Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2574496

Open-Source and FAIR Research Software for Proteomics

Perez-Riverol, Yasset [European Bioinformatics Institute, Cambridge (United Kingdom). European Molecular Biology Laboratory] (ORCID:0000000165796941)·Bittremieux, Wout [Univ. of Antwerp (Belgium)] (ORCID:0000000231051359)·Noble, William S. [Univ. of Washington, Seattle, WA (United States)] (ORCID:0000000172834715)·Martens, Lennart [VIB-UGent Center for Medical Biotechnology, Ghent (Belgium); Ghent Univ. (Belgium)] (ORCID:000000034277658X)·Bilbao, Aivett [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Environmental Molecular Sciences Laboratory (EMSL); USDOE Agile BioFoundry, Emeryville, CA (United States)] (ORCID:0000000329858249)·Lazear, Michael R. [Belharra Therapeutics, San Diego, CA (United States)] (ORCID:0000000153134262)·Grüning, Bjorn [Albert-Ludwigs University, Freiburg (Germany)] (ORCID:0000000230796586)·Katz, Daniel S. [Univ. of Illinois at Urbana-Champaign, IL (United States). National Center for Supercomputing Applications (NCSA)] (ORCID:0000000159347525)·MacCoss, Michael J. [Univ. of Washington, Seattle, WA (United States)] (ORCID:0000000318530256)·Dai, Chengxin [Beijing Institute of Life Omics (China). National Center for Protein Sciences]·Eng, Jimmy K. [Univ. of Washington, Seattle, WA (United States)] (ORCID:0000000163526737)·Bouwmeester, Robbin [VIB-UGent Center for Medical Biotechnology, Ghent (Belgium); Ghent Univ. (Belgium)] (ORCID:0000000168077029)·Shortreed, Michael R. [Univ. of Wisconsin, Madison, WI (United States)] (ORCID:0000000346260863)·Audain, Enrique [Carl von Ossietzky University, Oldenburg (Germany). University Medicine Oldenburg]·Sachsenberg, Timo [University of Tübingen (Germany)] (ORCID:0000000228336070)·Van Goey, Jeroen [InstaDeep London (United Kingdom)]·Wallmann, Georg [Max Planck Institute of Biochemistry, Martinsried (Germany)]·Wen, Bo [Univ. of Washington, Seattle, WA (United States)]·Käll, Lukas [KTH Royal Inst. of Technology, Stockholm (Sweden). Science for Life Laboratory] (ORCID:0000000156899797)·Fondrie, William E. [Talus Bioscience, Seattle, WA (United States)] (ORCID:0000000215543716)
View original source ↗Read source document ↗Export metadata ↓

Abstract

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.

Explore related subjects

97 MATHEMATICS AND COMPUTINGalgorithmscomputational chemistryoptical propertiesproteomicssoftware

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Perez-Riverol, Yasset [European Bioinformatics Institute, Cambridge (United Kingdom). European Molecular Biology Laboratory] (ORCID:0000000165796941), Bittremieux, Wout [Univ. of Antwerp (Belgium)] (ORCID:0000000231051359), Noble, William S. [Univ. of Washington, Seattle, WA (United States)] (ORCID:0000000172834715), Martens, Lennart [VIB-UGent Center for Medical Biotechnology, Ghent (Belgium); Ghent Univ. (Belgium)] (ORCID:000000034277658X), Bilbao, Aivett [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Environmental Molecular Sciences Laboratory (EMSL); USDOE Agile BioFoundry, Emeryville, CA (United States)] (ORCID:0000000329858249), Lazear, Michael R. [Belharra Therapeutics, San Diego, CA (United States)] (ORCID:0000000153134262), Grüning, Bjorn [Albert-Ludwigs University, Freiburg (Germany)] (ORCID:0000000230796586), Katz, Daniel S. [Univ. of Illinois at Urbana-Champaign, IL (United States). National Center for Supercomputing Applications (NCSA)] (ORCID:0000000159347525), MacCoss, Michael J. [Univ. of Washington, Seattle, WA (United States)] (ORCID:0000000318530256), Dai, Chengxin [Beijing Institute of Life Omics (China). National Center for Protein Sciences], Eng, Jimmy K. [Univ. of Washington, Seattle, WA (United States)] (ORCID:0000000163526737), Bouwmeester, Robbin [VIB-UGent Center for Medical Biotechnology, Ghent (Belgium); Ghent Univ. (Belgium)] (ORCID:0000000168077029), Shortreed, Michael R. [Univ. of Wisconsin, Madison, WI (United States)] (ORCID:0000000346260863), Audain, Enrique [Carl von Ossietzky University, Oldenburg (Germany). University Medicine Oldenburg], Sachsenberg, Timo [University of Tübingen (Germany)] (ORCID:0000000228336070), Van Goey, Jeroen [InstaDeep London (United Kingdom)], Wallmann, Georg [Max Planck Institute of Biochemistry, Martinsried (Germany)], Wen, Bo [Univ. of Washington, Seattle, WA (United States)], Käll, Lukas [KTH Royal Inst. of Technology, Stockholm (Sweden). Science for Life Laboratory] (ORCID:0000000156899797), Fondrie, William E. [Talus Bioscience, Seattle, WA (United States)] (ORCID:0000000215543716). 2025-04-23. Open-Source and FAIR Research Software for Proteomics. https://doi.org/10.1021/acs.jproteome.4c01079

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

TANTE: Time-adaptive operator learning via neural Taylor expansion

Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics. However, most existing methods rely on fixed time step sizes during rollout, which limits their ability to adapt to varying temporal complexity and often leads to error accumulation. In this work, we propose the Time-Adaptive Transformer with Neural Taylor Expansion (TANTE), a novel operator-learning framework that produces continuous-time predictions with adaptive step sizes. TANTE predicts future states by performing a Taylor expansion at the current state, where neural networks learn both the higher-order temporal derivatives and the local radius of convergence. This allows the model to dynamically adjust its rollout based on the local behavior of the solution, thereby reducing cumulative error and improving computational efficiency. We demonstrate the effectiveness of TANTE across a wide range of PDE benchmarks, achieving superior accuracy and adaptability compared to fixed-step baselines, delivering accuracy gains of 60-80 % and speed-ups of 30-40 % at inference time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Structured illumination for surface-resolved grazing-incidence X-ray scattering

Grazing-incidence (GI) scattering techniques are widely used to characterize thin films, offering high surface sensitivity and insight into morphology and structure. However, these approaches typically provide statistical averaged information due to elongated footprint or limited spatial resolution due to beam size. Here we introduce a method that combines structured illumination with GI X-ray scattering and leverages our computational imaging approach to resolve local structural details. We demonstrate that our method captures local features of an organic semiconductor thin film without the need for sample rotation as in tomography. The method expands GI techniques from statistical averaging to high-resolution imaging, thereby providing the capability for detailed analysis of local material properties, such as domain shape, orientation and polymorphism, which are critical for advancing material design towards more efficient and tailored materials.

97 MATHEMATICS AND COMPUTING↗