The figure was generated by workflow.R.
- bioinfo.practice
- demo functions you need to implement in the class
- buffalo
- demo R package development
- SQLite
- GenBank
- Alignment
- Make
- git/github
- Reproducible research
- Differential gene analysis
- Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509-1517 (2008).
- Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
- Anders, S. et al. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat Protoc 8, 1765-1786 (2013).
- Lund, S. P., Nettleton, D., McCarthy, D. J. & Smyth, G. K. Detecting differential expression in RNA-sequence data using quasilikelihood with shrunken dispersion estimates. Stat Appl Genet Mol Biol 11, (2012).
- Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
- Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
- GSEA
- Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A. 102, 15545-15550 (2005).
- https://yulab-smu.github.io/clusterProfiler-book
- https://bioconductor.org/packages/release/BiocViews.html#___GeneSetEnrichment
- Multiple Sequence Alignment
- Phylogeny