The new generation of omics technologies pose new challenges in bioinformatics and computational biology. For example, current high throughput sequencers produce 4 billion nucleotides of DNA sequence in a single run of the apparatus. By the end of this year, the output is expected to reach 95 billion nucleotides. In particular, the machines allow us to sequence entire microbial systems from samples taken from our environment or measure expression levels of mRNA transcripts or epigenomics for an organism or tumor. Beyond laboratory management software to efficiently process this information, these applications also require sophisticated new analytic approaches rooted in statistical inference and machine learning in order to distill this mass of information into physiologically relevant, testable hypotheses. A number of our researchers including Blanchette, Majewski and Hallett have worked with these areas and analogous proteomics-related efforts.