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homework2.py
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homework2.py
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#!/usr/bin/env python
"""Algorithms for DNA Sequencing - Programming Homework 2"""
from bm_preproc import BoyerMoore
from kmer_index import Index
import bisect
def main():
chr1 = readGenome('chr1.GRCh38.excerpt.fasta')
p = 'GGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGG'
p_bm = BoyerMoore(p)
print(naive(p, chr1)[2])
print(naive(p, chr1)[1])
print(boyer_moore(p, p_bm, chr1)[2])
p = 'GGCGCGGTGGCTCACGCCTGTAAT'
print(len(approximate_match(p, chr1, 2)[0]))
print(approximate_match(p, chr1, 2)[1])
print(approximate_match_subseq(p, chr1, 2, 3)[1])
def boyer_moore(p, p_bm, t):
""" Do Boyer-Moore matching. p=pattern, t=text,
p_bm=BoyerMoore object for p """
i = 0
occurrences = []
comparisons = 0
alignments = 0
while i < len(t) - len(p) + 1:
alignments += 1
shift = 1
mismatched = False
for j in range(len(p)-1, -1, -1):
comparisons += 1
if p[j] != t[i+j]:
skip_bc = p_bm.bad_character_rule(j, t[i+j])
skip_gs = p_bm.good_suffix_rule(j)
shift = max(shift, skip_bc, skip_gs)
mismatched = True
break
if not mismatched:
occurrences.append(i)
skip_gs = p_bm.match_skip()
shift = max(shift, skip_gs)
i += shift
return occurrences, comparisons, alignments
def naive(p, t):
occurrences = []
comparisons = 0
alignments = 0
for i in range(len(t) - len(p) + 1): # loop over alignments
alignments += 1
match = True
for j in range(len(p)): # loop over characters
comparisons += 1
if t[i+j] != p[j]: # compare characters
match = False
break
if match:
occurrences.append(i) # all chars matched; record
return occurrences, comparisons, alignments
def readGenome(filename):
genome = ''
with open(filename, 'r') as f:
for line in f:
# ignore header line with genome information
if not line[0] == '>':
genome += line.rstrip()
return genome
def approximate_match(p, t, n):
segment_length = int(round(len(p) / (n+1)))
all_matches = set()
p_idx = Index(t, segment_length)
idx_hits = 0
for i in range(n+1):
start = i*segment_length
end = min((i+1)*segment_length, len(p))
matches = p_idx.query(p[start:end])
# Extend matching segments to see if whole p matches
for m in matches:
idx_hits += 1
if m < start or m-start+len(p) > len(t):
continue
mismatches = 0
for j in range(0, start):
if not p[j] == t[m-start+j]:
mismatches += 1
if mismatches > n:
break
for j in range(end, len(p)):
if not p[j] == t[m-start+j]:
mismatches += 1
if mismatches > n:
break
if mismatches <= n:
all_matches.add(m - start)
return list(all_matches), idx_hits
def approximate_match_subseq(p, t, n, ival):
segment_length = int(round(len(p) / (n+1)))
all_matches = set()
p_idx = SubseqIndex(t, segment_length, ival)
idx_hits = 0
for i in range(n+1):
start = i
matches = p_idx.query(p[start:])
# Extend matching segments to see if whole p matches
for m in matches:
idx_hits += 1
if m < start or m-start+len(p) > len(t):
continue
mismatches = 0
for j in range(0, len(p)):
if not p[j] == t[m-start+j]:
mismatches += 1
if mismatches > n:
break
if mismatches <= n:
all_matches.add(m - start)
return list(all_matches), idx_hits
class SubseqIndex(object):
""" Holds a subsequence index for a text T """
def __init__(self, t, k, ival):
""" Create index from all subsequences consisting of k characters
spaced ival positions apart. E.g., SubseqIndex("ATAT", 2, 2)
extracts ("AA", 0) and ("TT", 1). """
self.k = k # num characters per subsequence extracted
self.ival = ival # space between them; 1=adjacent, 2=every other, etc
self.index = []
self.span = 1 + ival * (k - 1)
for i in range(len(t) - self.span + 1): # for each subseq
self.index.append((t[i:i+self.span:ival], i)) # add (subseq, offset)
self.index.sort() # alphabetize by subseq
def query(self, p):
""" Return index hits for first subseq of p """
subseq = p[:self.span:self.ival] # query with first subseq
i = bisect.bisect_left(self.index, (subseq, -1)) # binary search
hits = []
while i < len(self.index): # collect matching index entries
if self.index[i][0] != subseq:
break
hits.append(self.index[i][1])
i += 1
return hits
if __name__ == '__main__':
main()