Create tree_generator.py

tree_generator.py

+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan 29 14:59:19 2019
+
+@author: Amaury Leroy
+"""
+
+import numpy as np
+from Bio.SeqRecord import SeqRecord
+from Bio.Cluster import treecluster
+import alignementseq
+from scipy.cluster.hierarchy import average, dendrogram
+
+seqs = [SeqRecord('TAACCCCAAAAGAACCAGA'), SeqRecord('TTCTGTAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAAAGAACCAGA'), SeqRecord('TTCTGTAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAAAGAACCAGA'),SeqRecord('GTAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAGAGAACCAGA'), SeqRecord('GAGCCGGATGAGAAGAAACTCTCATGTCCGGTTCTGTAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAGAGAACCAGA'), SeqRecord('TTTTCATTCGCGAGGAGCCGGATGAGAAGAAACTCTCATGTCCGGTTCTGTAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAGAGAACCAGA')]
+
+#Arguments : une liste de séquences, une fonction d'alignement
+score_gap = -8
+def tree_build(seqs, align):
+    len_seqs= [len(seq) for seq in seqs]
+    M = np.zeros((len(seqs),len(seqs))) 
+    for i in range (len(seqs)):
+        for j in range (i):
+            M[i][j], a, b = align(seqs[i], seqs[j])
+            M[i][j] = 1-abs(M[i][j]/(max(len_seqs)*(11-score_gap)))
+    print(type(M), M)
+#    tree = treecluster(None, distancematrix=M, method = 'a', dist = 'e',)
+    tree= average(M)
+    return tree
+
+
+tree = tree_build(seqs, alignementseq.align2steps)
+print(tree)
+dn = dendrogram(tree)