BBSR bioinformatics services include project design and analysis for high-throughput genomics data (e.g., RNA-seq, single-cell RNA-seq, ChIP-seq, ATAC-seq, microbiome, exome and whole genome sequencing, pathway, drug sensitivity, and advanced visualization), and the development of deep learning (e.g., convolution and graphical neural networks) for project-specific analyses, including knowledge generation and predictions.
The BBSR bioinformaticians provide assistance in experimental design, feasibility assessment, power analysis, sample size calculation, data analysis and interpretation, as well as grant application and manuscript preparation. The resource offers pre-processing and sequencing analyses, knowledge-based and public database analyses, and advanced statistical analyses. To detect and prevent data quality issues that may impact later data analysis, BBSR bioinformaticians employ various quality control strategies, including quality assessment of sequencing data, sample outlier detection, and batch effect determination. BBSR bioinformaticians assist in projects involving large-scale experimental data sets that are available through research community efforts such as GEO, TCGA, CCLE, and ENCODE database compendia. With the fast development of researches in genomics and bioinformatics, to ensure that bioinformatics analysis by BBSR provides accurate, robust, and advanced genomic information, BBSR bioinformaticians routinely review and update their knowledge bases, analysis methodologies and pipelines.
Services include:
- Experiment design for high-throughput genomic studies
- Next-Generation Sequencing data analysis including whole transcriptome/single-cell RNA-Seq, ChIP-Seq, ATAC-Seq, exome/Whole Genome Sequencing, 16S rRNA, and other NGS technologies
- Pathway/Gene Set Enrichment Analysis and gene network analysis
- Functional metagenomics analysis
- Biomarker discovery and prediction modeling for clinical outcomes
- LINCS data pre-processing and pathway integrative analysis.
- Drug sensitivity and genomic association analysis
- Genomic data integration using public database such as TCGA, CPTAC, GEO, ENCODE, etc.
- Integration analysis of genomic data across multiple platforms
- Genomic data visualization
- Bioinformatics new methodology and algorithm development
- Bioinformatics software development including Bioconductor/R package and web-based software
- Long read sequencing analysis
- Spatial genomics analysis
- AI modeling
Resources
- Two Dedicated servers (112 CPU cores, 1.5 TB memory, 50 TB)
- Access to high performance computing (HPC) cluster (16 x 64 core, 512 GB RAM; 2x64 core, 4 TB RAM; 1200 TB storage)
- Broad array of software (including R, SAS, STATA, PASS, STAR, GATK, MACS2, GSEA, Cell Ranger, IGV, Cytoscape, MATLAB, Python)
To request support from the BBSR, complete the Investigator Request for Statistical and Bioinformatics Request Form and submit by clicking the “Submit” button on the form or email the form to bbsr@med.miami.edu.