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This README accompanies my UCLA SGSI tutorial of Monday, July 30 2018. The tutorial has three coding portions, corresponding to three levels of summarization/queryability:

1. R code (jx1.Rmd) for exploring summarized exon-exon splice-junction data (henceforth: junction data) over tens of thousands of datasets at once. This mimics the kinds of analysis performed in the Intropolis paper:

   • Nellore A, Jaffe AE, Fortin JP, Alquicira-Hernández J, Collado-Torres L, Wang S, Phillips RA III, Karbhari N, Hansen KD, Langmead B, Leek JT. Human splicing diversity and the extent of unannotated splice junctions across human RNA-seq samples on the Sequence Read Archive. Genome Biol. 2016 Dec 30;17(1):266. https://doi.org/10.1186/s13059-016-1118-6

2. R code from the recount2 getting started guide, demonstrating an in-depth analysis of a few RNA-seq studies, and making use of different summary types. Similar to the analyses performed in the recount2 paper:

   • Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B, Leek JT. Reproducible RNA-seq analysis using recount2. Nat Biotechnol. 2017 Apr 11;35(4):319-321. https://doi.org/10.1038/nbt.3838

3. Python code for querying the Snaptron service

   • Wilks C, Gaddipati P, Nellore A, Langmead B. Snaptron: querying splicing patterns across tens of thousands of RNA-seq samples. Bioinformatics. 2018 Jan 1;34(1):114-116. https://doi.org/10.1093/bioinformatics/btx547

Thus, all the relevant code can be found in these files:

• jx1.Rmd
• recount.Rmd
• snaptron.Rmd

• recount2
  • Front page: search study metadata and download data
  • SciServer: use Jupyter/R/Python & computers at JHU to work with recount2 data without downloading it
  • Paper & Workflow paper
• Snaptron
  • Front page: API docs, installation instructions
  • Raw data, which we used in the first coding portion
  • Paper

1. Langmead B, Nellore A. Cloud computing for genomic data analysis and collaboration. Nat Rev Genet. 2018 May;19(5):325. https://doi.org/10.1038/nrg.2017.113
2. Wilks C, Gaddipati P, Nellore A, Langmead B. Snaptron: querying splicing patterns across tens of thousands of RNA-seq samples. Bioinformatics. 2018 Jan 1;34(1):114-116. https://doi.org/10.1093/bioinformatics/btx547
3. Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B, Leek JT. Reproducible RNA-seq analysis using recount2. Nat Biotechnol. 2017 Apr 11;35(4):319-321. https://doi.org/10.1038/nbt.3838
4. Nellore A, Jaffe AE, Fortin JP, Alquicira-Hernández J, Collado-Torres L, Wang S, Phillips RA III, Karbhari N, Hansen KD, Langmead B, Leek JT. Human splicing diversity and the extent of unannotated splice junctions across human RNA-seq samples on the Sequence Read Archive. Genome Biol. 2016 Dec 30;17(1):266. https://doi.org/10.1186/s13059-016-1118-6
5. Nellore A, Collado-Torres L, Jaffe AE, Alquicira-Hernández J, Wilks C, Pritt J, Morton J, Leek JT, Langmead B. Rail-RNA: scalable analysis of RNA-seq splicing and coverage. Bioinformatics. 2017 Dec 15;33(24):4033-4040. https://doi.org/10.1093/bioinformatics/btw575