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Instructor Name

Dr. Omics (OP)

Category

CADD

Reviews

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Course Requirements

  • Motivation to Learn: A strong desire to engage with and understand the material.
  • Basic Knowledge: Familiarity with biology and molecular biology concepts.
  • Interest in Technology: Eagerness to learn about the latest technologies in Next-Generation Sequencing (NGS).
  • Software: Free software will be utilized, so no additional software purchases are necessary.
  • Hardware: A laptop with a minimum of 4GB RAM and 100GB of hard disk space.

Course Description

  • Bioinformatics Fundamentals: Introduction to genomic bioinformatics and key databases like GenBank and PubMed. Exploration of essential bioinformatics tools such as BLAST and Mega.
  • Next Generation Sequencing (NGS) Basics: Understanding the role of bioinformatics in NGS and its applications. Overview of NGS techniques, platforms, and data analysis methods.
  • Linux Essentials for Bioinformatics: Introduction to Linux system basics for efficient data management and processing.
  • Conclusion and Resources: Summary of key concepts and practical implications. Provision of additional resources for further learning in bioinformatics and NGS.
  • Introduction to Cloud Technology: Brief overview of cloud technology, focusing on AWS for bioinformatics applications.
  • Pipeline Engineering Basics: Introduction to pipeline engineering for streamlined data analysis workflows.

Course Outcomes

  • Gain a foundational understanding of genomic bioinformatics and key databases like GenBank and PubMed.
  • Master essential bioinformatics tools such as BLAST and Mega for sequence analysis.
  • Understand the role of bioinformatics in NGS and its applications, along with an overview of NGS techniques and platforms.
  • Acquire basic Linux system skills for efficient data management and processing in bioinformatics.
  • Summarize key concepts and provide additional resources for further learning in bioinformatics and NGS.
  • Familiarize with cloud technology, particularly AWS, and the basics of pipeline engineering for streamlined data analysis.


Rules & Regulations

  •  Attendance and Participation: Maintain a minimum of 75% attendance. Regular assessments and attendance contribute to performance evaluation.
  •  Discipline: Maintain punctuality and respect in live classes. Engage actively and interact respectfully with instructors and peers.
  • Course Fee Payment: Pay course fees on time to avoid suspension or cancellation of access.
  • Assignments and Projects: Complete assignments and projects sincerely and submit them on time.
  • Feedback and Communication: Maintain open communication with instructors and provide constructive feedback.
  • Certification: A certificate will be awarded upon course completion.

