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Outline

Course setup

  1. Intro to DNAnexus and the GOLD learning system

  2. Learning Objectives

  3. Unix Bootcamp

  4. Brief Review of R (for R scripts in later analyses)

Introduction to RNA Sequencing

  1. Central Dogma of Molecular Biology

  2. What is RNA Sequencing?

  3. How to Design an RNA-Seq Experiment

  4. Biological and Technical Replicates

Steps of an RNA-Seq Data Analysis

  1. Getting the data

  2. Unpacking the data

  3. QA/QC with FASTQC and MULTIQC

  4. Adapter trimming

  5. Alignment

  6. Getting a Reference Genome

  7. Genome Indexing

  8. Spliced reads

  9. Non-spliced reads

  10. No Reference Genome? Working with a Transcriptome (non-model organisms)

  11. Pseudo-alignment (kallisto, salmon)

  12. Quantification of Reads (gene, transcript)

File Formats for RNA-Seq

  1. FASTA
  2. FASTQ
  3. GTF
  4. SAM/BAM

RNA-Seq test dataset(s) (toy datasets, HBR/UHR with spike-in or other, GEO)

  1. toy dataset
  2. HBR/UHR with spike-in
  3. GEO

Introduction to Differential Expression Analysis

Computing differential expression (DESeq2, edger)

Viewing the results (heatmaps)

Meaningful results

Pathway Analysis

RNA Isoform Analysis

Course wrap-up

  1. RNA-Seq analysis on the NIH High-Performance Unix Cluster Biowulf
  2. NCI/CCR resources for RNA-Seq Analysis