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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

[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.

[2019-05] QCB student Jiasheng Wang joined the group as a graduate student. Welcome, Jiasheng!

graphic-abstract-v4[2019-05] The CPTAC study Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities has been published in Cell. This is an extension of the 2014 CPTAC colorectal cancer study. In addition to confirming the value of proteogenomic integration in uncovering novel cancer biology, this new study further demonstrated the utility of proteogenomics in therapeutic hypothesis generation. For example, phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. As another example, proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. The primary and processed datasets are available in publicly accessible data repositories and portals (e.g., LinkedOmics), which we hope will allow new biological discoveries and therapeutic hypothesis generation.

[2019-04] QCB student Wen Jiang (Faye) joined the group as a graduate student. Welcome, Faye!

[2019-03] Dr. Xinpei Yi joined the lab as a postdoctoral research fellow. Xinpei just graduated from the Institute of Applied Mathematics, Chinese Academy of Sciences, Beijing, with a PhD degree in Probability Theory and Mathematical Statistics and Bioinformatics. Welcome, Xinpei!

[2019-01] Bo’s paper  PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations has been published in Genome Research. Congratulations, Bo! PepQuery is a peptide-centric search engine that allows quick and easy proteomic validation of genomic alterations without customized database construction. Next generation sequencing-based genomic studies continuously identify new genomic alterations that may lead to novel protein sequences, which are attractive candidates for disease biomarkers and therapeutic targets after proteomic validation. The popular approach for proteomic validation requires customized database construction and a full evaluation of all possible spectrum-peptide pairs, which is time-consuming. We implemented PepQuery as both stand-alone and web-based applications. The stand-alone version supports batch analysis and user-provided MS/MS data. The web version provides access to more than half a billion MS/MS spectra from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and other cancer proteomic studies, making MS/MS data directly available and useful to scientists outside the proteomics community. PepQuery can be accessed at http://www.pepquery.org.

[2019-01] Dr. Jonathan Lei joined the lab as a postdoctoral research fellow supported by the Breast Center T32 training program. Jonathan just got his PhD in Translational Biology & Molecular Medicine from BCM. Welcome, Jonathan!

[2019-01] Ahmed Gad from TBMM interdisciplinary program joined the lab for a research rotation. Welcome, Ahmed!

[2019-01] Jiasheng Wang from the QCB program joined the group for a research rotation. Welcome, Jiasheng!

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