读书笔记 Bioinformatics Algorithms Chapter5
程序员文章站
2022-05-15 11:27:00
Chapter5 HOW DO WE COMPARE DNA SEQUENCES Bioinformatics Algorithms－An_Active Learning Approach http://bioinformaticsalgorithms.com/ 一、 1983年，Russell D ......
chapter5 how do we compare dna sequences
bioinformatics algorithms－an_active learning approach
一、
1983年，russell doolitte 将血小板源生长因子[platelet derived growth factor(pdgf)，一种刺激细胞增殖的物质]和其它已知基因比对，发现它的序列和原癌基因（oncogene）的序列有很高的相似度。这让科学家猜测某些癌症是因为基因在不合适的时机作用所致（scientists hypothesized that some forms of cancer might be caused by a good gene doing the right thing at the wrong time.）。
二、提出问题 序列比对：动态规划法
1.全局比对：
1 '''
2 code challenge: solve the global alignment problem.
3 input: two protein strings written in the single-letter amino acid alphabet.
4 output: the maximum alignment score of these strings followed by an alignment achieving this maximum score. use the
5 blosum62 scoring matrix for matches and mismatches as well as the indel penalty σ = 5.
6 ----------
7 sample input:
8 pleasantly
9 meanly
10 ----------
11 sample output:
12 8
13 pleasantly
14 -mea--n-ly
15 ----------
16 @ lo kowngho 2018.9
17 '''
18 import numpy
19 from os.path import dirname
20
21 def grade(symb1,symb2):
22 indx1 = symbollist[symb1]
23 indx2 = symbollist[symb2]
24 return matrix[indx1][indx2]
25
26 def init_graph_global(l1,l2):
27 graph = numpy.zeros([l2,l1])
28 for x in range(1,l2):
29 graph[x][0] = graph[x-1][0]-5
30 for y in range(1,l1):
31 graph[0][y] = graph[0][y-1]-5
32 return graph
33
34 def init_path(l1,l2):
35 path = numpy.zeros([l2,l1])
36 for x in range(1,l2):
37 path[x][0] = 1
38 for y in range(1,l1):
39 path[0][y] = 2
40 return path
41
42 def buildglobalalignmentgraph(text1,text2):
43
44 l1 = len(text1)
45 l2 = len(text2)
46 graph = init_graph_global(l1, l2)
47 path = init_path(l1, l2)
48
49 for x in range(1,l2):
50 for y in range(1,l1):
51 graph[x][y] = max(graph[x-1][y]-5, graph[x][y-1]-5, graph[x-1][y-1]+grade(text1[y],text2[x]))
52 if graph[x-1][y]-5==graph[x][y]:
53 path[x][y]=1
54 elif graph[x][y-1]-5==graph[x][y]:
55 path[x][y]=2
56 else:
57 path[x][y]=3
58 return [graph,path]
59
60
61 def outputglobalaligement(path,graph,text1,text2):
62 outt1 = ''
63 outt2 = ''
64 l1 = len(text1)
65 l2 = len(text2)
66 x = l2-1
67 y = l1-1
68 while(x!=0 or y!=0):
69 if path[x][y]==1:
70 outt1 += '-'
71 outt2 += text2[x]
72 x -= 1
73 elif path[x][y]==2:
74 outt1 += text1[y]
75 outt2 += '-'
76 y -= 1
77 else:
78 outt1 += text1[y]
79 outt2 += text2[x]
80 x -= 1
81 y -= 1
82 return [outt1[::-1],outt2[::-1]]
83
84 def importscorematrix():
85 dataset = open(dirname(__file__)+'blosum62.txt').read().strip().split('\n')
86 symbollist = dataset[0].split()
87 for i in range(len(symbollist)):
88 symbollist[i]=[symbollist[i],i]
89 symbollist = dict(symbollist)
90 print(symbollist)
91 matrix = []
92 for i in range(1,len(dataset)):
93 matrix.append(dataset[i].split()[1:])
94 for l in range(len(matrix)):
95 for i in range(len(matrix[l])):
96 matrix[l][i]=int(matrix[l][i])
97 return [matrix,symbollist]
98
99 if __name__ == '__main__':
100
101 [matrix,symbollist] = importscorematrix()
102
103 dataset = open(dirname(__file__)+'dataset.txt').read().strip().split()
104 text1 = '0'+dataset[0]
105 text2 = '0'+dataset[1]
106
107 [graph,path] = buildglobalalignmentgraph(text1, text2)
108
109 [outt1,outt2] = outputglobalaligement(path,graph,text1,text2)
110
111 print(int(graph[-1][-1]))
112 print(outt1)
113 print(outt2)
2. 局部比对
可以把局部比对想象成下面的free taxi场景，在开始和结尾都不受罚分约束，只在中间的某一过程受罚分约束。
在全局比对的基础上，状态转移方程在加上一个0，表示每一个点，既可以由→、↓、↘经过罚分得到，也可以直接由起点，不经罚分得到（free taxi）。
1 '''
2 code challenge: solve the local alignment problem.
