Metadata-Version: 2.1
Name: crema-ms
Version: 0.0.5
Summary: Confidence estimation for proteomics experiments
Home-page: https://github.com/noble-lab/crema
Author: William E. Fondrie
Author-email: fondriew@gmail.com
License: Apache 2.0
Project-URL: Documentation, https://crema-ms.readthedocs.io
Project-URL: Bug Tracker, https://github.com/noble-lab/crema/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: dev
License-File: LICENSE

<img src="https://raw.githubusercontent.com/Noble-Lab/crema/master/static/crema_logo.svg" width=300>
 
---

Confidence Estimation for Mass Spectrometry Proteomics

**crema** is a Python package that implements various methods to estimate false discovery rates (FDR)
in mass spectrometry proteomics experiments. crema focuses on
methods that rely on the concept of "target-decoy competition." The sole purpose of crema is to do decoy-based FDR
estimation, and to do it well. As a result, crema is lightweight and flexible. It has minimal dependencies and
supports a wide range of input and output formats. On top of that, it is extremely simple to use.

For more information, check out our
[documentation](https://crema-ms.readthedocs.io).  

## Installation  

crema requires Python 3.6+ and can be installed with pip:  

```
$ pip3 install crema-ms
```

## Basic Usage  

Before using crema, you need one or more files, each containing a collection of
peptide-spectrum matches (PSMs) in tab-delimited format. Note that crema defaults
to reading files via [crux](http://crux.ms/index.html) format, but can easily be
manipulated to accept files in formats that use differing column headers.

Simple crema calculations can be performed at the command line:

```Bash
$ crema data/tide-search.target.psms.txt data/tide-search.decoy.psms.txt
```

Alternatively, the Python API can be used to calculate confidence estimates in the Python
interpreter and affords greater flexibility:

```Python
    >>> import crema
    >>> input_files = ["data/tide-search.target.psms.txt", "data/tide-search.decoy.psms.txt"]
    >>> psms = crema.read_crux(input_files)
    >>> results =  psms.assign_confidence()
    >>> results.to_txt(ouput_dir="example_output_dir")
```

Check out our [documentation](hhttps://crema-ms.readthedocs.io) for more details
on how to make full use of crema.

