ABSTRACT: Modern life sciences applications need to analyze and manage 
large volumes of complex biological data. Many of these datasets are 
growing at a rate faster than Moore's Law. Unfortunately, existing 
methods for analyzing these datasets often do not scale with 
increasing data sizes. To make matters worse, biologists want to 
perform increasingly complex analyses on these datasets. Existing 
solutions are inadequate to meet these demands and threaten to slow 
the rate of progress in modern data-driven life sciences applications. 
The central premise of this talk is that methods inspired by the 
"database-style" of analyzing large datasets can provide viable 
solutions to many of these problems. This talk describes ongoing work 
in the Periscope project that aims to build efficient, effective, and 
expressive tools for querying biological data. This talk will 
highlight indexing and query processing techniques that we have 
developed for analyzing biological graphs and sequences. Compared to 
existing methods, our techniques are often orders of magnitude faster. 
A more significant aspect of our work is that these methods are far 
more expressive and effective in terms of the quality of results, 
which has allowed biologists to generate insights from data-driven 
analysis that was not possible with other existing tools.