Bioinformatics & Computational Biology





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Course Contents

Introduction to bioinformatics, biological databases and their growth, Concept of homology and definition of associated terms, pair wise sequence alignment, dot matrix plot, dynamic programming algorithm, global (Needleman Wunsch) and local (Smith Waterman) alignments, BLAST Scoring matrices (PAM and BLOSUM families), gap penalty, statistical significance of alignment Multiple sequence alignment, Sum of pairs method, CLUSTAL W, Genetic Algorithm Pattern finding in protein and DNA sequencing, Gibbs Sampler, Hidden Markov Model, Profile construction and searching, PSIBLAST Introduction to phylogeny, maximum parsimony method, distance method (neighbor joining), maximum likelihood method Gene prediction in prokaryotes and eukaryotes, homology and a binitio methods Genome analysis and annotation, comparative genomics.




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