Jennifer G. Abelin

The paper develops a deep learning model to predict peptide-HLA II interactions for post-translationally modified peptides, showing accurate predictions on unseen modifications.

The paper identifies 25% of 7,264 non-canonical open reading frames (ncORFs) as producing translated gene products, supported by over 3,000 peptides from 3.8 billion mass spectra.

This study identifies rare tumor-reactive T cells and conserved antigen repertoires in multiple myeloma and acute myeloid leukemia, developing a TCR fitness model to predict immunotherapy response.

This study uses Massively Parallel Ribosome Profiling to identify thousands of viral ORFs, including non-canonical ones, and shows their impact on HLA-I peptides and protein synthesis regulation.

This paper discusses the current state of research on non-canonical open reading frames (ORFs) and proposes standards for their investigation and reporting using Ribo-seq and proteomics.

Specter improves DIA proteomics by directly deconvoluting spectra using linear algebra, enhancing sensitivity for similar peptides and challenging cases.

The paper creates a proteomic resource profiling 90 drugs in six cell lines, revealing cell-specific responses and enabling integration with transcriptional data to identify therapeutic opportunities.