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Scientists ask hard questions about how Tesorai generates more identifications. We think that's exactly right. These explainers answer them directly — written for scientists, with the data to back it up.

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

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.

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

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.

The Tesorai team has updated the bioRxiv preprint describing the implementation of Tesorai Search, including detailed benchmarking of Tesorai against other leading software across multiple datasets.

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.

Proteomics studies all proteins in the body, revealing how genes drive cellular functions. Unlike the stable genome, the dynamic proteome reflects real-time health and responses to diet, environment, and disease.

Tesorai Search re-centers proteomics on its foundation — the spectrum and the sequence — using a large pre-trained model that delivers both higher sensitivity and speed.

Biology now relies on big-data, high-throughput workflows to process and interpret signals from instruments like DNA sequencers and mass spectrometers. These workflows convert raw data into meaningful biological insights, helping identify disease drivers, drug targets, and biomarkers.

Tools like Percolator can boost sensitivity, but overfitting can cause models to mistake artifacts for real signals, leading to more false positives.

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

Accurate protein identification and quantification are central to extracting meaningful biological insights from mass spectrometry (MS)-based proteomics.