csi5126 algorithms in bioinformatics
play

CSI5126 . Algorithms in bioinformatics Multiple Sequence Alignment - PowerPoint PPT Presentation

Apr 16, 2023 •254 likes •1.92k views

. Recent methods . . . . . . . Preamble SOP Exact Progressive Benchmarks Preamble . SOP Exact Progressive Benchmarks Recent methods CSI5126 . Algorithms in bioinformatics Multiple Sequence Alignment (MSA) Marcel Turcotte School

  1. . d2rmbi_ KGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTN d1rmha_ KGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTN d1ak4b_ KGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTN d2rmbc_ KGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTN Now what ? KGSCFHRIIPGFXCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSXANAGPNTN Motivation Recent methods Benchmarks Progressive Exact SOP Preamble Recent methods d1awtf_ d1cyna_ Progressive * Marcel Turcotte ... . : . . . .. : KNSKFHRVIKDFMIQGGDFTRGDGTGGKSIYGERFPDENFKLKHYGPGWVSMANAGKDTN ------AIDKPFLLPIEDVFSISGRG--TVVTGRVERGIIKVGEEVEIVGIKETQKSTCT d1efca1 NNTTFHRVIPGFMIQGGGFTEQMQQ--KKPNPPIKNEADNGLRNTRGTIAMARTADKDSA d1clh__ KGSTFHRVIKNFMIQGGDFTKGDGTGGESIYGGMFDDEEFVMKHDEPFVVSMANKGPNTN d1c5fg_ KGSIFHRVIKDFMIQGGDFTARDGTGGMSIYGETFPDENFKLKHYGIGWVSMANAGPDTN d2rmce_ Benchmarks Exact . . . . . . . . . . . . . . . . . . . . . SOP . Preamble . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  2. . d2rmbi_ KGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTN d1rmha_ KGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTN d1ak4b_ KGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTN d2rmbc_ KGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTN Now what ? KGSCFHRIIPGFXCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSXANAGPNTN Motivation Recent methods Benchmarks Progressive Exact SOP Preamble Recent methods d1awtf_ d1cyna_ Progressive * Marcel Turcotte ... . : . . . .. : KNSKFHRVIKDFMIQGGDFTRGDGTGGKSIYGERFPDENFKLKHYGPGWVSMANAGKDTN ------AIDKPFLLPIEDVFSISGRG--TVVTGRVERGIIKVGEEVEIVGIKETQKSTCT d1efca1 NNTTFHRVIPGFMIQGGGFTEQMQQ--KKPNPPIKNEADNGLRNTRGTIAMARTADKDSA d1clh__ KGSTFHRVIKNFMIQGGDFTKGDGTGGESIYGGMFDDEEFVMKHDEPFVVSMANKGPNTN d1c5fg_ KGSIFHRVIKDFMIQGGDFTARDGTGGMSIYGETFPDENFKLKHYGIGWVSMANAGPDTN d2rmce_ Benchmarks Exact . . . . . . . . . . . . . . . . . . . . . SOP . Preamble . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  3. . Preamble . . . . . . Preamble SOP Exact Progressive Benchmarks Recent methods SOP . Exact Progressive Benchmarks Recent methods Computer Science ’s Point of View: Generalization An MSA ( multiple sequence alignment ) is a generalization of the pairwise sequence alignment. gaps are inserted so that 1) all the sequences have the same length and 2) the distance for the alignment is minimized (this can also be seen as to maximize the similarity). Global or local multiple alignment. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics Defjnition. Given k > 2 strings S = { S 1 , S 2 , . . . , S k } ,

  4. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Life Science ’s Point of View Conserved patterns , e.g. conserved cysteins forming disulphide bonds . Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  5. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Life Science ’s Point of View Conserved Pro and Gl y opposed to an insersion suggest the Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics presence of a loop .

