Catalog of Courses for Computational Biology
A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. Papers will be drawn from recent and seminal publications in computational biology.
A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. Papers will be drawn from recent and seminal publications in computational biology.
A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. Papers will be drawn from recent and seminal publications in computational biology.
Students will learn theoretical and practical foundations of computational methods for analysis of experimental data from various biological data types. Course will cover algorithms, statistical and computational methods, and application areas in computational biology, and will include both classical methods as well as recent advances. Prior coursework/experience in linear algebra, UNIX, and R & Python programming required.
Students will learn theoretical and practical foundations of computational methods for analysis of experimental data from various biological data types. Course will cover algorithms, statistical and computational methods, and application areas in computational biology, and will include both classical methods as well as recent advances. Prior coursework/experience in linear algebra, UNIX, and R & Python programming required.
Students will learn algorithms and applications of computational methods for analysis of experimental data from genome, epigenome, and transcriptome sequencing experiments. The course will cover various biological data types (whole genome sequencing, ChIP-seq, ATAC-seq, RNA-seq, and DNA methylation profiling/bisulfite sequencing), algorithms, statistical and computational methods, and application areas in genomics and systems biology.
This course will cover fundamental topics in statistical genetics with a focus on concepts and methods critical to a concrete understanding of the application of statistical genetics to public health genomics. Major topics covered in this course include modes of genetic inheritance, heritability analysis, linkage and association mapping, integrative analysis leveraging molecular 'omics' data, and genetic risk prediction modeling.
For doctorial dissertation, taken under the supervision of a dissertation director.