTGHGSLNVALRKQLDIFANVALFKSIPGVKTRLNNIDMVIIRENTEGEYSGLEHESVPGVVESLKIMTRAKSERIARFAFDFALKNNRKSVCAVHKANIMKLGDGLFRNTVNEIGANEYPELDVKNIIVDNASMQAVAKPHQFDVLVTPNLYGSILGNIGSALIGGPGLVPGANFGREYAVFEPGSRHVGLDIKGQNVANPTAMILSSTLMLRHLGLNAYADRISKATYDVISEGKSTTRDIGGSASMLRQGIAAQKKSFATLAAEQLLPKKYGGRYTVTLIPGDGVGKEVTDSVVKIFENENIPIDWETIDISGLENTENVQRAVESLKRNKVGLKGIWHTPADQTGHGSLNVALRKQLDIFANVALFKSIPGVKTRLNNIDMVIIRENTEGEYSGLEHESVPGVVESLKIMTRAKSERIARFAFDFA F(w 1 , w 2 ) = square error F(w 1 , w 2 ) = aa scoring matrix
  • 9. Gemoda proceeds in three steps: comparison, clustering, and convolution Jensen, K., Styczynski,M., Rigoutsos,I. and Stephanopoulos,G. (2005) A generic motif discovery algorithm for sequential data. Bioinformatics, in press
  • 10. The comparison stage is used to map the pairwise similarities between all windows in the data streams
    • Creates an distance matrix
      • Does an all-by-all comparison of windows in the data
      • 11. Comparison function is context-specific
    F(w 1 , w 2 )
  • 12. The clustering phase is used to find groups of mutually similar windows
    • Different clustering functions have different uses
      • Clique-finding is provably exhaustive
      • 13. K-means and other methods are faster
    • Output clusters become “elementary motifs” which are convolved to make longer, maximal motifs
  • 14. The convolution phase is used to “stitch” together the clusters into maximal motifs
    • The motifs should be as long as possible, without decreasing the support
    elementary motifs (clusters) window ordering
  • 15. Here we show a few representative ways in which Gemoda can be used Motif discovery in...
    • Protein sequences
      • (ppGpp)ase enzymes & finding known domains
    • DNA sequences
      • The LD-motif challenge problem
    • Protein structures
      • Conserved structures without conserved sequences
  • 16. Gemoda can be applied to amino acid sequences as well
    • Example: (ppGpp)ase family from ENZYME database
      • Guanosine-3',5'-bis(diphosphate) 3'-pyrophosphohydrolase enzymes
        • EC 3.1.7.2
        • 17. Ave. length ~700 amino acids
        • 18. 8 sequences from 8 species
      • Searched using Gemoda
        • Minimum length = 50 amino acids
        • 19. Minimum Blosum62 bit score = 50 bits
        • 20. Minimum support = 100% (8/8 sequences)
        • 21. Clustering method = clique finding
    Can Gemoda find this known motif? How sensitive is Gemoda to “noise?”