3 input: two protein strings written in the single-letter amino acid alphabet.
4 output: the maximum score of a local alignment of the strings, followed by a local alignment of these strings achieving the maximum
5 score. use the pam250 scoring matrix for matches and mismatches as well as the indel penalty σ = 5.
6 ---------------
7 sample input:
8 meanly
9 penalty
10 ---------------
11 sample output:
12 15
13 eanl-y
14 enalty
15 ---------------
16 lo kwongho 2018.9
17 '''
18 import numpy
19 from os.path import dirname
20
21 def grade(symb1,symb2):
22 indx1 = symbollist[symb1]
23 indx2 = symbollist[symb2]
24 return matrix[indx1][indx2]
25
26 def init_graph_local(l1,l2):
27 graph = numpy.zeros([l1,l2])
28 return graph
29
30 def init_path(l1,l2):
31 path = numpy.ones([l1,l2])*-1
32 for x in range(1,l1):
33 path[x][0] = 1
34 for y in range(1,l2):
35 path[0][y] = 2
36 return path
37
38 def buildlocalalignmentgraph(text1,text2):
39 l1 = len(text1)
40 l2 = len(text2)
41 graph = init_graph_local(l1, l2)
42 path = init_path(l1, l2)
43
44 for x in range(1,l1):
45 for y in range(1,l2):
46 graph[x][y] = max(graph[x-1][y]-5, graph[x][y-1]-5, graph[x-1][y-1]+grade(text1[x],text2[y]),0)
47 if graph[x-1][y]-5 == graph[x][y]:
48 path[x][y] = 1
49 elif graph[x][y-1]-5==graph[x][y]:
50 path[x][y] = 2
51 elif graph[x][y] == 0:
52 path[x][y] = 0
53 else:
54 path[x][y] = 3
55 maxval = 0
56 maxindx = [-1,-1]
57 for x in range(1,l1):
58 for y in range(1,l2):
59 if graph[x][y]>maxval:
60 maxval=graph[x][y]
61 maxindx=[x,y]
62
63 return [graph,path,maxindx]
64
65 def outputlocalaligement(path,graph,text1,text2,maxindx):
66 outt1 = ''
67 outt2 = ''
68 l1 = len(text1)
69 l2 = len(text2)
70 x = maxindx[0]
71 y = maxindx[1]
72 while(x!=0 or y!=0):
73 if path[x][y]==1:
74 outt1 += text1[x]
75 outt2 += '-'
76 x -= 1
77 elif path[x][y]==2:
78 outt1 += '-'
79 outt2 += text2[y]
80 y -= 1
81 elif path[x][y]==3:
82 outt1 += text1[x]
83 outt2 += text2[y]
84 x -= 1
85 y -= 1
86 else:
87 x=0
88 y=0
89 return [outt1[::-1],outt2[::-1]]
90
91
92 def importscorematrix():
93 dataset = open(dirname(__file__)+'pam250.txt').read().strip().split('\n')
94 symbollist = dataset[0].split()
95 for i in range(len(symbollist)):
96 symbollist[i]=[symbollist[i],i]
97 symbollist = dict(symbollist)
98 matrix = []
99 for i in range(1,len(dataset)):
100 matrix.append(dataset[i].split()[1:])
101 for l in range(len(matrix)):
102 for i in range(len(matrix[l])):
103 matrix[l][i]=int(matrix[l][i])
104 return [matrix,symbollist]
105
106 if __name__ == '__main__':
107 [matrix,symbollist] = importscorematrix()
108
109 dataset = open(dirname(__file__)+'dataset.txt').read().strip().split()
110 text1 = '0'+dataset[0]
111 text2 = '0'+dataset[1]
112
113 [graph,path,maxindx] = buildlocalalignmentgraph(text1,text2)
114
