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Pairwise Sequence Alignment Exercise 2

Pairwise Sequence Alignment Exercise 2. Motivation.

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Pairwise Sequence Alignment Exercise 2

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  1. Pairwise Sequence AlignmentExercise 2

  2. Motivation ATGGTGAACCTGACCTCTGACGAGAAGACTGCCGTCCTTGCCCTGTGGAACAAGGTGGACGTGGAAGACTGTGGTGGTGAGGCCCTGGGCAGGTTTGTATGGAGGTTACAAGGCTGCTTAAGGAGGGAGGATGGAAGCTGGGCATGTGGAGACAGACCACCTCCTGGATTTATGACAGGAACTGATTGCTGTCTCCTGTGCTGCTTTCACCCCTCAGGCTGCTGGTCGTGTATCCCTGGACCCAGAGGTTCTTTGAAAGCTTTGGGGACTTGTCCACTCCTGCTGCTGTGTTCGCAAATGCTAAGGTAAAAGCCCATGGCAAGAAGGTGCTAACTTCCTTTGGTGAAGGTATGAATCACCTGGACAACCTCAAGGGCACCTTTGCTAAACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGATCCTGAGAATTTCAAGGTGAGTCAATATTCTTCTTCTTCCTTCTTTCTATGGTCAAGCTCATGTCATGGGAAAAGGACATAAGAGTCAGTTTCCAGTTCTCAATAGAAAAAAAAATTCTGTTTGCATCACTGTGGACTCCTTGGGACCATTCATTTCTTTCACCTGCTTTGCTTATAGTTATTGTTTCCTCTTTTTCCTTTTTCTCTTCTTCTTCATAAGTTTTTCTCTCTGTATTTTTTTAACACAATCTTTTAATTTTGTGCCTTTAAATTATTTTTAAGCTTTCTTCTTTTAATTACTACTCGTTTCCTTTCATTTCTATACTTTCTATCTAATCTTCTCCTTTCAAGAGAAGGAGTGGTTCACTACTACTTTGCTTGGGTGTAAAGAATAACAGCAATAGCTTAAATTCTGGCATAATGTGAATAGGGAGGACAATTTCTCATATAAGTTGAGGCTGATATTGGAGGATTTGCATTAGTAGTAGAGGTTACATCCAGTTACCGTCTTGCTCATAATTTGTGGGCACAACACAGGGCATATCTTGGAACAAGGCTAGAATATTCTGAATGCAAACTGGGGACCTGTGTTAACTATGTTCATGCCTGTTGTCTCTTCCTCTTCAGCTCCTGGGCAATATGCTGGTGGTTGTGCTGGCTCGCCACTTTGGCAAGGAATTCGACTGGCACATGCACGCTTGTTTTCAGAAGGTGGTGGCTGGTGTGGCTAATGCCCTGGCTCACAAGTACCATTGA || || ||||| ||| || || ||||||||||||||||||| MVHLTPEEKTAVNALWGKVNVDAVGGEALGRLLVVYPWTQRFFE… MVNLTSDEKTAVLALWNKVDVEDCGGEALGRLLVVYPWTQRFFE…

  3. What is sequence alignment? Alignment: Comparing two (pairwise) or more (multiple) sequences. Searching for a series of identical or similar characters in the sequences. MVNLTSDEKTAVLALWNKVDVEDCGGE |||| ||||| ||| |||| || MVHLTPEEKTAVNALWGKVNVDAVGGE

  4. Why sequence alignment? Predict characteristics of a protein – Premised on: similar sequence (or structure) similar function

  5. Local vs. Global Global alignment: forces alignment in regions which differ • Global alignment – finds the best alignment across the whole two sequences. • Local alignment – finds regions of high similarity in parts of the sequences. ADLGAVFALCDRYFQ |||| |||| | ADLGRTQN-CDRYYQ Local alignment concentrates on regions of high similarity ADLG CDRYFQ |||| |||| | ADLG CDRYYQ

  6. Evolutionary changes in sequences Three types of changes: • Substitution – a replacement of one (or more) sequence letter by another: • Insertion - an insertion of a letter or several letters to the sequence: • Deletion - deleting a letter (or more) from the sequence: AAGA  AACA AAG A T A A GA Insertion + Deletion Indel

  7. Choosing an alignment: • Many different alignments are possible: AAGCTGAATTCGAA AGGCTCATTTCTGA AAGCTGAATT-C-GAA AGGCT-CATTTCTGA- A-AGCTGAATTC--GAA AG-GCTCA-TTTCTGA- Which alignment is better?

  8. Exercise: compute both alignment scores • Match: +1 • Mismatch: -2 • Indel: -1 AAGCTGAATT-C-GAA AGGCT-CATTTCTGA- A-AGCTGAATTC--GAA AG-GCTCA-TTTCTGA-

  9. Scoring systems: accounting for biological context • Which is true about the scores in a pairwise alignment of nucleotide sequences? • Tr > Tv > 0 • Tr < Tv < 0 • 0 > Tr > Tv • 0 > Tv > Tr Tr = Transition Tv = Transversion

  10. Scoring systems: accounting for biological context • Which is true about the scores in a pairwise alignment of amino-acid sequences? • Asp->Asn > Asp->Glu • Arg->His > Ala->Phe • Arg->His < Thr->Met

  11. Substitutions matrices • Nucleic acids: • Transition-transversion • Amino acids: • Evolutionary (empirical data) based: (PAM, BLOSUM) • Physico-chemical properties based (Grantham, McLachlan)

