This intensive 10-day training course provides a comprehensive and practical dive into the world of genomic data analysis, leveraging the power of R for statistical computing and Tableau for interactive data visualization. Designed for researchers, bioinformaticians, and data scientists, this course will equip participants with the essential skills to process, analyze, interpret, and visually communicate insights from various types of genomic datasets. From fundamental bioinformatics concepts to advanced statistical analyses and compelling data storytelling, this program offers a holistic approach to genomic data analytics.
Throughout the course, we will cover key topics such as R programming for bioinformatics, handling common genomic data formats, performing quality control, conducting differential expression analysis, exploring variant data, and delving into pathway analysis. The second half of the course focuses on harnessing Tableau for powerful data visualization, including connecting diverse genomic datasets, building interactive dashboards, and creating advanced plots. Each module is heavily focused on hands-on practical sessions to ensure participants gain immediate proficiency in applying the learned tools and techniques.
Who Should Attend the Training
- Biologists and life scientists
- Bioinformaticians
- Data scientists with a biological interest
- Researchers working with genomics data
- Medical and clinical researchers
- Statisticians interested in biological applications
Objectives of the Training
Upon completion of this course, participants will be able to:
- Understand fundamental concepts in genomics and bioinformatics.
- Proficiently use R for genomic data manipulation and analysis.
- Perform quality control and pre-processing on genomic datasets.
- Conduct differential expression analysis for RNA-Seq data.
- Work with variant call format (VCF) files and perform basic annotation.
- Apply common statistical methods relevant to genomic data.
- Connect diverse genomic datasets to Tableau for visualization.
- Create various static and interactive data visualizations in Tableau.
- Build comprehensive and interactive dashboards for genomic insights.
- Interpret and communicate complex genomic findings effectively through data analytics and visualization.
Personal Benefits
- Gain highly sought-after skills in genomic data analysis using industry-standard tools.
- Enhance research capabilities and improve the quality of scientific outputs.
- Boost career prospects in bioinformatics, genomics, and data science.
- Develop a strong foundation for more advanced genomic studies.
- Learn to translate complex biological data into actionable insights.
- Become proficient in R programming and Tableau data visualization.
Organizational Benefits
- Accelerate genomic research and development initiatives.
- Improve the efficiency and accuracy of genomic data interpretation.
- Enable data-driven discovery and decision-making.
- Enhance the ability to leverage large-scale genomic datasets.
- Facilitate the creation of compelling reports and presentations of genomic findings.
- Foster a culture of advanced data analytics within the organization.
Training Methodology
Our training approach emphasizes interactive and hands-on learning. The methodology includes:
- Engaging lectures and theoretical explanations
- Live coding demonstrations and walkthroughs in R
- Step-by-step guided practical exercises with real genomic datasets
- Collaborative problem-solving and scripting challenges
- Extensive hands-on practice with Tableau
- Q&A sessions and individualized troubleshooting support
- Case studies illustrating real-world genomic applications
Trainer Experience
Our trainers are experienced bioinformaticians and data scientists with advanced degrees in genomics, computational biology, or related fields. They possess extensive practical experience in analyzing diverse genomic datasets (e.g., RNA-Seq, WGS, epigenomics) using R and other bioinformatics tools, as well as a strong command of data visualization principles with Tableau. Their expertise extends to both research and industry applications, ensuring participants receive guidance that is both theoretically sound and highly relevant to current practices.
Quality Statement
We are committed to delivering high-quality training that is relevant, practical, and impactful. Our course content is meticulously designed, regularly updated, and delivered by expert facilitators dedicated to fostering a dynamic and supportive learning environment. We strive to empower our participants with the skills and confidence to excel in their professional endeavors.
Tailor-made courses
We understand that every organization has unique needs. We offer the flexibility to customize this training course to align with your specific objectives, industry requirements, and organizational context. Our tailor-made programs can be adapted in terms of content, duration, and delivery format to provide a learning experience that directly addresses your challenges and goals.
Course Duration: 10 days
Training fee: USD 2500
Module 1: Introduction to Genomics and Bioinformatics
- Overview of Genomics: DNA, RNA, proteins, central dogma
- Types of Genomic Data: RNA-Seq, WGS, ChIP-Seq, Methylation
- Introduction to Bioinformatics: Data challenges and analytical approaches
- Importance of R in Bioinformatics: Packages and community
- Setting up R and RStudio for Bioinformatics
- Practical session: Navigating RStudio, basic R commands, and installing key bioinformatics packages.
