Phanks.

anicksaha · Nov 7, 2019 · 0f8befa · 0f8befa
1 parent 72c8862
commit 0f8befa
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Showing 7 changed files with 146 additions and 154 deletions.
diff --git a/edges.txt b/edges.txt
@@ -5,8 +5,8 @@
 63	64	0.00376827912568
 64	69	0.0114938796716
 69	70	0.00240415262502
-70	42	0.095761467563
 70	19	0.0913179402432
+70	42	0.095761467563
 69	6	0.068928283445
 64	74	0.0138257703957
 74	43	0.0701951689325
@@ -85,8 +85,8 @@
 83	91	0.013850279425
 91	102	0.00423269087876
 102	104	0.00461971797755
-104	13	0.0802724654919
 104	51	0.0825808319509
+104	13	0.0802724654919
 102	105	0.00656803437777
 105	110	0.0177473177689
 110	50	0.0824905753015

diff --git a/main.py b/main.py
@@ -10,16 +10,15 @@ def main(filename):
     ids, sequences = utils.read_fasta_file(filename)
 
     # Then generate the distance matrix
-    distMatrix = utils.get_distMatrix(ids, sequences)
+    distance_matrix = utils.get_distMatrix(ids, sequences)
 
     # write the distances.txt file (distance matrix)
-    utils.write_distMatrix(ids, distMatrix)
+    utils.write_distMatrix(ids, distance_matrix)
 
-    #This is used to number the sequences/nodes in the tree to be constructed
-    seqCounter = 120
-
-    Neighbour_Joining_instance = neighbour_joining.Neighbour_Joining()
-    root = Neighbour_Joining_instance.nei_saitou(ids, distMatrix, seqCounter)
+    # This is used to number the sequences/nodes in the tree to be constructed
+    sequence_counter = 120
+    # Run the nei saitu algorithm. Returns the root of the tree
+    root = neighbour_joining.nei_saitou(ids, distance_matrix, sequence_counter)
 
     # Writing the edge file
     utils.write_edge_file(root)

diff --git a/neighbour_joining.py b/neighbour_joining.py
@@ -1,90 +1,85 @@
 from node import Node
-import sys
-import random
-import utils
+import utils_nj as utils
 
-# Holds the methods to join neighbours
-class Neighbour_Joining:
+# Applies nei_saitou to calculte the distances recursively and get the phylogeny tree
+def nei_saitou(original_ids, original_distMatrix, seqCounter):
+    #Using a global root holder
+    global root
+    root = None
+    # Maintaining a map for the relationships between child and parent
+    relationship_map = {}
 
-    #Applies nei_saitou to calculte the distances recursively and get the phylogeny tree
-    def nei_saitou(self, original_ids, original_distMatrix, seqCounter):
-        #Using a global root holder
+    # Using a mapping for seqId and new ids.
+    seqId_orderId_map = {}
+    for i, id in enumerate(original_ids):
+        seqId_orderId_map[id] = str(i + 1)
+
+    def calculate_next_neighbours(ids, distMatrix, seqCounter, seqId_orderId_map):
         global root
-        root = None
-        # Maintaining a map for the relationships between child and parent
-        relationship_map = {}
+        N  = len(distMatrix)
 
