The K-INBRE Bioinformatics Core provides services and collaborative research support to KU Medical Center and K-INBRE investigators engaged in ‘omics studies. We provide the following services for a reduced charge to researchers in the K-INBRE network. Consultation and help in the preparation of grant applications is free to K-INBRE researchers, with flat-fee budgets provided for one-off projects.

For grant proposals in which bioinformatics and statistical genomics will be an integrated component, we would suggest that you consider collaborative research. In these cases, we will be glad to participate in proposal preparation, and specialized persons may be assigned to work on the project (if funded).

Below is a list of some of the services we provide:

• Study design and sample size/power calculations
• Data collection and development of databases
• Statistical analysis of ‘omic datasets to test hypotheses of interest (i.e., differential gene expression analysis)
• Single nucleotide polymorphism (SNP) selection for custom genotyping panels
• Pre-processing and quality control for array-based ‘omic platforms, followed by association analysis with endpoints of interest (e.g., differential expression analysis, differential DNA methylation analysis for CpG sites/islands).
• Study design and analysis of genome-wide association studies (GWAS) and candidate gene/SNP studies.
• Analyses for studies dealing with admixture and population genetics.
• Analyses for pharmacogenomic studies.
• Analysis of RT-PCR and other assay-based data.
• Illumina next-generation sequencing data pre-processing, with work flows available for the characterization of:
  
  » Single nucleotide variations, indels, copy number variations, translocations and inversions from whole-exome and whole-genome DNA sequencing (DNA-seq)
  
  » Level of gene expression, splice variants and fusion genes from RNA sequencing (RNA-seq)
  
  » Level of microRNA expression from microRNA sequencing (miRNA-seq)
  
  » Methylation sites, transcription factor binding sites and chromatin modifications from methylation sequencing (methyl-seq) and ChIP sequencing (ChIP-seq)
  
  
• Data annotation and pathways analysis
• Connectivity Map (CMAP) Analysis
• Clustering, profile and signature development
• Assessment and analysis of external genomics data sets (e.g., ENCODE, GEO, dbGaP, TCGA, CCLE)