  6. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Life Science ’s Point of View Similarity . Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  7. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Life Science ’s Point of View Chemical properties . Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  8. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Life Science ’s Point of View Patterns of conservation/substitution can indicate a preference for solvent exposure . Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  9. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Life Science ’s Point of View Secondary structure elements? Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  10. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Life Science ’s Point of View Gaps are good indicators of loop regions. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  11. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Life Science ’s Point of View History (phylogeny) Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  12. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Life Science ’s Point of View Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  13. . Recent methods . . . . . . . Preamble SOP Exact Progressive Benchmarks Preamble . SOP Exact Progressive Benchmarks Recent methods Life Science ’s Point of View “ Multiple alignments are among the most useful objects in bioinformatics ” [Wallace 2005] Phylogenetic trees inference Identifying functional residues Structure prediction etc. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  14. . Benchmarks . . . . . . . . Preamble SOP Exact Progressive Recent methods . Preamble SOP Exact Progressive Benchmarks Recent methods Pairwise vs Multiple Sequence Alignment Pairwise : the question is “ are the two sequences related? ” Multiple : the sequences are assumed to be related from the start. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  15. . Benchmarks . . . . . . . . Preamble SOP Exact Progressive Recent methods . Preamble SOP Exact Progressive Benchmarks Recent methods Pairwise vs Multiple Sequence Alignment Pairwise : the question is “ are the two sequences related? ” Multiple : the sequences are assumed to be related from the start. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  16. . Exact . . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Progressive . Benchmarks Recent methods Multiple Sequence Alignment Rectangular table such that: Rows are (related, homologous) sequences Residues in a given column (site): 1. Evolved from a position in some ancestral sequence (homologous) 2. Can be superimposed in three-dimension in a structural alignment 3. Have the same functional role All three criteria might not be simultaneously met, especially for sequences that are not closely related. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  17. . Exact . . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Progressive . Benchmarks Recent methods Multiple Sequence Alignment Rectangular table such that: Rows are (related, homologous) sequences Residues in a given column (site): 1. Evolved from a position in some ancestral sequence (homologous) 2. Can be superimposed in three-dimension in a structural alignment 3. Have the same functional role All three criteria might not be simultaneously met, especially for sequences that are not closely related. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  18. . Exact . . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Progressive . Benchmarks Recent methods Multiple Sequence Alignment Rectangular table such that: Rows are (related, homologous) sequences Residues in a given column (site): 1. Evolved from a position in some ancestral sequence (homologous) 2. Can be superimposed in three-dimension in a structural alignment 3. Have the same functional role All three criteria might not be simultaneously met, especially for sequences that are not closely related. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  19. . Exact . . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Progressive . Benchmarks Recent methods Multiple Sequence Alignment Rectangular table such that: Rows are (related, homologous) sequences Residues in a given column (site): 1. Evolved from a position in some ancestral sequence (homologous) 2. Can be superimposed in three-dimension in a structural alignment 3. Have the same functional role All three criteria might not be simultaneously met, especially for sequences that are not closely related. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  20. . Exact . . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Progressive . Benchmarks Recent methods Multiple Sequence Alignment Rectangular table such that: Rows are (related, homologous) sequences Residues in a given column (site): 1. Evolved from a position in some ancestral sequence (homologous) 2. Can be superimposed in three-dimension in a structural alignment 3. Have the same functional role All three criteria might not be simultaneously met, especially for sequences that are not closely related. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  21. . Benchmarks 1 BLOSUM62. k n Objective function : sum-of-pairs Recent methods Progressive n Exact SOP Preamble Recent methods . Progressive Exact c 1 Preamble LGB2LUPLU Marcel Turcotte sum-of-pairs (SP) score. Problem: Compute the (global) alignment that maximizes the ...IAGADNGAGV... GLB1GLYDI k ...FNA--NIPKH... ...VKG------D... HBA_HUMAN j-> GLB2HCHITP ...VEA--DVAGH... MYG_PHYCA i-> ...V----NVDEV... HBB_HUMAN ...VGA--HAGEY... SOP Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . . . . . . . . . Given a multiple alignment M of k sequences and n columns. k − 1 ∑ ∑ ∑ sp ( M ) = s ( m ci , m cj ) c = 1 i = 1 j = i + 1 where s ( a , b ) is a substitution matrix such as PAM250 or

  22. . Benchmarks . . . . . . . . Preamble SOP Exact Progressive Recent methods . Preamble SOP Exact Progressive Benchmarks Recent methods Remarks Unlike pairwise aligment, the sum-of-pairs score used by the MSA methods has no theoretical fundation , no interpretation in terms of an underlying evolutionary model. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  23. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Sum of pairs Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . C C C A C C C T A C A A T C T T

  24. . Progressive . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Exact Benchmarks . Recent methods Remarks I. M. Wallace, G. Blackshields, and D. G. Higgins. Multiple sequence alignments. Curr Opin Struct Biol , 15(3):261–6, Jun 2005. “Assembling a suitable MSA is not, however, a trivial task, and none of the existing methods have yet managed to deliver biologically perfect MSAs. ” “ Manually refjned alignments continue to be superior to purely automated methods;” “The wealth of available methods and their increasingly similar accuracies makes it harder than ever to objectively choose one over the others.” Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  25. . Progressive . . . . . . . . Preamble SOP Exact Benchmarks . Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods 2 , 3 , k , go ! Optimal alignment of 2 sequences Optimal alignment of 3 sequences Optimal alignment of k sequences Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  26. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 2 Sequences Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics V V

  27. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 2 Sequences V V Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics V V

  28. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 2 Sequences V - Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics V W

  29. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 2 Sequences - W Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics V W

  30. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 3 Sequences Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics V V V

  31. . Progressive . . . . . . . . Preamble SOP Exact Benchmarks . Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 3 Sequences V V V Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics V V V

  32. . Progressive . . . . . . . . Preamble SOP Exact Benchmarks . Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 3 Sequences V V - Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics W V V

  33. . Progressive . . . . . . . . Preamble SOP Exact Benchmarks . Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 3 Sequences - V V Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics V W V

  34. . Progressive . . . . . . . . Preamble SOP Exact Benchmarks . Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 3 Sequences V - V Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics V V W

  35. . Progressive . . . . . . . . Preamble SOP Exact Benchmarks . Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 3 Sequences V - - Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics L V W

  36. . Progressive . . . . . . . . Preamble SOP Exact Benchmarks . Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 3 Sequences - W - Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics L V W

  37. . Progressive . . . . . . . . Preamble SOP Exact Benchmarks . Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 3 Sequences - - L Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics L V W

  38. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Optimal alignment of 3 Sequences Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  39. S N G N S G N A . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . .. . . . . .

