Mercurial > repos > rmarenco > multi_fasta_glimmer_hmm
comparison multi_glimmer.py @ 0:0ddb5ee32ff6 draft default tip
planemo upload for repository https://github.com/remimarenco/multi_fasta_glimmerhmm.git commit 28bd73b26b50165eded1d9ba995979acdf005ad1-dirty
author | rmarenco |
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date | Thu, 18 Aug 2016 18:50:00 -0400 |
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-1:000000000000 | 0:0ddb5ee32ff6 |
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1 #!/usr/bin/python | |
2 # -*- coding: utf8 -*- | |
3 | |
4 import argparse | |
5 import os | |
6 import subprocess | |
7 import sys | |
8 | |
9 | |
10 def main(): | |
11 parser = argparse.ArgumentParser(description='Get a multi-fasta, the trained_dir and the output file as inputs, ' | |
12 'to generate GlimmerHMM gene prediction over all contigs') | |
13 | |
14 parser.add_argument('--multi_fasta', help='Multi fasta file to run GlimmerHMM on', required=True) | |
15 | |
16 parser.add_argument('--trained_dir', help='Path to the GlimmerHMM trained_dir', required=True) | |
17 | |
18 parser.add_argument('--output', help='file to output the result into', required=True) | |
19 | |
20 args = parser.parse_args() | |
21 | |
22 multi_fasta = args.multi_fasta | |
23 trained_dir = args.trained_dir | |
24 # TODO: Temporary fix for the issue with config.file in human/. Next: GC Content to select the appropriate folder | |
25 if trained_dir.split('/')[-1] == "human": | |
26 trained_dir = os.path.join(trained_dir, "Train0-43") | |
27 | |
28 output_file = args.output | |
29 temp_contig = "temp_contig" | |
30 | |
31 def exec_glimmer(contig_file, first_time=False): | |
32 p = subprocess.Popen(["glimmerhmm", contig_file, trained_dir, "-g"], | |
33 stdout=subprocess.PIPE, stderr=subprocess.PIPE) | |
34 output, errors = p.communicate() | |
35 | |
36 p.wait() | |
37 # Process the error if != "Done" | |
38 if not errors or (errors.split()[0] != "Done"): | |
39 raise Exception("Error in glimmer: {0}".format(errors)) | |
40 else: | |
41 sys.stdout.write(errors) | |
42 # If not first time, we need to remove the first comments | |
43 if not first_time: | |
44 output = "\n".join(output.split("\n")[1:]) | |
45 | |
46 return output | |
47 | |
48 with open(output_file, 'w+') as o: | |
49 with open(multi_fasta, 'r') as mf: | |
50 is_first_time = True | |
51 for i, line in enumerate(mf): | |
52 if line[0] == '>': | |
53 # If it is the first time we finish to read a contig, we let glimmer add the full comments | |
54 # and write into the output the result | |
55 if is_first_time is True and i != 0: | |
56 o.write(exec_glimmer(temp_contig, first_time=is_first_time)) | |
57 is_first_time = False | |
58 # Else we call glimmer and say this is not the first time (so remove the first comment) | |
59 # and dump into the output file the result | |
60 elif i > 0: | |
61 o.write(exec_glimmer(temp_contig)) | |
62 | |
63 # Because we are on an indication of a beginning of a sequence, we need to create an empty file | |
64 # to dump the line into | |
65 with open(temp_contig, 'w+') as tc: | |
66 tc.write(line) | |
67 else: | |
68 # We are in the sequence of a contig, so we append the line in the file | |
69 with open(temp_contig, 'a+') as tc: | |
70 tc.write(line) | |
71 # The file is terminate, we did read another contig so we need to save this last one | |
72 o.write(exec_glimmer(temp_contig, first_time=is_first_time)) | |
73 | |
74 if __name__ == "__main__": | |
75 main() |