The course is suitable for students as well as for statisticians without extensive backgrounds in genomics. Programming experience in Julia is NOT required. Below is a short description. If anyone has a question they can email Janet Sinsheimer (firstname.lastname@example.org). If people act soon (by 5/31 they get the early bird rate for JSM registration if they register by 6/29 they get the pre registration rate for the class.
Julia Meets Mendel: Algorithms and Software for Modern Genomic Data Analysis (ADDED FEE) – Professional Development Continuing Education Course ASA Instructor(s): Kenneth Lange, UCLA, Janet Sinsheimer, UCLA, Eric Sobel, UCLA, Hua Zhou, UCLA 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.