Xiaowen Liu

The paper introduces TopRepo, a comprehensive TD-MS spectral repository, and shows it improves proteoform identification and supports DL model training for spectral prediction.

The paper creates a high-resolution zebrafish embryonic proteome atlas across 16 time points, revealing dynamic protein expression patterns and discrepancies between transcript and protein levels during early development.

This study evaluates top-down and middle-down mass spectrometry approaches to enhance proteoform sequence coverage using in-source fragmentation and partial digestion, achieving over 90% coverage for three proteins.

The paper demonstrates that capillary zone electrophoresis-mass spectrometry (CZE-MS) can reproducibly measure peptides, proteoforms, and complexoforms across multiple biological samples.

This study performs a global proteomic analysis of colorectal cancers stratified by microsatellite instability subtype, revealing protein differences associated with Lynch syndrome and other subtypes.

This paper introduces TopMPI, a tool for identifying proteoforms from multiplexed TD-MS/MS spectra, showing improved identification and reduced errors.

TopLib is a software package for building and searching top-down mass spectral libraries, offering faster and more reproducible proteoform identification than conventional methods.

TopDIA is a software tool for proteoform identification using TD-DIA-MS, showing improved identification compared to TD-DDA-MS in E. coli.

TopFD is a tool for top-down proteomics that improves proteoform feature detection accuracy and reproducibility compared to existing methods.

The paper introduces PTM-TBA, a software pipeline that identifies and localizes post-translational modifications in proteoforms using top-down and bottom-up mass spectrometry combined with UniProt annotations.

The paper develops computational methods to process sub-nanopore nanospectra and predict electrical blockade currents from protein sequences for protein identification.

The paper evaluates machine learning models for predicting proteoform retention and migration times in top-down mass spectrometry, finding high accuracy with Prosit and a neural network model.

This paper introduces TopPG, a tool for identifying proteoforms with genetic alterations and alternative splicing using top-down mass spectrometry data from colorectal cancer cells.