BIO 351: Getting Science done in Ruby
summary
Sequencing costs fall from billions to under ten thousand dollars and projects like 1000 Genomes and dbSNP report 60 million variants in dbSNP and public cohorts exceed millions of samples so workflows must scale. Short reads produce 300 million reads per run in BAM files that get aligned and summarized. Pipelines combine Python JITs and C-backed libraries, Ruby gems like bio-ngs and nmatrix, and notebooks; an open tool named gender (genetics + pandas) glues analysis and visualization and supports published GWAS hits (atrial fibrillation loci) that replicate.
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chapters (7)
- 0:00Start
- 1:16Large Gains in Fighting Cardiovascular Disease
- 2:58Central Dogma of Nature
- 8:50Sequencing
- 9:31Tools
- 16:14Future Directions
- 17:35Questions?
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