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SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets.

Garrett M Ginell^1,2, Aidan J Flynn^1,2, Alex S Holehouse^1,2

¹Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110, United States.

Bioinformatics (Oxford, England)

|August 4, 2023

Summary

SHEPHARD simplifies large-scale integrative protein bioinformatics by providing a Python framework for easy analysis of complex protein sequence annotations. This tool facilitates novel biological discoveries from proteomic datasets.

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Analyzing Large Protein Complexes by Structural Mass Spectrometry

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Area of Science:

Bioinformatics
Computational Biology
Proteomics

Background:

High-throughput experiments and computational predictions generate vast amounts of protein sequence annotation data.
Analyzing and integrating these complex datasets presents significant logistical challenges for researchers.
A major barrier exists for superficial integrative bioinformatics due to data complexity.

Purpose of the Study:

To develop a user-friendly Python framework for large-scale integrative protein bioinformatics.
To simplify the annotation, integration, and analysis of complex protein sequence data.
To enable programmatic interrogation of proteomic datasets with millions of annotations.

Main Methods:

Developed SHEPHARD, a Python framework with an object-oriented hierarchical data structure.
Integrated database-like features for programmatic data handling.
Utilized the framework for analyzing proteome-wide questions linking protein sequence to molecular function.

Main Results:

SHEPHARD trivializes large-scale integrative protein bioinformatics, making it accessible.
The framework allows for easy, Pythonic interrogation of millions of protein annotations.
Demonstrated the ability to uncover novel biology by examining orthogonal proteome-wide questions.

Conclusions:

SHEPHARD effectively addresses the technical burden of analyzing complex proteomic data.
The framework facilitates novel biological insights from large-scale protein sequence annotations.
SHEPHARD is available as a stand-alone package and a Google Colab notebook for accessibility.