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Epidemiology, Biostatistics, Bioinformatics


Experimental design

  • Observational studies
    • Cohort Studies
    • Cross-sectional studies
    • Case control studies
  • Comparative Effectiveness studies
    • Randomized clinical trials
    • Non-randomized studies
  • Cost Effectiveness studies


Statistical analysis

  • Standard descriptive and inferential statistics
  • General linear models
  • Survival analysis
  • Classification/tree regression
  • Propensity score methods
  • Bayesian analysis
  • Multiple imputation of missing data


  • Next generation sequencing and experimental design and data analysis
    • Whole genome sequencing (WGS)
    • Whole exome sequencing (WES)
    • RNA transcriptomics (RNA-seq)
    • Small RNA sequencing (piRNA, miRNA)
    • Metagenomics shotgun sequencing
  • Germline and / or somatic variant analysis
  • Differential gene expression analysis
  • Access to state-of-the-art computational infrastructure, including a high-performance computing (HPC) cluster consisting of both CPU and GPU powered nodes
  • Data analytics for machine learning
  • Database design, normalization, and standardization
    • Including RedCap support
  • Data integration and analytics for “big data” projects

ACCEL DE-CTR’s BERD core has services available to accelerate clinical and translational research by providing support to researchers of all levels, whether looking for a collaborative research partner, a short-term consultant, or a technical/statistical advisor. ACCEL resources are available to the ACCEL community at no cost and are provided by expert epidemiologists, experienced investigators, and information technology professionals. BERD team members with considerable methodologic experience and strong publication track records from the University of Delaware, the Medical University of South Carolina, Nemours, and the Christiana Care Health System are available to assist researchers with developing projects in support of community and health care services.

Our team members can assist at any stage of research, from inception to dissemination. We can help ensure sound study design, guide in appropriate analyses, and aid in the interpretation of results. Involving BERD can help ensure research is effective and competitive. Our members have extensive experience in a wide variety of research designs, reviewing IRB protocols, identifying and applying for funding, participating on data safety and monitoring boards, and with authoring proposals, data management plans, statistical analysis plans, grants, manuscripts, and posters and presentations. These services are provided to improve the quality of research produced in the states of Delaware and South Carolina by facilitating research opportunities and forging beneficial collaborations.

The Medical Bioinformatics team at Nemours supports ACCEL funded research through the BERD core and via collaborations with research scientists within the network. Our team has developed both the wet-bench applications and bioinformatic pipelines for conducting next generation sequencing (NGS), with the goal of identifying relevant biological insights for complex clinical translational research projects.  The team has a state-of-the-art sequencing machine, NextSeq, and a robust computational infrastructure that includes a high-performance computing (HPC) cluster consisting of both both a GPU and CPU powered nodes.

Our team focuses on training and education such that PIs have a clear understanding of these analyses from start to finish. Some of our pipelines include industry standards such as GATK Best Practices for germline variant discovery in whole-genome sequencing (WGS). This is a tried and tested analysis standardized by the Broad Institute which allows for the discovery of high-confidence germline variants. The ENCODE consortium has a set of guidelines and best practices for RNAseq which we used as a basis for the framework of our RNA sequencing pipeline. This differential expression analysis is capable of determining which genes exhibit statistically significant changes in expression between two conditions or samples.

A number of our pipelines, however, are much more specialized with some being designed by our team to fit the needs of a single project. For example, in instances where RNAseq is performed on an organism without an annotated reference genome, we built an analysis around Trinity which performs a de novo reconstruction of the transcriptome. For those interested in studying microbiomes, our metagenomics pipeline is capable of performing kmer-based read classification down to the species level, assembling a combined metagenome from multiple samples, annotating that metagenome, and performing differential gene representation analysis across sample sets. Our small RNA (smRNA) pipeline allows for the identification and quantification of known miRNAs and piRNAs within samples, the discovery of novel miRNAs, and differential expression analysis across samples.