BMC Bioinformatics, Vol. 13, No. 1. (2012), 150, doi:10.1186/1471-2105-13-150 BACKGROUND:Genomic technologies are, by their very nature, designed for hypothesis generation. In some cases, the hypotheses that are generated require that genome scientists confirm findings about specific genes or proteins. But one major advantage of high-throughput technology is that global genetic, genomic, transcriptomic, and proteomic behaviors can be observed. Manual confirmation of every statistically significant genomic result is prohibitively expensive. This has led researchers in genomics to adopt the strategy of confirming only a handful of the most statistically significant results, a small subset chosen for biological interest, or a small random subset. But there is no standard approach for selecting and quantitatively evaluating validation targets.RESULTS:Here we present a new statistical method and approach for statistically validating lists of significant results based on confirming only a... - Daniel Swan