Spectronaut 21.1: DIA Proteomics & Multiomics Platform

Spectronaut 21.1 is the latest version of Biognosys’ flagship software for data-independent acquisition (DIA) proteomics, officially launched in June 2025 . This release delivers significant advancements in four key areas: next‑generation directDIA, AI‑driven PTM analysis, immunopeptidomics control, and large‑scale cohort performance optimization .

Key New Features in Version 21

Enhanced directDIA & Identification Depth

Spectronaut 21 delivers substantial improvements for library‑free DIA discovery workflows, powered by next‑generation directDIA with advanced machine and deep learning :

  • ~10% more protein groups across 65 benchmark proteomics datasets, and ~11% more peptides across 27 immunopeptidomics datasets

  • AI/ML scoring enhancements for both tryptic and unspecific peptides

  • Automated sub‑sample runs to boost efficiency for large‑scale cohort analyses

  • Up to 15% faster directDIA processing for timsTOF data

Advanced PTM Analysis

The 21.1 update significantly improves post‑translational modification (PTM) analysis:

  • Deep learning models enable confident detection of both common and rare modifications

  • Enhanced site localization driven by next‑generation scoring, including ion mobility metrics, providing sharper precision and more complete PTM mapping

  • Improved site‑specific statistics, stoichiometry, and post‑analysis visualization for deeper biological insights

Immunopeptidomics & ISF Control

For immunopeptidomics workflows, Spectronaut 21.1 introduces new tools to improve reliability :

  • In‑Source Fragmentation (ISF) classification to distinguish real endogenous peptides from fragmentation artifacts that can inflate identification counts

  • Key result fields now include EG.InSourceFragmentationClass, EG.InSourceFragmentationParentID, and EG.LikelyInSourceFragmentation

Performance & File Size Optimization

Processing large‑scale cohorts is now more efficient:

  • 15–50% reduction in analysis file sizes, minimizing data transfer and storage requirements

  • Up to 30% faster parallel processing for experiment‑level merging

  • ~50% smaller SNE files when saving with XIC information

  • Cloud‑ready performance with smart orchestration of parallel per‑sample processing