v3.4.2
New since last release:
- MS²PIP can now be downloaded and installed from PyPI using
pip install ms2pip. MS²PIP on PyPI is already compiled, so it can be installed in seconds (without slow and memory inefficient compilation step). - MS²PIP will use all available CPUs by default.
- Speed improvements after multiprocessing step (especially for large numbers of predictions).
- Moved to semantic versioning
Includes the following models:
| Model | Current version | Train-test dataset (unique peptides) | Evaluation dataset (unique peptides) | Median Pearson correlation on evaluation dataset |
|---|---|---|---|---|
| HCD | v20190107 | MassIVE-KB (1 623 712) | PXD008034 (35 269) | 0.903786 |
| CID | v20190107 | NIST CID Human (340 356) | NIST CID Yeast (92 609) | 0.904947 |
| iTRAQ | v20190107 | NIST iTRAQ (704 041) | PXD001189 (41 502) | 0.905870 |
| iTRAQphospho | v20190107 | NIST iTRAQ phospho (183 383) | PXD001189 (9 088) | 0.843898 |
| TMT | v20190107 | Peng Lab TMT Spectral Library (1 185 547) | PXD009495 (36 137) | 0.950460 |
| TTOF5600 | v20190107 | PXD000954 (215 713) | PXD001587 (15 111) | 0.746823 |
| HCDch2 | v20190107 | MassIVE-KB (1 623 712) | PXD008034 (35 269) | 0.903786 (+) and 0.644162 (++) |
| CIDch2 | v20190107 | NIST CID Human (340 356) | NIST CID Yeast (92 609) | 0.904947 (+) and 0.813342 (++) |