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Next-Generation Sequencing (NGS) Data Analysis Course
Original price was: USD $99.00.USD $59.00Current price is: USD $59.00.
Master Next-Generation Sequencing (NGS) Data Analysis teaches you how to analyze and interpret high-throughput sequencing data used in modern genomics research. Through hands-on projects, you’ll learn how to process sequencing datasets, perform quality control, identify genetic variations, and extract meaningful biological insights from complex genomic data.
About the Course
Next-Generation Sequencing (NGS) technologies generate massive volumes of data that drive modern research in Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), RNA-Seq, metagenomics, cancer genomics, and population genetics. However, raw sequencing reads are only the starting point. Meaningful insights depend on structured, reproducible, and statistically sound analysis.
This course provides a practical pathway through the major steps of NGS data analysis, including quality control, trimming and filtering, alignment to a reference genome, variant calling or expression quantification, downstream statistical analysis, and biological interpretation. Participants will work with widely adopted bioinformatics tools and workflows used in research and applied genomics settings.
“Sequencing technologies produce data at scale, but biological meaning comes from careful analysis. This course connects computational workflows with genomic interpretation, helping participants move from raw reads to defensible conclusions.”
The program integrates:
- NGS quality control and preprocessing workflows
- Sequence alignment and file management
- Variant calling and annotation strategies
- RNA-Seq expression analysis
- Reproducible bioinformatics practices
The focus remains on reproducibility, statistical rigor, and biological relevance so that participants can build genomic analysis workflows that are technically sound and scientifically meaningful.
Why This Topic Matters
NGS is central to modern research in:
- Precision medicine
- Cancer genomics
- Infectious disease surveillance
- Agricultural genomics
- Evolutionary biology
Without proper analysis, sequencing data can lead to false-positive variants, misleading expression results, poor reproducibility, and incorrect biological or clinical conclusions. Professionals trained in NGS data analysis are therefore in high demand across academia, biotech companies, clinical laboratories, pharmaceutical industries, and public health genomics programs.
What Participants Will Learn
• Understand NGS technologies and data formats
• Perform QC and preprocessing of sequencing data
• Align sequencing reads to reference genomes
• Conduct variant calling and annotation
• Perform RNA-Seq differential expression analysis
• Interpret genomic variants in biological context
• Work with command-line bioinformatics tools
• Manage large genomic datasets efficiently
• Design reproducible NGS workflows
Course Structure / Table of Contents
Module 1 — Introduction to NGS Technologies
- Overview of sequencing platforms
- Types of NGS applications
- Data formats: FASTQ, SAM, BAM, VCF
Module 2 — Quality Control and Preprocessing
- FASTQC analysis
- Adapter trimming and filtering
- Read quality metrics
Module 3 — Sequence Alignment
- Reference genome concepts
- Alignment tools: BWA, Bowtie
- SAM/BAM file handling using SAMtools
Module 4 — Variant Calling and Analysis
- SNP and INDEL detection
- GATK best practices pipeline
- VCF file interpretation
- Variant annotation tools
Module 5 — RNA-Seq Data Analysis
- Read alignment with HISAT2
- Transcript assembly
- Gene expression quantification
- Differential expression analysis using DESeq2
Module 6 — Functional Genomics & Pathway Analysis
- Gene ontology analysis
- Pathway enrichment
- Biological interpretation of NGS results
Module 7 — Metagenomics & Advanced Applications
- Microbial community analysis
- Taxonomic classification
- Environmental and clinical metagenomics
Module 8 — Reproducible Bioinformatics Workflows
- Command-line basics (Linux environment)
- Workflow management tools
- Data management and documentation
Module 9 — Final Applied Project
- Analyze a complete NGS dataset
- Perform QC, alignment, and downstream analysis
- Interpret biological findings
- Prepare a structured analysis report
Real-World Applications
This course supports work in genomics research laboratories, clinical genomics and diagnostics, cancer research institutes, pharmaceutical and biotech companies, agricultural genomics programs, and public health genomics. In research settings, it strengthens reproducible genomic analysis. In clinical and translational contexts, it supports accurate variant detection, expression analysis, and biologically grounded interpretation.
Tools and Platforms Covered
FASTQC
Trimmomatic
BWA / Bowtie
SAMtools
GATK
HISAT2
DESeq2
Linux Command Line
NCBI / Ensembl
Trimmomatic
BWA / Bowtie
SAMtools
GATK
HISAT2
DESeq2
Linux Command Line
NCBI / Ensembl
Who Should Attend
This course is ideal for:
- Biotechnology and life science students
- Bioinformatics beginners and intermediate learners
- Molecular biologists working with sequencing data
- PhD scholars and research fellows
- Clinical researchers entering genomics
- Professionals transitioning into bioinformatics
It is especially suited for learners with a serious interest in genomics and computational biology.
Prerequisites: Recommended basic molecular biology knowledge and introductory understanding of genetics. Familiarity with command-line basics and elementary statistics is helpful but not mandatory. No prior advanced programming experience is required.
Why This Course Stands Out
Many genomics courses remain heavily theoretical, while some computational programs overlook biological interpretation. This course bridges that divide by integrating NGS data processing pipelines, hands-on tool usage, statistical analysis, genomic interpretation, and reproducible research practices. The final project requires participants to analyze a full NGS dataset from raw reads to biological conclusions, closely reflecting real research and applied bioinformatics workflows.
Frequently Asked Questions
What is NGS data analysis?
It involves processing and interpreting high-throughput sequencing data to identify variants, expression changes, and broader genomic patterns.
Does the course include RNA-Seq analysis?
Yes. RNA-Seq alignment, quantification, and differential expression analysis are covered.
Will I learn variant calling?
Yes. The course includes SNP and INDEL detection using widely adopted bioinformatics workflows and tools.
Is coding required?
Basic command-line usage is included and explained. Advanced programming is not mandatory for this course.
Will I work with real datasets?
Yes. Hands-on activities include real or publicly available NGS datasets for applied analysis.
Is this suitable for beginners?
It is best suited for learners with basic molecular biology knowledge who want a structured introduction to practical NGS analysis.
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