  40. G G - S N G N S G N A . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . .. . . . . .

  41. GN GN -N S N G N S G N A . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . .. . . . . .

  42. GN- GNA -N- S N G N S G N A . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . .. . . . . .

  43. GN-S GNA- -N-S S N G N S G N A . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . .. . . . . .

  44. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks . Preamble SOP Exact Progressive Benchmarks Recent methods Exact alignment of 3 Sequences Given : x , y and z three strings. given by: Marcel Turcotte . Recent methods . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . . . . . . . . V ( i , j , k ) is the optimal SP score to align x [ 1 .. i ] , y [ 1 .. j ] and z [ 1 .. k ] is  V ( i − 1 , j − 1 , k − 1 ) + s ( x i , y j , z k ) ,    V ( i − 1 , j − 1 , k ) − s ( x i , y j , − ) ,     V ( i , j − 1 , k − 1 ) − s ( − , y j , z k ) ,     V ( i , j , k ) = max V ( i − 1 , j , k − 1 ) − s ( x i , − , z k ) , V ( i − 1 , j , k ) − s ( x i , − , − ) ,      V ( i , j − 1 , k ) − s ( − , y j , − ) ,      V ( i , j , k − 1 ) − s ( − , − , z k ) .  ⇒ For non-boundary cells only.

  45. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Exact alignment of 3 Sequences At the boundaries: Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . V ( 0 , 0 , 0 ) = 0 , V ( i , j , 0 ) = V ( x i , y j ) − ( i + j ) × d , V ( i , 0 , k ) = V ( x i , z k ) − ( i + k ) × d , V ( 0 , j , k ) = V ( y j , z k ) − ( j + k ) × d .

  46. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods . SOP Exact Progressive Benchmarks Recent methods Exact alignment of k Sequences Marcel Turcotte . Preamble . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . . . . . . . . Given : x 1 , x 2 and x k , k sequences. The optimum SP alignment for k sequences, V ( i 1 , i 2 , . . . , i k ) , to align x 1 [ 1 .. i 1 ] , x 2 [ 1 .. i 2 ] , . . . , x k [ 1 .. i k ]  V ( i 1 − 1 , i 2 − 1 , . . . , i k − 1 ) + s ( i 1 , i 2 , . . . , i k ) ,   V ( i 1 , i 2 − 1 , . . . , i k − 1 ) + s ( − , i 2 , . . . , i k ) ,     V ( i 1 − 1 , i 2 , . . . , i k − 1 ) + s ( i 1 , − , . . . , i k ) ,     . . .  V ( i 1 , i 2 , . . . , i k ) = max V ( i 1 − 1 , i 2 − 1 , . . . , i k ) + s ( i 1 , i 2 , . . . , − ) ,   . . .     V ( i 1 , i 2 , . . . , i k − 1 ) + s ( − , − , . . . , i k ) ,     . . .  ⇒ All the subsets (2 k ) except the empty one, which corresponds to − , − , . . . , − , hence, 2 k − 1 cases.

  47. O n k (memory cells) Time complexity is O 2 k n k Say k . Benchmarks Space complexity is Given: k sequences of approximately the same length, n Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 Remarks Recent methods Progressive 100 and k Exact SOP Preamble Recent methods Benchmarks Progressive Exact For n 10 10 5, n k 5, n Marcel Turcotte 16 million years! 100 takes 10, n For k 26 hours 100 takes For k Preamble 100 takes 0.2 millisecond n 2 (pairwise) and 10 20 10, n k 100 and k For n SOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  48. O n k (memory cells) Time complexity is O 2 k n k Say k . Benchmarks Space complexity is Given: k sequences of approximately the same length, n Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 Remarks Recent methods Progressive 100 and k Exact SOP Preamble Recent methods Benchmarks Progressive Exact For n 10 10 5, n k 5, n Marcel Turcotte 16 million years! 100 takes 10, n For k 26 hours 100 takes For k Preamble 100 takes 0.2 millisecond n 2 (pairwise) and 10 20 10, n k 100 and k For n SOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  49. Time complexity is O 2 k n k Say k O n k (memory cells) . Benchmarks Space complexity is Given: k sequences of approximately the same length, n Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 Remarks Recent methods Progressive 100 and k Exact SOP Preamble Recent methods Benchmarks Progressive Exact For n 10 10 5, n k 5, n Marcel Turcotte 16 million years! 100 takes 10, n For k 26 hours 100 takes For k Preamble 100 takes 0.2 millisecond n 2 (pairwise) and 10 20 10, n k 100 and k For n SOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  50. Time complexity is O 2 k n k Say k O n k (memory cells) . Benchmarks Space complexity is Given: k sequences of approximately the same length, n Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 Remarks Recent methods Progressive 100 and k Exact SOP Preamble Recent methods Benchmarks Progressive Exact For n 10 10 5, n k 5, n Marcel Turcotte 16 million years! 100 takes 10, n For k 26 hours 100 takes For k Preamble 100 takes 0.2 millisecond n 2 (pairwise) and 10 20 10, n k 100 and k For n SOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  51. O 2 k n k Say k . Exact Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 Remarks Recent methods Benchmarks Progressive SOP For n Preamble Recent methods Benchmarks Progressive Exact SOP Preamble Given: k sequences of approximately the same length, n 100 and k . 100 takes Marcel Turcotte 16 million years! 100 takes 10, n For k 26 hours 5, n 10, n k For k 100 takes 0.2 millisecond n 2 (pairwise) and Time complexity is 10 20 . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . . . . . . . . . . . . Space complexity is O ( n k ) (memory cells) For n = 100 and k = 5, n k = 10 10

  52. O 2 k n k Say k . Recent methods Benchmarks Progressive Exact SOP Preamble Benchmarks Remarks Progressive Exact SOP Preamble . . Recent methods Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 . Given: k sequences of approximately the same length, n Time complexity is 2 (pairwise) and n 100 takes 0.2 millisecond For k 5, n 100 takes 26 hours For k 10, n 100 takes 16 million years! Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics Space complexity is O ( n k ) (memory cells) For n = 100 and k = 5, n k = 10 10 For n = 100 and k = 10, n k = 10 20

