ABI Bioinformatics Guide 2024
  • INTRODUCTION
    • How to use the guide
  • MOLECULAR BIOLOGY
    • The Cell
      • Cells and Their Organelles
      • Cell Specialisation
      • Quiz 1
    • Biological Molecules
      • Carbohydrates
      • Lipids
      • Nucleic Acids (DNA and RNA)
      • Quiz 2
      • Proteins
      • Catalysis of Biological Reactions
      • Quiz 3
    • Information Flow in the Cell
      • DNA Replication
      • Gene Expression: Transcription
      • Gene Expression: RNA Processing
      • Quiz 4
      • Chromatin and Chromosomes
      • Regulation of Gene Expression
      • Quiz 5
      • The Genetic Code
      • Gene Expression: Translation
    • Cell Cycle and Cell Division
      • Quiz 6
    • Mutations and Variations
      • Point mutations
      • Genotype-Phenotype Interactions
      • Quiz 7
  • PROGRAMMING
    • Python for Genomics
    • R programming (optional)
  • STATISTICS: THEORY
    • Introduction to Probability
      • Conditional Probability
      • Independent Events
    • Random Variables
      • Independent, Dependent and Controlled Variables
    • Data distribution PMF, PDF, CDF
    • Mean, Variance of a Random Variable
    • Some Common Distributions
    • Exploratory Statistics: Mean, Median, Quantiles, Variance/SD
    • Data Visualization
    • Confidence Intervals
    • Comparison tests, p-value, z-score
    • Multiple test correction: Bonferroni, FDR
    • Regression & Correlation
    • Dimentionality Reduction
      • PCA (Principal Component Analysis)
      • t-SNE (t-Distributed Stochastic Neighbor Embedding)
      • UMAP (Uniform Manifold Approximation and Projection)
    • QUIZ
  • STATISTICS & PROGRAMMING
  • BIOINFORMATICS ALGORITHMS
    • Introduction
    • DNA strings and sequencing file formats
    • Read alignment: exact matching
    • Indexing before alignment
    • Read alignment: approximate matching
    • Global and local alignment
  • NGS DATA ANALYSIS & FUNCTIONAL GENOMICS
    • Experimental Techniques
      • Polymerase Chain Reaction
      • Sanger (first generation) Sequencing Technologies
      • Next (second) Generation Sequencing technologies
      • The third generation of sequencing technologies
    • The Linux Command-line
      • Connecting to the Server
      • The Linux Command-Line For Beginners
      • The Bash Terminal
    • File formats, alignment, and genomic features
      • FASTA & FASTQ file formats
      • Basic Unix Commands for Genomics
      • Sequences and Genomic Features Part 1
      • Sequences and Genomic Features Part 2: SAMtools
      • Sequences and Genomic Features Part 3: BEDtools
    • Genetic variations & variant calling
      • Genomic Variations
      • Alignment and variant detection: Practical
      • Integrative Genomics Viewer
      • Variant Calling with GATK
    • RNA Sequencing & Gene expression
      • Gene expression and how we measure it
      • Gene expression quantification and normalization
      • Explorative analysis of gene expression
      • Differential expression analysis with DESeq2
      • Functional enrichment analysis
    • Single-cell Sequencing and Data Analysis
      • scRNA-seq Data Analysis Workflow
      • scRNA-seq Data Visualization Methods
  • FINAL REMARKS
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  • Introduction
  • Why Do We Need NGS Data?

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NGS DATA ANALYSIS & FUNCTIONAL GENOMICS

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Introduction

We will explore Next-Generation Sequencing (NGS) data analysis, starting from raw data processing to final interpretation. We will begin by learning how to handle and analyze raw NGS data, understanding the main data formats used to store genomic information. We will cover Whole Genome (WG) data handling, focusing on the key steps of alignment and variant calling. By the end of this course, you will have a solid understanding of the complete workflow involved in NGS data analysis, including the tools and techniques essential for accurate and efficient genomic data processing.

Why Do We Need NGS Data?

1. Genome Assembly Genome assembly involves reconstructing the complete sequence of an organism's DNA from small, overlapping fragments generated by sequencing. This is essential for studying organisms without a reference genome or for improving existing assemblies. NGS provides the high-throughput data needed to piece together these fragments accurately, enabling a comprehensive view of an organism's genome.

2. Variant Detection Variant detection identifies differences between an individual's genome and a reference genome, such as single nucleotide polymorphisms (SNPs), insertions, deletions, or structural variations. These variants are crucial for understanding genetic diversity, disease mechanisms, and traits of interest in both research and clinical settings. NGS's sensitivity and resolution make it ideal for uncovering even rare or complex variants. Learn more about genetic variation

3. Gene Expression Analysis Gene expression analysis determines which genes are actively transcribed and at what levels under specific conditions or in different tissues. NGS, provides a precise and quantitative method for measuring gene expression. This allows researchers to identify gene regulation patterns, compare expression across samples, and study pathways involved in biological processes and diseases.

Beyond these, NGS has many more applications, such as epigenomics, metagenomics, and transcriptome profiling, making it an indispensable tool in modern biology.

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