remove old code

frag_package/fragmentation.py

-import random
-
-
-dna_seq = {
-    "ATAACATGTGGATGGCCAGTGGTCGGTTGTTACACGCCTACCGCGATGCTGAATGACCCGGACTAGAGTGGCGAAATTTATGGCGTGTGACCCGTTATGC": 100,
-    "TCCATTTCGGTCAGTGGGTCATTGCTAGTAGTCGATTGCATTGCCATTCTCCGAGTGATTTAGCGTGACAGCCGCAGGGAACCCATAAAATGCAATCGTA": 100
-}
-
-mean_length = 12
-std = 1
-
-term_frags = []
-for seq, counts in dna_seq.items():
-    for _ in range(counts):
-        n_cuts = int(len(seq)/mean_length)
-        cuts = random.sample(range(1,len(seq)-1), n_cuts)
-        cuts.sort()
-        cuts.insert(0,0)
-        term_frag = ""
-        for i, val in enumerate(cuts):
-            if i == len(cuts)-1:
-                fragment = seq[val:cuts[-1]]
-            else:
-                fragment = seq[val:cuts[i+1]]
-            if mean_length-std <= len(fragment) <= mean_length+std:
-                term_frag = fragment
-        if term_frag == "":
-            continue
-        else:
-            term_frags.append(term_frag)
-    
-with open('terminal_frags.txt', 'w') as f:
-    for line in term_frags:
-        f.write(line)
-        f.write('\n')
-
+import re
+
+import numpy as np
+import pandas as pd
+
+
+def fasta_process(fasta_file):
+    with open(fasta_file, "r") as f:
+        lines = f.readlines()
+
+        ident_pattern = re.compile('>(\S+)')
+        seq_pattern = re.compile('^(\S+)$')
+
+        genes = {}
+        for line in lines:
+            if ident_pattern.search(line):
+                seq_id = (ident_pattern.search(line)).group(1)
+            elif seq_id in genes.keys():
+                genes[seq_id] += (seq_pattern.search(line)).group(1)
+            else:
+                genes[seq_id] = (seq_pattern.search(line)).group(1)
+    return genes
+
+def fragmentation(fasta_file, counts_file, mean_length, std, a_prob, t_prob, g_prob, c_prob):
+    fasta = fasta_process(fasta_file)
+    seq_counts = pd.read_csv(counts_file, names = ["seqID", "count"])
+
+    # nucs = ['A','T','G','C']
+    # mononuc_freqs = [0.22, 0.25, 0.23, 0.30]
+    nuc_probs = {'A':a_prob, 'T':t_prob, 'G':g_prob, 'C':c_prob} # calculated using https://www.nature.com/articles/srep04532#MOESM1
+
+    term_frags = [] 
+    for seq_id, seq in fasta.items():
+        counts = seq_counts[seq_counts["seqID"] == seq_id]["count"]
+        for _ in range(counts): 
+            n_cuts = int(len(seq)/mean_length)
+            
+            # non-uniformly random DNA fragmentation implementation based on https://www.nature.com/articles/srep04532#Sec1
+            # assume fragmentation by sonication for NGS workflow
+            cuts = []
+            cut_nucs = np.random.choice(list(nuc_probs.keys()), n_cuts, p=list(nuc_probs.values())) 
+            for nuc in cut_nucs:
+                nuc_pos = [x.start() for x in re.finditer(nuc, seq)]
+                pos = np.random.choice(nuc_pos)
+                while pos in cuts:
+                    pos = np.random.choice(nuc_pos)
+                cuts.append(pos)
+
+            cuts.sort() 
+            cuts.insert(0,0)
+            term_frag = ""
+            for i, val in enumerate(cuts):
+                if i == len(cuts)-1:
+                    fragment = seq[val+1:cuts[-1]]
+                else:
+                    fragment = seq[val:cuts[i+1]]
+                if mean_length-std <= len(fragment) <= mean_length+std:
+                    term_frag = fragment
+            if term_frag == "":
+                continue
+            else:
+                term_frags.append(term_frag)
+    return term_frags
+

frag_package/fragmentation_v2.py

-import re
-
-import numpy as np
-import pandas as pd
-
-
-def fasta_process(fasta_file):
-    with open(fasta_file, "r") as f:
-        lines = f.readlines()
-
-        ident_pattern = re.compile('>(\S+)')
-        seq_pattern = re.compile('^(\S+)$')
-
-        genes = {}
-        for line in lines:
-            if ident_pattern.search(line):
-                seq_id = (ident_pattern.search(line)).group(1)
-            elif seq_id in genes.keys():
-                genes[seq_id] += (seq_pattern.search(line)).group(1)
-            else:
-                genes[seq_id] = (seq_pattern.search(line)).group(1)
-    return genes
-
-def fragmentation(fasta_file, counts_file, mean_length, std, a_prob, t_prob, g_prob, c_prob):
-    fasta = fasta_process(fasta_file)
-    seq_counts = pd.read_csv(counts_file, names = ["seqID", "count"])
-
-    # nucs = ['A','T','G','C']
-    # mononuc_freqs = [0.22, 0.25, 0.23, 0.30]
-    nuc_probs = {'A':a_prob, 'T':t_prob, 'G':g_prob, 'C':c_prob} # calculated using https://www.nature.com/articles/srep04532#MOESM1
-
-    term_frags = [] 
-    for seq_id, seq in fasta.items():
-        counts = seq_counts[seq_counts["seqID"] == seq_id]["count"]
-        for _ in range(counts): 
-            n_cuts = int(len(seq)/mean_length)
-            
-            # non-uniformly random DNA fragmentation implementation based on https://www.nature.com/articles/srep04532#Sec1
-            # assume fragmentation by sonication for NGS workflow
-            cuts = []
-            cut_nucs = np.random.choice(list(nuc_probs.keys()), n_cuts, p=list(nuc_probs.values())) 
-            for nuc in cut_nucs:
-                nuc_pos = [x.start() for x in re.finditer(nuc, seq)]
-                pos = np.random.choice(nuc_pos)
-                while pos in cuts:
-                    pos = np.random.choice(nuc_pos)
-                cuts.append(pos)
-
-            cuts.sort() 
-            cuts.insert(0,0)
-            term_frag = ""
-            for i, val in enumerate(cuts):
-                if i == len(cuts)-1:
-                    fragment = seq[val+1:cuts[-1]]
-                else:
-                    fragment = seq[val:cuts[i+1]]
-                if mean_length-std <= len(fragment) <= mean_length+std:
-                    term_frag = fragment
-            if term_frag == "":
-                continue
-            else:
-                term_frags.append(term_frag)
-    return term_frags
-