  53. O 2 k n k Say k . Recent methods Benchmarks Progressive Exact SOP Preamble Benchmarks Remarks Progressive Exact SOP Preamble . . Recent methods Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 . Given: k sequences of approximately the same length, n Time complexity is 2 (pairwise) and n 100 takes 0.2 millisecond For k 5, n 100 takes 26 hours For k 10, n 100 takes 16 million years! Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics Space complexity is O ( n k ) (memory cells) For n = 100 and k = 5, n k = 10 10 For n = 100 and k = 10, n k = 10 20

  54. O 2 k n k Say k . Recent methods Benchmarks Progressive Exact SOP Preamble Benchmarks Remarks Progressive Exact SOP Preamble . . Recent methods Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 . Given: k sequences of approximately the same length, n Time complexity is 2 (pairwise) and n 100 takes 0.2 millisecond For k 5, n 100 takes 26 hours For k 10, n 100 takes 16 million years! Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics Space complexity is O ( n k ) (memory cells) For n = 100 and k = 5, n k = 10 10 For n = 100 and k = 10, n k = 10 20

  55. . Recent methods . . . . . . . Preamble SOP Exact Progressive Benchmarks Preamble . SOP Exact Progressive Benchmarks Recent methods Remarks Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 Given: k sequences of approximately the same length, n For k 10, n 100 takes 16 million years! Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . . Space complexity is O ( n k ) (memory cells) For n = 100 and k = 5, n k = 10 10 For n = 100 and k = 10, n k = 10 20 Time complexity is O ( 2 k n k ) Say k = 2 (pairwise) and n = 100 takes 0.2 millisecond For k = 5, n = 100 takes ∼ 26 hours

  56. . Recent methods . . . . . . . Preamble SOP Exact Progressive Benchmarks Preamble . SOP Exact Progressive Benchmarks Recent methods Remarks Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 Given: k sequences of approximately the same length, n For k 10, n 100 takes 16 million years! Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . . Space complexity is O ( n k ) (memory cells) For n = 100 and k = 5, n k = 10 10 For n = 100 and k = 10, n k = 10 20 Time complexity is O ( 2 k n k ) Say k = 2 (pairwise) and n = 100 takes 0.2 millisecond For k = 5, n = 100 takes ∼ 26 hours

  57. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Remarks Recall that peta- (P) = 10 15 , tera- (T) 10 12 , giga- (G) 10 9 Given: k sequences of approximately the same length, n Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics Space complexity is O ( n k ) (memory cells) For n = 100 and k = 5, n k = 10 10 For n = 100 and k = 10, n k = 10 20 Time complexity is O ( 2 k n k ) Say k = 2 (pairwise) and n = 100 takes 0.2 millisecond For k = 5, n = 100 takes ∼ 26 hours For k = 10, n = 100 takes ∼ 16 million years!

  58. . Benchmarks . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Recent methods . What’s next ? The exact algorithm cannot be applied; prohibitive space and time complexity. What can be done? Use a difgerent optimization technique , something else than dynamic programming; Suggestions? Genetic algorithms SAGA (Notredame and Higgins 1996) Branch-and-bound MSA (Gupta et al 1995), DCA (Stoye et al 1997) Solve a simpler problem: Progressive sequence alignment problem; most widely used approach. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  59. . Recent methods . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks What’s next ? . The exact algorithm cannot be applied; prohibitive space and time complexity. What can be done? Use a difgerent optimization technique , something else than dynamic programming; Suggestions? Genetic algorithms SAGA (Notredame and Higgins 1996) Branch-and-bound MSA (Gupta et al 1995), DCA (Stoye et al 1997) Solve a simpler problem: Progressive sequence alignment problem; most widely used approach. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  60. . Benchmarks . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Recent methods . What’s next ? The exact algorithm cannot be applied; prohibitive space and time complexity. What can be done? Use a difgerent optimization technique , something else than dynamic programming; Suggestions? Genetic algorithms SAGA (Notredame and Higgins 1996) Branch-and-bound MSA (Gupta et al 1995), DCA (Stoye et al 1997) Solve a simpler problem: Progressive sequence alignment problem; most widely used approach. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  61. . Benchmarks . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Recent methods . What’s next ? The exact algorithm cannot be applied; prohibitive space and time complexity. What can be done? Use a difgerent optimization technique , something else than dynamic programming; Suggestions? Genetic algorithms SAGA (Notredame and Higgins 1996) Branch-and-bound MSA (Gupta et al 1995), DCA (Stoye et al 1997) Solve a simpler problem: Progressive sequence alignment problem; most widely used approach. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  62. . Benchmarks . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Recent methods . What’s next ? The exact algorithm cannot be applied; prohibitive space and time complexity. What can be done? Use a difgerent optimization technique , something else than dynamic programming; Suggestions? Genetic algorithms SAGA (Notredame and Higgins 1996) Branch-and-bound MSA (Gupta et al 1995), DCA (Stoye et al 1997) Solve a simpler problem: Progressive sequence alignment problem; most widely used approach. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  63. . Benchmarks . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Recent methods . What’s next ? The exact algorithm cannot be applied; prohibitive space and time complexity. What can be done? Use a difgerent optimization technique , something else than dynamic programming; Suggestions? Genetic algorithms SAGA (Notredame and Higgins 1996) Branch-and-bound MSA (Gupta et al 1995), DCA (Stoye et al 1997) Solve a simpler problem: Progressive sequence alignment problem; most widely used approach. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  64. . Recent methods . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks What’s next ? . The exact algorithm cannot be applied; prohibitive space and time complexity. What can be done? Use a difgerent optimization technique , something else than dynamic programming; Suggestions? Genetic algorithms SAGA (Notredame and Higgins 1996) Branch-and-bound MSA (Gupta et al 1995), DCA (Stoye et al 1997) Solve a simpler problem: Progressive sequence alignment problem; most widely used approach. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  65. . Benchmarks . . . . Preamble SOP Exact Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Recent methods . What’s next ? The exact algorithm cannot be applied; prohibitive space and time complexity. What can be done? Use a difgerent optimization technique , something else than dynamic programming; Suggestions? Genetic algorithms SAGA (Notredame and Higgins 1996) Branch-and-bound MSA (Gupta et al 1995), DCA (Stoye et al 1997) Solve a simpler problem: Progressive sequence alignment problem; most widely used approach. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  66. . Recent methods . . . . . . . Preamble SOP Exact Progressive Benchmarks Preamble . SOP Exact Progressive Benchmarks Recent methods Progressive alignment methods Idea. 1. Two sequences are chosen and aligned by standard dynamic programming algorithm 2. A third sequence is chosen and aligned to the fjrst alignment 3. Iterate until all sequences have been aligned Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics ⇒ Most commonly used approach.