115 [outt1,outt2]=outputlocalaligement(path,graph,text1,text2,maxindx)
116 print(int(graph[maxindx[0]][maxindx[1]]))
117 print(outt1)
118 print(outt2)
3. overlarp alignment
1 ''' 2 code challenge: solve the overlap alignment problem. 3 >>input: two strings v and w, each of length at most 1000. 4 >>output: the score of an optimal overlap alignment of v and w, followed by an alignment of a suffix v' of v and a prefix w' of w. 5 achieving this maximum score. use an alignment score in which matches count +1 and both the mismatch and indel penalties are 2. 6 ------------------- 7 sample input: 8 pawheae 9 heagawghee 10 ------------------- 11 sample output: 12 1 13 heae 14 heag 15 ------------------- 16 coder: lo kwongho 17 ''' 18 19 import numpy 20 from os.path import dirname 21 22 def init_graph_overlap(l1,l2): 23 graph = numpy.zeros([l1,l2]) 24 for y in range(1,l2): 25 graph[0][y] = graph[0][y-1]-1 26 return graph 27 28 def init_path(l1,l2): 29 path = numpy.ones([l1,l2])*-1 30 for x in range(1,l1): 31 path[x][0] = 1 32 for y in range(1,l2): 33 path[0][y] = 2 34 return path 35 36 def buildoverlapalignmentgraph(text1,text2): 37 l1 = len(text1) 38 l2 = len(text2) 39 graph = init_graph_overlap(l1, l2) 40 path = init_path(l1,l2) 41 for x in range(1,l1): 42 for y in range(1,l2): 43 if text1[x]==text2[y]: 44 graph[x][y]=max(graph[x-1][y-1]+1,graph[x-1][y]-2,graph[x][y-1]-2) 45 else: 46 graph[x][y]=max(graph[x-1][y-1]-2,graph[x-1][y]-2,graph[x][y-1]-2) 47 if graph[x][y]==graph[x-1][y]-2: 48 path[x][y]=1 49 elif graph[x][y]==graph[x][y-1]-2: 50 path[x][y]=2 51 else: 52 path[x][y]=3 53 54 maxval = 0 55 maxindx = -1 56 for i in range(l2): 57 if graph[l1-1][i]>maxval: 58 maxval=graph[l1-1][i] 59 maxindx=i 60 61 return [graph,path,maxindx,maxval] 62 63 def outputoverlapaligement(path,graph,text1,text2,maxindx): 64 outt1 = '' 65 outt2 = '' 66 l1 = len(text1) 67 l2 = len(text2) 68 x = l1-1 69 y = maxindx 70 while(y!=0): 71 if path[x][y]==1: 72 outt1 += text1[x] 73 outt2 += '-' 74 x -= 1 75 elif path[x][y]==2: 76 outt1 += '-' 77 outt2 += text2[y] 78 y -= 1 79 elif path[x][y]==3: 80 outt1 += text1[x] 81 outt2 += text2[y] 82 x -= 1 83 y -= 1 84 else: 85 x=0 86 y=0 87 return [outt1[::-1],outt2[::-1]] 88 89 90 if __name__ == '__main__': 91 dataset = open(dirname(__file__)+'dataset.txt').read().strip().split() 92 text1 = '0'+dataset[0] 93 text2 = '0'+dataset[1] 94 l1 = len(text1) 95 l2 = len(text2) 96 [graph,path,maxindx,maxval] = buildoverlapalignmentgraph(text1,text2) 97 #print(graph) 98 99 [outtext1,outtext2]=outputoverlapaligement(path, graph, text1, text2, maxindx) 100 101 print(int(maxval)) 102 print(outtext1) 103 print(outtext2)
4.fitting alignment
