Installation

splicescope needs Python 3.10 or newer and runs on Linux, macOS and Windows. Its runtime dependencies are NumPy, pandas, SciPy, Matplotlib and scikit-learn, and pip installs them automatically.

From PyPI

Once the package has been released on PyPI:

pip install splicescope

Optional extras add the pieces that not every user needs:

pip install "splicescope[app]"    # the Streamlit dashboard

From source

Until the first PyPI release, or to work on the code, install from GitHub:

pip install "git+https://github.com/elmasnuryilmaz/splicescope.git"

or clone it and install in editable mode, with the test and notebook tools:

git clone https://github.com/elmasnuryilmaz/splicescope.git
cd splicescope
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev,docs]"     # add ",app" for the dashboard, ",site" for this website

extra	what it adds	needed for
app	Streamlit	streamlit run app/streamlit_app.py
dev	pytest, pytest-cov, Hypothesis, ruff	running the test suite and the linter
docs	nbformat, nbclient, ipykernel, Pillow	rebuilding the tutorial notebook and the figures in docs/
site	MkDocs, Material for MkDocs, mkdocstrings	building this website with mkdocs serve

Check the installation

splicescope --version
splicescope simulate --outdir demo_data

From a source checkout, the full test suite is the most thorough check:

pytest

Other tools you may need

• An aligner that writes splice junctions. splicescope reads STAR's SJ.out.tab. For alignments from another aligner, validation/extract_sj.sh derives the same table from any coordinate-sorted BAM file.
• A FASTA index for the genome (.fai, for example from samtools faidx), only for protein-consequence prediction.
• Nextflow 23.04 or newer, only for the pipeline.