  67. . Recent methods . . . . . . . Preamble SOP Exact Progressive Benchmarks Preamble . SOP Exact Progressive Benchmarks Recent methods Progressive Aligments P. Hogeweg and B. Hesper. The alignment of sets of sequences and the construction of phyletic trees: an integrated method. J Mol Evol , 20(2):175–186, 1984. D. G. Higgins and P. M. Sharp. Clustal: a package for performing multiple sequence alignment on a microcomputer. Gene , 73(1):237–44, Dec 1988. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  68. . Recent methods . . . . . . . Preamble SOP Exact Progressive Benchmarks Preamble . SOP Exact Progressive Benchmarks Recent methods Remarks (digression) Publications are the currency of academia! The number of citations demonstrates the impact of the work in the fjeld. As of 2018-10-03, Des Higgins, the author of Clustal, has 125,800 citations (Scopus, 164,298 citations on Google Scholar)! Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics

  69. . Benchmarks . . . . . . . . Preamble SOP Exact Progressive Recent methods . Preamble SOP Exact Progressive Benchmarks Recent methods Progressive alignment i and j 2. Build a guide tree 3. From the deepest node up to the root build all the pairwise partial alignments (bottom-up) Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics 1. Calculate d i , j , distance between sequences i and j , for all

  70. . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . .. . . . . .

  71. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods CLUSTALW Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics YDGGPAVEAL YDGGPEAL FEGGPILVEAL FDGGILVQAV YEGGAVVQAL

  72. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods CLUSTALW Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics YDGGPAVEAL YDGGPEAL FEGGPILVEAL FDGGILVQAV YEGGAVVQAL

  73. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods CLUSTALW Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics YDGGPAVEAL YDGGP−−EAL FEGGPILVEAL FDGGILVQAV YEGGAVVQAL

  74. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods CLUSTALW Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics YDGGPAVEAL YDGGP−−EAL FEGGPILVEAL FDGGILVQAV YEGGAVVQAL

  75. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods CLUSTALW Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics YDGGPAVEAL YDGGP−−EAL FEGGPILVEAL FDGGILVQAV YEGGAVVQAL

  76. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods CLUSTALW Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics YDGGPAVEAL YDGGP−−EAL FEGGPILVEAL FDGGILVQAV YEGGAVVQAL

  77. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods CLUSTALW Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics YDGGPA−VEAL YDGGP−−−EAL FEGGPILVEAL FDGGILVQAV YEGGAVVQAL

  78. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods CLUSTALW Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics YDGGPA−VEAL YDGGP−−−EAL FEGGPILVEAL FDGGILVQAV YEGGAVVQAL

  79. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods CLUSTALW Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics YDGGPA−VEAL YDGGP−−−EAL FEGGPILVEAL FDGGIL−VQAV YEGGAV−VQAL

  80. . SOP . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods CLUSTALW Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics YDGGPA−VEAL YDGGP−−−EAL FEGGPILVEAL FDGGIL−VQAV YEGGAV−VQAL

  81. . Exact . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Progressive sequence alignment: take 2 Source code available on the course Web site, as well as the Appendix. Marcel Turcotte . . . . . . . . . . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . S1 YDGGPAVEAL S3 S2 YDGGPEAL S5 S4 FEGGPILVEAL S9 S6 FDGGILVQAV S8 S7 YEGGAVVQAL

  82. Sequence vs Sequence , Sequence vs MSA , of the alignment. the sequence. MSA vs MSA ... 0 1 2 3 n − a 1 a a a n 2 3 − 0 b 1 1 b 2 2 ... b m m a 1 , a 2 . . . a n represents a sequence or an alignment When it represents a sequence, the a i are the symbols of When it represents an alignment, the a i are the columns Similarly, for b 1 , b 2 . . . b m . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . .. . . . . .

