Biological Sequence Analysis

Description.

Stochastic study of biological sequences (Entropy, Relative Entropy, Mutual Information). Pairwise sequence alignment (dynamic programming, global and local alignment – Needleman and Wunch algorithm, Smith and Waterman algorithm, statistical significance of alignment, similarity matrices, gap penalties, heuristic methods for database search- BLAST, FASTA). Multiple sequence alignment (Multi-dimensional dynamic programming, heuristic methods for multiple alignment – CLUSTAL, KALIGN, MUSCLE, T-Coffee, evaluation of multiple alignments). Search for patterns in sequences (regular expressions, PROSITE, weight matrices, profiles, PSSMs, PSI-BLAST, PHI-BLAST). Phylogenetic analysis (basic principles of phylogenetics, trees, stochastic models of the evolutionary process, character-based methods, distance-based methods). Protein and DNA sequence-based prediction algorithms (protein and RNA secondary structure prediction, prediction of transmembrane protein fragments and their orientation, finding possible genes in DNA sequences). Hidden Markov Models (The forward and backward algorithms, decoding, parameter estimation, special modifications of the Hidden Markov Model for biological data). Neural Networks in Bioinformatics (Introduction to Neural Networks, Applications). Comparative and computational genomics (methods of genome analysis, applications). Structural bioinformatics (Representation of biological structures, protein fold recognition, adaptation and superposition of structures in space, homology-based comparative standardization, structure anchoring). Computational Grammar (Chomsky’s hierarchy, examples and applications – RNA, protein folding).

Bibliography:
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