# Mass Spectrometry DDA Tensor (MSDT)

Proteomics has plenty of public mass spectra, but mixed formats, missing metadata and a human-heavy species mix make them hard to train deep models on. MassNet gathers DDA data from 648 projects across 35 species, holds 548 million PSMs, and stores spectra in the tensor-based MSDT format with traceable metadata. On a benchmark spanning 15 species and 9.66 million PSMs, the XuanjiNovo model trained on this corpus reaches peptide recall of 0.45 on human and 0.68 on mouse data, and 0.25 on tomato, above the listed de novo sequencing models.

Proteomics has plenty of public mass spectra, but mixed formats, missing metadata and a human-heavy species mix make them hard to train deep models on. MassNet gathers DDA data from 648 projects across 35 species, holds 548 million PSMs, and stores spectra in the tensor-based MSDT format with traceable metadata. On a benchmark spanning 15 species and 9.66 million PSMs, the XuanjiNovo model trained on this corpus reaches peptide recall of 0.45 on human and 0.68 on mouse data, and 0.25 on tomato, above the listed de novo sequencing models.

MassNet is an AI-ready mass spectrometry proteomics corpus of about 1.54 billion tandem mass spectra from 648 projects across 35 species, paired with a columnar tensor storage format called MSDT. Previously, mass spectra were scattered across heterogeneous formats such as MGF and mzML, with peak lists stored separately from peptide annotations and coverage skewed toward human samples. XuanjiNovo, a non-autoregressive model trained on this corpus, improves over state-of-the-art methods by 38.35% to 143.57% on human Astral data and also supports retention time prediction and PSM rescoring.

MassNet is an AI-ready mass spectrometry proteomics corpus of about 1.54 billion tandem mass spectra from 648 projects across 35 species, paired with a columnar tensor storage format called MSDT. Previously, mass spectra were scattered across heterogeneous formats such as MGF and mzML, with peak lists stored separately from peptide annotations and coverage skewed toward human samples. XuanjiNovo, a non-autoregressive model trained on this corpus, improves over state-of-the-art methods by 38.35% to 143.57% on human Astral data and also supports retention time prediction and PSM rescoring.