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.
[2021-11] Faye’s paper Deep learning-derived evaluation metrics enable effective benchmarking of computational tools for phosphopeptide identification has been published in Molecular & Cellular Proteomics. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods. Congratulations to Faye!
[2021-10] Congratulations to Jonathan on winning the 2nd place prize for his oral presentation at the 16th Annual Breast Center Retreat!
[2021-09] Xinpei’s paper caAtlas: An immunopeptidome atlas of human cancer has been published in iScience. This paper reports an immunopeptidome atlas of human cancer constructed through an extensive collection of 43 published immunopeptidomic datasets and standardized analysis of 81.6 million MS/MS spectra using Open-pFind. The study greatly expands the current knowledge of MHC-bound antigens, including an unprecedented characterization of post-translationally modified antigens. Further analysis of these data provides evidence for tumor specific-presentation of post translationally modified antigens, cancer testis antigens and cancer type-specific tumor associated antigens. All these data together with annotated MS/MS spectra supporting identification of each antigen are available in an easily browsable web portal named cancer antigen atlas (caAtlas). caAtlas provides a central resource for the selection and prioritization of MHC-bound peptides for in vitro HLA binding assay and immunogenicity testing, which will pave the way to eventual development of cancer immunotherapies. Congratulations to Xinpei, Yuxing, and all coauthors!
[2021-09] The CPTAC study Proteogenomic characterization of pancreatic ductal adenocarcinoma has been published in Cell. Congratulations to Chen and all co-authors!
[2021-08] The CPTAC study A proteogenomic portrait of lung squamous cell carcinoma has been published in Cell. Congratulations to Sara and all co-authors!
[2021-06] Kuan Huang joined the lab as a postdoctoral research associate. Welcome, Kuan!
[2021-05] The paper describing the crowdsourcing precisionFDA NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge has been published in Patterns. Congratulations to Seungyeul, Zhiao, Bo, SoonJye, and all co-authors! The final collaborative product, COSMO, can be accessed at https://github.com/bzhanglab/COSMO.
[2021-04] Zhiao’s paper Feature selection methods for protein biomarker discovery from proteomics or multi-omics data has been published in Molecular & Cellular Proteomics. The algorithms ProMS and ProMS_mo show good performance, enable functional interpretation of the identified markers, and provide alternative choices for each identified marker to facilitate a robust transition to the verification and validation platforms. The software can be downloaded at https://github.com/bzhanglab/proms.
[2021-04] Xinpei received the AACR Women in Cancer Research (WICR) Scholar Award for her work on caAtlas, which was presented at the AACR 2021 conference. Meanwhile, Jonathan received an AACR Doreen J. Putrah Cancer Research Foundation Scholar-in-Training Award recognizing his work on chemotherapy response and potential therapeutic targets to overcome resistance in triple-negative breast cancer. Congratulations!
[2021-03] QCB student Chang In Moon (James) joined the group as a graduate student. Welcome, James!
[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.