SpliceAI2

This repository contains the source code for SpliceAI2, a deep learning model that predicts variant effects on splicing through quantitative modeling of splice sites, splice junctions, and transcripts.

Pretrained model weights and precomputed variant effect predictions within protein-coding genes (GENCODE v48, GRCh38), covering all possible single nucleotide variants (4 billion) and indels observed in human populations (150 million), are available on Hugging Face for academic and non-commercial research use. For commercial use, please contact AI_licensing@illumina.com.

SpliceAI2 predictions are summarized into a single spliceai2_summary_score ranging from 0 to 1, with higher values indicating larger predicted effects on splicing. Recommended thresholds, with their SpliceAI equivalents, are:

Installation

SpliceAI2 is available through PyPI and can be installed with:

pip install spliceai2

Alternatively, clone the source repository and install SpliceAI2 in editable mode to work directly with the source code:

git clone https://github.com/Illumina/SpliceAI2.git
cd SpliceAI2
pip install -e .

A CUDA-capable GPU is required. If PyTorch raises a CUDA error, install a matching build from pytorch.org.

Variant effect prediction

Provide variants in a tsv file with the following columns:

Run SpliceAI2 with the pretrained model weights and a reference genome fa file:

spliceai2 \
    --model_folders /path/to/model_01 /path/to/model_02 \
    --var_tsv_file /path/to/variants.tsv \
    --fasta_file /path/to/reference.fa

Add --no_compile to skip torch.compile when scoring only a few variants or if it fails on your system.

The output file is saved alongside the input file, with .spliceai2 appended to its name. Beyond the spliceai2_summary_score, the output contains 260 columns providing detailed splicing predictions organized as {effect_type}_{field}_{k}, as defined below.

k indexes the 10 strongest changes per effect type and ranges from 0 to 9, with 0 indicating the strongest change. The spliceai2_summary_score is the maximum of donor_gain_delta_score_0, donor_loss_delta_score_0, acceptor_gain_delta_score_0, and acceptor_loss_delta_score_0.

Eight of the 260 columns correspond one-to-one with SpliceAI's outputs: DS_DG, DS_DL, DS_AG, DS_AL, DP_DG, DP_DL, DP_AG, and DP_AL map to donor_gain_delta_score_0, donor_loss_delta_score_0, acceptor_gain_delta_score_0, acceptor_loss_delta_score_0, donor_gain_dist_0, donor_loss_dist_0, acceptor_gain_dist_0, and acceptor_loss_dist_0, respectively.

Transcript prediction

SpliceAI2 can also predict transcripts directly from sequence. For variant analysis, generate predictions separately for the reference and alternate sequences.

import torch
from spliceai2.model import SpliceAI2
from spliceai2.dataset import _one_hot_encode
from spliceai2.utils import decode_topk_tx

seq = "ACGT..."
# L + 131,072 nt on the sense strand, with L a multiple of 16
# Predictions span the central L positions

x = torch.tensor(_one_hot_encode(seq, "GRCh38")[None]).float().cuda()
models = [SpliceAI2.load_from_checkpoint(f"{f}/model.ckpt").eval().cuda()
          for f in ["/path/to/model_01", "/path/to/model_02"]]

with torch.no_grad():
    ftrs_list = [model.forward(x) for model in models]
    out_ss = torch.stack([
        model.forward_1d(ftrs) for model, ftrs in zip(models, ftrs_list)
    ]).mean(dim=0)
    out_1d_len = out_ss.shape[2]
    out_2d_len = int((out_ss.amax(dim=1).sigmoid() > 0.01).sum())
    out_2d_idxs = out_ss.amax(dim=1).topk(out_2d_len, dim=1)[1].sort(dim=1).values
    out_jxn = torch.stack([
        model.forward_2d(ftrs, out_2d_idxs) for model, ftrs in zip(models, ftrs_list)
    ]).mean(dim=0)
    out_tx = (
        out_ss[:, 0].gather(1, out_2d_idxs).unsqueeze(2)
        + out_jxn
        + out_ss[:, 1].gather(1, out_2d_idxs).unsqueeze(1)
    )

topk_tx = decode_topk_tx(
    out_tx[0].cpu().numpy(), out_2d_idxs[0].cpu().numpy(), out_1d_len, out_2d_len, k=3
)
# k × L array, one transcript per row; 1 is exonic and 0 is intronic

Citation

Jaganathan K, Chen J, Liu X, Zhang Y, et al. A unified framework for quantitative splicing and transcript prediction. Unpublished manuscript (2026).

Contact

Kishore Jaganathan: kjaganathan@illumina.com