Justin Sanders

Improvements to Casanovo, a deep learning de novo peptidesequencer

The paper improves Casanovo, a deep learning de novo peptidesequencer, by enhancing interpretability, generalizing its use in database search, speeding up performance, and adding tools for adoption and result interpretation.

Puget predicts gene expression across cell types using sequence and 3D chromatin organization data

Puget predicts gene expression across cell types using sequence and Hi-C data, improving generalization to new biosamples and species.

Accounting for digestion enzyme bias in Casanovo

The paper modifies the Casanovo model to account for digestion enzyme bias but finds no significant improvement in sequencing accuracy.

A learned score function improves the power of mass spectrometry database search

The paper re-purposes a machine learning model for de novo peptide sequencing as a score function for database search, improving detection power over existing methods.

A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data

This paper introduces a transformer-based model called Cascadia for de novo sequencing of data-independent acquisition mass spectrometry data, achieving state-of-the-art performance.

HiCRep.py: Fast comparison of Hi-C contact matrices in Python

HiCRep.py provides a faster Python implementation of HiCRep for comparing Hi-C contact matrices, demonstrating improved performance on high-resolution and single-cell Hi-C data.