Mission
To deliver rigorous, reproducible and high-quality bioinformatics solutions through collaborative research that enables investigators to generate meaningful biological insights and advance cancer discovery.
Common assays
The bioinformatics core analyzes a wide range of high-throughput data types.
Overview
The bioinformatics team, part of the UF Health Cancer Institute Biostatistics & Computational Biology Shared Resource, provides comprehensive and collaborative support for cancer research across a spectrum of omics data analysis. Our core mission is to deliver rigorous, reproducible and high-quality bioinformatics solutions through collaborative research that enables investigators to generate meaningful biological insights and advance cancer discovery.
Our team specializes in the end-to-end analysis of raw high-throughput omics data, including genomics, transcriptomics, epigenomics, proteomics and single-cell sequencing, using robust, standardized workflows. By integrating advanced computational approaches and industry-standard best practices, we help transform complex datasets into interpretable findings that drive impactful research.
In addition to data analysis, we actively collaborate with scientists and investigators on experimental study design, development of custom analysis pipelines and web applications, and the bioinformatics components of grant proposals and manuscripts. We ensure seamless coordination with sequencing facilities for efficient data transfers and deliver detailed, user-friendly reports to support scientific decision-making. We leverage the University of Florida’s HiPerGator high-performance computing system to efficiently process and analyze large-scale omics datasets.
We also contribute to the development of informatics infrastructure for secure data sharing, management and storage, enabling efficient handling of large-scale cancer omics datasets. Our team also offers educational programs, including seminar series, journal clubs, workshops, and walk-in clinics, to build bioinformatics capacity in the research community.
Through these efforts, the bioinformatics team serves as a trusted partner, enabling rigorous, innovative and reproducible research that accelerates progress in cancer science.
Common assays in bioinformatics research
The bioinformatics team advises researchers to involve us from the beginning, offering guidance in experimental design and protocol selection, followed by data analysis, interpretation and visualization. Here are some assay types that the team works with:
WES / WGS
Sequencing of the entire exome (coding regions) or genome (coding and non-coding regions).
The standard analysis includes QC processing, alignment, base recalibration, somatic / germline variant calling, filtering, and annotation. Customized analyses such as TMB calculation, ancestry analysis, and population-level analysis are also offered.
RNA-Seq
Bulk transcriptome to quantify gene and transcript expression across conditions or time points.
The standard analysis involves an end-to-end process from QC, trimming, alignment, quantification, differential analysis, and summary report generation. Downstream analysis, such as overrepresentation analysis or GSEA, is also supported.
ATAC-Seq
Genome-wide mapping of open chromatin to reveal regulatory and transcription site accessibility
The analysis includes QC, trimming, alignment, Tn5 shifting, peak calling, consensus peak set, TSS enrichment/FRiP score, and summary report generation. Customized analysis, such as differential accessibility analysis, motif enrichment, TF footprinting, and multi-omics integration, is offered.
CUT&RUN / CUT&Tag / ChIP-Seq
Targeted profiling of protein–DNA interactions with low background and high resolution.
The analysis involves QC, trimming, alignment, peak calling, spike-in–aware normalization if applicable, and summary report generation. Customized downstream analysis involves differential binding, motif and footprint analyses, feature enrichment, and multi-omics integration.
single cell RNA-Seq
Transcriptome profiling on a single-cell resolution to study cellular heterogeneity, states, and trajectories.
A typical bioinformatic analysis involves demultiplexing and QC/alignment (as needed), quantification, and matrix generation. Customized analyses such as filtering, normalization, cell type annotation, differential analysis, trajectory inference, etc., are supported.
Proteomics and Metabolomics
Comprehensive profiling of small molecule metabolites or proteins using mass spectrometry or hybridization platforms. Analysis approaches are flexible and based on the technology used. Customized analyses such as differential expression analysis, network analysis, machine learning approaches, multi-omics integration, and pathway analysis are supported.
CRISPR Screens
Positive and negative selection screens using sgRNA guides to identify essential genes in drug resistance, cell survival, drug sensitivity, etc.