  • 22. (ppGpp)ase example: the comparison phase shows many regions of local similarity Dots indicate 50aa windows that are pairwise similar Streaks indicate regions that will probably be convolved into a maximal motif
  • 23. (ppGpp)ase example: the clustering phase shows elementary motifs conserved between all 8 enzyme sequences
  • 24. (ppGpp)ase example: the final motifs match the known rela_spot domain and the HD domain from NCBI's conserved domain database Maximal motif (one of three, ~100 aa in length) This particular cluster represents the first set of 8 50aa windows in the above motif. Results are insensitive to “noise”
  • 25. The LD-motif problem models the subtle binding site discovery problem GACTCGATAGCGACG Sequence #1: ATGAT GA G TC T ATTG C G C CG CGATCAGCTAGCTAGCTACTATCTTATTCGACTAGTACGACTACGTACTACGATCTATCTATCAG... Sequence #2: ATGAGCTAGCTAGCTACTATCTTATTCGACTAGTACGACTACGTACTACGAATCAGCT CT CTCGAT T GCGAC T TTCGACTAGCTA... Sequence #3: ATGTACTACGA G T CTC C ATAGCG TT G CTCTATCTATCAGTACTACGACTCGTCGACTAGCTAGCTGACTCTATCTATCAGGATTT... Sequence #4: ATGACTATAGCTACTATCTTATTCGACTAGTACGACTATAGCTACTACGACTATAGCTATCTTATTCGAC GACTCG TGG GCG G CG ... ... Sequence #m: ATGCTACTATCTTATTCGACTAGTACGACTATAGCTACT GA T TCG TA AG G GACG ATAGCTACTATCTTATTCGACTAGTACGACT... Pevzner & Sze, Proc. ISMB, 2000
  • 26. Gemoda can solve both the LD-motif problem and a more generalized version of the same GG GACTCGATAGCGACG CCG Sequence #1: ATGAT GA G TC T ATTG C G C CG CGATCAGCTAGCTAGCTACTATCTTATTCGACTAGTACGACTACGTACTACGATCTATCTATCAG... Sequence #2: ATGAGCTAGCTAGCTACTATCTTATTCGACTAGTACGACTACGTACTACGAATCAGCTCGATCGCTAGCTTTTAAATCTCTTCGACTAGCTA... Sequence #3: ATGTACTACGA G T CTC C ATAGCG TT G CTCTATCTATCAGTACTACGACTCGTCGACTAGCTAGCTGACTCTATCTATCAGGATTT... Sequence #4: ATGACTATAGCTACTATCTTATTCGACTAGTACGACTATAGCTACTACGACTATAGCTATCTTATTCGAC GACTCG TGG GCG G CG ... ... Sequence #m: ATGCTACTATCTTATTCGACTAGTACGACTATAGCTACT GA T TCG T TAG G GACG ATAGCTACTATCTTATTCGACTAGTACGACT... Total motif length ? Styczynski,M., Jensen,K., Rigoutsos,I. and Stephanopoulos,G. (2004) An extension and novel solution to the Motif Challenge Problem. Genome Informatics, 15 (2).
  • 27. Gemoda can solve both the LD-motif problem and a more generalized version of the same GACTCGATAGCGACG X All sequences ? Sequence #1: ATGAT GA G TC T ATTG C G C CG CGATCAGCTAGCTAGCTACTATCTTATTCGACTAGTACGACTACGTACTACGATCTATCTATCAG... Sequence #2: ATGAGCTAGCTAGCTACTATCTTATTCGACTAGTACGACTACGTACTACGAATCAGCTCGATCGCTAGCTTTTAAATCTCTTCGACTAGCTA... Sequence #3: ATGTACTACGA G T CTC C ATAGCG TT G CTCTATCTATCAGTACTACGACTCGTCGACTAGCTAGCTGACTCTATCTATCAGGATTT... Sequence #4: ATGACTATAGCTACTATCTTATTCGACTAGTACGACTATAGCTACTACGACTATAGCTATCTTATTCGAC GACTCG TGG GCG G CG ... ... Sequence #m: ATGCTACTATCTTATTCGACTAGTACGACTATAGCTACT GA T TCG T TAG G GACG ATAGCTACTATCTTATTCGACTAGTACGACT...
  • 28. Gemoda can solve both the LD-motif problem and a more generalized version of the same GACTCGATAGCGACG Number of mutations ? Sequence #1: ATGAT GA G TC T ATTG C G C CG CGATCAGCTAGCTAGCTACTATCTTATTCGACTAGTACGACTACGTACTACGATCTATCTATCAG... Sequence #2: ATGAGCTAGCTAGCTACTATCTTATTCGACTAGTACGACTACGTACTACGAATCAGCTCGATCGCTAGCTTTTAAATCTCTTCGACTAGCTA... Sequence #3: ATGTACTACGA G T CTC C ATAGCG TT G CTCTATCTATCAGTACTACGACTCGTCGACTAGCTAGCTGACTCTATCTATCAGGATTT... Sequence #4: ATGACTATAGCTACTATCTTATTCGACTAGTACGACTATAGCTACTACGACTATAGCTATCTTATTCGAC GACTCG TGG GCG G CG ... ... Sequence #m: ATGCTACTATCTTATTCGACTAGTACGACTATAGCTACT GA T TCG T TAG G GACG ATAGCTACTATCTTATTCGACTAGTACGACT...