-        # Using a mapping for seqId and new ids.
-        seqId_orderId_map = {}
-        for i, id in enumerate(original_ids):
-            seqId_orderId_map[id] = str(i + 1)
-
-        def calculate_next_neighbours(ids, distMatrix, seqCounter, seqId_orderId_map):
-            global root
-            N  = len(distMatrix)
-
-            # Base case for recursion
-            if N <= 2:
-                # Assigning the ids to these nodes according to the requirements
-                if ids[0] in seqId_orderId_map:
-                    tip_1 = seqId_orderId_map[ids[0]]
-                else:
-                    tip_1 = ids[0]
-                if ids[1] in seqId_orderId_map:
-                    tip_2 = seqId_orderId_map[ids[1]]
-                else:
-                    tip_2 = ids[1]
-                # Adding their relationship to the tree
-                relationship_map[tip_1] = (tip_2, distMatrix[0][1])
-                # Since these are the last two nodes, any of these can be chosen to be the root
-                root = tip_2
-                return
-
-            #First step is to get the closest two tips from the distance matrix
-            mini, minj, minVal = utils.construct_QMatrix(distMatrix)
-
-            edge_i, edge_j = utils.calculate_edge_lengths(distMatrix, mini, minj, N)
-            #print(edge_i, edge_j)
-            tip_1 = tip_2 = None
-            # Setting the new ids to the tips/ internal node
-            if ids[mini] in seqId_orderId_map:
-                tip_1 = seqId_orderId_map[ids[mini]]
+        # Base case for recursion
+        if N <= 2:
+            # Assigning the ids to these nodes according to the requirements
+            if ids[0] in seqId_orderId_map:
+                tip_1 = seqId_orderId_map[ids[0]]
             else:
-                tip_1 = ids[mini]
-            if ids[minj] in seqId_orderId_map:
-                tip_2 = seqId_orderId_map[ids[minj]]
+                tip_1 = ids[0]
+            if ids[1] in seqId_orderId_map:
+                tip_2 = seqId_orderId_map[ids[1]]
             else:
-                tip_2 = ids[minj]
-
-            # Add them to the tree relationships
-            relationship_map[tip_1] = (str(seqCounter), edge_i)
-            relationship_map[tip_2] = (str(seqCounter), edge_j)
+                tip_2 = ids[1]
+            # Adding their relationship to the tree
+            relationship_map[tip_1] = (tip_2, distMatrix[0][1])
+            # Since these are the last two nodes, any of these can be chosen to be the root
+            root = tip_2
+            return
+
+        #First step is to get the closest two tips from the distance matrix
+        mini, minj, minVal = utils.construct_QMatrix(distMatrix)
+
+        edge_i, edge_j = utils.calculate_edge_lengths(distMatrix, mini, minj, N)
+        #print(edge_i, edge_j)
+        tip_1 = tip_2 = None
+        # Setting the new ids to the tips/ internal node
+        if ids[mini] in seqId_orderId_map:
+            tip_1 = seqId_orderId_map[ids[mini]]
+        else:
+            tip_1 = ids[mini]
+        if ids[minj] in seqId_orderId_map:
+            tip_2 = seqId_orderId_map[ids[minj]]
+        else:
+            tip_2 = ids[minj]
+
+        # Add them to the tree relationships
+        relationship_map[tip_1] = (str(seqCounter), edge_i)
+        relationship_map[tip_2] = (str(seqCounter), edge_j)
 
-            # Remove the used ids and add the new seqCounter
-            ids = [id for id in ids if id not in (ids[mini], ids[minj])] + [str(seqCounter)]
+        # Remove the used ids and add the new seqCounter
+        ids = [id for id in ids if id not in (ids[mini], ids[minj])] + [str(seqCounter)]
 
-            seqCounter -=1
-            new_distMatrix = utils.calculate_new_distMatrix(distMatrix, mini, minj, N)
+        seqCounter -=1
+        new_distMatrix = utils.calculate_new_distMatrix(distMatrix, mini, minj, N)
 
-            # recursively call with the new distance matrix
-            calculate_next_neighbours(ids, new_distMatrix, seqCounter, seqId_orderId_map)
+        # recursively call with the new distance matrix
+        calculate_next_neighbours(ids, new_distMatrix, seqCounter, seqId_orderId_map)
 
-        calculate_next_neighbours(original_ids, original_distMatrix, seqCounter, seqId_orderId_map)
+    calculate_next_neighbours(original_ids, original_distMatrix, seqCounter, seqId_orderId_map)
 
-        # construct tree from the relationship_map relations
-        rootNode = Node(root)
-        queue = [rootNode]
-        # bfs for tree construction
-        while len(queue) > 0:
-            nodeList = []
-            for node in queue:
-                for child, (parent, distance) in relationship_map.items():
-                    if parent == node.id:
-                        childNode = Node(child)
-                        node.add_child(childNode, distance)
-                        nodeList.append(childNode)
-            for node in nodeList:
-                del relationship_map[node.id]
-            queue = nodeList
-        return rootNode
+    # construct tree from the relationship_map relations
+    rootNode = Node(root)
+    queue = [rootNode]
+    # bfs for tree construction
+    while len(queue) > 0:
+        nodeList = []
+        for node in queue:
+            for child, (parent, distance) in relationship_map.items():
+                if parent == node.id:
+                    childNode = Node(child)
+                    node.add_child(childNode, distance)
+                    nodeList.append(childNode)
+        for node in nodeList:
+            del relationship_map[node.id]
+        queue = nodeList
+    return rootNode
 
