TIMOTHY I. SHAW Ph.D. Assistant Member, Department of Biostatistics and Bioinformatics. Dr. Shaw's research focuses on systems immuno-oncology, particularly in developing computational tools to study immune interactions and associated regulatory networks. My program consists of the following three aims: 1) Leverage multi-omics data to identify key regulators and biomarkers of immune suppression. 2) Identify cancer-specific isoforms for immunotherapy. 3) Developing a web-based ecosystem to accelerate research for basic researchers and clinicians. We aim to connect translational and basic research by facilitating data interpretation and access to basic and clinical research data. Our group's goal is to facilitate the accelerated development of pharmaceutics and immunologics for Florida patients at Moffitt and beyond.
Shaw TI* et al. Multi-omics approach to identifying isoform variants as therapeutic targets in cancer patients.Frontiers in Oncology. 2022 Dec; 12. doi: 10.3389/fonc.2022.1051487.
Shaw TI, et al. Integrative network analysis reveals USP7 haploinsufficiency inhibits E-protein activity in pediatric T-lineage acute lymphoblastic leukemia (T-ALL).Sci Rep.2021 Mar 4;11(1):5154. doi: 10.1038/s41598-021-84647-2.
Tan H*, Yang K*, Li Y*,Shaw TI*, et al. Integrative Proteomics and Phosphoproteomics Profiling Reveals Dynamic Signaling Networks and Bioenergetics Pathways Underlying T Cell Activation.Immunity. 2017 Mar 21;46(3):488-503. doi: 10.1016/j.immuni.2017.02.010.
Active Grant Funding
"Program control of CD8+ T cell response to tumors and tumor memory." NIH R01CA293755. MPI (Avram, Shaw). 8/13/2025-4/30/2030.
"Characterizing the Role of Aberrant FGD1 Splicing in Brain and Bone Metastasis in Clear Cell Renal Cell Carcinoma." DoD CDMRP HT94252510691. Co-PI (Manley, Shaw). 7/15/2025-7/14/2029
"Targeting ER Stress in Pediatric Acute Myeloid Leukemia." FDOH-Live Like Bella. 04/01/2023-03/31/2026
"Developing ShinyEvents: a generalizable framework for the analysis of longitudinal molecular and clinical data." Moffitt Cancer Center. 07/01/2024-06/30/2025
Alyssa Obermayer M.S. A Research Data Analyst in the Biostatistics and Bioinformatics department in the lab of Dr. Tim Shaw. Specializing in developing and maintaining R Shiny web applications for genomic and clinical data analysis. Contributes to implementing bioinformatic tools integrating genomic data with patient outcome and regression methods to identify clinically relevant predictive biomarkers. Assists in streamlining multi-omics analysis for collaboration efforts for the advancement of cancer immunology and therapeutics research.
Select Publications
Davis JT*, Obermayer A*, et al. BatchFLEX: feature-level equalization of X-batch. Bioinformatics. 2024 Oct 1;40(10):btae587. doi: 10.1093/bioinformatics/btae587. PMID: 39360977; PMCID: PMC11486499. (* Co-First Authors)
Nobles, G., Obermayer, A., et al. “BERLIN: Basic explorer for single-cell RNAseq analysis and cell lineage determination” bioRxiv (2023) DOI: https://doi.org/10.1101/2023.07.13.548919
Obermayer, A et al. “PATH-SURVEYOR: Pathway Level Survival Enquiry for Immuno-Oncology and Drug Repurposing.” BMC Bioinformatics 24, 266 (2023) DOI: https://doi.org/10.1186/s12859-023-05393-y
Obermayer, A et al. “DRPPM-EASY: A Web-Based Framework for Integrative Analysis of Multi-Omics Cancer Datasets.” Biology vol. 11,2 260 (2022): DOI: 10.3390/biology11020260
Joshua Davis, PharmD, PhD, from Auburn University is a T32 postdoctoral researcher in bioinformatics. He aims to combine his background in pharmacology, pharmacogenomics, and oncologic drug resistance to create applications to assist clinicians and researchers in identifying molecular mechanisms of drug resistance at a patient and systemic level to develop personalized therapeutic regimens and novel therapeutics to prevent, delay, and overcome oncologic drug resistance. He is currently focused on developing a drug resistance resource to help identify therapeutics and therapeutic combinations with high barriers to drug resistance. He is also interested in leveraging machine learning and artificial intelligence approaches to predict the most likely pathway of resistance based on initial patient sample data to allow clinicians to tailor regimens based on patient-specific resistance characteristics.
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Davis JT*,Obermayer A*, et al. BatchFLEX: feature-level equalization of X-batch. Bioinformatics. 2024 Oct 1;40(10):btae587. doi: 10.1093/bioinformatics/btae587. PMID: 39360977; PMCID: PMC11486499. (* Co-First Authors)
Divya Priyanka Talada, Bachelor of Dental Surgery, Masters in Bioinformatics. She recently finished her master's degree in bioinformatics @ USF and is currently receiving training in single-cell and spatial transcriptomics analysis. Her research aims to build on her knowledge of human physiology to generate a knowledge-based annotation protocol for single-cell types. Divya's goal is to pursue a career in research, particularly in fields that accelerate human progress against catastrophic diseases. Divya is currently co-trained with Dr Dorina Avram's lab.
Thac Duong is an undergraduate student @ USF. He joined the lab Spring 2023. He's training toward a career in bioinformatics and is currently interested in alternative splicing analysis. He's interested in CAR T cell research and is jointly mentored by Dr Fabiana Perna.
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Davis JT, Obermayer AN, Soupir AC, Hesterberg RS, Duong T, Yang CY, Dao KP, Manley BJ, Grass GD, Avram D, Rodriguez PC, Fridley BL, Yu X, Teng M, Wang X, Shaw TI.BatchFLEX: feature-level equalization of X-batch. Bioinformatics.2024 Oct 1;40(10). doi: 10.1093/bioinformatics/btae587. PubMed PMID: 39360977; PubMed Central PMCID: PMC11486499
