Gene co-expression analysis for functional classification and gene–disease predictions

Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a…

• UO
  University of Liverpool
• RG
  Rijksuniversiteit Groningen
• UM
  Universitair Medisch Centrum Groningen
• DF
  Directorate for Biological Sciences
• BA
  Biotechnology and Biological Sciences Research Council

  Award: BB/K016741/1