diff --git a/solution/hw3-nj-solution-tree.tre → solution/hw3-nj-solution-tree.txt b/solution/hw3-nj-solution-tree.tre → solution/hw3-nj-solution-tree.txt
diff --git a/tree.txt b/tree.txt
@@ -1 +1 @@
-(((4439229:0.0994636314408,(164216:0.0805450227767,146154:0.0648116394037):0.0142644977651):0.00735813002246,((720729:0.097762461851,(((((100149:0.0486748871823,(4468812:0.0261929524043,166838:0.0343723235042):0.0135727575014):0.0312959330949,348167:0.0893299080895):0.0107871348319,(286686:0.0867460556338,((779409:0.04678825286,593544:0.060210401245):0.00697756765851,4387271:0.0431570218435):0.0165513871656):0.00338682209946):0.0117075956228,(11136:0.104185713798,(5364:0.0459632162084,(3148512:0.0387720577239,106543:0.0419816434874):0.0112373221496):0.024851971263):0.00389111736513):0.00387210426656,(((1920715:0.120428860031,(260976:0.0822133814411,646961:0.0759292834311):0.0141606419883):0.0116483249952,((((4378332:0.0506567246683,523025:0.0846057248606):0.0147056847708,(511006:0.0515193738372,(((273179:0.0226913456411,188674:0.020377295005):0.000565372968216,319214:0.0219783686199):0.0109189939435,197222:0.0380379374159):0.0254489976298):0.0132847257272):0.00561348628869,(806723:0.0722060713208,(1009440:0.0708603722105,((954340:0.0772832314566,947627:0.0727840632945):0.00484880367808,(4164932:0.0605401041029,(565897:0.0476464546292,144260:0.0337801940922):0.0454491287369):0.00415186926943):0.00135530746646):0.00572966219195):0.0100726310145):0.00375090334222,4397763:0.0781236103481):0.0106890635478):0.00388775124005,((((664158:0.0802724654919,822879:0.0825808319509):0.00461971797755,((3906666:0.0824905753015,(816087:0.0812659224188,4448694:0.0762037949433):0.0255175000687):0.0177473177689,(4431405:0.0543373959424,809189:0.0614095757938):0.0302843107641):0.00656803437777):0.00423269087876,((2034243:0.094206517777,208595:0.0686467796658):0.00510333767336,996519:0.0891093137398):0.025745017735):0.013850279425,((152801:0.115587289276,3855581:0.124654971827):0.0215198171439,4345542:0.132248688913):0.188580743455):0.0120725313848):0.0120473718793):0.00536520381635):0.00336721222622,((2263183:0.0648454483874,((586332:0.0726342497929,1136782:0.103677997852):0.00763606407822,2963287:0.0704258470927):0.0074964089477):0.00238916628989,(555079:0.0352536797343,553272:0.0313681237651):0.0385765133871):0.0120786458936):0.00753624506626):0.000959080610108);
+(((4439229:0.0994636314408,(164216:0.0805450227767,146154:0.0648116394037):0.0142644977651):0.00735813002246,((720729:0.097762461851,(((((100149:0.0486748871823,(4468812:0.0261929524043,166838:0.0343723235042):0.0135727575014):0.0312959330949,348167:0.0893299080895):0.0107871348319,(286686:0.0867460556338,((779409:0.04678825286,593544:0.060210401245):0.00697756765851,4387271:0.0431570218435):0.0165513871656):0.00338682209946):0.0117075956228,(11136:0.104185713798,(5364:0.0459632162084,(3148512:0.0387720577239,106543:0.0419816434874):0.0112373221496):0.024851971263):0.00389111736513):0.00387210426656,(((1920715:0.120428860031,(260976:0.0822133814411,646961:0.0759292834311):0.0141606419883):0.0116483249952,((((4378332:0.0506567246683,523025:0.0846057248606):0.0147056847708,(511006:0.0515193738372,(((273179:0.0226913456411,188674:0.020377295005):0.000565372968216,319214:0.0219783686199):0.0109189939435,197222:0.0380379374159):0.0254489976298):0.0132847257272):0.00561348628869,(806723:0.0722060713208,(1009440:0.0708603722105,((954340:0.0772832314566,947627:0.0727840632945):0.00484880367808,(4164932:0.0605401041029,(565897:0.0476464546292,144260:0.0337801940922):0.0454491287369):0.00415186926943):0.00135530746646):0.00572966219195):0.0100726310145):0.00375090334222,4397763:0.0781236103481):0.0106890635478):0.00388775124005,((((822879:0.0825808319509,664158:0.0802724654919):0.00461971797755,((3906666:0.0824905753015,(816087:0.0812659224188,4448694:0.0762037949433):0.0255175000687):0.0177473177689,(4431405:0.0543373959424,809189:0.0614095757938):0.0302843107641):0.00656803437777):0.00423269087876,((2034243:0.094206517777,208595:0.0686467796658):0.00510333767336,996519:0.0891093137398):0.025745017735):0.013850279425,((152801:0.115587289276,3855581:0.124654971827):0.0215198171439,4345542:0.132248688913):0.188580743455):0.0120725313848):0.0120473718793):0.00536520381635):0.00336721222622,((2263183:0.0648454483874,((586332:0.0726342497929,1136782:0.103677997852):0.00763606407822,2963287:0.0704258470927):0.0074964089477):0.00238916628989,(555079:0.0352536797343,553272:0.0313681237651):0.0385765133871):0.0120786458936):0.00753624506626):0.000959080610108);
diff --git a/utils.py b/utils.py
@@ -1,5 +1,3 @@
-# This file contains the util functions to be used
-
 # Function that reads the given fasta file and gets the sequences.
 def read_fasta_file(filename):
     sequences = {}
@@ -16,7 +14,7 @@ def read_fasta_file(filename):
     return ids, sequences
 
