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Alignment-based methods ,[object Object],[object Object],[object Object],[object Object],[object Object]
 
Local alignment Finds domains and short regions of similarity between  a pair of sequences.  The two sequences under  comparison do not necessarily need to have high levels  of similarity over their entire length in order to receive  locally high similarity scores.  This feature of local  similarity searches give them the advantage of being  useful when looking for domains within proteins or  looking for regions of genomic DNA that contain  introns.  Local similarity searches do not have the  constraint that similarity between two sequences  needs to be observed over the entire length of each  gene.
Global alignment Finds the optimal alignment over the entire length of  the two sequences under comparison.  Algorithms of  this nature are not particularly suited to the  identification of genes that have evolved by  recombination or insertion of unrelated regions of  DNA.  In instances such as this, a global similarity  score will be greatly reduced.  In cases where genes  are being aligned whose sequences are of comparable  length and also whose entire gene is homologous  (descendant from a common ancestor), global  alignment might be considered appropriate.
Terminology ,[object Object],[object Object],[object Object],[object Object]
Needleman-Wunsch Exact global alignment method. Not particularly good in many cases (database searches, looking for small regions of similarity, alignment of sequences with vastly differing lengths), but the most rigorous and thorough method if the task is to align sequences that have not evolved by exon shuffling, domain insertion/deletion etc.  In other words, it is the best method if you have sequences that are of ‘similar’ length and have evolved from a common ancestor by point processes (point mutation, small indels).
Smith-Waterman Exact local alignment There is no requirement for the alignment to extend along the entirety of the sequences.  This is a very good algorithm for database searching, multiple alignment and pairwise alignment. It is exhaustive and can be very slow (compared to the heuristics described later).  The difference between this and the N-W algorithm is that alignments starting at all possible positions must be considered, not just the ones that start at the beginning and end at the end.
 
FastA algorithm ,[object Object],[object Object],[object Object],[object Object]
 
FastA algorithm ,[object Object],[object Object],[object Object]
z-opt E() < 20  0  0 : * 22  0  0 : * 24  0  0 : * 26  0  0 : * 28  0  3 : * 30  0  18 : * 32  11  70 :=  * 34  73  190 :====  * 36  430  389 :================ * == 38  969  644 :=========================== * =============== 40  1086  898 :======================================= * ======== 42  1332  1097 :=============================================== * ========== 44  1252  1211 :==================================================== * == 46  1022  1233 :=============================================  * 48  1041  1181 :==============================================  * 50  982  1077 :===========================================  * 52  846  947 :=====================================  * 54  716  809 :================================  * 56  650  676 :============================= * 58  547  555 :======================== * 60  409  449 :==================  * 62  369  360 :=============== * = 64  289  287 :============ * 66  232  226 :========= * = 68  176  178 :======= * 70  163  140 :====== * = 72  124  109 :==== * = 74  88  85 :=== * 76  73  66 :== * = 78  73  51 :== * = 80  44  40 := * 82  32  31 := * 84  23  24 := * 86  19  19 : * 88  15  14 : * 90  8  11 : * 92  11  9 : *   :======== * == 94  3  7 : *   :===  * 96  2  5 : *   :==  * 98  6  4 : *   :=== * == 100  2  3 : *   :== * 102  4  2 : *   := * == 104  3  2 : *   := * = 106  0  1 : *   : * 108  0  1 : *   : * 110  1  1 : *   : * 112  0  1 : *   : * 114  1  1 : *   : * 116  0  0 : *   * 118  0  0 : *   * >120  1  0 : *   * = Results of a FastA search
The best scores are:  initn init1 opt  z-sc E(13127) HP0793 polypeptide deformylase (def) {Escherichia  66  66  100 126.9  0.71 AF2215 methylmalonyl-CoA mutase, subunit alpha, N  45  45  94 113.9  1.2 AF1231 hypothetical protein  50  50  86 104.9  4.4 MJ1169 tungsten formylmethanofuran dehydrogenase,  45  45  85 102.7  4.8 AF0267 hypothetical protein  71  71  84 101.2  5.5 AF1486 hypothetical protein  83  83  84 102.4  6.1 AF0262 medium-chain acyl-CoA ligase (alkK-2) {Pse  50  50  82 99.2  7.8 AF0229 conserved hypothetical protein {Methanococ  58  58  83 103.0  8.2 D09_orf125.gseg, 378 bases, 5AC53121 checksum.  50  50  85 110.0  8.5 SL251_1.UVRC  1797 residues  40  40  81 97.5  8.9 slr2049 hypothetical protein  83  83  83 105.5  9.9 AF0868 alkyldihydroxyacetonephosphate synthase {C  45  45  80 97.7  12 AF1320 GMP synthase (guaA-2) {Methanococcus janna  35  35  82 104.5  12 SL159_1.PKSK  13344 residues  99  74  74 79.2  12 slr1771  40  40  79 95.6  13 sll1018 dihydroorotase (pyrC)  60  60  79 96.6  14 slr2102 cell division protein FtsY (ftsY)  77  77  78 94.7  15 AF0946 hypothetical protein  67  67  76 88.8  16 AF1325 multidrug resistance protein {Methanococcu  55  55  77 95.0  20 SL194_2.BFMBB  1272 residues  75  75  76 93.1  22
Original BLAST ,[object Object],[object Object],[object Object]
Original BLAST ,[object Object],[object Object]
Gapped BLAST ,[object Object],[object Object],[object Object]
Gapped BLAST ,[object Object],[object Object],[object Object]
Two-Hit Method ,[object Object],[object Object],[object Object]
How does this affect the process of searching a database? ,[object Object],[object Object]
 
