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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  1. PROGRAMMING

R programming (optional)

PreviousPython for GenomicsNextSTATISTICS: THEORY

Last updated 5 months ago

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R is a powerful programming language widely used in bioinformatics for data analysis, statistical computing, and data visualization. With its extensive range of libraries and tools designed specifically for biological data, R is an essential tool for anyone working in genomics, transcriptomics, or other areas of bioinformatics. By learning R, you'll be able to efficiently manipulate and analyze complex biological datasets, perform statistical analyses, and visualize results in meaningful ways. This course will introduce you to the fundamentals of R programming, providing a solid foundation for tackling real-world bioinformatics problems.

This course will help you:

  • Set up R and RStudio to create a robust programming environment.

  • Understand core programming concepts such as data types, control structures, and functions in R.

  • Learn to read, write, and manipulate data, including handling missing values and subsetting data structures.

  • Create and debug custom functions to streamline your data analysis workflow.

  • Enhance the efficiency of your code through profiling and optimization techniques.

  • Organize and comment your code effectively to ensure clarity and reproducibility.

Access hints

To access this course, you’ll need to create a Coursera account. Financial aid is available, allowing you to take the course for free. The application process is simple and straightforward, so don’t hesitate—just go for it!

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