proffer 0.5.3

Fast and flexible tool for protein inference

Proffer is fast and flexible tool for protein inference in bottom-up proteomics experiments.

Installation

This library requires Python 3.10+ and can be installed with pip:

pip install proffer

Basic Usage

After installation, you can run the proffer CLI tool:

proffer data/percolator.txt --format tsv

You can also load results directly from any URI supported by fsspec:

# HTTP(S)
proffer https://github.com/seerbio/proffer/raw/refs/heads/main/data/percolator.txt --format tsv

# S3
pip install proffer[s3]  # ensure libraries for S3 support are installed
proffer s3://bucket/key/results.parquet --format parquet

Basic support is included for thresholding peptide identifications before inference:

proffer data/percolator.txt --format tsv --qvalue-threshold 0.01

By default output is written in JSON format to stdout. Alternatively, the -o flag can be used to write results to Parquet:

proffer data/percolator.txt --output proffer-results.parquet

It is possible to configure the column names used, as well as the approach used to pick protein groups. To learn more about the available options you can run:

proffer --help

Python usage

To use Proffer in your Python code, call proffer.infer with a Polars DataFrame:

import proffer

result_frame = proffer.infer(polars_frame)

It is possible to configure the column names used, as well as the approach used to pick protein groups. To learn more about the available options you can run:

help(proffer.infer)

Usage with Fulcrum Pipeline

Utilities for working with Spark-based datasets can be found in proffer.spark. These use interfaces from wheely-mammoth (compatible with Fulcrum Pipeline™) to efficiently read peptide and protein information and compute inference.

For typical use cases, it will be easiest to employ a pre-built Fulcrum workflow that uses Proffer for inference, rather than call these utilities directly.

By default, installing Proffer will not install Spark dependencies; to use Proffer in an environment where these are not already installed, run:

pip install 'proffer[spark]'