Welcome to Dr. Bing Zhang’s Lab at the Baylor College of Medicine. We develop and use integrative bioinformatics approaches to extract biological meanings from experimental data and generate hypotheses for experimental validation. Please explore our website to learn more about our people and our research.
[2020-04] Bo’s paper Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis has been published in Nature Communications. Congratulations, Bo! Identifying mutation-derived neoantigens by proteogenomics requires robust strategies for quality control. In this paper, we propose peptide retention time as an evaluation metric for proteogenomics quality control methods, and develop a deep learning algorithm AutoRT for accurate retention time prediction. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.
[2020-02] The CPTAC study Proteogenomic Characterization of Endometrial Carcinoma has been published in Cell. Congratulations to Yongchao and all co-authors!
[2020-01] Our collaborative study with Drs. Ellis, Carr and other colleagues entitled Microscaled proteogenomic methods for precision oncology has been published in Nature Communications. Congratulations to Eric and all co-authors!
[2019-12] Congratulations to Faye for passing the QCB PhD Qualifying Exam!
[2019-10] Chaozhong Liu from the QCB program joined the lab for a research rotation. Welcome, Chaozhong!
[2019-07] Zifan Zhao from the Cancer & Cell Biology program joined the lab for a research rotation. Welcome, Zifan!
[2019-07] Linhua Wang from the QCB program joined the group for a research rotation. Welcome, Linhua!
[2019-05] Sara and Zhiao’s paper Graph algorithms for condensing and consolidating gene set analysis results has been published in Molecular & Cellular Proteomics. Two graph algorithms were used to integrate gene set analysis results from multiple experiments, such as multi-omics or pan-cancer studies. Specifically, a weighted set cover algorithm was used to reduce redundancy of gene sets identified in a single experiment, and then affinity propagation was used to consolidate similar gene sets identified from multiple experiments into clusters and to automatically determine the most representative gene set for each cluster. This has been implemented in an R package Sumer, which is available at https://github.com/bzhanglab/sumer.
[2019-05] Yuxing’s paper WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs has been published in Nucleic Acids Research. There are five major changes in this new version: 1) We have completely redesigned result visualizations and user interfaces to improve user-friendliness and to provide multiple types of interactive and publication-ready figures. 2) To address the growing and unique need for phosphoproteomics data interpretation, we have implemented phosphosite set analysis to identify important kinases from phosphoproteomics data. 3) To facilitate comprehension of the enrichment results, we have implemented two methods to reduce redundancy between enriched gene sets. 4) We introduced a web API for other applications to get data programmatically from the WebGestalt server or pass data to WebGestalt for analysis. 5) We wrapped the core computation into an R package called WebGestaltR for users to perform analysis locally or in third party workflows.