v4.0.0-dev5

Full Changelog: v4.0.0-dev4...v4.0.0-dev5

v4.0.0-dev4

Full Changelog: v4.0.0-dev3...v4.0.0-dev4

v4.0.0-dev3

What's Changed

• Timstof models by @ArthurDeclercq in #203

Full Changelog: v4.0.0-dev2...v4.0.0-dev3

v4.0.0-dev2

Full Changelog: v4.0.0-dev1...v4.0.0-dev2

v4.0.0-dev1

Full Changelog: v4.0.0-dev0...v4.0.0-dev1

v4.0.0-dev0

Early development release of v4.0.0, which includes:

• Fully refactored Python API
• New command-line interface
• Support for all file formats readable by psm_utils
• Support for both MGF and mzML spectrum files
• Support for ProForma 2.0 peptide notation (no modification configuration required anymore)
• Support for multiple peptides/PSMs per spectrum
• ...

3.11.0

Added

• fasta2speclib: Improved workflow for generating spectral libraries starting from a FASTA file, with new configuration options. ⚠️ These changes break compatibility with the previous configuration files. ⚠️ (PR #193, fixes #188)
  • Support for C-terminal modifications
  • Differentiate between peptide and protein termini for variable modifications
  • Allow filtering of peptides based on precursor m/z
  • Allow semi-specific cleavage
  • Allow non-specific cleavage
  • Allow setting of a maximum of variable modifications per peptide
  • Add tests for modification assignment
• Add figures for 2023 manuscript (PR #194)

Changed

• Change logging of model configuration to debug level (PR #193)

Removed

• fasta2speclib: Removed support for Elude-based RT predictions, RT predictions file, PEPREC filter, saving temporary PEPREC files (PR #193)

Fixed

• Remove unsupported argument for mzml.read (PR #193)
• spectrum_output: Fix CSV output to always use \n line terminators (PR #193)
• spectrum_output: Use semi-colon for spectronaut CSV output (PR #193)
• DeepLC integration: Disable PyGAM for default calibration on iRT peptides (led to poor calibration) (PR #193)

v3.10.1

Added

• If the precursor charge is not found in the MGF file, the charge from the PeptideRecord file is used instead. (#189)
• Added tests for fasta2speclib modification generation. (#190)

Fixed

• Fixed issue in fasta2speclib where fixed modifications were added one residue to the left of the actual site. This bug was introduced in commit 6f41d44 and released in v3.10.0. (#190)

v3.10.0

Added

• Added support for mzML spectrum files (both for evaluating models and for extracting feature vectors).
• New argument spectrum_id_pattern: Regular expression pattern to apply to spectrum titles before matching to peptide file entries.
• When using MS²PIP as class instance, the resulting pred_and_emp dataframe can also be returned (instead of writing to a file) when setting return_results to True.
• If requested, retention time prediction with DeepLC is now also enabled if spectrum file is given. This feature was previously only enabled if only a peptide file was given.

Changed

• Improved logging: Use Rich library for logging, show time stamps and message log levels.
• MS²PIP now shows a progress bar instead of a wall of text to display prediction progress.
• fasta2speclib: Improved algorithm for variable modification assignment. Combinatorial explosion from variable modifications is now reduced by setting a maximum of modified residues per peptide, instead of arbitrarily selecting a maximum of potentially modified sites per peptide.
• Update README.md (Switch from BadGen to Shields.io).
• Switch to Pyteomics MGF reader.
• Avoid SciPy dependency.
• More optimal use of Numpy in calc_correlations.
• Remove poetry.lock (not used, avoid unneeded Dependabot PRs).

Fixed

• Vastly improved computational speed and reduced memory usage when using XGBoost model files for prediction in combination with providing a spectrum file (XGB prediction step is now moved out of multiprocessing).
• For optimal performance, feature vectors for predictions from XGBoost model files now also uses the traditional ms2pipC.py multiprocessing system.
• fasta2speclib: Fixed issue where modified versions of peptide were duplicated.
• spectrum_output: Various fixes in MSP spectral library file writing for DIA-NN compatibility: Write m/z error of 0.0 for each predicted peak in peak annotation string, ensure modifications in MSP Mods field are sorted by position, use RetentionTime instead of RTINSECONDS in comments field.
• Fixed double spectrum_utils entry in requirements.
• Updated python_requires to minimal 3.7, following previously updated test grid.
• Fix spectrum_utils modification off-by-one bug (had no consequences except for plot annotations).
  • Fixes #170
• Fix typo in write_amino_acid_masses function name.
• Fix missing comma in the setup.py.

Removed

• Removed unsupported Tableau output file option

v3.9.0

New and improved 🚀

• New prediction model for CID-TMT: TMT-labelled peptide spectra acquired on ion trap (trap-type CID), often used for "MultiNotch MS3" (https://dx.doi.org/10.1021/ac502040v) (PR #157)
• Support for Python 3.9 and 3.10; dropped support for end-of-life Python 3.6 (PR #156, fixes #126)
• Support for alternative cleavage rules (digestion enzymes) in fasta2speclib (PR #166, fixes #96)

Bugfixes 🐛

• Fixed missing support for XGBoost models in single-prediction mode (PR #157, fixes #155)
• Use oldest-supported-numpy for build in CI testing (PR #157)

Refactoring and minor changes 🔧

• Replaced C models files with their XGBoost counterpart (except for HCD2019 and TMT): Faster compilation, smaller Python package (PR #157)
• Add model_dir option to set custom directory for model downloads (CLI, single-prediction CLI, Python API) (PR #169, fixes #165)
• Add docstring to MS2PIP class and add example to README.md (PR #167, fixes #131)
• Relaxed click version requirements (PR #157, fixes #158)
• Removed XGBoost warnings from the CLI output (PR #157)
• Various fasta2speclib improvements (PR #166)
  • Add deeplc option to default config
  • Suppress tensorflow warnings
  • Replace deprecated pandas append with concat
• Add missing sptm and gptm to example config.toml (#167)

New prediction models

Model	Current version	Train-test dataset (unique peptides)	Evaluation dataset (unique peptides)	Median Pearson correlation on evaluation dataset
CID-TMT	v20220104	[in-house dataset] (72 138)	PXD005890 (69 768)	0.851085