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HMMs for alignments & Sequence pattern discovery

I519 Introduction to Bioinformatics. HMMs for alignments & Sequence pattern discovery. Contents. Motifs We have seen motifs in regular expression Profiles & consensus Motif search

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HMMs for alignments & Sequence pattern discovery

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  1. I519 Introduction to Bioinformatics HMMs for alignments & Sequence pattern discovery

  2. Contents • Motifs • We have seen motifs in regular expression • Profiles & consensus • Motif search • sequence motifs represent critical positions that are conserved in evolution, so search algorithms employing motifs may be used to identify more divergent sequences than methods based on global sequence similarity • PSI-BLAST (similarity search using PSSM, Position Specific Scoring Matrix) • HMM of protein family (a very brief introduction)

  3. a G g t a c T t C c A t a c g t Alignment a c g t T A g t a c g t C c A t C c g t a c g G A3 0 1 0 31 1 0 ProfileC24 0 0 14 0 0 G 0 1 4 0 0 0 31 T 0 0 0 5 1 0 14 Consensus A C G T A C G T Line up the patterns by their start indexes s = (s1, s2, …, st) Construct matrix profile with frequencies of each nucleotide in columns Consensus nucleotide in each position has the highest score in column Motifs: Profiles and Consensus

  4. Aligned DNA sequences can be represented by a 4 ·n profile matrix reflecting the frequencies of nucleotides in every aligned position. Profile Representation of Protein Families Protein family can be represented by a 20·nprofile representing frequencies of amino acids.

  5. Profiles and HMMs • HMMs can also be used for aligning a sequence against a profile representing protein family. • A 20·n profile P corresponds to n sequentially linked match states M1,…,Mnin the profile HMM of P.

  6. Multiple Alignments and Protein Family Classification • Multiple alignment of a protein family shows variations in conservation along the length of a protein • Example: after aligning many globin proteins, the biologists recognized that the helices region in globins are more conserved than others.

  7. What are Profile HMMs ? • A Profile HMM is a probabilistic representation of a multiple alignment. • A given multiple alignment (of a protein family) is used to build a profile HMM. • This model then may be used to find and score less obvious potential matches of new protein sequences.

  8. Profile HMM A profile HMM

  9. Building a Profile HMM • Multiple alignment is used to construct the HMM model. • Assign each column to a Match state in HMM. Add Insertion and Deletion state. • Estimate the emission probabilities according to amino acid counts in column. Different positions in the protein will have different emission probabilities. • Estimate the transition probabilities between Match, Deletion and Insertion states • The HMM model gets trained to derive the optimal parameters.

  10. States of Profile HMM • Match states M1…Mn (plus begin/end states) • Insertion states I0I1…In • Deletion states D1…Dn

  11. Transition Probabilities in Profile HMM • log(aMI)+log(aIM) = gap initiation penalty • log(aII) = gap extension penalty

  12. Emission Probabilities in Profile HMM • Probabilty of emitting a symbol a at an • insertion state Ij: • eIj(a) = p(a) • where p(a) is the frequency of the • occurrence of the symbol a in all the • sequences.

  13. Profile HMM Alignment • Define vMj(i)as the logarithmic likelihood score of the best path for matching x1..xi to profile HMM ending with xi emitted by the state Mj. • vIj (i) and vDj (i) are defined similarly.

  14. vMj-1(i-1) + log(aMj-1,Mj ) vMj(i) = log (eMj(xi)/p(xi)) + max vIj-1(i-1) + log(aIj-1,Mj ) vDj-1(i-1) + log(aDj-1,Mj ) vMj(i-1) + log(aMj, Ij) vIj(i) = log (eIj(xi)/p(xi)) + max vIj(i-1) + log(aIj, Ij) vDj(i-1) + log(aDj, Ij) Profile HMM Alignment: Dynamic Programming

  15. Paths in Edit Graph and Profile HMM A path through an edit graph and the corresponding path through a profile HMM

  16. Making a Collection of HMM for Protein Families • Use Blast to separate a protein database into families of related proteins • Construct a multiple alignment for each protein family. • Construct a profile HMM model and optimize the parameters of the model (transition and emission probabilities). • Align the target sequence against each HMM to find the best fit between a target sequence and an HMM

  17. Application of Profile HMM to Modeling Globin Proteins • Globins represent a large collection of protein sequences • 400 globin sequences were randomly selected from all globins and used to construct a multiple alignment. • Multiple alignment was used to assign an initial HMM • This model then get trained repeatedly with model lengths chosen randomly between 145 to 170, to get an HMM model optimized probabilities.

  18. hmmer package • Tools for making HMMs and for hmmscan • hmmer3 (as fast as blast)

  19. Sequence Pattern (Motif) Discovery • Finding patterns in multiple alignments, or in unaligned sequences • eMotif (a protein pattern database); eBLOCKs • Gibbs and MEME • To infer patterns in unaligned sequences • Gibbs program starts with a fixed pattern length of W and a random set of locations of the pattern in given input sequences (i.e., the initial pattern is random); and then one sequence is selected at a time randomly and an attempt is made to improve its pattern position. • MEME uses many similar concepts, but uses the EM (expectation maximization) method.

  20. Utilization of Multiple Alignments • Residue conservation • Jalview • Subfamilies • SCI-PHY • FunShift

  21. Readings • Chapter 6

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