Week 1, Stream 1

Description: RNA-Seq refers to high throughput sequencing methods that probes the entire transcriptomic landscape of a given tissue or sample of interest. The data acquired from such experiments can be used to explore the overall RNA profile of a sample as well as comparing samples under various conditions. While extremely powerful, RNA-Seq is susceptible to numerous experimental pitfalls and requires intimate knowledge of the experimental procedures and data analysis methods. When conducted properly RNA-Seq can reveal information about gene/transcript expression, splicing and the effects of mutations. In this session we will take a thorough look at a comprehensive RNA-Seq pipeline, from sample processing methods to final differential expression analysis. Relevant R / BioConductor packages will be introduced. We will have the opportunity to investigate numerous quality control metrics, perform genomic alignment, differential expression and pathway enrichment analysis. We will cover several “gotcha”s and common mistakes in experimental design and data analysis. Basic familiarity with R and Linux command line will be beneficial but not required. All necessary commands and parameters will be explained during the class. Participants will be offered hands-on practice in which they will use RStudio to run R/BioConductor scripts for data analysis as well as the Integrative Genomic Viewer (IGV) software to visualize genomic data on their laptops

Teachers: Alper Celik (HPC4Health, Centre for Computational Medicine SickKids) and Lauren Liang (HPC4Health, Centre for Computational Medicine SickKids)

Level: Intermediate

Format: Lecture + Hands-on

Certificate: Attendance

Prerequisites: Basic R and Linux beneficial but not required

Description: This workshops explores the use of artificial intelligence, especially large language models, for processing healthcare data such as electronic health records. Learners will examine how LLMs can be used to extract structured information from unstructured text, automate documentation, and uncover patterns in complex datasets. The presentation highlights benefits such as improved efficiency and richer insights, while addressing risks including hallucinations, model biases and privacy concerns. Students will study pitfalls unique to healthcare data and gain an understanding of key technical challenges: data standardization, model validation, safety monitoring, and ensuring transparency and privacy in clinically sensitive and resource constrained environments. We will talk about open source packages and open weight models and how they can be used effectively for clinical data. 

Teachers: Alper Celik (HPC4Health, Centre for Computational Medicine SickKids) 

Level: Introductory, Intermediate

Format: Lecture + Hands-on (first half lecture, second half hands-on)

Certificate: Attendance

Prerequisites: Basic R and BASH

Description: Rapid prototyping for biological sequence analysis requires tools that let researchers move quickly from idea to working workflow without sacrificing performance. Today, this is typically done in one of two ways. The first is by chaining specialized command-line tools, either directly or through workflow systems such as Nextflow or Snakemake. The second is by scripting interactively in high-level languages such as Python, using libraries like BioPython or cogent3 in notebooks. Each approach carries significant performance trade-offs. Pipeline-based tools incur costly memory round-trips — data must be repeatedly transferred off and back onto accelerator memory (e.g., GPU) between chained commands. Python libraries, meanwhile, largely lack built-in support for CPU parallelism (e.g., BioPython) or GPU acceleration (e.g., cogent3), limiting scalability for performance-critical workloads. This course introduces GNX, a modern C++20 library and toolkit purpose-built for high-performance rapid prototyping of biological sequence analysis. GNX addresses the limitations of both paradigms through three core design principles: zero-copy operations that enable toolkit commands to chain directly through device memory without redundant data transfers; SIMD and multi-level parallelism for hardware-aware acceleration on modern CPUs; and seamless backend portability across OpenMP, CUDA, and ROCm targets. Crucially, GNX also exposes an interactive scripting interface through the xeus-cling Jupyter kernel, bringing C++-native performance to the familiar notebook environment. By the end of this 90-minute course, participants will understand GNX's architecture, be able to construct accelerated bioinformatics pipelines using its toolkit and interactively prototype analyses in a Jupyter notebook — all without sacrificing performance.

Teachers: Armin Sobhani (SHARCNET)

Level: Intermediate

Format: Lecture

Certificate: Attendance

Prerequisites: None

Description: R Shiny has become one of the most widely used tools in bioinformatics for building interactive data analysis applications—without requiring web development expertise. For researchers, analysts, and students working with high dimensional biological data, Shiny provides a way to turn static analyses into dynamic, user friendly dashboards that make exploration simpler, faster, and more intuitive.

This course is intended to help learners bridge the gap between data analysis and data communication. Instead of generating dozens of plots manually, you can build a Shiny app where users adjust parameters, filter data, and visualize results in real time. This is especially valuable in genomics, where datasets are large, multidimensional, and frequently explored by multidisciplinary teams.

Teachers: Prajkta Kallurkar (HPC4Health, SickKids)

Level: Introductory

Format: Lecture + Hands-On

Certificate: Completion

Prerequisites: Basic R knowledge.