--- title: RNA-Seq - NGS Aligners linkTitle: "RNA-Seq Aligners" description: > type: docs weight: 402 ---

## 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 + Compare the RNA-Seq aligner HISAT2 with at least 1-2 other aligners, such as Rsubread, Star or Kallisto. Evaluate the impact of the aligner on the downstream analysis results including: + Read counts + Differentially expressed genes (DEGs) + Generate plots to compare the results efficiently ## References + Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. doi: 10.1038/nbt.3519 [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/27043002) + 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](http://www.ncbi.nlm.nih.gov/pubmed/24098335) + 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](http://www.ncbi.nlm.nih.gov/pubmed/23618408) + Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12: 357–360 [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/25751142) + 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](http://www.ncbi.nlm.nih.gov/pubmed/23558742)