Merge pull request #25 from wfondrie/quant

Add support for downstream quantitation tools

CHANGELOG.md

@@ -1,5 +1,31 @@
 # Changelog for mokapot  
 
+## [unreleased]
+### Added
+- Support for downstream peptide and protein quantitation with
+  [FlashLFQ](https://github.com/smith-chem-wisc/FlashLFQ). This is accomplished
+  through the `mokapot.to_flashlfq()` function or the `to_flashlfq()` method of
+  `LinearConfidence` objects. Note that to support the FlashLFQ format, you'll
+  need to specify additional columns in `read_pin()` or use a PepXML input file
+  (`read_pepxml()`). 
+- Added a top-level function for exporting confident PSMs, peptides, and
+  proteins from one or more `LinearConfidence` objects as a tab-delimited file:
+  `mokapot.to_txt()`.
+- Added a top-level function for reading FASTA files for protein-level 
+  confidence estimates: `mokapot.read_fasta()`.
+- Tests accompanying the support for the features above.
+- Added a "mokapot cookbook" to the documentation with helpful code snippets.
+
+### Changed
+- Corresponding with support for new formats, the `mokapot.read_pin()` function
+  and the `LinearPsmDataset` constructor now have many new optional parameters.
+  These specify the columns containing the metadata needed to write the added
+  formats.
+- Starting mokapot should be slightly faster for Python >= 3.8. We were able to
+  eliminate the runtime call to setuptools, because of the recent addition of
+  `importlib.metadata` to the standard library, saving a few hundred
+  milliseconds.
+
 ## [0.6.2] - 2021-03-12  
 ### Added
 - Now checks to verify there are no debugging print statements in the code

docs/source/api/functions.rst

@@ -7,8 +7,10 @@ Primary Functions
 -----------------
 .. autofunction:: read_pin
 .. autofunction:: read_pepxml
+.. autofunction:: read_fasta
 .. autofunction:: brew
-
+.. autofunction:: to_txt
+.. autofunction:: to_flashlfq
 
 Utility Functions
 -----------------

docs/source/api/index.rst

@@ -32,6 +32,7 @@ Finally, custom machine learning models can be created using the
    model.rst
    dataset.rst
    confidence.rst
+   proteins.rst
 
 Functions
 ---------
@@ -44,7 +45,10 @@ Primary Functions
 
    read_pin
    read_pepxml
+   read_fasta
    brew
+   to_txt
+   to_flashlfq
 
 Utility Functions
 *****************
@@ -100,3 +104,20 @@ optionally, proteins.
 
    LinearConfidence
    .. CrossLinkedConfidence
+
+
+Protein Sequences
+-----------------
+
+To calculate protein-level confidence estimates, mokapot needs the original
+protein sequences and digestion parameters used for the database search. These
+are created using the :py:func:`mokapot.read_fasta()` function, which return a
+:py:class:`Proteins` object. :py:class:`Proteins` objects store the mapping of
+peptides to the proteins that may have generated them and the mapping of
+target protein sequences to their corresponding decoys.
+
+.. currentmodule:: mokapot.proteins
+.. autosummary::
+   :nosignatures:
+
+   Proteins

docs/source/api/proteins.rst

@@ -0,0 +1,3 @@
+Protein Sequences
+=================
+.. automodule:: mokapot.proteins