Course Curriculum

1 4 Nov 2024 Day-01
59 Min


2 5 Nov 2024 Day-02
1 Hour


3 6 Nov 2024 Day-03
1 Hour


4 7 Nov 2024 Day-04
59 Min


5 8 Nov 2024 Day-05
1 Hour


1 M1T1 = Introduction to Bioinformatics
58 Min


2 M1T2 = NCBI Database Overview
1 Hour


3 M1T3 = Genbank Database Practical Exercises
1 Hour


4 M1T4 = UCSC Genome Browser Overview
59 Min


5 M1T5 = UCSC Genome Browser Hands-on Exercises
59 Min


6 M1T6 = Pubmed Database Introduction
59 Min


7 M1T7 = Clinvar Database Overview
1 Hour


8 M1T8 = KEGG Database Overview and Exercises
59 Min


9 M1T9 = Protein Databases (UniProt)
1 Hour


10 M1T10 = Protein Databases (PDB)
1 Hour


11 M1T11 = Online BLAST Introduction and Exercises
1 Hour


12 M1T12 = Standalone BLAST Setup and Exercises
1 Hour


13 M1T13 = Standalone BLAST Advanced Exercises
59 Min


14 M1T14 = Multiple Sequence Alignment with ClustalW
1 Hour


15 M1T15 = Multiple Sequence Alignment with MEGA
1 Hour


1 M2T1 = Overview and Installation of Linux
1 Hour


2 M2T2 = Basic Linux Commands
1 Hour


3 M2T3 = Advanced Linux Commands
1 Hour


4 M2T4 = Package Management using Repository
1 Hour


5 M2T5 = Package Management using Source Code
1 Hour


1 M3T1 = Introduction to Python
59 Min


2 M3T2= Data Types
1 Hour


3 M3T3= String Handling
1 Hour


4 M3T4= Data Structure
1 Hour


5 M3T5=Control Structure
59 Min


6 M3T8= Data Manipulation
1 Hour


7 M3T6 = Function
1 Hour


8 M3T7= File Handling
1 Hour


9 M3T9= Data Visualization
1 Hour


10 M3T10= Biopython
1 Hour


1 M4T1 = Introduction and Installation of R
1 Hour


2 M4T2= Data Types in R
1 Hour


3 M4T3= Data Structure
1 Hour


4 M4T4= File Handling
1 Hour


5 M4T5=Control Structure
1 Hour


6 M4T6 = Function
1 Hour


7 M4T7= Package Management
1 Hour


8 M4T8= Data Manipulation
1 Hour


9 M4T9= Data Visualization
1 Hour


10 M4T10= Statistical Analysis
1 Hour


1 M6T1 =Introduction to Drug Discovery Process
1 Hour


2 M6T2=Role of Computational Methods
59 Min


3 M6T3= Hands-on: Chemical Structure Visualization
1 Hour


4 M6T4= Biomolecules and Their Properties
1 Hour


5 M6T5= Structure of Proteins and Ligands
1 Hour


6 M6T6= Hands-on: Protein Structure Visualization
1 Hour


7 M7T7= Molecular Visualization Tools
1 Hour1 Min


8 M6T8= Molecular Mechanics and Dynamics Simulations
1 Hour


9 M6T9= Molecular Mechanics and Dynamics Simulations (continued)
1 Hour


10 M6T10= Chemical Databases and Data Mining
1 Hour


11 M6T11= Ligand and Structure-Based Virtual Screening
59 Min


12 M6T12= Hands-on: Chemical Data Exploration
1 Hour


13 M6T13= Advanced Virtual Screening Techniques
1 Hour


14 M6T14= Virtual Screening using Autodock Vina
1 Hour


15 M6T15= Principles of Molecular Docking
1 Hour


16 M6T16= Scoring Functions in Docking
59 Min


17 M6T17= Hands-on: Molecular Docking
59 Min


18 M6T18= Introduction to Molecular Dynamics
59 Min


19 M6T19= Simulation Software (e.g., GROMACS)
59 Min


20 M6T20= Hands-on: Analyzing MD Data
59 Min


21 M6T21= Chemoinformatics: Data Analysis and Visualization
1 Hour


22 M6T22= Protein-Ligand Interaction Analysis
1 Hour


23 M6T23= Hands-on Protein-Ligand Interaction Analysis
1 Hour


24 M6T24= Pharmacophore Modeling and Applications
1 Hour


25 M6T25= Chemoinformatics: Data Analysis and Visualization (continued)
59 Min


26 M6T26= Structure-Based Drug Design
1 Hour


27 M6T27= Ligand-Based Drug Design
59 Min


28 M6T28= Hands-on Structure-Based and Ligand-Based Drug Design
59 Min


29 M6T29= ADMET in Drug Development
59 Min


30 M6T30= Course Conclusion
59 Min


1 M7T1= Basics of Machine Learning
59 Min


2 M7T2= Supervised, Unsupervised, and Reinforcement Learning
59 Min


3 M7T3= Hands-on: Learn the Basics with scikit-learn Library in Python
1 Hour


4 M7T4= Data Cleaning and Feature Selection
59 Min


5 M7T5= Handling Molecular Data
59 Min


6 M7T6= Hands-on: Use Pandas and NumPy for Data Preprocessing
1 Hour


7 M7T7= Regression and Classification Algorithms
1 Hour


8 M7T8= Deep Learning in Drug Discovery
59 Min


9 M7T9= Hands-on: Implement Machine Learning Models using scikit-learn
59 Min


10 M7T10= Hands-on: Implement Machine Learning Models using TensorFlow/Keras
1 Hour


11 M7T11= Predicting Drug-Target Interactions
1 Hour


12 M7T12= QSAR Modeling
59 Min


13 M7T13= Hands-on: Apply Machine Learning to Real Datasets with RDKit
1 Hour


14 M7T14= Hands-on: Apply Machine Learning to Real Datasets with Cheminformatics
1 Hour


15 M7T15= Structure-Activity Relationship (SAR) Analysis
1 Hour


16 M7T16= Hands-on: Use RDKit for SAR Analysis
1 Hour


17 M7T17= De Novo Drug Design using ML
59 Min


18 M7T18= Explore De Novo Design Tools
59 Min


19 M7T19= Advanced Machine Learning Techniques in Drug Design
1 Hour


20 M7T20= Integration of Omics Data in Drug Discovery
59 Min


21 M7T21= Clinical Trial Design and Data Analysis
59 Min


22 M7T22= Ethical Considerations in Drug Design and Machine Learning
1 Hour


23 M7T23= Real-World Applications
1 Hour


24 M7T24= Q&A and discussion
1 Hour


25 M7T25= Conclusion
1 Hour


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