Whole Genome Assembly Crash Course-1
Master the fundamentals of whole genome assembly using modern bioinformatics tools and AI-assisted genomic analysis.
Course Description
The Whole Genome Assembly Crash Course is designed to provide a practical understanding of genome assembly workflows used in modern genomics and bioinformatics research. This course covers the principles of next-generation sequencing (NGS), genome assembly strategies, and assembly quality evaluation. Participants will learn how to process raw sequencing reads and perform de novo genome assembly using industry-standard tools. The course also introduces hybrid assembly approaches and AI-assisted bioinformatics techniques for genomic data analysis. Learners will gain hands-on exposure to genome polishing, scaffolding, and annotation concepts. Real-world datasets and case studies are included to strengthen practical knowledge. The program is suitable for researchers, students, and professionals interested in genomics and precision medicine. By the end of the course, participants will be able to confidently perform and interpret whole genome assembly projects. No advanced programming expertise is required to begin learning.
What You'll Learn
Fundamentals of genome sequencing technologies.
Principles of de novo and reference-guided genome assembly.
Pre-processing and quality control of sequencing reads.
Assembly workflows for short-read and long-read sequencing data.
Hybrid genome assembly approaches.
Assembly polishing and error correction methods.
Genome scaffolding and gap closing techniques.
Assembly quality assessment and validation metrics.
Introduction to genome annotation concepts.
Curriculum
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Module 1: Foundational paradigms of de novo sequence assembly, understanding data challenges, and setting up Linux-based processing environments.
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Module 2: Pre-assembly quality assessment, data filtering metrics, and calculating optimal k-mer distributions for graph building.
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Module 3: Executing short-read assembly via SPAdes and long-read structural construction using Flye, Raven, or Shasta assemblers.
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Module 4: Deploying machine learning consensus algorithms and deep-learning models for raw assembly polishing and nucleotide correction.
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Module 5: Advanced scaffolding execution, gap-closing mechanics, and calculating quantitative validation scores for final genome submission.
Lesson
-
Module 1: Foundational paradigms of de novo sequence assembly, understanding data challenges, and setting up Linux-based processing environments.
Lesson -
Module 2: Pre-assembly quality assessment, data filtering metrics, and calculating optimal k-mer distributions for graph building.
Lesson -
Module 3: Executing short-read assembly via SPAdes and long-read structural construction using Flye, Raven, or Shasta assemblers.
Lesson -
Module 4: Deploying machine learning consensus algorithms and deep-learning models for raw assembly polishing and nucleotide correction.
Lesson -
Module 5: Advanced scaffolding execution, gap-closing mechanics, and calculating quantitative validation scores for final genome submission.
Lesson
