Marshaling Public Data for Lean and Powerful Splicing Studies
The Sequence Read Archive (SRA) now contains over a million accessions. Such archives are potential gold mines for researchers but they are not organized for everyday use by scientists. The situation resembles the early days of the World Wide Web, before search engines made the web easy to use. I will describe our work on making making large public RNA sequencing datasets easy to use. I will describe our multi-layered design, with one layer for scalable and uniform analysis (Rail-RNA), another for forming easy-to-use summarized (recount2), and a third for indexing the summaries and making them queryable (Snaptron). Altogether, the system allows scientists to pose scientific questions over vast gene expression and splicing summaries. I will describe collaborations where these tools were applied to (a) evaluate hypotheses about prevalence or specificity of splicing patterns, (b) characterize completeness of the gene annotations we use to understand splicing patterns, and (c) reveal patterns in public data that ultimately changed the study design and allowed more targeted hypotheses to be tested with less new data generation.
This is joint work with Chris Wilks, Abhinav Nellore, Jonathan Ling, Seth Blackshaw, Luigi Marchionni, Jeff Leek, Kasper Hansen, Andrew Jaffe and others.