1 ''' 2 fitting alignment problem: construct a highest-scoring fitting alignment between two strings. 3 >>input: strings v and w as well as a matrix score. 4 >>output: a highest-scoring fitting alignment of v and w as defined by the scoring matrix score. 5 ------------------- 6 sample input: 7 gtaggcttaaggtta 8 tagata 9 ------------------- 10 sample output: 11 2 12 taggctta 13 taga--ta 14 ------------------- 15 coder: lo kwongho 16 ''' 17 18 import numpy 19 from os.path import dirname 20 21 def init_graph_fiting(l1,l2): 22 graph = numpy.zeros([l1,l2]) 23 for y in range(1,l2): 24 graph[0][y] = graph[0][y-1]-1 25 return graph 26 27 def init_path(l1,l2): 28 path = numpy.ones([l1,l2])*-1 29 for x in range(1,l1): 30 path[x][0] = 1 31 for y in range(1,l2): 32 path[0][y] = 2 33 return path 34 35 def buildfittingalignmentgraph(text1,text2): 36 l1 = len(text1) 37 l2 = len(text2) 38 graph = init_graph_fiting(l1, l2) 39 path = init_path(l1,l2) 40 for x in range(1,l1): 41 for y in range(1,l2): 42 if text1[x]==text2[y]: 43 graph[x][y]=max(graph[x-1][y-1]+1,graph[x-1][y]-1,graph[x][y-1]-1) 44 else: 45 graph[x][y]=max(graph[x-1][y-1]-1,graph[x-1][y]-1,graph[x][y-1]-1) 46 if graph[x][y]==graph[x-1][y]-1: 47 path[x][y]=1 48 elif graph[x][y]==graph[x][y-1]-1: 49 path[x][y]=2 50 else: 51 path[x][y]=3 52 53 maxval = 0 54 maxindx = -1 55 for i in range(l1): 56 if graph[i][l2-1]>maxval: 57 maxval=graph[i][l2-1] 58 maxindx=i 59 60 return [graph,path,maxindx,maxval] 61 62 def outputfittingaligement(path,graph,text1,text2,maxindx): 63 outt1 = '' 64 outt2 = '' 65 l1 = len(text1) 66 l2 = len(text2) 67 x = maxindx 68 y = l2-1 69 while(y!=0): 70 if path[x][y]==1: 71 outt1 += text1[x] 72 outt2 += '-' 73 x -= 1 74 elif path[x][y]==2: 75 outt1 += '-' 76 outt2 += text2[y] 77 y -= 1 78 elif path[x][y]==3: 79 outt1 += text1[x] 80 outt2 += text2[y] 81 x -= 1 82 y -= 1 83 else: 84 x=0 85 y=0 86 return [outt1[::-1],outt2[::-1]] 87 88 89 if __name__ == '__main__': 90 dataset = open(dirname(__file__)+'dataset.txt').read().strip().split() 91 text1 = '0'+dataset[0] 92 text2 = '0'+dataset[1] 93 l1 = len(text1) 94 l2 = len(text2) 95 [graph,path,maxindx,maxval] = buildfittingalignmentgraph(text1,text2) 96 97 [outtext1,outtext2]=outputfittingaligement(path, graph, text1, text2, maxindx) 98 #print(graph) 99 print(int(maxval)) 100 print(outtext1) 101 print(outtext2)
这四种比对的关系如图：
全局比对 局部比对
overlarp alignment fitting alignment
5、基因的插入和删除，通常都是连续的一段，故在比对出现的连续空位，应该把它们当作一个整体看待。在比对的空位罚分中，生物学家认为，在每一条链上新开一个空位，应罚重分，而空位的延续，罚分应较少：
解决问题的方法是：开三个矩阵，每个矩阵代表一种方向。在→、↓方向上行走，代表产生空位。故每当从↘转移到→、↓，或者→、↓间转移，代表在某链上产生新空位，重罚，而在→、↓内转移，代表空位延续，轻罚。
1 '''
2 code challenge: solve the alignment with affine gap penalties problem.
3 >>input: two amino acid strings v and w (each of length at most 100).
4 >>output: the maximum alignment score between v and w, followed by an alignment of v and w achieving this maximum score. use the
5 blosum62 scoring matrix, a gap opening penalty of 11, and a gap extension penalty of 1.
