A brief overview of reproducible research issues, reasons, costs, initiatives, and tools that help ensure computational reproducibility. For Bioinformatics 101 class

Enrichment analysis of genes and genomic regions, gene ontology, pathways and genomic annotation databases and tools

Reproducibility is the foundation of scientific progress. Failure to reproduce published research affects medical care, delays scientific development, wastes limited research funding. In the era of data-driven research our ability to reproducibly analyze massive amounts of data became a cornerstone of data science. This presentation outlines the scope of reproducible research, examples and cost of irreproducibility, and give an overview of initiatives and best practices for reproducible data analysis. Several computational tools and practical approaches to enhance reproducibility are presented. Following these practices will improve our confidence in the results of computational data analysis, and speed up scientific progress.