Open-Source and FAIR Research Software for Proteomics

doi:10.1021/acs.jproteome.4c01079

Open-Source and FAIR Research Software for Proteomics

2025-04-01

0

OA

AI

William Stafford Noble

DOI：10.1021/acs.jproteome.4c01079

原文链接

查看原文

摘要

En 中文

Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms, predictive models, and workflows, but the field faces challenges, including the increasing complexity of mass spectrometry data, limited reproducibility due to proprietary software, and difficulties integrating with other omics disciplines. Closed-source, platform-specific tools exacerbate these issues by restricting innovation, creating inefficiencies, and imposing hidden costs on the community. Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization. Drawing on lessons from other omics fields, we present a vision for a future where OSS and FAIR principles underpin a transparent, accessible, and innovative proteomics community.

Keyword:

FAIR principles

open source

computationalproteomics

best practices

data reuse

open data

mass spectrometry

proteomics

Open-Source and FAIR Research Software for Proteomics

摘要

AI总结

期刊

机构