Improve understanding of the molecular mechanisms of healthy aging and age-related disease through excellence in computational analysis of high-throughput genomic data.
Support manipulation, applied statistical analysis, and post-processing of high-throughput genomic data generated by Buck Institute laboratories in studies of the biology of aging.
A given high-throughput functional genomics, sequencing, or transcriptome profiling experiment can generate terabytes of raw data. The challenge for the scientist is to convert such strings of numbers to biological insight. A computational genomics effort aims to pick out in these data—from among a genome's worth of measurement noise—observations that really tell us something about the organism. In the study of the biology of aging, a successful bioinformatics analysis uses high-throughput data to make predictions about genes and molecules that mediate lifespan or other age-associated traits. And these predictions then feed back to the laboratory to be tested in additional rounds of experiments. Professor Rachel Brem and the Bioinformatics Core team offer a collaborative service to meet this goal, supporting low-level manipulation, normalization and statistical analysis, and high-level post-processing of genomic data, as well as data integration, transfering, and retrieval.
Any researcher with a computational-genomic challenge, no matter how large or small, is encouraged to consult with members of our core to develop a project plan. The Bioinformatics Core team will work with you to:
- identify pre-existing computational tools applicable to your project, and train you in their usage
- develop new computational and statistical methods for your project when appropriate
- interpret and present results in collaboration with you
Please contact us the Bioinformatics Core team: