Spaces:
Running
on
Zero
Running
on
Zero
| title: De Novo Peptide Sequencing With InstaNovo and InstaNovo+ | |
| emoji: π | |
| colorFrom: green | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 5.23.1 | |
| app_file: app.py | |
| pinned: true | |
| license: apache-2.0 | |
| thumbnail: >- | |
| https://cdn-uploads.huggingface.co/production/uploads/6189aee17d9b289cdebafbd6/tb9e-8Z2_pDsRMkGglcvh.png | |
| short_description: Translate fragment ion peaks into sequence of amino acids | |
| # _De Novo_ Peptide Sequencing With InstaNovo and InstaNovo+ | |
| This Space provides a web interface for the [InstaNovo](https://github.com/instadeepai/InstaNovo) models for _de novo_ peptide sequencing from mass spectrometry data. | |
| **Features:** | |
| * Upload MS/MS data in common formats (`.mgf`, `.mzml`, `.mzxml`). | |
| * Choose between fast Greedy Search or more accurate but slower Knapsack Beam Search. | |
| * View predictions directly in the interface. | |
| * Download full results as a CSV file. | |
| **How to Use:** | |
| 1. Upload your mass spectrometry data file. | |
| 2. Select the desired decoding method. | |
| 3. Click "Predict Sequences". | |
| 4. View the results table and download the CSV if needed. | |
| **Model:** | |
| This demo uses the pretrained model checkpoint. | |
| * Predictions use version `instanovo-v1.1.0` for the transformer-based InstaNovo model and version `instanovoplus-v1.1.0` for the diffusion-based InstaNovo+ model. | |
| **Note:** Processing large files can take time, depending on the file size and the chosen decoding method. Knapsack generation can also add to the initial startup time. |