The official code repository for InstaNovo. This repo contains the code for training and inference of InstaNovo and InstaNovo+. InstaNovo is a transformer neural network with the ability to translate fragment ion peaks into the sequence of amino acids that make up the studied peptide(s). InstaNovo+, inspired by human intuition, is a multinomial diffusion model that further improves performance by iterative refinement of predicted sequences.
Publication in Nature Machine Intelligence: InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments
The full documentation is available at https://instadeepai.github.io/InstaNovo/ and consists of the following sections.
- Tutorials
- How to install InstaNovo, make your first prediction and evaluate InstaNovo's performance.
- An end-to-end starter notebook that you can run in Google Colab .
- How-to guides:
- How to perform predictions with InstaNovo with iterative refinement of InstaNovo+, or how to use each model separately.
- Guide for preparing your own data for use with InstaNovo and InstaNovo+.
- Details how to train your own InstaNovo and InstaNovo+ models.
- Reference:
- Overview of the
instanovocommand-line interface. - List of the supported post translational modifications.
- Description of the columns in the prediction output CSV
- Code reference API
- Overview of the
- Explanation:
- Explains our performance metrics and benchmarking results
- A detailed explanation of the
SpectrumDataFrameclass and its features.
- Blog:
- For Developers:
- How to set up a development environment.
- How to run the tests and lint the code.
- View the test coverage and test report.
- How to Cite:
- Bibtex references for our peer-reviewed publication on InstaNovo and InstaNovo+ and our preprints on InstaNovo-P, InstaNexus and Winnow.
- License:
- Code is licensed under the Apache License, Version 2.0
- The model checkpoints are licensed under Creative Commons Non-Commercial (CC BY-NC-SA 4.0)
Developed by: