BenLangmead / cgsi18

Scripts for downloading and querying raw Snaptron data

Langmead UCLA CGSI Tutorial

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

Related links

• 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

Related papers

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