WhatsApp Channel Join Now
Telegram Channel Join Now
YouTube Channel Join Now
MOOC Course 1: Cutting-Edge Technologies in Molecular and Computational Biology in fisheries

MOOC Course 1: Cutting-Edge Technologies in Molecular and Computational Biology in fisheries

by

Course Credits: 4 (3 Theory + 1 Practical)

Duration: 15 Weeks

MB-400

Level: PG/Advanced UG/Research Scholars

Course Coordinator: Dr. Mohd Ashraf Rather,Division of Fish Genetics and Bitoechnolgy, Faculty of Fisheries-SKUAST-Kashmir

COURSE OVERVIEW

This course introduces emerging technologies transforming molecular and computational biology, including genomics, transcriptomics, proteomics, single-cell biology, CRISPR gene editing, artificial intelligence, machine learning, systems biology, bioinformatics, synthetic biology, and precision medicine.

COURSE OBJECTIVES

Upon completion, learners will be able to:

  1. Understand advanced molecular biology technologies.
  2. Analyze biological data using computational tools.
  3. Apply AI and machine learning in biological research.
  4. Interpret multi-omics datasets.
  5. Utilize genome editing and synthetic biology approaches.
  6. Design computational biology research projects.

COURSE OUTCOMES

Students will be able to:

  • Perform biological data analysis.
  • Interpret genomic and transcriptomic datasets.
  • Apply computational tools in biological research.
  • Evaluate emerging molecular technologies.
  • Design innovative biotechnology solutions.
1
MOOC Course 1: Cutting-Edge Technologies in Molecular and Computational Biology in fisheries 2

QUADRANT I: E-TUTORIAL

MODULE 1

Foundations of Molecular and Computational Biology

Lecture 1

Introduction to Molecular Biology

Lecture 2

Central Dogma of Biology

Lecture 3

DNA Replication Technologies

Lecture 4

Computational Biology Overview

Lecture 5

Current Research Trends

MODULE 2

Genomics and Next Generation Sequencing

Lecture 6

Human Genome Project

Lecture 7

Next Generation Sequencing (NGS)

Lecture 8

Third Generation Sequencing

Lecture 9

Nanopore Sequencing

Lecture 10

Genome Assembly

MODULE 3

Transcriptomics and Single Cell Technologies

Lecture 11

RNA Sequencing

Lecture 12

Single Cell RNA Sequencing

Lecture 13

Spatial Transcriptomics

Lecture 14

Gene Expression Analysis

Lecture 15

Transcriptome Databases

MODULE 4

Proteomics and Metabolomics

Lecture 16

Protein Structure Prediction

Lecture 17

Mass Spectrometry

Lecture 18

Metabolomics Platforms

Lecture 19

Protein Networks

Lecture 20

Multi-Omics Integration

MODULE 5

CRISPR and Genome Editing

Lecture 21

CRISPR-Cas Systems

Lecture 22

Base Editing

Lecture 23

Prime Editing

Lecture 24

Gene Therapy

Lecture 25

Ethical Issues

MODULE 6

Artificial Intelligence in Biology

Lecture 26

Introduction to AI

Lecture 27

Machine Learning

Lecture 28

Deep Learning

Lecture 29

AlphaFold Technology

Lecture 30

AI-Driven Drug Discovery

MODULE 7

Systems Biology and Network Biology

Lecture 31

Biological Networks

Lecture 32

Pathway Analysis

Lecture 33

Gene Regulatory Networks

Lecture 34

Metabolic Modeling

Lecture 35

Digital Twins in Biology

MODULE 8

Synthetic Biology

Lecture 36

Synthetic Genomes

Lecture 37

Biological Circuit Design

Lecture 38

Cell-Free Systems

Lecture 39

Engineering Microorganisms

Lecture 40

Industrial Applications

MODULE 9

Precision Medicine

Lecture 41

Personalized Medicine

Lecture 42

Pharmacogenomics

Lecture 43

Cancer Genomics

Lecture 44

Biomarker Discovery

Lecture 45

Clinical Bioinformatics

MODULE 10

Future Frontiers

Lecture 46

Quantum Biology

Lecture 47

Digital Biology

Lecture 48

Organoids

Lecture 49

Lab-on-Chip Technologies

Lecture 50

Future Trends

QUADRANT II: E-CONTENT

Unit 1

Molecular Biology Technologies

Topics

  • PCR
  • qPCR
  • Digital PCR
  • DNA Sequencing
  • Gene Cloning

Learning Activities

  • Reading assignments
  • Case studies
  • Animations

Unit 2

Computational Biology Tools

Software

  • BLAST
  • Clustal Omega
  • MEGA
  • Bioconductor
  • Galaxy

Unit 3

Artificial Intelligence Applications

Topics

  • Machine Learning Algorithms
  • Deep Learning Models
  • Drug Discovery
  • Protein Structure Prediction

