4 modules
Explore the fundamentals of Frontier Technologies
5 Level NCrF
National Credit Framework
90 Hours
Flexible Learning
Beginner Level
No prior experience required
Micro Credit Course
03 Credit
Course Overview
This course offers a comprehensive introduction to computational genomics, equipping students with the skills to analyze genomic data and understand biological variations. It covers genomic variation and the differences between individual and reference genomes, as well as pattern matching algorithms for DNA fragment alignment and genome mapping. Students will learn to apply Hidden Markov Models (HMMs) to identify protein structure and function from mutated sequences and analyze protein families using bioinformatics software and machine learning-based approaches. The course also emphasizes practical mutation analysis using publicly available datasets and tools, enabling learners to gain hands-on experience in genomic research and computational biology applications.
Key Learning Highlights
Understand genomic variation and differences between individual and reference genomes
Apply pattern matching algorithms for DNA fragment alignment and genome mapping
Use Hidden Markov Models (HMMs) to predict protein structure and function from mutated sequences
Analyze protein families using bioinformatics software and machine learning approaches
Perform mutation analysis using publicly available datasets and computational tools
Tools & Platform Used
Learning Outcome
By the end of this course, students will be able to:
- Explain genomic variation and the differences between individual and reference genomes
- Apply pattern matching algorithms for DNA fragment alignment and genome mapping
- Utilize Hidden Markov Models (HMMs) to identify protein structure and function from mutated sequences
- Analyze protein families using bioinformatics software and machine learning-based methods
- Perform mutation analysis using publicly available datasets and computational tools
Master the course with just 4 Modules
This course begins with an Introduction to Genome Variation, covering types of mutations, differences between individual and reference genomes, and the role of mutation detection in disease research. Learners then explore DNA Fragment Mapping Algorithms, including combinatorial pattern matching, indexing, and alignment techniques such as Burrows-Wheeler Transform and suffix arrays. The course continues with Protein Mutation Analysis, focusing on protein structure, the impact of mutations on function, and sequence versus structure conservation. Finally, students study Hidden Markov Models (HMMs) in Bioinformatics, learning their applications in protein family modeling, motif discovery, and function prediction using profile HMMs.
Introduction to Genome Variation
- Understand different types of mutations and their biological significance
- Compare individual genomes with reference genomes
- Explore the role of mutation detection in disease research
DNA Fragment Mapping Algorithms
- Learn combinatorial pattern matching techniques for mapping DNA reads
- Study indexing and alignment methods such as Burrows-Wheeler Transform and suffix arrays
- Apply algorithms for efficient genome mapping and sequence analysis
Introduction to Protein Mutation Analysis
- Understand protein structure and its relationship to function
- Analyze the impact of mutations on protein function
- Compare sequence conservation versus structural conservation
Hidden Markov Models in Bioinformatics
- Learn the fundamentals of Hidden Markov Models (HMMs)
- Apply HMMs for protein family modeling and motif discovery
- Use profile HMMs for predicting protein function
Roles
Computational Biologist
Bioinformatics Analyst
Genomics Data Scientist
Protein Structure Analyst
Research Scientist in Genetics or Molecular Biology
Clinical Bioinformatics Specialist
Biotechnology Analyst