 # Function to calculate the distance between two sequences as a helper 
-def distance(seq1,seq2):
+def get_distance(seq1,seq2):
     total = len(seq1)
     mismatch = 0
     for i in range(total):
@@ -33,7 +31,7 @@ def get_distMatrix(ids, sequences):
     matrix = [[0] * total for _ in range(total)]
     for i, id1 in enumerate(ids):
         for j, id2 in enumerate(ids):
-            matrix[i][j] = distance(sequences[id1], sequences[id2])
+            matrix[i][j] = get_distance(sequences[id1], sequences[id2])
     return matrix
 
 
@@ -46,30 +44,6 @@ def write_distMatrix(ids, matrix):
         for i in range(num_ids):
             f.write(ids[i] + '\t' + '\t'.join(map(str, matrix[i])) + '\n')
 
-
-# Calculates the Q matrix from the distance matrix and returns the two closest tips and their distance
-def construct_QMatrix(distMatrix):
-    N = len(distMatrix)
-    # Matrix to store the Q values
-    QMatrix = [[0] * N for _ in range(N)]
-
-    # We also want to return the two closest tips
-    mini = minj = 0
-    minVal = float('inf')
-
-    for i in range(N):
-        for j in range(N):
-            if i == j:
-                continue
-            QMatrix[i][j] = (N - 2) * distMatrix[i][j] - sum(distMatrix[i]) - sum(distMatrix[j])
-            # keep track of the minimum value and indices in the Q matrix
-            if QMatrix[i][j] < minVal:
-                mini = i
-                minj = j
-                minVal = QMatrix[i][j]
-    return mini, minj, minVal
-
-
 # uses post order traversal to write the newick file
 def write_newick_file(seqIds, root):
     # recursive postorder traversal
@@ -90,45 +64,6 @@ def postorder_traversal(node):
     with open('tree.txt', 'w') as f:
         f.write(newick)
 
