Program

Day 1 - Gene expression analysis

  • Lecture 1: General processing of NGS data
  • Lab 1: Hands-on processing of RNA-seq data using FastQC, trim_galore, and alignment.
  • Lecture 2: Quantification of gene expression
  • Lab 2: Quantification and differential gene expression by RNA-seq

Day 2 - Chromatin accessibility

  • Lecture 3: Introduction to general epigenomics concepts
  • Lab 3: Processing ATAC-seq/MNase-seq data
  • Lecture 4: Peak calling and accessibility visualization
  • Lab 4: Peak calling and coverage visualization

Day 3 - Chromatin composition

  • Lecture 5: Introduction to ChIP-seq
  • Lab 5: Processing ChIP-seq data
  • Lecture 6: DNA motif methodologies and resources
  • Lab 6: Meme and TFBSTools for motif enrichment analysis in R

Day 4 - Chromatin interactions

  • Lecture 7: Profiling chromatin contacts and visualize Hi-C data
  • Lab 7: Alignments and quality assessment using hicstuff
  • Lecture 8: Identifying structural features from Hi-C data
  • Lab 8: Levering chromosight to study chromatin contacts

Day 5 - Data integration and multi-omics

  • Lecture 9: Integrating multi-omics data through GO analysis
  • Lab 9: Combining RNA-seq, ATAC-seq, ChIP-seq and Hi-C data with Bioconductor

Day 1 - RNA-seq

  • 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, …

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, …

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, …

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

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
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