BIOINFORMATICS I

This course introduces fundamental concepts and methods for bioinformatics. Main topics of the course include: bioinformatics databases, analysis of DNA or protein sequences, sequence alignment, phylogenetic trees, key methods and tools used in bioinformatics, Artificial Neural Networks, Hidden Markov Models (HMMs).

Objectives

The main aim of the course is to familiarize students with the topics of Bioinformatics and the tools that they can use in many biological queries and to help them acquire skills regarding the use of databases and software packages that are widely prevalent in the science of Bioinformatics.

Prerequisites

No previous knowledge of the specific subject is required

Syllabus

Introduction: definition of bioinformatics, divisions of bioinformatics, types of data in Bioinformatics. Databases: bibliographic databases, protein and DNA sequence databases, Entrez and SRS databases. Sequence alignment: methods of finding similarities in sequences, homology and sequence similarity and their importance, dynamic programming algorithms, Global Alignment - algorithm of Needleman and Wunch, Local Alignment - algorithm of Smith and Waterman, calculation of statistical significance of alignment, penalties for gaps, heuristic methods to find similarities in databases - BLAST, FASTA, etc.. Multiple sequence alignment: Multidimensional dynamic programming algorithms, Heuristic methods for multiple sequence alignment - CLUSTAL, DIALIGN, MULTALIN etc. - phylogenetic trees and multiple alignments. Prediction Algorithms: prediction of secondary structure of proteins, prediction of transmembrane regions of proteins and their orientation, finding possible genes in DNA sequences, multiple sequence alignments using Hidden Markov Models, ranking sequences into families. Structural Bioinformatics: Representation of biological structures, identification of protein folding, comparative modeling by sequence homology.