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.
[2021-03] Xinpei has been selected for the trainee award by the US HUPO 2021 conference committee for her work on caAtlas and will give an award talk at the conference. Meanwhile, Faye has received honorable mention for her work on “Deep learning-derived evaluation metrics for benchmarking computational pipelines for the analysis of large-scale phosphoproteomic datasets”, which has also been selected for an oral presentation. Congratulations!
[2021-01] The CPTAC study Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma has been published in Cancer Cell. Congratulations to Chen, Sara, and all co-authors! In addition to providing more complete biological understanding of HPV-negative HNSCC, this study demonstrates the potential of proteogenomics as a therapeutic hypothesis generator. First, focused characterization of the target proteins and pathways of the standard-of-care or investigational drugs identifies biomarkers that may help match HNSCC patients to available treatments. Moreover, unbiased exploratory analysis of proteogenomic data further reveals new putative therapeutic targets for further experimental validation.
[2021-01] Diyahir Campos joined the lab as a bioinformatics programmer. Welcome, Diyahir!
[2020-11] The Proteomics special issue, “Computational Proteomics: Focus on Deep Learning“, is now online. This special issue, edited by Bo and Dr. Zhang, brings together 17 original research, review, and perspective articles on applying novel computational technologies, with a focus on deep learning methods, to the analysis of MS-based proteomics data.
[2020-10] Jiayi Luo from the CCB program joined the lab for a research rotation. Welcome, Jiayi!
[2020-10] James Moon from the QCB program joined the lab for a research rotation. Welcome, James!
[2020-09] A review article led by Bo, Deep learning in proteomics, has been published in Proteomics. This paper provides a comprehensive overview of deep learning applications in proteomics including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding affinity prediction, and protein structure prediction. A list of the applications described in the paper can be found at our GitHub site.
[2020-08] Kai’s paper DeepRescore: leveraging deep learning to improve peptide identification in immunopeptidomics has been published in Proteomics. Congratulations, Kai! DeepRescore integrates peptide features derived from deep learning predictions, namely accurate retention time and MS/MS spectra predictions, into the rescoring of peptide-spectrum matches, which increases both the sensitivity and reliability of the identification of MHC-bound peptides and neoantigens. DeepRescore is developed using NextFlow and Docker and is available at https://github.com/bzhanglab/DeepRescore.
[2020-08] Congratulations to Michael on successfully defending his PhD thesis!
[2020-08] Mariah Berner from the CCB program joined the lab for a research rotation. Welcome, Mariah!
[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.