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Progressive MSA

Progressive MSA. Do pair-wise alignment Develop an evolutionary tree Most closely related sequences are then aligned, then more distant are added. Genetic distance - number of mismatched positions divided by the total number of matched positions (gaps not considered). Example.

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Progressive MSA

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  1. Progressive MSA • Do pair-wise alignment • Develop an evolutionary tree • Most closely related sequences are then aligned, then more distant are added. • Genetic distance - number of mismatched positions divided by the total number of matched positions (gaps not considered).

  2. Example • Domain: a segment of a protein that can fold to a 3D structure independent of other segments of the protein. • Card Domain • Caspase recruitment domains (CARDs) are modules of 90 - 100 amino acids involved in apoptosis signaling pathways. • http://www.mshri.on.ca/pawson/card.html

  3. These are equivalent trees B A A B C C C C B A A B

  4. Previous tree was Rooted These are Unrooted trees

  5. Gaps • Clustalw attempts to place gaps between conserved domains. • In known sequences, gaps are preferentially found between secondary structure elements (alpha helices, beta strands). • Clustalw attempts to place gaps between conserved domains. • In known sequences, gaps are preferentially found between secondary structure elements (alpha helices, beta strands).

  6. Problem with Progressive Alignment: Errors made in early alignments are propagated throughout the MSA

  7. Profiles & Gaps • From an MSA, a conserved region identified and a scoring matrix (profile) constructed for that region. • Each position has a score associated with an amino acid substitution or gap. • Blocks- also extracted from MSA, but no gaps are permitted.

  8. Block Server • Results • TLE short form • TLEl Long form

  9. Hidden Markov Models • Probabilistic model of a Multiple sequence alignment. • No indel penalties are needed • Experimentally derived information can be incorporated • Parameters are adjusted to represent observed variation. • Requires at least 20 sequences

  10. D1 D2 D3 D4 D5 D6 I0 I1 I2 I3 I4 I5 I6 B M1 M2 M3 M4 M5 M6 E • The bottom line of states are the main states (M) • These model the columns of the alignment • The second row of diamond shaped states are called the insert states (I) • These are used to model the highly variable regions in the alignment. • The top row or circles are delete states (D) • These are silent or null states because they do not match any residues, they simply allow the skipping over of main states.

  11. The Evolution of a Sequence • Over long periods of time a sequence will acquire random mutations. • These mutations may result in a new amino acid at a given position, the deletion of an amino acid, or the introduction of a new one. • Over VERY long periods of time two sequences may diverge so much that their relationship can not see seen through the direct comparison of their sequences.

  12. Hidden Markov Models • Pair-wise methods rely on direct comparisons between two sequences. • In order to over come the differences in the sequences, a third sequence is introduced, which serves as an intermediate. • A high hit between the first and third sequences as well as a high hit between the second and third sequence, implies a relationship between the first and second sequences.Transitive relationship

  13. Introducing the HMM • The intermediate sequence is kind of like a missing link. • The intermediate sequence does not have to be a real sequence. • The intermediate sequence becomes the HMM.

  14. Introducing the HMM • The HMM is a mix of all the sequences that went into its making. • The score of a sequence against the HMM shows how well the HMM serves as an intermediate of the sequence. • How likely it is to be related to all the other sequences, which the HMM represents.

  15. B M1 M2 M3 M4 E Match State with no Indels MSGL MTNL Arrow indicates transition probability. In this case 1 for each step

  16. B M1 M2 M3 M4 E Match State with no Indels MSGL MTNL S=0.5 T=0.5 M=1 Also have probability of Residue at each positon

  17. B M1 M2 M3 M4 E Typically want to incorporate small probability for all other amino acids. MSGL MTNL S=0.5 T=0.5 M=1

  18. B M1 M2 M3 M4 E Permit insertion states MS.GL MT.NL MSANI I0 I1 I2 I3 I4 Transition probabilities may not be 1

  19. B M1 M2 M3 M4 E Permit insertion states MS..GL MT..NL MSA.NI MTARNL I0 I1 I2 I3 I4

  20. DELETE PERMITS INCORPORATION OF LAST TWO SITES OF SEQ1 MS..GL-- MT..NLAG MSA.NIAG MTARNLAG D1 D2 D3 D4 D5 D6 I0 I1 I2 I3 I4 I5 I6 B M1 M2 M3 M4 M5 M6 E

  21. D1 D2 D3 D4 D5 D6 I0 I1 I2 I3 I4 I5 I6 B M1 M2 M3 M4 M5 M6 E • The bottom line of states are the main states (M) • These model the columns of the alignment • The second row of diamond shaped states are called the insert states (I) • These are used to model the highly variable regions in the alignment. • The top row or circles are delete states (D) • These are silent or null states because they do not match any residues, they simply allow the skipping over of main states.

  22. Dirichlet Mixtures • Additional information to expand potential amino acids in individual sites. • Observed frequency of amino acids seen in certain chemical environments • aromatic • acidic • basic • neutral • polar

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