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Computational Biology

3 Credit Hour Course

Prerequisite:   None

Introduction to molecular biology and genetics: central dogma, regulation of gene expression, Mendel’s laws; Sequencing based assays: RNA-seq, ChIP-seq; Genome sembly: overlap, string and de Bruijn graphs; Read mapping: suffix tree, suffix array, Burrows-Wheeler transform (BWT), hash table; Sequence alignment and multiple sequence alignment: Needleman-Wunsch algorithm, Smith-Waterman algorithm; Phylogenetics: neighbor joining, statistical phylogenetics; Transcript abundance estimation: EM algorithm; Association mapping: hypothesis testing, correcting for multiple tests; Population structure estimation: principle component analysis (PCA), correction of confounding factors in association mapping; Topics in population genetics: mutation, fixation, selection, drift, migration; Genome annotation: gene finding, regulatory motifs; Gene expression analysis: clustering, classification; Systems biology: networks; RNA and protein structure prediction: meta-heuristic search.

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