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Algorithms in computational Biology
Algorithms and probabilistic models that develop in different computational biology applications, consisting of:
- Pattern matching with strings: suffix trees, suffix varieties, Burrows Wheeler Transform, inexact matches with vibrant programming, useful DNA sequence alignment
- Alignment of biological networks
- Phylogenetic: inference of phylogenetic trees, perfect phylogeny, probability estimation
- Hidden Markov models with application to chromatin state annotation.
Introduction to computational problems on evaluating biological information. The focus of this course is on style and examination of algorithms with applications to bioinformatics, and programs to manipulate biological information using Algorithms for Computational Biology using Python. Subjects include (1) nucleic acid and protein sequence alignment, (2) phylogenetic algorithms, and (3) gene clustering and practical genomics.
- To introduce students to the standard ideas and techniques on Computational Biology and Bioinformatics.
- To develop computational skills of developing algorithms to evaluate biological information.
- To get experience of programming to manipulate biological information using Algorithms for Computational Biology using Python.
- BINF 3350, Genomics and Bioinformatics
- CSI 3344, Introduction to Algorithms
- An Intro to Bioinformatics Algorithms, by Neil C. Jones and Pave A. Pevsner, The MIT Press
- Algorithms in Bioinformatics: A Practical Intro, by W.-K. Sung, CRC Press
- Bioinformatics and Practical Genomics, by Jonathan Pevsner, Wiley-Less
- Advanced Analysis of Gene Expression Microarray Data, by Assisting Zhang, World Scientific
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- Programs Algorithms for Computational Biology using Python, by Mark Lutz, O'Reilly
- Bioinformatics Setting Using Algorithms for Computational Biology using Python, by Mitchell L Model, O'Reilly
- Start Perl for Bioinformatics, by James Tidal, O'Reilly
- Intro to Algorithms, by T. H. Carmen, C. E. Leadsperson, R. L. Rives, and C. Stein, McGraw-Hill
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