Data Analysis

While no two sequencing projects have exactly the same goals, most projects utilize one or more of the following analyses. We have also performed various custom analyses over the years, so if your project requires something different please contact us to discuss your planned analyses.

• Differential expression analysis: Normalized read counts are compared across all samples in a project to identify genes associated with particular conditions, treatments, or stages of disease.
  
  
• Gene set enrichment analysis: Once differentially expressed genes have been identified, they are compared to gene sets curated from previous research studies to look for similarities and describe the overall mechanisms or biological pathways that are represented and/or cell types that may be dominating the observed response.
  
  
• Cell type annotation: Published or user-provided gene panels are used to assign specific cell types to single cell transcriptomic data to identify cells of interest and understand response to treatment or infection.
  
  
• Repertoire discovery: Heavy and light chain immunoglobulins can be reconstructed to analyze changes in the clonotypes present in response to infection, disease, or treatment.
  
  
• SNP discovery: After mapping the reads to a reference genome, SNPs or other polymorphisms within a set of samples can be identified and quantified. The genomic location of all polymorphisms can be further analyzed for statistical significance or visualized within a genome browser.
  
  
• De novo assembly: For non-standard genomes, we can perform de novo genome assembly utilizing both whole genome and transcriptome sequencing.