Initial commit

model/predict.py

+import numpy as np
+import gffpandas.gffpandas as gffpd
+from typing import Tuple
+import re
+
+with open("on_target/model/reg_coef.pkl", "br") as handle:
+    coef = np.load(handle, allow_pickle=True)
+
+bases = ["A", "T", "G", "C"]
+
+
+def encode(seq):
+    '''One-hot encoding of a sequence (only non-ambiguous bases (ATGC) accepted)'''
+    return np.array([[int(b == p) for b in seq] for p in bases])
+
+
+# Quartiles: q1 > 0.4 > q2 > -0.08 > q3 > -0.59 > q4
+def predict(X):
+    return [np.sum(x * coef) for x in X]
+
+
+def rev_comp(seq):
+    comp = str.maketrans("ATGC", "TACG")
+    return seq.translate(comp)[::-1]
+
+
+def find_targets(seq):
+    repam = "[ATGC]GG"
+    L = len(seq)
+    seq_revcomp = rev_comp(seq)
+    alltargets = [
+        dict(
+            [
+                ("target", m.group(1)),
+                ("guide", m.group(1)[:20]),
+                ("start", L - m.start() - 20),
+                ("stop", L - m.start()),
+                ("pam", L - m.start() - 22),
+                ("ori", "-"),
+            ]
+        )
+        for m in re.finditer("(?=([ATGC]{6}" + repam + "[ATGC]{16}))", seq_revcomp)
+    ]
+    return alltargets
+
+
+def on_target_predict(seq):
+    seq = seq.upper()  # make uppercase
+    seq = re.sub(r"\s", "", seq)  # removes white space
+    alltargets = find_targets(seq)
+    if alltargets:
+        X = np.array(
+            [encode(tar["target"][:7] + tar["target"][9:]) for tar in alltargets] #encore and remove GG of PAM
+        )
+        X = X.reshape(X.shape[0], -1)
+        preds = predict(X)
+        for i, target in enumerate(alltargets):
+            target.update({"pred": preds[i]})
+        return alltargets
+    else:
+        return []

tests/predict_test.py

+import pytest
+from on_target.model.predict import on_target_predict
+
+
+def test_on_target_predict_empty():
+    # Empty sequence
+    predicted_target = on_target_predict("")
+    assert len(predicted_target) == 0, "the list is non empty"
+
+
+def test_on_target_predict_size_guide():
+    size_guide = 20
+    predicted_targets = on_target_predict(
+        "TGCCTGTTTACGCGCCGATTGTTGCGAGATTTGGACGGACGTTGACGGGGTCTATACCTGCGACCCGCGTCAGGTGCCCGATGCGAGGTTGTTGAAGTCGATGTCCTACCAGGAAGCGATGGAGCTTTCCTACTTCGGCG"
+    )
+    guides = (predicted_target["guide"] for predicted_target in predicted_targets)
+    for guide in guides:
+        assert len(guide) == size_guide, (
+            "the guide do not have a length of " + size_guide
+        )
+    pams = (
+        (predicted_target["pam"], predicted_target["start"])
+        for predicted_target in predicted_targets
+    )
+    for pam in pams:
+        (pam_val, start_val) = pam
+        assert (
+            start_val - pam_val == 2
+        ), "the difference between start and pam position is different than 2"
+