Bioinformatics Data Analysis with R Bioconductor
About This Course
This Bioinformatics Data Analysis with R Bioconductor course equips participants with the necessary skills to analyze complex bioinformatics datasets using R and Bioconductor. The course begins with an introduction to bioinformatics and sequence analysis, followed by variant calling and protein structure analysis. You will explore transcriptomics and genomics data, learning to identify gene expressions, perform differential expression analysis, and detect genomic variants.
Advanced topics include machine learning applications in bioinformatics, where participants will develop predictive models, classify biological data, and uncover trends using clustering techniques. The course also emphasizes data visualization, teaching learners to create insightful visualizations using tools like ggbio and heatmaps. By the end of the course, learners will be able to facilitate discussions on bioinformatics issues and derive actionable insights from large datasets.
What You'll Learn
LO1: Recognize significant trends and aberrant results in bioinformatics data using statistical techniques
LO2: Use statistical tests and data analytics tools to estimate uncertainties and determine data acceptability.
LO3: Review datasets to uncover trends or patterns and identify potential causes of unacceptable data.
LO4: Develop new methods for analyzing large, complex bioinformatics datasets using specialized modeling software.
LO5: Facilitate discussions on applying big data analytics to examine bioinformatics issues and derive insights.
Learning Outcome:
Topic 1: Introduction to Bioconductor and Sequence Analysis
• Overview of Bioinformatics
• Understanding Sequence Alignment
• Introduction to Bioconductor
• Multiple Sequence Analysis
• Interpreting Sequence Similarity Scores
Topic 2: Structural Bioinformatics and Variant Calling
• Introduction to Structural Bioinformatics
• Analysing Protein Structures
• Variant Calling Using Bioconductor
• Evaluating Relationships Between Protein Structures and Functions
Topic 3: Transcriptomes, Genomics, and Variant Analysis
• Introduction to Transcriptome Data Analysis
• Genomic Data Analysis and Visualization
• Gene Expression Analysis and Differential Expression
• Variant Detection and Annotation in Genomic Data
Topic 4: Machine Learning for Bioinformatics
• Introduction to Machine Learning
• Machine Learning for Predictive Modelling in Bioinformatics
• Clustering and Classification of Biological Data
• Predictive Models for Genomic and Transcriptomic Data
Topic 5: Data Visualization for Bioinformatics
• Creating Data Visualizations using ggbio
• Heatmaps for Transcriptomic and Genomic Data
• Visualizing Biological Pathways and Networks
• Communicating Biological Insights through Effective Visualizations
Entry Requirements
Knowledge and Skills
• Able to operate using computer functions
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience
Target Age Group: 21-65 years old