  83. . Preamble . . . . . . . . . . . . Exact Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Marcel Turcotte . SOP . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . . . . . . . . S 1 vs S 2 ... 0 1 2 3 n − a 1 a a a n 2 3 − 0 b 1 1 b 2 2 ... b m m a = S 2 = YDGGPEAL b = S 1 = YDGGPAVEAL

  84. Y 30][ 24][ 18][ 6][ -5][ V [ -42][ -26][ -16][ 20] 26][ 24][ 28] 12][ 1][ D 12] 18][ 24][ 30][ 30][ E [ -48][ -32][ -22][ -11][ 7][ 22] YDGGPAVEAL YDGGP--EAL 30] 18][ 10][ 0][ L [ -60][ -44][ -34][ -23][ -12][ 24][ 0][ 16][ 6][ -6][ A [ -54][ -38][ -28][ -17][ 27] 24][ 22][ 12][ 18][ A [ -36][ -20][ -10][ -4][ Y [ 4][ D [ -12][ -8][ -14][ -20][ -26][ -32] -2][ P [ -30][ -14][ 10][ -6][ -6][ -12][ -18][ -24][ -30][ -36][ -42][ -48] 8][ 0][ [ L A E P G G 14][ 4][ 2][ -8][ 0] -4][ -10][ -16][ -22] 12][ 18][ 24][ 13][ 2][ 6][ G [ -24][ -5][ -11] 1][ 7][ 13][ 19][ 8][ -2][ G [ -18][ . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . .. . . . . .

  85. . . . . . . . . . . . . Preamble Exact . Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods YDGGP--EAL Marcel Turcotte . SOP . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . . . . . . . . S 3 vs S 4 ... 0 1 2 3 n − a 1 a a a n 2 3 − 0 b 1 1 b 2 2 ... b m m a = S 4 = FEGGPILVEAL b = S 3 = YDGGPAVEAL

  86. F 58][ 50][ 26][ 4][ E E [ -62][ -36][ -16][ 14] 25][ 37][ 49][ 59][ 62][ 34][ 60][ 12][ V - [ -54][ -28][ -10][ 12] 24][ 34][ 46][ E 69][ 70][ 46][ 24][ 58][ 59][ A - [ -42][ -16][ L L [ -78][ -52][ -32][ -12][ YDGGPA-VEAL YDGGP---EAL FEGGPILVEAL 85] 63][ 62][ 66][ 68][ 50][ 34][ 10][ 55] 61][ 67][ 61][ 62][ 56][ 50][ 40][ 16][ -6][ A A [ -72][ -46][ -26][ 37] 49][ 2][ 57][ 10] -2][ G G [ -17][ -2][ -14][ -26][ -38][ -50][ -62][ -74] 10][ 22][ 22][ 16][ D D [ -10][ 0][ -12][ -24][ -36][ -48][ -60][ -72][ -84][ -96] 12][ 24][ Y Y [ 27][ 0][ -12][ -24][ -36][ -48][ -60][ -72][ -84][ -96][-108][-120][-132] [ L A E V L I P G G 9][ 34][ 49][ 4][ 34][ 46][ 58][ 70][ 82][ 37][ 36][ 14][ -4][ P P [ -30][ -8][ -20] 58][ 16][ 28][ 40][ 52][ 64][ 42][ 20][ 2][ G G [ -24][ 1][ -11][ -23][ -35][ -47] 13][ 25][ . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . .. . . . . .

  87. . Preamble . . . . . . . . . . . . Exact Progressive Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods Marcel Turcotte . SOP . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . . . . . . . . S 5 vs S 6 ... 0 1 2 3 n − a 1 a a a n 2 3 − 0 b 1 1 b 2 2 ... b m m a = S 6 = YEGGAVVQAL b = S 5 = FDGGILVQAV

  88. Y V [ -42][ -29][ -20][ 10] 14][ 19][ 25][ 17][ 8][ 2][ -9][ 6] -4][ 8][ 14][ 20][ 21][ E 8][ -3][ L [ -36][ -23][ -14][ -6] Q [ -48][ -35][ -26][ -15][ 2][ 6][ 25] YEGGAVVQAL FDGGILVQAV 33] 25][ 17][ 9][ 2][ -8][ V [ -60][ -47][ -38][ -27][ -16][ 31][ 11][ 23][ 13][ 5][ -2][ A [ -54][ -41][ -32][ -21][ -10][ 17] 23][ 29][ 19][ 0][ 13][ 12][ -6][ -12][ -18][ -24][ -30][ -36][ -42][ -48][ -54][ -60] 10][ 1][ D [ -12][ -5][ -11][ -17][ -23][ -29][ -35][ -41][ -47] 18][ 7][ -6][ F [ 0][ -2][ [ L A Q V V A G G 4][ 1][ -8][ -14][ -20][ -26][ -32][ -38] 20][ 19][ 14][ 3][ G [ -18][ I [ -30][ -17][ -4][ -10][ -16] 2][ 8][ 14][ -8][ 9][ -2][ G [ -24][ -11][ -9][ -15][ -21][ -27] -3][ 3][ 9][ 15][ 4][ -5][ . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . .. . . . . .

  89. . . . . . . . . . . . Preamble SOP Progressive . Benchmarks Recent methods Preamble SOP Exact Progressive Benchmarks Recent methods YEGGAVVQAL YDGGP---EAL YDGGPA-VEAL Marcel Turcotte . Exact . . . . . . . . . . . . . . CSI5126 . Algorithms in bioinformatics . . . . . . . . . . . . . S 7 vs S 8 ... 0 1 2 3 n − a 1 a a a n 2 3 − 0 b 1 1 b 2 2 ... b m m a = S 8 = FDGGILVQAV b = S 7 = FEGGPILVEAL

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

CSI5126 . Algorithms in bioinformatics Overview of the course content and expectations Marcel

CSI5126 . Algorithms in bioinformatics Overview of the course content and expectations Marcel

. Jobs . . . . . . . . Preamble Discussion Syllabus Biology . Preamble Discussion Syllabus Jobs Biology CSI5126 . Algorithms in bioinformatics Overview of the course content and expectations Marcel Turcotte School of Electrical

