Mercurial > repos > jay > pdaug_uversky_plot

annotate PDAUG_Peptide_Ngrams/PDAUG_Peptide_Ngrams.py @ 2:5637b997244e draft

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"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit 8b18552f6d2b2261efebe1075ff4c18a295b94dd"
author jay
date Tue, 29 Dec 2020 18:23:37 +0000
parents 070fc1da6df7
children
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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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17 def Run_ngrams(fasta1, fasta2, OutFile ):
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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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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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27 result_seq = fs_aa(alphasyn_seq)
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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],
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32 metric='taxi', radius=1.0))
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33 result_seq2 = fs_hp(alphasyn_seq)
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34 result_freq = ngram_count(alphasyn_seq, n=2)
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35 result_fit = zipf_law_fit(amyload_pos_seq, n=3, verbose=True)
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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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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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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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53 plt.savefig(OutFile)
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55 if __name__=="__main__":
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58 import argparse
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60 parser = argparse.ArgumentParser()
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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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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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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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79 args = parser.parse_args()
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81 Run_ngrams(args.Fasta1, args.Fasta2, args.OutFile)
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