Tanya

fragmentation_2.py

+import pandas as pdimport randomimport numpy as npimport re
dna_seq = {
    "ATAACATGTGGATGGCCAGTGGTCGGTTGTTACACGCCTACCGCGATGCTGAATGACCCGGACTAGAGTGGCGAAATTTATGGCGTGTGACCCGTTATGC": 100,
    "TCCATTTCGGTCAGTGGGTCATTGCTAGTAGTCGATTGCATTGCCATTCTCCGAGTGATTTAGCGTGACAGCCGCAGGGAACCCATAAAATGCAATCGTA": 100}nucs = ['A','T','G','C']mononuc_freqs = [0.22, 0.25, 0.23, 0.30]
# A_dinuc_freqs = {'AA':0.27,'AT':0.25, 'AG':0.22, 'AC':0.26}
# T_dinuc_freqs = {'TA':0.27, 'TT':0.24, 'TG':0.24, 'TC':0.25}
# C_dinuc_freqs = {'CA':0.23, 'CT':0.21, 'CG':0.35, 'CC':0.21}
# G_dinuc_freqs = {'GA':0.25, 'GT':0.25, 'GG':0.23, 'GC':0.27}

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)
        
        # non-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(nucs, n_cuts, p=mononuc_freqs) 
        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)
    
with open('terminal_frags.txt', 'w') as f:
    for line in term_frags:
        f.write(line)
        f.write('\n')
\ No newline at end of file