RNA-Seq Workflow

  1. Read quality assessment, filtering and trimming
  2. Map reads against reference genome
  3. Perform read counting for required ranges (e.g. exonic gene ranges)
  4. Normalization of read counts
  5. Identification of differentially expressed genes (DEGs)
  6. Clustering of gene expression profiles
  7. Gene set enrichment analysis

Challenge Project: Comparison of RNA-Seq Aligners

  • Run workflow from start to finish (steps 1-7) on RNA-Seq data set from Howard et al. (2013)
  • Challenge project tasks
    • Run at least 2-3 RNA-Seq alignment tools such as Bowtie2/Tophat2, HISAT and Kallisto, and evaluate the impact of the aligner on:
      • Read counts
      • Differentially expressed genes (DEGs)
      • Generate plots to compare the results efficiently

References

  • Howard, B.E. et al., 2013. High-throughput RNA sequencing of pseudomonas-infected Arabidopsis reveals hidden transcriptome complexity and novel splice variants. PloS one, 8(10), p.e74183. PubMed
  • Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. doi: 10.1186/gb-2013-14-4-r36 PubMed
  • Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12: 357–360 PubMed
  • Liao Y, Smyth GK, Shi W (2013) The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res 41: e108 PubMed
  • Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. doi: 10.1038/nbt.3519 PubMed