criscross 0.1.3

CPU-friendly sequence-only CRISCross off-target prediction with a scikit-learn-style API.

criscross

CPU-friendly sequence-only CRISCross off-target prediction with a scikit-learn-style API. The pretrained model weights ship inside the wheel (fp16 + zstd-compressed) so no extra downloads are required.

Install

pip install criscross

CPU-only install (no CUDA libraries pulled in):

pip install criscross --extra-index-url https://download.pytorch.org/whl/cpu

Quickstart

from criscross import sequence_model
import pandas as pd

# Single datapoint (dict)
prob = sequence_model.predict({
    "Guide_sequence":   "GCTCGGGGACACAGGATCCCTGG",     # 23 nt
    "off_target_512nt": "GCAG...TGCC",                 # 512 nt, RC for - strand
    "strand_id":        1,                             # 1 for +, 0 for -
})
print(prob)   # float in [0, 1]

# Dataset (DataFrame or CSV path)
df = pd.read_csv("examples/sample_input.csv")
probs = sequence_model.predict(df)           # -> np.ndarray, shape [N]
probs = sequence_model.predict("examples/sample_input.csv")  # same

Preparing inputs from a genome scan (Cas-OFFinder)

If you have guide RNA(s) and a reference genome FASTA, you can generate the Guide_sequence/off_target_512nt/strand_id table with Cas-OFFinder and feed it directly into sequence_model.predict(...).

from criscross import offinder, sequence_model

X = offinder.prepare(
    guide_rnas=["GCTCGGGGACACAGGATCCCTGG"],
    fasta="/path/to/GRCh38.primary_assembly.genome.fa",
    pam="NGG",            # default
    max_mismatches=6,     # default
)

# X is a DataFrame you can pass straight to criscross
probs = sequence_model.predict_proba(X)

Requirements:

• Cas-OFFinder needs an OpenCL runtime even for CPU mode. On Linux, the simplest CPU runtime is PoCL, e.g. conda install -c conda-forge pocl ocl-icd-system (or sudo apt install pocl-opencl-icd).
• Cas-OFFinder 2.4.1 is bundled inside the criscross wheel. If you prefer to use your own build, set CAS_OFFINDER=/path/to/cas-offinder (or pass cas_offinder_path=).

Accepted inputs to predict(X)

X	Returned
dict / pandas.Series with the 3 required keys	float
(guide, off_target_512nt, strand_id) 3-tuple	float
pandas.DataFrame with the 3 required columns	np.ndarray shape [N]
list of dicts	np.ndarray shape [N]
str / pathlib.Path pointing to a CSV with the 3 columns	np.ndarray shape [N]

Required columns/keys:

key	dtype	meaning
Guide_sequence	23nt string	sgRNA guide sequence
off_target_512nt	512nt string	candidate off-target window, already reverse-complemented for - strand
strand_id	int 0/1	1 for + strand, 0 for -

CLI

criscross predict --csv examples/sample_input.csv --out preds.csv

If the input CSV also has a label column (0/1), AUPRC is printed to stderr.

Loading a custom checkpoint

from criscross import sequence_model
sequence_model.load("path/to/my_model.pt")           # fp32 raw .pt
sequence_model.load("path/to/my_model.pt.zst")       # zstd-compressed fp16

Inspecting the model

sequence_model.config()    # hyperparameters used to build CRISCross(**config)
sequence_model.metadata()  # versions, training-time test_auprc, input/output signature, seed

Citation

If you use this package in research, please cite the upstream CRISCross work. This package is a CPU-only, sequence-only repackaging of that model.