-
-# Calculates the edge lengths to the u node and returns the lengths.
-def calculate_edge_lengths(distMatrix, mini, minj, N):
-    # Distances to the new internal node
-    edge_i = 1/ 2.0 * distMatrix[mini][minj] + 1 / (2.0  * (N - 2))* (sum(distMatrix[mini]) - sum(distMatrix[minj]))
-    # distance from minj node to u
-    edge_j = distMatrix[mini][minj] - edge_i
-    return edge_i, edge_j
-
-
-def calculate_new_distMatrix(distMatrix, mini, minj, N):
-    updatedDistances = [[0] * (N + 1) for _ in range(N + 1)]
-    for i in xrange(N):
-        for j in xrange(N):
-            updatedDistances[i][j] = distMatrix[i][j]
-    # update the distances to the new node
-    for k in range(N):
-        updatedDistances[N][k] = (0.5) * (distMatrix[mini][k] + distMatrix[minj][k] - distMatrix[mini][minj])
-        updatedDistances[k][N] = updatedDistances[N][k]
-
-    # Creates a new distance matrix 
-    new_distMatrix = [[0] * (N - 1) for _ in range(N - 1)]
-    keep_i = keep_j = 0
-    for i in range(N + 1):
-        # Replacing these two with a new node
-        if i == mini or i == minj:
-            continue
-        keep_j = 0
-        for j in range(N + 1):
-            # Replacing these two with the new node
-            if j == mini or j == minj:
-                continue
-            new_distMatrix[keep_i][keep_j] = updatedDistances[i][j]
-            keep_j += 1
-        keep_i += 1
-
-    return new_distMatrix
-
-
 # write the percentages in the bootstrap file
 def write_bootstrap(percentages):
     with open('boot.txt', 'w') as f:

diff --git a/utils_nj.py b/utils_nj.py
@@ -0,0 +1,63 @@
+# This file contains the util functions 
+# used by the nei-saitou neighbor-joining algorithm
+
+# Calculates the Q matrix from the distance matrix and returns the two closest nodes and their distance
+def construct_QMatrix(distance_matrix):
+    N = len(distance_matrix)
+    # Matrix to store the Q values
+    QMatrix = [[0] * N for _ in range(N)]
+
+    # We also want to return the two closest nodes
+    # keep track of the minimum value and indices in the Q matrix
+    mini = minj = 0
+    minVal = float('inf')
+
+    for i in range(N):
+        for j in range(N):
+            if i == j:
+                continue
+            QMatrix[i][j] = (N - 2) * distance_matrix[i][j] - sum(distance_matrix[i]) - sum(distance_matrix[j])
+
+            if QMatrix[i][j] < minVal:
+                mini = i
+                minj = j
+                minVal = QMatrix[i][j]
+
+    return mini, minj, minVal
+
+# Calculates the edge lengths to the u node and returns the lengths.
+def calculate_edge_lengths(distMatrix, mini, minj, N):
+    # Distances to the new internal node
+    edge_i = 1/ 2.0 * distMatrix[mini][minj] + 1 / (2.0  * (N - 2))* (sum(distMatrix[mini]) - sum(distMatrix[minj]))
+    # distance from minj node to u
+    edge_j = distMatrix[mini][minj] - edge_i
+    return edge_i, edge_j
+
+
+def calculate_new_distMatrix(distMatrix, mini, minj, N):
+    updatedDistances = [[0] * (N + 1) for _ in range(N + 1)]
+    for i in xrange(N):
+        for j in xrange(N):
+            updatedDistances[i][j] = distMatrix[i][j]
+    # update the distances to the new node
+    for k in range(N):
+        updatedDistances[N][k] = (0.5) * (distMatrix[mini][k] + distMatrix[minj][k] - distMatrix[mini][minj])
+        updatedDistances[k][N] = updatedDistances[N][k]
+
+    # Creates a new distance matrix 
+    new_distMatrix = [[0] * (N - 1) for _ in range(N - 1)]
+    keep_i = keep_j = 0
+    for i in range(N + 1):
+        # Replacing these two with a new node
+        if i == mini or i == minj:
+            continue
+        keep_j = 0
+        for j in range(N + 1):
+            # Replacing these two with the new node
+            if j == mini or j == minj:
+                continue
+            new_distMatrix[keep_i][keep_j] = updatedDistances[i][j]
+            keep_j += 1
+        keep_i += 1
+
+    return new_distMatrix
Original file line number	Diff line number	Diff line change
		@@ -1 +1 @@
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