RNA-Seq - Differential Exon Usage Analysis
2 minute read
RNA-Seq Workflow
- Read quality assessment, filtering and trimming
- Map reads against reference genome
- Perform read counting for required ranges (e.g. exonic gene ranges)
- Normalization of read counts
- Identification of differentially expressed genes (DEGs)
- Clustering of gene expression profiles
- Gene set enrichment analysis
Challenge Projects
Differential exon usage analysis with DEXseq
- Run workflow from start to finish (steps 1-7) on RNA-Seq data set from Howard et al. (2013)
- Challenge project tasks
- Perform differential exon analysis with DEXseq. Assess the results as follows:
- Identify genes that show differential exon usage according to DEXseq. Optionally, perform functional gene set enrichment analysis on the obained gene set.
- Compare the results with the findings of the splice variant analysis reported by Howard et al (2013).
- Perform differential exon analysis with DEXseq. Assess the results as follows:
References
- Anders S, Reyes A, Huber W (2012) Detecting differential usage of exons from RNA-seq data. Genome Res 22: 2008–2017 PubMed
- 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
- Guo Y, Li C-I, Ye F, Shyr Y (2013) Evaluation of read count based RNAseq analysis methods. BMC Genomics 14 Suppl 8: S2 PubMed
- Hardcastle TJ, Kelly KA (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11: 422 PubMed
- Liu R, Holik AZ, Su S, Jansz N, Chen K, Leong HS, Blewitt ME, Asselin-Labat M-L, Smyth GK, Ritchie ME (2015) Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res. doi: 10.1093/nar/gkv412. PubMed
- Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550 PubMed
- Zhou X, Lindsay H, Robinson MD (2014) Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res 42: e91 PubMed
Last modified 2022-05-25: some edits (2a1d3f13a)