Mercurial > repos > jay > pdaug_merge_dataframes
annotate PDAUG_Peptide_Ngrams/PDAUG_Peptide_Ngrams.py @ 7:b2e270976283 draft
"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit 32b9c48c81639a81be24bb3e2f48dc0a81c0deca"
author | jay |
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date | Sun, 09 Jan 2022 03:46:58 +0000 |
parents | 5bb52d4bf172 |
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5bb52d4bf172
"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit a9bd83f6a1afa6338cb6e4358b63ebff5bed155e"
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1 import matplotlib |
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2 matplotlib.use('Agg') |
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3 import os |
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4 import sys |
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5 sys.path.insert(0, os.path.abspath('..')) |
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6 import quantiprot |
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7 from quantiprot.utils.io import load_fasta_file |
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8 from quantiprot.utils.feature import Feature, FeatureSet |
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9 from quantiprot.metrics.aaindex import get_aa2hydropathy |
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10 from quantiprot.metrics.basic import identity |
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11 from quantiprot.metrics.ngram import pattern_match, pattern_count |
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12 from quantiprot.analysis.ngram import ngram_count |
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13 from quantiprot.analysis.ngram import zipf_law_fit |
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14 from matplotlib import pyplot as plt |
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15 |
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16 |
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17 def Run_ngrams(fasta1, fasta2, OutFile ): |
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18 |
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19 alphasyn_seq = load_fasta_file(fasta1) |
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20 amyload_pos_seq = load_fasta_file(fasta2) |
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21 |
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22 fs_aa = FeatureSet("aa patterns") |
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23 fs_aa.add(identity) |
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24 fs_aa.add(pattern_match, pattern='VT', padded=True) |
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25 fs_aa.add(pattern_count, pattern='VT') |
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26 |
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27 result_seq = fs_aa(alphasyn_seq) |
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28 |
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29 fs_hp = FeatureSet("hydropathy patterns") |
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30 fs_hp.add(Feature(get_aa2hydropathy())) |
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31 fs_hp.add(Feature(get_aa2hydropathy()).then(pattern_match, pattern=[0.0, 2.0], |
5bb52d4bf172
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32 metric='taxi', radius=1.0)) |
5bb52d4bf172
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33 result_seq2 = fs_hp(alphasyn_seq) |
5bb52d4bf172
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34 result_freq = ngram_count(alphasyn_seq, n=2) |
5bb52d4bf172
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35 result_fit = zipf_law_fit(amyload_pos_seq, n=3, verbose=True) |
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36 |
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37 counts = sorted(result_fit["ngram_counts"], reverse=True) |
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38 ranks = range(1, len(counts)+1) |
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39 |
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40 slope = result_fit["slope"] |
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41 harmonic_num = sum([rank**-slope for rank in ranks]) |
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42 fitted_counts = [(rank**-slope) / harmonic_num * sum(counts) for rank in ranks] |
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43 |
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44 plt.plot(ranks, counts, 'k', label="empirical") |
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45 plt.plot(ranks, fitted_counts, 'k--', |
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46 label="Zipf's law\nslope: {:.2f}".format((slope))) |
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47 plt.xlabel('rank') |
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48 plt.ylabel('count') |
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49 plt.xscale('log') |
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50 plt.yscale('log') |
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51 plt.legend() |
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52 |
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53 plt.savefig(OutFile) |
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54 |
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55 if __name__=="__main__": |
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56 |
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57 |
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58 import argparse |
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59 |
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60 parser = argparse.ArgumentParser() |
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61 |
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62 parser.add_argument("-f1", "--Fasta1", |
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63 required=True, |
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64 default=None, |
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65 help="First fasta file") |
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66 |
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67 parser.add_argument("-f2", "--Fasta2", |
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68 required=True, |
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69 default=None, |
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70 help="Second fasta file") |
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71 |
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72 |
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73 parser.add_argument("--OutFile", |
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74 required=True, |
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75 help="HTML out file", |
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76 default="report.html") |
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77 |
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78 |
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79 args = parser.parse_args() |
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80 |
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81 Run_ngrams(args.Fasta1, args.Fasta2, args.OutFile) |
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82 |