sadie: A Python repository from jwillis0720

Sequencing Analysis and Data Library for Immunoinformatics Exploration

About

Documentation: https://sadie.jordanrwillis.com

Source Code: https://github.com/jwillis0720/sadie

Colab: https://colab.research.google.com/github/jwillis0720/sadie

SADIE is the Sequencing Analysis and Data library for Immunoinformatics Exploration. The key feautures include:

Provide pre-built command line applications for popular immunoinformatics applications.
Provide a low-level API framework for immunoinformatics developers to build higher level tools.
Provide a testable and reusable library that WORKS!
Provide a customizable and verified germline reference library.
Maintain data formats consistent with standards governed by the AIRR community
Portability ready to use out the box.

SADIE is billed as a "complete antibody library", not because it aims to do everything, but because it aims to meet the needs of all immunoinformatics users. SADIE contains both low, mid and high level functionality for immunoinformatics tools and workflows. You can use SADIE as a framework to develop your own tools, use many of the prebuilt contributed tools, or run it in a notebook to enable data exploration. In addition, SADIE aims to port all code to python because relies heavily on the Pandas library, the workhorse of the data science/machine learning age.

Installation

Installation is handled using the python package installer pip

$ pip install sadie-antibody

Development installation.

Pull requests are highly encouraged here. The development installation uses pre-commit, flake8 linting and black style formatting to maintain code readability and reausability.

$ git clone git@github.com/jwillis0720/sadie.git
$ pip install poetry
$ poetry install --with dev

Quick Usage

Consult the documentation for complete usage. Or checkout our Colab notebook

Command Line Usage

Annotate antibody sequences only from functional human imgt antibodies to a gzip output

$ sadie airr my_sequence.fasta

API

from sadie.airr import Airr
# define a single sequence
pg9_seq = """
    CAGCGATTAGTGGAGTCTGGGGGAGGCGTGGTCCAGCCTGGGTCGTCCCTGAGACTCTCCTGTGCAGCGT
    CCGGATTCGACTTCAGTAGACAAGGCATGCACTGGGTCCGCCAGGCTCCAGGCCAGGGGCTGGAGTGGGT
    GGCATTTATTAAATATGATGGAAGTGAGAAATATCATGCTGACTCCGTATGGGGCCGACTCAGCATCTCC
    AGAGACAATTCCAAGGATACGCTTTATCTCCAAATGAATAGCCTGAGAGTCGAGGACACGGCTACATATT
    TTTGTGTGAGAGAGGCTGGTGGGCCCGACTACCGTAATGGGTACAACTATTACGATTTCTATGATGGTTA
    TTATAACTACCACTATATGGACGTCTGGGGCAAAGGGACCACGGTCACCGTCTCGAGC""".replace(
    "\n", ""
)

# initialize the api
air_api = Airr("human")

# run single sequence string
airr_table = air_api.run_single("PG9", pg9_seq)

jwillis0720/sadie