TY - JOUR
T1 - SHEPHARD
T2 - a modular and extensible software architecture for analyzing and annotating large protein datasets
AU - Ginell, Garrett M.
AU - Flynn, Aidan J.
AU - Holehouse, Alex S.
N1 - Funding Information:
This work was supported by Dewpoint Therapeutics, National Science Foundation (NSF) grant 2128068, and the Longer Life Foundation: an RGA/Washington University Collaboration, all to A.S.H. This work was also funded in part by WALII, supported by the National Science Foundation Division of Biological Infrastructure (NSF DBI ) grant 2213983.
Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press.
PY - 2023/8/1
Y1 - 2023/8/1
N2 - Motivation: The emergence of high-throughput experiments and high-resolution computational predictions has led to an explosion in the quality and volume of protein sequence annotations at proteomic scales. Unfortunately, sanity checking, integrating, and analyzing complex sequence annotations remains logistically challenging and introduces a major barrier to entry for even superficial integrative bioinformatics. Results: To address this technical burden, we have developed SHEPHARD, a Python framework that trivializes large-scale integrative protein bioinformatics. SHEPHARD combines an object-oriented hierarchical data structure with database-like features, enabling programmatic annotation, integration, and analysis of complex datatypes. Importantly SHEPHARD is easy to use and enables a Pythonic interrogation of largescale protein datasets with millions of unique annotations. We use SHEPHARD to examine three orthogonal proteome-wide questions relating protein sequence to molecular function, illustrating its ability to uncover novel biology. Availability and implementation: We provided SHEPHARD as both a stand-alone software package (https://github.com/holehouse-lab/shep hard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shep hard-colab).
AB - Motivation: The emergence of high-throughput experiments and high-resolution computational predictions has led to an explosion in the quality and volume of protein sequence annotations at proteomic scales. Unfortunately, sanity checking, integrating, and analyzing complex sequence annotations remains logistically challenging and introduces a major barrier to entry for even superficial integrative bioinformatics. Results: To address this technical burden, we have developed SHEPHARD, a Python framework that trivializes large-scale integrative protein bioinformatics. SHEPHARD combines an object-oriented hierarchical data structure with database-like features, enabling programmatic annotation, integration, and analysis of complex datatypes. Importantly SHEPHARD is easy to use and enables a Pythonic interrogation of largescale protein datasets with millions of unique annotations. We use SHEPHARD to examine three orthogonal proteome-wide questions relating protein sequence to molecular function, illustrating its ability to uncover novel biology. Availability and implementation: We provided SHEPHARD as both a stand-alone software package (https://github.com/holehouse-lab/shep hard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shep hard-colab).
UR - http://www.scopus.com/inward/record.url?scp=85167847658&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btad488
DO - 10.1093/bioinformatics/btad488
M3 - Article
C2 - 37540173
AN - SCOPUS:85167847658
SN - 1367-4803
VL - 39
JO - Bioinformatics
JF - Bioinformatics
IS - 8
M1 - btad488
ER -