Advances in Next Generation Sequencing (NGS) technologies allow us to generate data at unprecedented speed and throughput. As a consequence, we can now study biological systems at the level of whole genomes and whole transcriptomes instead of at the single gene level. However, the biggest challenge for utilizing the power of such data is our limited ability to quickly and reliably obtain insights from this data. In the Big Data in Biology FRI stream, supported by the W.M. Keck Foundation, students explore methods for analyzing such large-scale biological datasets using computational algorithms, statistical tools, and supercomputers. A unique aspect of our stream is that we collaborate with many other labs and streams to help analyze their data and answer their biological questions. Many labs at UT generate large-scale datasets, but may not have the expertise to analyze them. Therefore, a student with the computational skills required to analyze such data is always in high demand. We have collaborated with 5 different labs and 2 other FRI streams to answer such questions as what genes and pathways are linked to alcohol addiction and how are flowering response genes affected by changes in day length in a biofuel candidate plant. Our students also mine and perform meta-analysis using publicly available NGS datasets, for example, to model immune system dysregulation in brain disorders or to demonstrate the hourglass developmental model in variety of species. As a technology stream, we not only teach the students technologically relevant skills, but we also expose to them how such computational skills are used in industry. They hear talks from bioinformaticians working in the industry and also get to work closely with one industry expert to develop a product idea.