599 views • 59 slides
CSI5126 . Algorithms in bioinformatics Hidden Markov Models (continued) Marcel Turcotte School of

CSI5126 . Algorithms in bioinformatics Hidden Markov Models (continued) Marcel Turcotte School of

. Estimation . . . . . . . Preamble Example Decoding Likelihood Model Specifjcation Preamble . Example Decoding Likelihood Model Specifjcation Estimation CSI5126 . Algorithms in bioinformatics Hidden Markov Models (continued)

1.01k views • 98 slides
CSI5126 . Algorithms in bioinformatics Phylogeny Marcel Turcotte School of Electrical

CSI5126 . Algorithms in bioinformatics Phylogeny Marcel Turcotte School of Electrical

. Preamble . . . . . . . . . . Character-based . Maximum likelihood Preamble Character-based Maximum likelihood CSI5126 . Algorithms in bioinformatics Phylogeny Marcel Turcotte School of Electrical Engineering and Computer

1.51k views • 134 slides
CSI5126 . Algorithms in bioinformatics Deterministic Sequence Motifs Marcel Turcotte School of

CSI5126 . Algorithms in bioinformatics Deterministic Sequence Motifs Marcel Turcotte School of

. PRINTS . . . . . . . . . Preamble Words Regular Expressions . Preamble Words PRINTS Regular Expressions CSI5126 . Algorithms in bioinformatics Deterministic Sequence Motifs Marcel Turcotte School of Electrical Engineering and

1.71k views • 145 slides
CSI5126 . Algorithms in bioinformatics Pairwise Sequence Alignment Marcel Turcotte School of

CSI5126 . Algorithms in bioinformatics Pairwise Sequence Alignment Marcel Turcotte School of

. Local . . . . . . . . Preamble Edit graph Global Gaps . Preamble Edit graph Global Local Gaps CSI5126 . Algorithms in bioinformatics Pairwise Sequence Alignment Marcel Turcotte School of Electrical Engineering and Computer

1.01k views • 82 slides
CSI5126 . Algorithms in bioinformatics Essential Cellular Biology (continued) Marcel Turcotte

CSI5126 . Algorithms in bioinformatics Essential Cellular Biology (continued) Marcel Turcotte

. Transcription . . . . . . . . Preamble Central Dogma Replication Translation . Preamble Central Dogma Replication Transcription Translation CSI5126 . Algorithms in bioinformatics Essential Cellular Biology (continued) Marcel

1.54k views • 132 slides
CSI5126 . Algorithms in bioinformatics Essential Cellular Biology Marcel Turcotte School of

CSI5126 . Algorithms in bioinformatics Essential Cellular Biology Marcel Turcotte School of

. kingdoms of life . . . . . . . . . Preamble The Cell Macromolecules . Preamble The Cell kingdoms of life Macromolecules CSI5126 . Algorithms in bioinformatics Essential Cellular Biology Marcel Turcotte School of Electrical

897 views • 85 slides
CSI5126 . Algorithms in bioinformatics Hidden Markov Models Marcel Turcotte School of Electrical

CSI5126 . Algorithms in bioinformatics Hidden Markov Models Marcel Turcotte School of Electrical

. Profjle HMM . . . . . . . . . Preamble Motivation Defjnitions . Preamble Motivation Profjle HMM Defjnitions CSI5126 . Algorithms in bioinformatics Hidden Markov Models Marcel Turcotte School of Electrical Engineering and

632 views • 34 slides
CSI5126 . Algorithms in bioinformatics Substitution Score Marcel Turcotte School of Electrical

CSI5126 . Algorithms in bioinformatics Substitution Score Marcel Turcotte School of Electrical

. PAM . . . . . . . Preamble Signifjcance Models Substitutions Markov Chains Preamble . Signifjcance Models Substitutions Markov Chains PAM CSI5126 . Algorithms in bioinformatics Substitution Score Marcel Turcotte School of

1.18k views • 115 slides
CSI5126 . Algorithms in bioinformatics Suffjx Trees Marcel Turcotte School of Electrical

CSI5126 . Algorithms in bioinformatics Suffjx Trees Marcel Turcotte School of Electrical

. Boyer-Moore . . . . . . . . . Preamble Notation Suffjx Trees . Preamble Notation Boyer-Moore Suffjx Trees CSI5126 . Algorithms in bioinformatics Suffjx Trees Marcel Turcotte School of Electrical Engineering and Computer Science

1.29k views • 113 slides
CSI5126 . Algorithms in bioinformatics RNA Secondary Structure Search Problem Marcel Turcotte

CSI5126 . Algorithms in bioinformatics RNA Secondary Structure Search Problem Marcel Turcotte

. Preamble . . . . . . . . . . Inference problem . Search problem Preamble Inference problem Search problem CSI5126 . Algorithms in bioinformatics RNA Secondary Structure Search Problem Marcel Turcotte School of Electrical

2.12k views • 197 slides
CSI5126 . Algorithms in bioinformatics Suffjx Trees Marcel Turcotte School of Electrical

CSI5126 . Algorithms in bioinformatics Suffjx Trees Marcel Turcotte School of Electrical

. LCE . . . . . . . Preamble Repeats Generalized Suffjx Tree More Repeats LCA Preamble . Repeats Generalized Suffjx Tree More Repeats LCA LCE CSI5126 . Algorithms in bioinformatics Suffjx Trees Marcel Turcotte School of

1.59k views • 158 slides
CSI5126 . Algorithms in bioinformatics Probabilistic Sequence Motifs Marcel Turcotte School of