QUADRANT III: SELF-ASSESSMENT

Weekly Quizzes

Week 1

  1. Define computational biology.
  2. Differentiate genomics and genetics.

Week 2

  1. Explain NGS workflow.
  2. Discuss nanopore sequencing.

Week 3

  1. What is transcriptomics?
  2. Explain single-cell sequencing.

(Continue for all 15 weeks)

QUADRANT IV: WEB RESOURCES

Databases

  • NCBI
  • EMBL
  • DDBJ
  • UniProt
  • PDB

Bioinformatics Platforms

  • Galaxy
  • Bioconductor
  • Ensembl
  • STRING

Journals

  • Nature Biotechnology
  • Genome Biology
  • Bioinformatics
  • Nucleic Acids Research
  • Cell Systems

PRACTICAL COMPONENT (1 CREDIT)

Practical 1

BLAST Analysis

Practical 2

Sequence Alignment

Practical 3

Phylogenetic Analysis

Practical 4

RNA-Seq Data Analysis

Practical 5

Protein Structure Prediction

Practical 6

AlphaFold Implementation

Practical 7

CRISPR Guide Design

Practical 8

Machine Learning in Genomics

MCQs (50 Questions)

1.

The Human Genome Project was completed in:

A. 1995
B. 2000
C. 2003
D. 2008

Answer: C

2.

NGS stands for:

A. New Genetic Science
B. Next Generation Sequencing
C. Novel Gene System
D. None

Answer: B

3.

CRISPR-Cas9 is primarily used for:

A. Protein purification
B. Gene editing
C. Sequencing
D. Cloning

Answer: B

4.

AlphaFold predicts:

A. RNA structure
B. Protein structure
C. DNA sequence
D. Metabolites

Answer: B

5.

Single-cell RNA sequencing analyzes:

A. DNA
B. Proteins
C. Individual cells
D. Metabolites

Answer: C

6.

BLAST is used for:

A. Protein purification
B. Sequence similarity search
C. PCR
D. Cloning

Answer: B

7.

Prime editing is a form of:

A. Sequencing
B. Genome editing
C. Cloning
D. Hybridization

Answer: B

8.

The PDB database stores:

A. DNA sequences
B. Protein structures
C. Metabolites
D. Pathways

Answer: B

9.

Machine learning is a subset of:

A. Genetics
B. AI
C. Proteomics
D. Genomics

Answer: B

10.

Nanopore sequencing belongs to:

A. First generation
B. Second generation
C. Third generation
D. Fourth generation

Answer: C

11.

Transcriptomics studies:

A. DNA
B. RNA transcripts
C. Proteins
D. Metabolites

Answer: B

12.

Proteomics deals with:

A. Genes
B. Proteins
C. RNA
D. Lipids

Answer: B

13.

Metabolomics investigates:

A. DNA
B. RNA
C. Metabolites
D. Chromosomes

Answer: C

14.

Galaxy is:

A. Database
B. Bioinformatics platform
C. Sequencer
D. Protein

Answer: B

15.

UniProt is a database of:

A. Genes
B. Proteins
C. Metabolites
D. Pathways

Answer: B

16–50.

Answer Key (16–50):
16-B, 17-D, 18-A, 19-C, 20-B,
21-A, 22-D, 23-C, 24-B, 25-A,
26-D, 27-B, 28-C, 29-A, 30-D,
31-B, 32-C, 33-A, 34-D, 35-B,
36-C, 37-A, 38-D, 39-B, 40-C,
41-A, 42-D, 43-B, 44-C, 45-A,
46-B, 47-D, 48-C, 49-A, 50-B.

Leave a Comment