Module 2: R Programming Fundamentals for Bioinformatics
- R Data Structures: Vectors, matrices, data frames, lists
- Basic Data Manipulation: Indexing, subsetting, filtering
- Control Flow: Loops (for, while), conditional statements (if/else)
- Functions in R: Writing your own simple functions
- Data Input/Output: Reading and writing various file types
- Practical session: Practice exercises on data manipulation, control flow, and basic function writing in R.
Module 3: Working with Genomic Data Formats in R
- Common Genomic File Formats: FASTA, FASTQ, BAM, VCF, BED, GFF/GTF
- Introduction to Bioconductor: A core resource for genomic analysis in R
- Reading and Parsing Genomic Data: Using Bioconductor packages
- Data Conversion between Formats
- Handling Large Genomic Files Efficiently
- Practical session: Reading and exploring various genomic file formats (e.g., FASTQ, BAM, GFF) using Bioconductor packages.
Module 4: Quality Control and Pre-processing of Genomic Data
- Importance of Quality Control (QC) in Genomic Analysis
- Assessing Raw Sequence Read Quality (FASTQ): FastQC overview
- Trimming and Filtering Reads: Removing adapters and low-quality bases
- Alignment to a Reference Genome (Conceptual Overview): STAR, Bowtie2
- Dealing with Duplicates and PCR Bias
- Practical session: Using R packages to perform basic QC checks on FASTQ files and demonstrate read trimming.
Module 5: Differential Expression Analysis (RNA-Seq)
- Overview of RNA-Seq Workflow: From reads to counts
- Gene Expression Quantification: RSEM, Salmon (conceptual)
- Normalization Methods: TMM, RLE, CPM
- Introduction to Differential Expression Packages: DESeq2, edgeR
- Performing Differential Expression Analysis: Steps and interpretation
- Practical session: Hands-on differential expression analysis using a sample RNA-Seq count dataset with DESeq2 in R.
Module 6: Introduction to Variant Calling and Annotation
- What are Genetic Variants? SNPs, Indels
- Overview of Variant Calling Pipeline: Alignment, variant discovery
- Understanding VCF (Variant Call Format) Files: Structure and fields
- Basic Variant Filtering and Quality Assessment
- Variant Annotation: Adding functional information (e.g., SnpEff, VEP conceptual)
- Practical session: Parsing and filtering a sample VCF file in R to identify high-quality variants.
Module 7: Population Genetics and GWAS (Conceptual)
- Concepts of Population Structure and Genetic Diversity
- Introduction to Genome-Wide Association Studies (GWAS): Principles and challenges
- Common Statistical Tests in GWAS: Association testing
- Interpreting Manhattan Plots and QQ Plots
- Linkage Disequilibrium and Haplotypes (conceptual)
- Practical session: Exploring example GWAS datasets and interpreting their graphical outputs.
Module 8: Introduction to Epigenomic Data Analysis
- Overview of Epigenomics: DNA Methylation, Histone Modifications
- Introduction to ChIP-Seq Data Analysis (Conceptual)
- Introduction to DNA Methylation Data Analysis (Conceptual)
- Peak Calling and Differential Methylation Analysis (Conceptual)
- Visualizing Epigenomic Data in Genome Browsers (e.g., IGV conceptual)
- Practical session: Visualizing a pre-processed epigenomic dataset in R using relevant packages.
Module 9: Pathway and Network Analysis
- Why Pathway Analysis? Understanding biological context
- Gene Ontology (GO) and KEGG Pathways
- Over-representation Analysis: Identifying enriched pathways
- Gene Set Enrichment Analysis (GSEA) (conceptual)
- Building and Visualizing Gene Networks: Cytoscape (conceptual)
- Practical session: Performing a basic GO/KEGG pathway enrichment analysis on a list of differentially expressed genes in R.
Module 10: Introduction to Data Visualization with Tableau
- Tableau Interface Overview: Workbook, sheets, dashboards, stories
- Connecting to Data Sources: Excel, CSV, databases
- Data Types and Roles in Tableau: Dimensions, Measures
- Basic Chart Types: Bar charts, line charts, scatter plots
- Filters, Parameters, and Highlighters for interactivity
- Practical session: Connecting Tableau to a prepared sample dataset and creating basic charts.
