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Bio-Informatics:Algorithms and Applications

Indian Institute of Technology Madras via NPTEL

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Bioinformatics is an interdisciplinary field of science for analyzing and interpreting vast biological data using computational techniques. In this course, we aim to give a walkthrough of the major aspects of bioinformatics such as the development of databases, computationally derived hypothesis, algorithms, and computer-aided drug design. During the first section of the course, we will focus on DNA and protein sequence databases and analysis, secondary structures and 3D structural analysis. The second section will be devoted to applications such as prediction of protein structure, folding rates, stability upon mutation, and intermolecular interactions. Further, we will cover computer-aided drug design using docking and QSAR studies. This course is designed to nurture skills and knowledge required for aspiring students, young biologists and research scholars to develop algorithms and tools in bioinformatics.

Students, PhD scholars, teachers, industry


Week 1  :  Introduction, DNA sequence analysis, DNA Databases
Week 2  :   Protein structure and function, protein sequence databases, sequence alignment
Week 3  :  PAM matrix, Global and local alignment, BLAST: features and scores
Week 4  :  Multiple sequence alignment, Conservation score, phylogenetic trees
Week 5  :  Protein sequence analysis, hydrophobicity profiles, non-redundant datasets
Week 6  :  Protein secondary structures, Ramachandran plot, propensity, secondary structure prediction
Week 7  :  Protein tertiary structure, Protein Data Bank, visualization tools, structural classification, contact maps
Week 8  :  Protein structural analysis, protein structure prediction
Week 9  :  Protein stability, energetic contributions, database, stabilizing residues, stability upon mutations
Week 10 :  Protein folding rates, proteins interactions, binding site residues
Week 11  : Computer aided drug design, docking, screening, QSAR
Week 12 :  Development of algorithms, awk programming, machine learning techniques, applications using WEKA

Taught by

Prof. Michael Gromiha

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