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Abstract:
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Challenges in statistical genomics and precision medicine are enormous. Datasets are becoming bigger and more varied, demanding complex data structures and integration across multiple biological scales. Analysis pipelines juggle many programs, implemented in different languages, running on different platforms, and requiring different I/O formats. This heterogeneity erects barriers to communication, data exchange, data visualization, biological insight and replication of results. Statisticians spend inordinate time coding/debugging low-level languages instead of creating better methods and interpreting results. The benefits of parallel and distributed computing are largely ignored.
The time is ripe for better statistical genomic computing approaches. This short course reviews current statistical genomics problems and introduces efficient computational methods to (1) enable interactive and reproducible analyses with visualization of results, (2) allow integration of varied genetic data, (3) embrace parallel, distributed and cloud computing, (4) scale to big data, and (5) facilitate communication between statisticians and their biomedical collaborators. We present statistical genomic examples and offer participants hands on coding exercises in Julia as part of the OpenMendel project (https://openmendel.github.io). Julia is a new open source programming language with a more flexible design and superior speed over R and Python. R and Matlab users quickly adapt to Julia.
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