6 ---------------------
7 sample input:
8 prteins
9 prtwpsein
10 ---------------------
11 sample output:
12 8
13 prt---eins
14 prtwpsein-
15 ---------------------
16 coder: lo kwongho
17 '''
18 import numpy
19 from os.path import dirname
20 neginfinity = -999
21 #penalties
22 gs = -10 #gap_start
23 ge = -1 #gap_extend
24 #
25 def grade(symb1,symb2):
26 indx1 = symbollist[symb1]
27 indx2 = symbollist[symb2]
28 return matrix[indx1][indx2]
29
30 def initgraph(l1,l2):
31 graph = [numpy.zeros([l1,l2] ,dtype=int) for i in range(3)]
32
33 graph[1][0][0] = neginfinity
34 graph[2][0][0] = neginfinity
35 for x in range(1,l1):
36 graph[0][x][0]=neginfinity
37 if x==1:
38 graph[1][x][0]=ge+gs
39 else:
40 graph[1][x][0]=graph[1][x-1][0]+ge
41 graph[2][x][0]=neginfinity
42 for y in range(1,l2):
43 graph[0][0][y]=neginfinity
44 if y ==1:
45 graph[2][0][y]=ge+gs
46 else:
47 graph[2][0][y]=graph[2][0][y-1]+ge
48 graph[1][0][y]=neginfinity
49 return graph
50
51 def init_path(l1,l2):
52 path = [numpy.ones([l1,l2])*-1 for i in range(3)]
53 '''for x in range(1,l1):
54 path[x][0] = 1
55 for y in range(1,l2):
56 path[0][y] = 2'''
57 return path
58
59 def buildalignmentgraph(text1,text2,l1,l2):
60
61 graph = initgraph(l1,l2)
62 #path = #init_path(l1,l2)
63 for x in range(1,l1):
64 for y in range(1,l2):
65 # x ######
66 graph[1][x][y]=max(gs+ge+graph[0][x-1][y],gs+ge+graph[2][x-1][y],ge+graph[1][x-1][y])
67
68
69 # y ######
70 graph[2][x][y]=max(gs+ge+graph[0][x][y-1],gs+ge+graph[1][x][y-1],ge+graph[2][x][y-1])
71
72 # m ######
73 graph[0][x][y]=grade(text1[x], text2[y])+max(graph[0][x-1][y-1],graph[1][x-1][y-1],graph[2][x-1][y-1])
74
75 maxval = 0
76 maxindx = -1
77 for i in range(3):
78 if graph[i][l1-1][l2-1]>maxval:
79 maxval=graph[i][l1-1][l2-1]
80 maxindx=i
81 return [graph,maxindx,maxval]
82
83 def trackback(graph,maxindx,text1,text2):
84 x = len(text1)-1
85 y = len(text2)-1
86 otext1 = ''
87 otext2 = ''
88 indx = maxindx
89 while(1):
90 if indx==0:
91 otext1 += text1[x]
92 otext2 += text2[y]
93 if x ==1:
94 break
95 if graph[0][x][y]==graph[1][x-1][y-1]+grade(text1[x], text2[y]):
96 indx = 1
97 elif graph[0][x][y]==graph[2][x-1][y-1]+grade(text1[x], text2[y]):
98 indx = 2
99 else:
100 indx = 0
101 x -= 1
102 y -= 1
103 elif indx==1:
104 otext1 += text1[x]
105 otext2 += '-'
106 if x == 1:
107 break
108 if graph[1][x][y]==graph[0][x-1][y]+ge+gs:
109 indx = 0
110 elif graph[1][x][y]==graph[2][x-1][y]+ge+gs:
111 indx = 2
112 else:
113 indx = 1
114 x -= 1
115 else:
116 otext1 += '-'
117 otext2 += text2[y]
118 if y == 1:
119 break
120 if graph[2][x][y]==graph[0][x][y-1]+ge+gs:
121 indx = 0
122 elif graph[2][x][y]==graph[1][x][y-1]+ge+gs:
123 indx = 1
124 else:
125 indx = 2
126 y -= 1
127
128 return [otext1[::-1],otext2[::-1]]
129
130 def importscorematrix():
131 dataset = open(dirname(__file__)+'blosum62.txt').read().strip().split('\n')
132 symbollist = dataset[0].split()
133 for i in range(len(symbollist)):
134 symbollist[i]=[symbollist[i],i]
135 symbollist = dict(symbollist)
136 matrix = []
137 for i in range(1,len(dataset)):
138 matrix.append(dataset[i].split()[1:])
139 for l in range(len(matrix)):
140 for i in range(len(matrix[l])):
141 matrix[l][i]=int(matrix[l][i])
142 return [matrix,symbollist]
143
144
145 if __name__ == '__main__':
146 [matrix,symbollist] = importscorematrix() # 打分矩阵
147
148 dataset = open(dirname(__file__)+'dataset.txt').read().strip().split()
149 text1 = '0'+dataset[0]
150 text2 = '0'+dataset[1]
151 l1 = len(text1)
152 l2 = len(text2)
153 [graph,maxindx,maxval] = buildalignmentgraph(text1, text2, l1, l2)
154 #print(graph)
155
156 [output_text1,output_text2] = trackback(graph,maxindx,text1,text2)
157 print(maxval)
158 print(output_text1)
159 print(output_text2)
160
6 * 一种线性空间的比对方法 space-efficient sequence alignment（分治+动态规划）
https://www.cs.rit.edu/~rlaz/algorithms20082/slides/spaceefficientalignment.pdf
1 '''
2 code challenge: implement linearspacealignment to solve the global alignment problem for a large dataset.
3 >>>input: two long (10000 amino acid) protein strings written in the single-letter amino acid alphabet.