  12. PAM matrices • Family of matrices PAM 80, PAM 120, PAM 250 • The number with a PAM matrix represents the evolutionary distance between the sequences on which the matrix is based • Greater numbers denote greater distances

  13. PAM - limitations • Based on only one original dataset • Examines proteins with few differences (85% identity) • Based mainly on small globular proteins so the matrix is biased

  14. BLOSUM matrices • Different BLOSUMn matrices are calculated independently from BLOCKS (ungapped local alignments) • BLOSUMn is based on a cluster of BLOCKS of sequences that share at least n percent identity • BLOSUM62 represents closer sequences than BLOSUM45

  15. Substitution matrices exercise • Pick the best substitution matrix (PAM and BLOSUM) for each pairwise alignment: • Human – chimp • Human - yeast • Human – fish PAM options: PAM60 PAM120 PAM250 BLOSUM options: BLOSUM45 BLOSUM62 BLOSUM80

  16. PAM Vs. BLOSUM PAM100 = BLOSUM90 PAM120 = BLOSUM80 PAM160 = BLOSUM60 PAM200 = BLOSUM52 PAM250 = BLOSUM45 More distant sequences • BLOSUM62 for general use • BLOSUM80 for close relations • BLOSUM45 for distant relations • PAM120 for general use • PAM60 for close relations • PAM250 for distant relations

  17. Gap penalty AAGCGAAATTCGAAC A-G-GAA-CTCGAAC AAGCGAAATTCGAAC AGG---AACTCGAAC • Which alignment is more likely? • Which alignment has a higher score?

  18. Web servers for pairwise alignment

  19. BLAST 2 sequences (bl2Seq) at NCBI Produces the local alignment of two given sequences using BLAST (Basic Local Alignment Search Tool)engine for local alignment • Does not use an exact algorithm but a heuristic

  20. Back to NCBI

  21. BLAST – bl2seq

  22. Bl2Seq - query • blastn – nucleotide blastp – protein

  23. Bl2seq results

  24. Bl2seq results Dissimilarity Low complexity Gaps Similarity Match

  25. BLAST – programs Query: DNA Protein Database: DNA Protein

  26. BLAST – Blastp

  27. Blastp - results

  28. Blastp – results (cont’)

  29. Blast scores: • Bits score– A score for the alignment according to the number of similarities, identities, etc. • Expected-score (E-value) –The number of alignments with the same score one can “expect” to see by chance when searching a random database of a particular size. The closer the e-value is to zero, the greater the confidence that the hit is really a homolog

  30. Blastp – acquiring sequences

  31. blastp – acquiring sequences

  32. Fasta format – multiple sequences >gi|4504351|ref|NP_000510.1| delta globin [Homo sapiens] MVHLTPEEKTAVNALWGKVNVDAVGGEALGRLLVVYPWTQRFFESFGDLSSPDAVMGNPKVKAHGKKVLG AFSDGLAHLDNLKGTFSQLSELHCDKLHVDPENFRLLGNVLVCVLARNFGKEFTPQMQAAYQKVVAGVAN ALAHKYH >gi|4504349|ref|NP_000509.1| beta globin [Homo sapiens] MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN ALAHKYH >gi|4885393|ref|NP_005321.1| epsilon globin [Homo sapiens] MVHFTAEEKAAVTSLWSKMNVEEAGGEALGRLLVVYPWTQRFFDSFGNLSSPSAILGNPKVKAHGKKVLT SFGDAIKNMDNLKPAFAKLSELHCDKLHVDPENFKLLGNVMVIILATHFGKEFTPEVQAAWQKLVSAVAI ALAHKYH >gi|6715607|ref|NP_000175.1| G-gamma globin [Homo sapiens] MGHFTEEDKATITSLWGKVNVEDAGGETLGRLLVVYPWTQRFFDSFGNLSSASAIMGNPKVKAHGKKVLT SLGDAIKHLDDLKGTFAQLSELHCDKLHVDPENFKLLGNVLVTVLAIHFGKEFTPEVQASWQKMVTGVAS ALSSRYH >gi|28302131|ref|NP_000550.2| A-gamma globin [Homo sapiens] MGHFTEEDKATITSLWGKVNVEDAGGETLGRLLVVYPWTQRFFDSFGNLSSASAIMGNPKVKAHGKKVLT SLGDATKHLDDLKGTFAQLSELHCDKLHVDPENFKLLGNVLVTVLAIHFGKEFTPEVQASWQKMVTAVAS ALSSRYH

  33. Searching for remote homologs • Sometimes BLAST isn’t enough • Large protein family, and BLAST only finds close members. We want more distant members • PSI-BLAST

  34. PSI-BLAST • Position Specific Iterated BLAST Regular blast Construct profile from blast results Blast profile search Final results

  35. PSI-BLAST • Advantage: PSI-BLAST looks for seq’s that are close to the query, and learns from them to extend the circle of friends • Disadvantage: if we obtained a WRONG hit, we will get to unrelated sequences (contamination). This gets worse and worse each iteration

  36. PSI-BLAST Which one(s) of the following is/are correct? • PSI-BLAST is expected to give more hits than BLAST • PSI-BLAST is an iterative search method • PSI-BLAST is faster than BLAST • Each iteration of PSI-BLAST can only improve the results of the previous iteration

  37. BLAST – PSI-Blast

  38. PSI-Blast - results

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