  • 29. Gemoda can solve both the LD-motif problem and a more generalized version of the same GACTCGATAGCGACG Sequence #1: ATGAT GA G TC T ATTG C G C CG CGATCAGCTAGCTAGCTACTATCTTATTCGACTAGTACGACTACGTACTACGATCTATCTATCAG... Sequence #2: ATGAGCTAGCTAGCTACTATCTTATTCGACTAGTACGACTACGTACTACGAATCAGCTCGATCGCTAGCTTTTAAATCTCTTCGACTAGCTA... Sequence #3: ATGTACTACGA G T CTC C ATAGCG TT G CTCTATCTATCAGTACTACGACTCGTCGACTAGCTAGCTGACTCTATCTATCAGGATTT... Sequence #4: ATGACTATAGCTACTATCTTATTCGACTAGTA TATCTGGTTCGACTT AGCTATCTATTCGAC GACTCG TGG GCG G CG ... ... Sequence #m: ATGCTAC TATCTTATTCGACTG AGTACGACTATAGCTACT GA T TCG T TAG G GACG ATAGCTACTATGACTAGTGACT... Number of unique motifs ?
  • 30. Gemoda can also be applied to protein structures
    • Treat protein structure as alpha-carbon trace
      • Series of x,y,z coordinates
    • Use a clustering function that compares x,y,z windows
      • Root mean square deviation (RMSD)
      • 31. unit-RMSD
    x 1 y 1 z 1 x 2 y 2 z 2 x 3 y 3 z 3 ........................... x M y M z M
  • 32. Protein structure example: human FIT vs. uridylyltransferase
  • 33. Questions?
  • 34. The Gemoda algorithm has guarantees of maximality and exhaustiveness
    • Maximality
      • Motifs are as long as possible
      • 35. Motifs are as specific as possible
      • 36. Motifs are not missing an occurrences
    • Exhaustiveness
      • All maximal motifs are found
      • 37. No non-maximal motifs are found
    = motif1 = motif2
  • 38. Gemoda may be used with nucleotide sequences to find regulatory motifs
    • The LD-motif problem: an example for the board
    >AACTG >AATTA >AATTG Look for motifs of at least 3 nucletides with a Hamming distance between any window of 3 of 1 or less given: 1 = AAC 2 = ACT 3 = CTG 4 = AAT 5 = ATT 6 = TTA 7 = AAT 8 = ATT 9 = TTG We get the following windows:
  • 39. A simple natural language example
    • Choosing a window length of L=4 gives 7 unique windows in the three sequences
    Seq 1: motif Seq 2: motor Seq 3: potion
  • 40. Here we show the comparison phase using two different similarity metrics
    • X's and dotted lines
      • Identify matrix: ¾
    • O's and solid lines
      • Consonant/vowel matrix: ¾
    Windows 1: moti 2: otif 3: moto 4: otor 5: poti 6: otio 7: tion Input sequences Seq 1: motif Seq 2: motor Seq 3: potion Similarity graph
  • 41. The clustered windows (elementary motifs) are different depending on the similarity function
    • Clustering phase
      • Clique-finding
      • 42. Support >= 2
    Windows 1: moti 2: otif 3: moto 4: otor 5: poti 6: otio 7: tion Cluster 1 1: moti 3: moto 5: poti Cluster 2 2: otif 4: otor 6: otio Cluster 1 1: moti 3: moto Cluster 2 1: moti 5: poti Cluster 3 2: otif 6: otio Solid lines (vowel/cons): Dotted lines (identity):
  • 43. Likewise, the final, convolved motifs depend on the similarity function choice Motif 1 motif motor potio Seq 1: motif Seq 2: motor Seq 3: potion Windows 1: moti 2: otif 3: moto 4: otor 5: poti 6: otio 7: tion Vowel/cons: Motif 1 motif potio Motif 2 moti moto Identity: Cluster 1 1: moti 3: moto 5: poti Cluster 2 2: otif 4: otor 6: otio Cluster 1 1: moti 3: moto Cluster 2 1: moti 5: poti Cluster 3 2: otif 6: otio Vowel/cons: Identity: