Program

Day 1 - Next-generation sequencing data processing

Lectures

  • Lecture 1: Epigenomics introduction
  • Lecture 2: General processing of NGS data

Demo

  • Fetching an MNase-seq dataset from GEO
  • Indexing a genome with bowtie2
  • Map paired-end reads with bowtie2
  • Generate sequencing-depth normalized track
  • Generate nucleosomes track

Labs

  • Fetching an MNase-seq dataset from GEO
  • Indexing a genome with bowtie2
  • Map paired-end reads with bowtie2
  • Generate sequencing-depth normalized track
  • Generate nucleosomes track
  • Check the relevance of filtering out duplicates

Day 2 - ATAC-seq

Lectures

  • Lecture 3: NGS worfklows: bash, Snakemake, Nextflow, …
  • Lecture 4: ATAC-seq processing
  • Lecture 5: R/Bioconductor 101: Data import, manipulating genomic ranges, …

Demo

  • Fetching two ATAC-seq replicates from GEO
  • Indexing a genome with bowtie2
  • Map paired-end reads with bowtie2
  • Generate sequencing-depth normalized track
  • Calling peaks form ATAC-seq data

Labs

  • Overlap ATAC-seq peaks with annotated REs
  • Check ATAC-seq fragment sizes
  • Overlap ATAC-seq peaks with annotated regulatory elements (REs)
  • Check tissue-specific enrichment of ATAC-seq peaks

Day 3 - ChIP-seq analysis

Lectures

  • Lecture 6: ChIP-seq processing
  • Lecture 7: R/Bioconductor 201: Rle, SummarizedExperiment, …

Demo

  • Manually process Scc1 ChIP-seq reads
  • Generate IP/input ratios with bamCoverage
  • Call peaks and inspect them visually

Labs

  • Find motifs enriched in a set of ChIP-seq peaks
  • Import a dozen of ChIP-seq peak sets in R
  • Check distribution of peaks comapred to genomic features
  • Check peak occurrence over tissue-specific regulatory elements

Day 4 - RNA-seq analysis

Lectures

  • Lecture 8: RNA-seq processing
  • Lecture 9: R/Bioconductor 301: Databases, resources, …

Demo

  • Manually process RNA-seq reads
  • Generate stranded RNA-seq tracks with bamCoverage
  • Estimate transcript abundance with featureCounts

Labs

  • Manually process RNA-seq reads
  • Generate stranded RNA-seq tracks with bamCoverage
  • Estimate transcript abundance with summarizeOverlaps

Day 5 - Data integration and multi-omics

Lectures

  • Lecture 10: Hi-C processing
  • Lecture 11: GO and GSEA analyses

Demo

  • Recovering chromatin states from the AnnotationHub
  • Intersecting GRanges
  • Recovering genes from genomic loci
  • Performing GO analysis

Labs

  • Visually inspect results from MNase-seq, Scc1 ChIP-seq and RNA-seq in yeast
  • Plot profiles of MNase-seq coverage @ TSSs
  • Plot profiles of RNA-seq @ Scc1 ChIP-seq