How Tesorai Search gets more identifications without cutting corners on error control — the scoring model, the FDR methodology, and how it handles both DDA and DIA, in one place.

Three concepts that determine whether you can trust your proteomics results — and what to look for when evaluating any search engine.

From how proteins are measured to why the software that analyzes the data matters as mush as the instrument that generates it.

How NYU researchers used large-scale immunopeptidomics and AI-enabled peptide identification to identify 28,446 tumor-specific antigens and accelerate therapeutic target discovery.

How UCSF researchers used deep proteomics and AI-enabled peptide identification to map 7.998 protien-verified RNA editing sites - an 18-fold increase over previously reported evidence.

Fine-tuning a single-cell foundation model on proteomics data matches or beats scaling to models over 40x larger — showing multimodal training can outperform parameter scaling alone.

Our collaborators at UCSF have deposited a preprint on bioRxiv describing how RNA editing reshapes the cephalopod proteome, using Tesorai to characterize thousands of recoding events at the peptide level.

Valuable biological information is lost in the practice of publishing our work.

The "protein table" behind differential analysis hides aggregation choices that can quietly shape your results.

Peter's lecture at MaxQuant Summer School 2024 describing the principles underlying how Search was built.

Tanja Janko with the ASEF spoke with Peter about the creation of Tesorai and the future of molecular analysis.

See Tesorai Chat in action and watch examples of how it can make data visualization faster and easier.

Learn how to set up your first analysis with Tesorai Search. This guide walks you through uploading your data, running a search, and reviewing the results.