Deep learning has notably advanced the field of liquid chromatography–mass spectrometry-based proteomics. Accurate prediction of peptide retention times significantly enhances our ability to match LC-...

De novo peptide sequencing enables peptide identification from fragmentation spectra without relying on sequence databases. However, incomplete spectra create ambiguity, making unambiguous identificat...

While LC retention time prediction of peptides and their modifications has proven useful, widespread adoption and optimal performance are hindered by variations in experimental parameters. These varia...

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.

Liquid chromatography–mass spectrometry (LC-MS/MS) extends the matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) Zooarcheology by Mass Spectrometry (ZooMS) “mass fingerprinting” approach to species identification by providing fragmentation spectra for each peptide. However, ancient bone samples generate sparse data containing only a few collagen proteins, rendering target–decoy strategies unusable and increasing uncertainty in peptide annotation. To ameliorate this issue, we present a ZooMS/MS data pipeline that builds on a manually curated Collagen database and comprises two novel algorithms: isoBLAST and ClassiCOL. isoBLAST first extends peptide ambiguity by generating all “potential peptide candidates” isobaric to the annotated precursor. The exhaustive set of candidates created is then used to retain or reject different potential paths at each taxonomic branching point from superkingdom to species, until the greatest possible specificity is reached. Uniquely, ClassiCOL allows for the identification of taxonomic mixtures, including contaminated samples, as well as suggesting taxonomies not represented in sequence databases, including extinct taxa. All considered ambiguity is then graphically represented with clear prioritization of the potential taxa in the sample. Using public as well as in-house data acquired on different instruments, we demonstrate the performance of this universal postprocessing and explore the identification of both genetic and sample mixtures. Diet reconstruction from 40,000-year-old cave hyena coprolites illustrates the exciting potential of this approach.

Peptide collisional cross-section (CCS) prediction is complicated by the tendency of peptide ions to exhibit multiple conformations in the gas phase. This adds further complexity to downstream analysi...

We are excited to announce DIA-NN 2.0, the most significant milestone in the history of DIA-NN development. Key Breakthroughs Proteoform Confidence mode: DIA-NN 2.0 solves the long-standing chall...

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 in...