CSI5126 . Algorithms in bioinformatics Probabilistic Sequence Motifs Marcel Turcotte School of

. Preamble . . . . Preamble Motivation Profjle HMM Defjnitions Example Decoding Likelihood Model Specifjcation Estimation Motivation . Profjle HMM Defjnitions Example Decoding Likelihood Model Specifjcation Estimation CSI5126

1.92k views • 122 slides
What is a phylogenetic tree? Bioinformatics Algorithms (Fundamental Algorithms, module 2)

What is a phylogenetic tree? Bioinformatics Algorithms (Fundamental Algorithms, module 2)

What is a phylogenetic tree? Bioinformatics Algorithms (Fundamental Algorithms, module 2) speciation events Zsuzsanna Lipt ak Masters in Medical Bioinformatics academic year 2018/19, II. semester species wolf cat lion horse rhino

854 views • 5 slides
Bioinformatics Algorithms (Fundamental Algorithms, module 2) Zsuzsanna Lipt ak Masters in

Bioinformatics Algorithms (Fundamental Algorithms, module 2) Zsuzsanna Lipt ak Masters in

Bioinformatics Algorithms (Fundamental Algorithms, module 2) Zsuzsanna Lipt ak Masters in Medical Bioinformatics academic year 2018/19, II semester Organisation Organisation Title of course: Bioinformatics Algorithms (Fundamental

14.56k views • 11 slides
Bioinformatics Algorithms (Fundamental Algorithms, module 2) Zsuzsanna Lipt ak Masters in

Bioinformatics Algorithms (Fundamental Algorithms, module 2) Zsuzsanna Lipt ak Masters in

Bioinformatics Algorithms (Fundamental Algorithms, module 2) Zsuzsanna Lipt ak Masters in Medical Bioinformatics academic year 2018/19, II semester Phylogenetics II 1 1 These slides are partially based on the Lecture Notes from Bielefeld

543 views • 32 slides
Lexico-Syntactic Influences in Spoken-Word Recognition Garance P ARIS Dept. of Psycholinguistics

Lexico-Syntactic Influences in Spoken-Word Recognition Garance P ARIS Dept. of Psycholinguistics

IGK Annual Meeting, July 2005 7/30/05 IGK Annual Meeting July 2005 Lexico-Syntactic Influences in Spoken-Word Recognition Garance P ARIS Dept. of Psycholinguistics Saarland University 1 IGK Annual Meeting, July 2005 7/30/05 1 Overview

229 views • 20 slides
An introduction to multiple alignments original version by Cdric Notredame, updated by Laurent

An introduction to multiple alignments original version by Cdric Notredame, updated by Laurent

An introduction to multiple alignments original version by Cdric Notredame, updated by Laurent Falquet Swiss Institute of Bioinf ormatics Institut Suisse de Bioinf ormatique CN+LF-2006.01 Overview Multiple alignments How-to, Goal,

500 views • 28 slides
January 8, 2013 MEMORANDUM TO: Chairman Macfarlane Commissioner Svinicki Commissioner

January 8, 2013 MEMORANDUM TO: Chairman Macfarlane Commissioner Svinicki Commissioner

January 8, 2013 MEMORANDUM TO: Chairman Macfarlane Commissioner Svinicki Commissioner Apostolakis Commissioner Magwood Commissioner Ostendorff FROM: Margaret M. Doane /RA/ General Counsel SUBJECT: BACKGROUND MATERIAL ON NRC ADJUDICATIONS

847 views • 37 slides
Image Formation CPSC 453 Fall 2018 Sonny Chan Outline for Today The Camera Obscura

Image Formation CPSC 453 Fall 2018 Sonny Chan Outline for Today The Camera Obscura

Image Formation CPSC 453 Fall 2018 Sonny Chan Outline for Today The Camera Obscura The geometry of perspective The anatomy of a camera The Camera Obsura ca. 4th century BC Camera Obscura, 1946 San Francisco, California Camera

711 views • 49 slides
Lecture 6: samples and populations Todays lecture Look at fundamental concepts of samples and

Lecture 6: samples and populations Todays lecture Look at fundamental concepts of samples and

Lecture 6: samples and populations Todays lecture Look at fundamental concepts of samples and populations Intended to reinforce similar material in MAS2901 Adopt a different perspective to MAS2901: use simulation rather than analytic

414 views • 29 slides
Exploitation Passenger pigeon hunted to extinction dye-hardwood tree Pau-Brazil vigorously

Exploitation Passenger pigeon hunted to extinction dye-hardwood tree Pau-Brazil vigorously

Species Comments Bison hunted almost to extinction Exploitation Passenger pigeon hunted to extinction dye-hardwood tree Pau-Brazil vigorously harvested 1500-1850 (coastal Brazilian forests) ongoing harvest driven by Mahagony economical

378 views • 3 slides
Example titles from last semester Coral reef resilience and susceptibility due to human

Example titles from last semester Coral reef resilience and susceptibility due to human

Example titles from last semester Coral reef resilience and susceptibility due to human interference The ripple effect: the consequences of biological control Overfishing: without immediate reform the problems of yesterday will be here to stay

606 views • 25 slides
FINAL REPORT An empirical relationship between changes in headrope length and catch for the NPF

FINAL REPORT An empirical relationship between changes in headrope length and catch for the NPF

FINAL REPORT An empirical relationship between changes in headrope length and catch for the NPF fleet A report to AFMA for the NPF RAG Bill Venables and Matthew Browne CMIS Report Number 07/80 29 June 2007 Commercial-in-confidence

380 views • 25 slides

More recommend