diff PDAUG_Peptide_Ngrams/PDAUG_Peptide_Ngrams.py @ 0:070fc1da6df7 draft

"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit a9bd83f6a1afa6338cb6e4358b63ebff5bed155e"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/PDAUG_Peptide_Ngrams/PDAUG_Peptide_Ngrams.py	Wed Oct 28 01:56:46 2020 +0000
@@ -0,0 +1,82 @@
+import matplotlib
+matplotlib.use('Agg')
+import os
+import sys
+sys.path.insert(0, os.path.abspath('..'))
+import quantiprot 
+from quantiprot.utils.io import load_fasta_file
+from quantiprot.utils.feature import Feature, FeatureSet
+from quantiprot.metrics.aaindex import get_aa2hydropathy
+from quantiprot.metrics.basic import identity
+from quantiprot.metrics.ngram import pattern_match, pattern_count
+from quantiprot.analysis.ngram import ngram_count
+from quantiprot.analysis.ngram import zipf_law_fit
+from matplotlib import pyplot as plt
+
+
+def Run_ngrams(fasta1, fasta2, OutFile ):
+
+    alphasyn_seq = load_fasta_file(fasta1)
+    amyload_pos_seq = load_fasta_file(fasta2)
+
+    fs_aa = FeatureSet("aa patterns")
+    fs_aa.add(identity)
+    fs_aa.add(pattern_match, pattern='VT', padded=True)
+    fs_aa.add(pattern_count, pattern='VT')
+
+    result_seq = fs_aa(alphasyn_seq)
+
+    fs_hp = FeatureSet("hydropathy patterns")
+    fs_hp.add(Feature(get_aa2hydropathy()))
+    fs_hp.add(Feature(get_aa2hydropathy()).then(pattern_match, pattern=[0.0, 2.0],
+                                                metric='taxi', radius=1.0))
+    result_seq2 = fs_hp(alphasyn_seq)
+    result_freq = ngram_count(alphasyn_seq, n=2)
+    result_fit = zipf_law_fit(amyload_pos_seq, n=3, verbose=True)
+
+    counts = sorted(result_fit["ngram_counts"], reverse=True)
+    ranks = range(1, len(counts)+1)
+
+    slope = result_fit["slope"]
+    harmonic_num = sum([rank**-slope for rank in ranks])
+    fitted_counts = [(rank**-slope) / harmonic_num * sum(counts) for rank in ranks]
+
+    plt.plot(ranks, counts, 'k', label="empirical")
+    plt.plot(ranks, fitted_counts, 'k--',
+             label="Zipf's law\nslope: {:.2f}".format((slope)))
+    plt.xlabel('rank')
+    plt.ylabel('count')
+    plt.xscale('log')
+    plt.yscale('log')
+    plt.legend()
+
+    plt.savefig(OutFile)
+
+if __name__=="__main__":
+    
+    
+    import argparse
+    
+    parser = argparse.ArgumentParser()
+    
+    parser.add_argument("-f1", "--Fasta1",
+                        required=True,
+                        default=None,
+                        help="First fasta file")
+                        
+    parser.add_argument("-f2", "--Fasta2",
+                        required=True,
+                        default=None,
+                        help="Second fasta file")   
+
+
+    parser.add_argument("--OutFile", 
+                        required=True, 
+                        help="HTML out file", 
+                        default="report.html")
+
+
+    args = parser.parse_args()        
+                                               
+    Run_ngrams(args.Fasta1, args.Fasta2, args.OutFile)
+