ArticleBioinformatics (Oxford, England)2023
SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Disordered but rhythmic-the role of intrinsic protein disorder in eukaryotic circadian timing.FEBS letters · 2026Review
- Sequence-based prediction of intermolecular interactions driven by disordered regions.Science (New York, N.Y.) · 2025Article
- Phosphorylation of disordered proteins tunes local and global intramolecular interactions.Biophysical journal · 2024Article
- Phosphorylation of disordered proteins tunes local and global intramolecular interactions.bioRxiv : the preprint server for biology · 2024Article
- Direct prediction of intermolecular interactions driven by disordered regions.bioRxiv : the preprint server for biology · 2024Article
- The molecular basis for cellular function of intrinsically disordered protein regions.Nature reviews. Molecular cell biology · 2024Review
- Direct prediction of intrinsically disordered protein conformational properties from sequence.Nature methods · 2024Article
- Aberrant phase separation is a common killing strategy of positively charged peptides in biology and human disease.bioRxiv : the preprint server for biology · 2023Article
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3 authors.
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Abstract
motivationThe 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.
resultsTo 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/shephard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shephard-colab).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.