 
PSI(  -BLAST ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PSI-BLAST ,[object Object],[object Object],[object Object]
PSI(  -BLAST ,[object Object],[object Object],[object Object]
PSI(  -BLAST ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
 
Significance of the similarity of two sequences ,[object Object],[object Object],[object Object]
Randomisation test ,[object Object],[object Object],[object Object],[object Object]

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BLAST

  • 1.
  • 2.  
  • 3. Local alignment Finds domains and short regions of similarity between a pair of sequences. The two sequences under comparison do not necessarily need to have high levels of similarity over their entire length in order to receive locally high similarity scores. This feature of local similarity searches give them the advantage of being useful when looking for domains within proteins or looking for regions of genomic DNA that contain introns. Local similarity searches do not have the constraint that similarity between two sequences needs to be observed over the entire length of each gene.
  • 4. Global alignment Finds the optimal alignment over the entire length of the two sequences under comparison. Algorithms of this nature are not particularly suited to the identification of genes that have evolved by recombination or insertion of unrelated regions of DNA. In instances such as this, a global similarity score will be greatly reduced. In cases where genes are being aligned whose sequences are of comparable length and also whose entire gene is homologous (descendant from a common ancestor), global alignment might be considered appropriate.
  • 5.
  • 6. Needleman-Wunsch Exact global alignment method. Not particularly good in many cases (database searches, looking for small regions of similarity, alignment of sequences with vastly differing lengths), but the most rigorous and thorough method if the task is to align sequences that have not evolved by exon shuffling, domain insertion/deletion etc. In other words, it is the best method if you have sequences that are of ‘similar’ length and have evolved from a common ancestor by point processes (point mutation, small indels).
  • 7. Smith-Waterman Exact local alignment There is no requirement for the alignment to extend along the entirety of the sequences. This is a very good algorithm for database searching, multiple alignment and pairwise alignment. It is exhaustive and can be very slow (compared to the heuristics described later). The difference between this and the N-W algorithm is that alignments starting at all possible positions must be considered, not just the ones that start at the beginning and end at the end.
  • 8.  
  • 9.
  • 10.  
  • 11.
  • 12. z-opt E() < 20 0 0 : * 22 0 0 : * 24 0 0 : * 26 0 0 : * 28 0 3 : * 30 0 18 : * 32 11 70 := * 34 73 190 :==== * 36 430 389 :================ * == 38 969 644 :=========================== * =============== 40 1086 898 :======================================= * ======== 42 1332 1097 :=============================================== * ========== 44 1252 1211 :==================================================== * == 46 1022 1233 :============================================= * 48 1041 1181 :============================================== * 50 982 1077 :=========================================== * 52 846 947 :===================================== * 54 716 809 :================================ * 56 650 676 :============================= * 58 547 555 :======================== * 60 409 449 :================== * 62 369 360 :=============== * = 64 289 287 :============ * 66 232 226 :========= * = 68 176 178 :======= * 70 163 140 :====== * = 72 124 109 :==== * = 74 88 85 :=== * 76 73 66 :== * = 78 73 51 :== * = 80 44 40 := * 82 32 31 := * 84 23 24 := * 86 19 19 : * 88 15 14 : * 90 8 11 : * 92 11 9 : * :======== * == 94 3 7 : * :=== * 96 2 5 : * :== * 98 6 4 : * :=== * == 100 2 3 : * :== * 102 4 2 : * := * == 104 3 2 : * := * = 106 0 1 : * : * 108 0 1 : * : * 110 1 1 : * : * 112 0 1 : * : * 114 1 1 : * : * 116 0 0 : * * 118 0 0 : * * >120 1 0 : * * = Results of a FastA search
  • 13. The best scores are: initn init1 opt z-sc E(13127) HP0793 polypeptide deformylase (def) {Escherichia 66 66 100 126.9 0.71 AF2215 methylmalonyl-CoA mutase, subunit alpha, N 45 45 94 113.9 1.2 AF1231 hypothetical protein 50 50 86 104.9 4.4 MJ1169 tungsten formylmethanofuran dehydrogenase, 45 45 85 102.7 4.8 AF0267 hypothetical protein 71 71 84 101.2 5.5 AF1486 hypothetical protein 83 83 84 102.4 6.1 AF0262 medium-chain acyl-CoA ligase (alkK-2) {Pse 50 50 82 99.2 7.8 AF0229 conserved hypothetical protein {Methanococ 58 58 83 103.0 8.2 D09_orf125.gseg, 378 bases, 5AC53121 checksum. 50 50 85 110.0 8.5 SL251_1.UVRC 1797 residues 40 40 81 97.5 8.9 slr2049 hypothetical protein 83 83 83 105.5 9.9 AF0868 alkyldihydroxyacetonephosphate synthase {C 45 45 80 97.7 12 AF1320 GMP synthase (guaA-2) {Methanococcus janna 35 35 82 104.5 12 SL159_1.PKSK 13344 residues 99 74 74 79.2 12 slr1771 40 40 79 95.6 13 sll1018 dihydroorotase (pyrC) 60 60 79 96.6 14 slr2102 cell division protein FtsY (ftsY) 77 77 78 94.7 15 AF0946 hypothetical protein 67 67 76 88.8 16 AF1325 multidrug resistance protein {Methanococcu 55 55 77 95.0 20 SL194_2.BFMBB 1272 residues 75 75 76 93.1 22
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.  
  • 21.  
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.  
  • 27.  
  • 28.  
  • 29.
  • 30.