diff --git a/terminal-fragment-selector/fragmentation.py b/terminal-fragment-selector/fragmentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a1e68a83728194a3aab9f0e156c3723a4c10d40
--- /dev/null
+++ b/terminal-fragment-selector/fragmentation.py
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+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
+