Advanced Bioconductor

Course Description

Welcome to "Advanced Bioconductor," an advanced-level course that delves deep into the world of genomic data analysis and visualization. This course builds upon the foundation laid in previous courses and takes your skills to the next level in handling, analyzing, and interpreting complex genomic data.

What You Will Learn

• Advanced techniques for visualizing genome-scale data
• Development of interactive graphical interfaces for efficient data exploration
• Reproducible research methods using knitr and rmarkdown
• Handling of large-scale data using out-of-memory approaches
• Integration of multiomic data using real-world examples
• Exploration of cloud-resident resources for genomic analysis

Prerequisites

To succeed in this course, students should have completed the following prerequisites:

• PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
• PH525.4x: High-Dimensional Data Analysis

Additionally, a strong background in R programming, statistics, and basic genomics concepts is essential.

Course Content

• Genome-scale data visualization techniques
• Interactive graphical interface development
• Reproducible research using knitr and rmarkdown
• Out-of-memory approaches for large data analysis
• Relational databases and HDF5 for data storage
• Multiomic data integration using The Cancer Genome Atlas
• Cloud-resident resources from the Encyclopedia of DNA Elements (ENCODE)
• Transcription factor binding, ATAC-seq, and RNA-seq analysis
• CRISPR interference in genomic studies

Who Should Take This Course

• Bioinformaticians and computational biologists
• Researchers in genomics and molecular biology
• Data scientists interested in applying their skills to genomics
• Advanced students in life sciences or computer science
• Professionals in the biotech and pharmaceutical industries

Real-World Applications

The skills acquired in this course have numerous real-world applications:

• Conducting cutting-edge genomics research in academic or industrial settings
• Developing innovative tools for genomic data analysis and visualization
• Contributing to large-scale genomics projects and collaborations
• Improving reproducibility in scientific research
• Analyzing complex multiomic datasets for drug discovery and personalized medicine
• Leveraging cloud-based resources for efficient and scalable genomic analysis

By mastering these advanced techniques, learners will be well-equipped to tackle complex challenges in genomics and contribute to groundbreaking discoveries in the field of life sciences.