The analysis includes using MAGeCK tools to perform QC, counts generation, normalizations, and statistical modelling. Different downstream analyses are also available.
Hi-C
Genome-wide assay to capture 3D chromatin architecture by long-range DNA-DNA interactions.
The analysis involves QC, alignment, interaction matrices generation, identifying chromatin loops, topologically associating domains (TADs), and performing network analysis. Multi-omics integration is also supported.
Spatial Transcriptomics
Spatially resolved gene expression mapped onto tissue architecture. The analysis involves using vendor-recommended pipelines for primary processing (e.g., 10x Space Ranger/Xenium software, GeoMX analysis) to produce count matrices, spot/ROI metrics, and QC. Customized downstream analysis for spatial QC and normalization, detection of spatially variable genes, cell type deconvolution, image alignment, and integration with histology/morphometrics is available.
Software and tools
Our bioinformatics team supports a wide range of analytical tools, with in-house and publicly available standardized tools and pipelines aligning with the ENCODE practices. Our pipelines are Nextflow or Snakemake-based, many of which are adapted from nf-core. Additionally, we develop interactive in-house applications to enhance data interpretation and visualization.
One of our most versatile tools, DECODeR (Differential Exploration of Counts Data in R), supports exploratory analysis of count-based matrices that provide users the freedom to explore the analyzed RNA-Seq / ATAC-Seq data by generating custom heatmaps, volcano plots, pathway analysis plots, and dimensionality reduction plots.
Our team can also assist in hosting publicly available tools upon request. This is ideal for researchers facing computational constraints or privacy concerns. For using our in-house bioinformatics tools and publicly available state-of-the-art software, refer to the BCB-SR tools website. These tools are hosted on HiPerGator.
Explore our Bioinformatics Hub, which is designed to provide thorough documentation and resources to support your bioinformatics analyses. Here, you’ll find information on upcoming events, seminars, best practice guidelines, recommended tools and software, our workflows, guidelines for do-it-yourself analysis, tutorials and much more. This platform serves as a valuable hub to support your research and enhance collaboration within our community.
Our team
Bioinformatics Unit Leader
Jason Orr Brant, Ph.D.
Jason Orr Brant, Ph.D., is an assistant professor in the Department of Biostatistics. His research interests are focused on the role of DNA methylation and chromatin structure in regulating gene expression and how perturbations in the epigenome can result in disease onset and progression.
BIOINFORMATICS ANALYST III
Heather Kates, Ph.D.
Heather Kates, Ph.D., is a bioinformatician who works to develop tools and strategies that bridge the gap between data and discovery in multi-omics biomedical research. She aims to design and execute thoughtful analyses using gold-standard methods while presenting findings in formats that are clear, interpretable and actionable for investigators.
BIOINFORMATICS ANALYST III
Kalyanee Shirlekar, M.S.
Kalyanee Shirlekar, M.S., is a bioinformatician who enjoys working with exciting NGS technologies. With a passion for developing novel approaches and bioinformatics tools, she focuses on exploratory analyses to uncover insights related to cancer oncogenesis, drug resistance, and treatment.
Consultation and contact
As part of the Biostatistics & Computational Biology Shared Resource, the bioinformatics team is also present at the weekly Biostatistics & Bioinformatics virtual walk-in clinic for consultation. Alternatively, if you would like to have a dedicated time slot for your project, our bioinformatics staff are available by appointment for personalized consultations. We are happy to support you with experimental design, selection of analytical approaches, software tools and troubleshooting during your data analysis journey.
We highly recommend engaging with our team early in your project planning phase. Early collaboration ensures robust experimental design and maximizes the success of downstream analyses. Our team operates under a collaborative research model, where bioinformatics staff scientists are included as key personnel on grant proposals, with salary support provided through the funded project.
To request support from the bioinformatics team, please complete and submit the support request form. To learn more about our services, approach and how we can collaborate, we invite you to schedule a meeting with Jason Orr Brant, Ph.D., bioinformatics unit leader, or drop an email to UFHCC-BCB-SR@ad.ufl.edu.