4 >>>output: the maximum alignment score of these strings, followed by an alignment achieving this maximum score. use the
5
6 blosum62 scoring matrix and indel penalty σ = 5.
7 ------------
8 sample input:
9 pleasantly
10 meanly
11 ------------
12 sample output:
13 8
14 pleasantly
15 -mea--n-ly
16 ------------
17 coder: lo kwongho in 2018.9
18 '''
19 from os.path import dirname
20 import numpy
21 #
22 indel = -5
23 neginf = -9999
24 #
25 #
26 def grade(symb1,symb2):
27 indx1 = symbollist[symb1]
28 indx2 = symbollist[symb2]
29 return matrix[indx1][indx2]
30
31 def importscorematrix():
32 dataset = open(dirname(__file__)+'blosum62.txt').read().strip().split('\n')
33 symbollist = dataset[0].split()
34 for i in range(len(symbollist)):
35 symbollist[i]=[symbollist[i],i]
36 symbollist = dict(symbollist)
37 matrix = []
38 for i in range(1,len(dataset)):
39 matrix.append(dataset[i].split()[1:])
40 for l in range(len(matrix)):
41 for i in range(len(matrix[l])):
42 matrix[l][i]=int(matrix[l][i])
43 return [matrix,symbollist]
44 #
45 def half_alignment(v,w):
46 nv = len(v)
47 mw = len(w)
48 s = numpy.zeros(shape=(nv+1,2),dtype=int)
49 for i in range(nv+1):
50 s[i,0] = indel*i
51 if mw==0:
52 return s[:,0] #
53 for j in range(mw):
54 s[0,1]=s[0,0]+indel
55 for i in range(nv):
56 s[i+1,1]=max(s[i,1]+indel,s[i+1,0]+indel,s[i,0]+grade(w[j],v[i]))
57 s[:,0]=s[:,1]
58 return s[:,1]
59
60 def midedge(v,w):
61 nv = len(v)
62 mw = len(w)
63 mid = int((mw-1)/2)
64 wl = w[:mid]
65 wr = w[mid+1:]
66 pre = half_alignment(v,wl)
67 suf = half_alignment(v[::-1],wr[::-1])[::-1]
68 hs = [pre[i]+suf[i]+indel for i in range(nv+1)]
69 ds = [pre[i]+suf[i+1]+grade(w[mid],v[i]) for i in range(nv)]
70 maxhs = max(hs)
71 maxds = max(ds)
72 if maxhs>maxds:
73 return ( (hs.index(maxhs),mid) ,(hs.index(maxhs),mid+1) )
74 else:
75 return ( (ds.index(maxds),mid) ,(ds.index(maxds)+1,mid+1) )
76
77 def build_alignment_track(v,w):
78 vn = len(v)
79 wm = len(w)
80 if vn==0 and wm==0:
81 return []
82 elif vn==0:
83 return ['-']*wm
84 elif wm==0:
85 return ['|']*vn
86 ((x1,y1),(x2,y2)) = midedge(v,w)
87 if x1==x2:
88 edge = ['-']
89 else:
90 edge = ['\\']
91 wleft = w[:y1]
92 wright = w[y2:]
93 vupper = v[:x1]
94 vbotm = v[x2:]
95
96 upper_left_track = build_alignment_track(vupper,wleft)
97 bottom_right_track = build_alignment_track(vbotm,wright)
98 return upper_left_track+edge+bottom_right_track
99
100 def tracktostring(v,w,track):
101 vi = 0
102 wj = 0
103 outv = ''
104 outw = ''
105 score = 0
106 for i in track:
107 if i == '|':
108 outv += v[vi]
109 outw += '-'
110 score += indel
111 vi += 1
112 elif i == '-':
113 outv += '-'
114 outw += w[wj]
115 score += indel
116 wj += 1
117 else:
118 outv += v[vi]
119 outw += w[wj]
120 score += grade(v[vi], w[wj])
121 vi += 1
122 wj += 1
123
124 return [score,outv,outw]
125
126 def linearspacealignment(v,w):
127 track = build_alignment_track(v,w)
128 [score,outv, outw] = tracktostring(v,w,track)
129 print(score)
130 print(outv)
131 print(outw)
132
133 if __name__ == '__main__':
134 dataset = open(dirname(__file__)+'dataset.txt').read().strip().split()
135 [matrix,symbollist] = importscorematrix()
136 v = dataset[0]
137 w = dataset[1]
138 linearspacealignment(v,w)