دورية أكاديمية
SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets.
| العنوان: | SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets. |
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| المؤلفون: | Ginell GM; Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110, United States.; Center for Biomolecular Condensates, Washington University in St. Louis, 1 Brookings Drive, Saint Louis, MO 63130, United States., Flynn AJ; Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110, United States.; Center for Biomolecular Condensates, Washington University in St. Louis, 1 Brookings Drive, Saint Louis, MO 63130, United States., Holehouse AS; Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110, United States.; Center for Biomolecular Condensates, Washington University in St. Louis, 1 Brookings Drive, Saint Louis, MO 63130, United States. |
| المصدر: | Bioinformatics (Oxford, England) [Bioinformatics] 2023 Aug 01; Vol. 39 (8). |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S. |
| اللغة: | English |
| بيانات الدورية: | Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Oxford : Oxford University Press, c1998- |
| مواضيع طبية MeSH: | Proteomics* , Proteome*, Software ; Computational Biology ; Molecular Sequence Annotation |
| مستخلص: | 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/shephard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shephard-colab). (© The Author(s) 2023. Published by Oxford University Press.) |
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| المشرفين على المادة: | 0 (Proteome) |
| تواريخ الأحداث: | Date Created: 20230804 Date Completed: 20230814 Latest Revision: 20230815 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC10423030 |
| DOI: | 10.1093/bioinformatics/btad488 |
| PMID: | 37540173 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1367-4811 |
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| DOI: | 10.1093/bioinformatics/btad488 |