Course schedule

The figure was generated by workflow.R.

Data

• test.db
• parsing genbank
  • AB115403.gb
  • AB115403.fasta
  • some gb files
• GEO
• Flu
  • flu_seq.fas
  • flu_seq_v2.fas
• Beta Coronavirus
  • accession number
  • genbank files
  • fasta sequence
  • partial rdrp fasta sequence
  • aligned partial rdrp fasta sequence
  • rdrp newick tree

R package

• bioinfo.practice
  • demo functions you need to implement in the class
• buffalo
  • demo R package development

Links and resources

• SQLite
  • https://www.sqlite.org/aff_short.html
  • How to Use SQLite with R
  • RSQLite Tutorial
  • SQLite in R
  • A Gentle Introduction to SQL Using SQLite
• GenBank
  • https://www.ncbi.nlm.nih.gov/Sitemap/samplerecord.html
  • https://www.ncbi.nlm.nih.gov/nuccore/AB115403
  • https://www.ncbi.nlm.nih.gov/books/NBK25501/
• Alignment
  • https://www.nature.com/articles/nbt0704-909
    • global.c
  • https://developer.ibm.com/articles/j-seqalign/
  • https://www.geeksforgeeks.org/sequence-alignment-problem/
  • https://mp.weixin.qq.com/s/4DL-pJVItOhkYJAbPEg7BQ
• Make
  • Make命令教程
  • A simple makefile tutorial
• git/github
  • https://help.github.com/en/articles/set-up-git
  • http://jaimeiniesta.github.io/learn.github.com/p/diff.html
  • https://git-scm.com/book/en/v2/Git-Branching-Basic-Branching-and-Merging
• Reproducible research
  • http://www.nature.com/news/reproducibility-1.17552
  • Repeatability of published microarray gene expression analyses
  • https://github.com/GuangchuangYu/plotting_tree_with_data
  • https://bookdown.org/yihui/rmarkdown/
• 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
  • https://www.bioconductor.org/packages/muscle/
  • https://www.bioconductor.org/packages/msa
  • https://cran.r-project.org/package=ape
  • http://www.mbio.ncsu.edu/BioEdit/bioedit.html
  • https://mp.weixin.qq.com/s/F0TxMk1CrYVNvxWqZgv-Aw
• Phylogeny
  • https://yulab-smu.github.io/treedata-book/
  • Evolution 101
  • How to read a phylogenetic tree
  • Reading a Phylogenetic Tree: The Meaning of Monophyletic Groups