  Personalized cancer vaccines are envisioned as the next generation rational
cancer immunotherapy. The key step in developing personalized therapeutic
cancer vaccines is to identify tumor-specific neoantigens that are on the
surface of tumor cells. A promising method for this is through de novo peptide
sequencing from mass spectrometry data. In this paper we introduce DeepNovoV2,
the state-of-the-art model for peptide sequencing. In DeepNovoV2, a spectrum is
directly represented as a set of (m/z, intensity) pairs, therefore it does not
suffer from the accuracy-speed/memory trade-off problem. The model combines an
order invariant network structure (T-Net) and recurrent neural networks and
provides a complete end-to-end training and prediction framework to sequence
patterns of peptides. Our experiments on a wide variety of data from different
species show that DeepNovoV2 outperforms previous state-of-the-art methods,
achieving 13.01-23.95\% higher accuracy at the peptide level.
