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Bioinformatics Advanced Internship - Online
Bioinformatics Virtual Internship Class Schedule
Bioinformatics Advanced Internship Class schedule
UNIT-1 Protein modelling
Day 1: Introduction, Methods: Homology Modelling (59:33)
Day 2: Fold Recognition method, Ab-initio Method, Concept of Protein Folding Demo on tools: Swiss model, I-TASSER, Phyre2 (83:55)
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UNIT-2 Computer aided drug design (CADD)
Day 3: Introduction, Drug discovery pipeline: Target identification and validation, Lead identification and optimization, Virtual screening (61:39)
Day 4: Molecular docking, ADMET properties, Pre-clinical and clinical trials (47:41)
Day 5: Brush up of Basics (49:20)
Day 6: Demo on Molecular docking softwares (59:50)
ADMET Studies for the lead optimization. (113:29)
Demo on tools: SWISS-ADME, Analysis and Interpretation; Boiled-Egg Model 21/04/2022 (60:20)
UNIT-3: Network and Pathway Analysis
Building Research Hypothesis (Null and Alternative). Practical Based Learning concepts on Protein-Drug Interaction. (59:34)
Protein-protein interaction network, STRING, and Cytoscape, Network terminology, Pathway enrichment analysis Demo on the tool: STRING and Cytoscape. (60:04)
UNIT-4: Molecular Dynamics and Simulation
Introduction to Molecular Dynamics and Simulation (MDS), Force fields (57:29)
Steps involved in MDS: Ligand preparation, Solvation and ionization, Energy minimization (57:25)
Molecular dynamics trajectories and Analysis Demo on tool: GROMACS (62:48)
UNIT-5: Genomics & Epigenetics
Human Genome project; Bacterial Genomics, Human Microbiome and Epigenetics Demo on tool: Mothur and QIIME (66:56)
Sanger sequencing, Next Generation Sequencing: DNA Sequencing, RNA Sequencing. miRNA Analysis. Demo on tool: Databases: dbSNP, Clinvar, Genome browsers: UCSC and Ensembl (63:06)
Demo: Differential expression analysis on bacterial genome (66:57)
Demo: Functional genomics and Comparative genomics. (61:33)
UNIT-6: Machine learning (ML) and Basics of programming
Introduction to Machine learning, Supervised learning: Classification, Regression, Unsupervised learning: Clustering (59:01)
Conceptual idea on BioLinux, BioJava, BioPearl, BioPython, and R language (59:03)
Introduction to Machine learning, Supervised learning: Classification, Regression, Unsupervised learning: Clustering
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