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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.

Lab News

[2022-11] Faye, James, and Lindsey were selected for oral presentation at the 17th Annual Breast Center Retreat, and James won the 2nd place prize for his presentation entitled “ClinicalOmicsDB – Bridging the gap between clinical omics data and machine learning”. Congratulations!

[2022-10] Bo’s paper OmicsEV: a tool for comprehensive quality evaluation of omics data tables has been published in Bioinformatics. This paper describes an R package for quality evaluation of omics data tables. For each data table, OmicsEV uses a series of methods to evaluate data depth, data normalization, batch effect, biological signal, platform reproducibility, and multi-omics concordance, producing comprehensive visual and quantitative evaluation results that help assess data quality of individual data tables and facilitate the identification of the optimal data processing method and parameters for the omics study under investigation. OmicsEV and documentation can be downloaded at https://github.com/bzhanglab/OmicsEV.

[2022-10] QCB students Jiaye Chen and Daniel Palacios joined the group for a research rotation. Welcome, Jiaye and Daniel!

[2022-08] Dr. Chenwei Wang joined the lab as a postdoctoral research associate. Welcome, Chenwei!

[2022-08] QCB student Xuqian Tan joined the group for a research rotation. Welcome, Xuqian!

[2022-07] Our U01 application entitled “Illuminating understudied druggable proteins using pan-cancer proteogenomics data” has been selected for funding by the Illuminating the Druggable Genome (IDG) consortium.

[2022-07] James was offered a position in the CTR Certificate of Added Qualification program and the program’s T32 grant. The CAQ training is designed to develop leaders in translational research who are well equipped to translate discoveries from the laboratory to the clinic to the benefit of human health. Congratulations!

[2022-06] The Office of Cancer Clinical Proteomics Research at the National Cancer Institute (NCI) has reaffirmed its commitment to furthering proteogenomics research by announcing the next round of Clinical Proteomic Tumor Analysis Consortium (CPTAC) centers. As part of this new phase, our lab will continue to serve as a Proteogenomic Data Analysis Center (PGDAC) over the next five years.

[2022-06] Xinpei and Sara gave oral presentations about their computational tools DeepRescore2 and IDPpub, respectively, at the 70th ASMS Conference on Mass Spectrometry and Allied Topics (ASMS 2022) took place 5-9 June 2022 in Minneapolis, MN, USA. DeepRescore2 leverages deep learning to improve phosphopeptide identification and site localization in phosphoproteomics, whereas IDPpub aims to illuminate the dark phosphoproteome through PubMed mining.

[2022-06] Byron Jia, a rising senior at Carleton College joined the group for a summer internship through the SMART program. Welcome, Byron!

[2022-05] CCB student Lindsey Olsen joined the group as a graduate student. Welcome, Lindsey!

[2022-03] Faye has been awarded funding on the CPRIT BCM Comprehensive Cancer Training Program to support her research on “Leveraging Artificial Intelligence to Illuminate the Cancer Phosphoproteome”. Congratulations!

[2022-02] Dr. Zhang is a recipient of a CPRIT Individual Investigator Research Award for Computational Systems Biology.

[2022-01] Congratulations to James for passing the QCB PhD Qualifying Exam!

[2021-12] We had a much needed and deserved Holiday Party at the Hermann Park. Great food and great fun! Special thanks to James for putting this superb event together!
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[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.


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[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.


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