Day 1 - Concepts in Bioinformatics

  • Introduction
  • Fundamental statistics
  • Microarray technology
  • Next-generation sequencing technology
  • Research example: bioinformatics in cancer research

Day 2 - Transcriptomics

  • Analysis of microarray data: normalization, detecting differentially expressed genes, clustering and classification
  • Applications of microarray-based gene expression profiling
  • Analysis of RNA-seq data: sequence alignment, read count normalization, detecting differentially expressed transcripts
  • Applications of transcriptional profiling via high-throughput sequencing

Day 3 - Genetic Sequence and Structural Variation

  • GWAS: SNP array genotyping & trait association
  • Copy number analysis: copy number detection from SNP arrays
  • Applications of copy number profiling
  • Analysis of deep DNA sequencing data: variant calling, detecting structural variation
  • Research example: deep sequencing in cancer research

Day 4 - Epigenomics

  • Analysis of DNA methylation microarrays
  • Research example: DNA methylation profiling in cancer
  • High-throughput Bisulphite sequencing: visualisation of quantitative sequence data
  • Immunoprecipitation and next-gen sequencing: ChIP-seq, MeDIP-seq, alignment and peak-finding
  • Research example: Next-gen sequencing for cancer epigenetics

Day 5 - The Bigger Picture

  • Making use of publically available data
  • Functional enrichment analysis: adding interpretation to results
  • Analysis of cell viability data from siRNA screens
  • Functional proteomics: reverse-phase protein arrays
  • Characterising metabolic profiles
  • Using networks to study biological systems