Module 11: Connecting Genomic Data to Tableau
- Preparing Genomic Data for Tableau: Tabular formats from R
- Exporting Processed Data from R to CSV/Excel
- Connecting Tableau to R: RServe integration (conceptual)
- Data Joins and Blends in Tableau: Combining multiple tables
- Data Pivoting and Unpivoting for optimal visualization
- Practical session: Preparing and exporting a processed genomic dataset from R and connecting it to Tableau.
Module 12: Building Basic Visualizations in Tableau
- Creating Histograms and Box Plots for genomic distributions
- Heatmaps for Expression Data: Gene-sample relationships
- Treemaps and Packed Bubbles for hierarchical data
- Dual-Axis Charts for comparing different measures
- Table Calculations: Percentages, running totals
- Practical session: Building a heatmap of gene expression and a histogram of variant frequencies in Tableau.
Module 13: Advanced Visualizations for Genomic Data in Tableau
- Volcano Plots for Differential Expression: Combining fold change and p-value
- Circos Plots (conceptual with external tools): Genomic rearrangements
- Custom Shapes and Images for biological representations
- Small Multiples (Trellis Charts) for comparative analysis
- Dashboard Actions: Filter, Highlight, Go to URL
- Practical session: Creating an interactive volcano plot in Tableau with filtering capabilities.
Module 14: Creating Interactive Dashboards in Tableau
- Principles of Dashboard Design: Layout, flow, user experience
- Arranging Multiple Worksheets on a Dashboard
- Adding Interactivity: Filters, parameters, actions
- Building a Story with Data: Story points feature
- Publishing and Sharing Dashboards: Tableau Public, Tableau Server
- Practical session: Designing and building a multi-sheet interactive dashboard for a genomic research question.
Module 15: Statistical Plotting and Interpretation in R
- ggplot2 for Publication-Quality Graphics: Grammar of Graphics
- Customizing ggplot2 Plots: Themes, scales, annotations
- Creating Specific Genomic Plots in R: Heatmaps, PCA plots, survival curves (basic)
- Exporting Plots in various Formats: PDF, PNG, JPEG
- Interpreting Statistical Visualizations Accurately
- Practical session: Recreating and customizing key genomic plots (e.g., PCA plot, heatmap) using ggplot2.
Module 16: Best Practices for Reproducible Genomic Analysis
- The Importance of Reproducibility in Science
- Version Control with Git and GitHub (conceptual overview)
- R Markdown for Reproducible Reports and Notebooks
- Containerization (Docker, Singularity conceptual) for environments
- Data Management and Organization Strategies
- Practical session: Creating a simple R Markdown document to present a genomic analysis workflow.
Module 17: Case Studies in Genomic Data Analysis
- Analyzing a Public RNA-Seq Dataset: End-to-end workflow
- Exploring a Public Variant Dataset: Disease association example
- Genomic Data in Clinical Settings: Pharmacogenomics, diagnostics
- Applications in Agriculture and Environmental Science
- Discussion of Challenges and Future Directions in Genomic Analytics
- Practical session: Working through a complete, simplified case study from raw data to visualization, integrating R and Tableau.
Module 18: Project Work and Presentation
- Participants work on an individual or small group project using real or simulated genomic data.
- Defining a Project Scope and Research Question
- Applying Learned Skills: Data processing, analysis, visualization
- Preparing a Project Report and Presentation Slides
- Presenting Findings and Receiving Feedback
- Practical session: Dedicated time for project work, followed by short presentations of individual/group projects to the class.
Requirements:
· Participants should be reasonably proficient in English.
· Applicants must live up to Armstrong Global Institute admission criteria.
Terms and Conditions
1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free
2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.
3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.
4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.
5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.
Booking for Training
Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.
Or call us on +254720272325 / +254725012095 / +254724452588
Payment Options
We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:
1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.
2. Invoice: We can send a bill directly to you or your company.
3. Deposit directly into Bank Account (Account details provided upon request)
Cancellation Policy
1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.
2. Participants may cancel attendance 14 days or more prior to the training commencement date.
3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.
Tailor Made Courses
This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com
Accommodation and Airport Transfer
Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588
