NSCCN/mokapot：用于蛋白质组学中肽段和蛋白质的假发现率估计

v0.11.0 RC1 (#132)

(feat) added auto handling of traditional pin and testing
(fix) added handling of default direction
(fix) changed intermediate files pin->tsv and fixed tests accordingly
(chore) formatting on docs and removed T20 from them
(chore) upgraded to upstream actions
(chore) removed unused dependency in docs
(chore) reformatted tests
Feature/confidence streaming (#127)
✨ cherry picks internal fixes from !68 and !70
Cherry pick feature/confidence_streaming branch
✨ adds filelock dependency for tests
💄 linting
💄 reformat to satisfy linter k
✨ imports type annotations from future for python 3.9
✨ make pytest and cli behave with type annotations in Python 3.9
✨ test dropping Python 3.9 support

inspired by https://github.com/wfondrie/mokapot/pull/126/files#diff-1db27d93186e46d3b441ece35801b244db8ee144ff1405ca27a163bfe878957fL20

Set scale_to_one to false in all cases
Fixed path problems probably causing errors under windows
Fix more possible path issues
Fix warning about bitwise not in python 3.12
Fix problem with numpy 2.x’s different str rep of floats
Make hashing of rows for splitting independent of numpy version and spectra columns
Feature/streaming fix windows (#48)
✨ log more infos
✨ uses uv for env setup; fix dependencies

Co-authored-by: Elmar Zander elmar.zander@googlemail.com

Small changes for FlashLFQ writer (#131)

Fixed retention time division by 60. Time is required in minutes for FlashLFQ, it’s already in minutues

Co-authored-by: William Fondrie fondriew@gmail.com

wip: formatting and rebasing fixes
chore: ruff format
wip,chore: re-adding flashlfq support
ci,fix: fixed confidence out and ci migration
format: eof newline
wip,fix: progress to re-add flashlfq output
chore: uv lock and formatting
chore: added pr template
wip: make brew generic again
wip,fix: added deleter to on psm dataset
feat: re-added flashlfq support
chore: linting + formatting
fix: fixtures and progess in definition of cols
test, fix: annotated/commented new fixtures
lint: formatting
ci: removed fail on codecov fail
ci: test speedup replacing RF with dtree
ci: attempt to fix test docker build
refactor: change stats to dataclass
refactor: extracted output writer factory
refactor: extracted level manager in confidence
refactor: extracted level writer group
refactor: extracted more writer builder work to class
feat: score propagation and unscored confidence
feat(confidence): add data reading api
feat,experiment: Experimental qvalue-fdr estimation
chore,docs: updated basic docs to curr api and updated typing
chore: updated basic n joint model docs code (md in progress)
chore: updated notebook
chore,confidence: update docstrings
chore,qvalue: removed commented out code
chore: fixed line length lints in docstrings
fix,sqlite: fixed path for sqlite writer
feat, wip: compound key on spectrum
refactor,wip: centralized column group logic
refactor,dataset: broke module into files and changed tdc implementation to remove numba
fix: fixed string to bool target col conversion and added notes on tests
ci: enabled lint and test on all PRs
chore: updated triqler and np versions
feat,doc: fixed empty cols in proteins and better column descriptions in docstrings
test: added content testing to cli testing + csv -> tsv
fix: flashlfq and misc fixes
ci: added extra xml to test makefile
chore: unify naming schemas
chore: self-review cleanup
fix,chore: updated makefile and fixed iterator
chore: self-review cleanup

Co-authored-by: Siegfried Gessulat s.gessulat@gmail.com Co-authored-by: Elmar Zander elmar.zander@googlemail.com Co-authored-by: Ivan Chudinov 44038974+unholyparrot@users.noreply.github.com Co-authored-by: William Fondrie fondriew@gmail.com

Basic Usage

Before you can use mokapot, you need PSMs assigned by a search engine available in the Percolator tab-delimited file format (often referred to as the Percolator input, or “PIN”, file format) or as a PepXML file.

Simple mokapot analyses can be performed at the command line:

$ mokapot psms.pin

Alternatively, the Python API can be used to perform analyses in the Python interpreter and affords greater flexibility:

import mokapot psms = mokapot.read_pin("psms.pin") results, models = mokapot.brew(psms) results.to_txt()

Check out our documentation for more details and examples of mokapot in action.

Fast and flexible semi-supervised learning for peptide detection.

mokapot is fundamentally a Python implementation of the semi-supervised learning algorithm first introduced by Percolator. We developed mokapot to add additional flexibility to our analyses, whether to try something experimental—such as swapping Percolator’s linear support vector machine classifier for a non-linear, gradient boosting classifier—or to train a joint model across experiments while retaining valid, per-experiment confidence estimates. We designed mokapot to be extensible and support the analysis of additional types of proteomics data, such as cross-linked peptides from cross-linking mass spectrometry experiments. mokapot offers basic functionality from the command line, but using mokapot as a Python package unlocks maximum flexibility.

For more information, check out our documentation.

Citing

If you use mokapot in your work, please cite:

Fondrie W. E. & Noble W. S. mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection. J Proteome Res (2021) doi: 10.1021/acs.jproteome.0c01010. PMID: 33596079. Link

Installation

mokapot requires Python 3.6+ and can be installed with pip or conda.

Using conda:

$ conda install -c bioconda mokapot

Using pip:

$ pip3 install mokapot

Additionally, you can install the development version directly from GitHub:

$ pip3 install git+git://github.com/wfondrie/mokapot