Calendar

DESCRIPTION: This hands-on course is designed for researchers who want to take their high-performance computing (HPC) workflows to the next level. Whether you're new to large-scale computing or looking to optimize your current practices, this course will guide you through the key steps to efficiently scale your applications on HPC systems.

Participants will begin by identifying their specific applications and learn how to properly compile their code for HPC environments. The course covers essential topics including running interactive sessions, performance tuning, and efficient batch job submission-complete with practical script examples. You'll also explore strategies for checkpointing, monitoring job progress, and effective debugging techniques.

Through a combination of lectures and hands-on exercises, this course offers real-world insights into improving performance, reducing run times, and making the most of shared computing resources. By the end, you'll be equipped with the tools and knowledge needed to run scalable, reliable, and efficient HPC workflows.

TEACHERS: Sergey Maschenko (SHARCNET, McMaster University) and Jaime Pinto (SciNet, University of Toronto)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITES: None

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, SickKids) and Lauren Liang (HPC4Health, SickKids)

LEVEL: Intermediate

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITES: Basic R and Linux beneficial but not required

DESCRIPTION: This hands-on course is designed for researchers who want to take their high-performance computing (HPC) workflows to the next level. Whether you're new to large-scale computing or looking to optimize your current practices, this course will guide you through the key steps to efficiently scale your applications on HPC systems.

Participants will begin by identifying their specific applications and learn how to properly compile their code for HPC environments. The course covers essential topics including running interactive sessions, performance tuning, and efficient batch job submission-complete with practical script examples. You'll also explore strategies for checkpointing, monitoring job progress, and effective debugging techniques.

Through a combination of lectures and hands-on exercises, this course offers real-world insights into improving performance, reducing run times, and making the most of shared computing resources. By the end, you'll be equipped with the tools and knowledge needed to run scalable, reliable, and efficient HPC workflows.

TEACHERS: Sergey Maschenko (SHARCNET, McMaster University) and Jaime Pinto (SciNet, University of Toronto)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITES: None

DESCRIPTION: Pathway enrichment analysis is a powerful computational approach used to identify biological pathways that are significantly overrepresented in a given set of differentially expressed genes, or any gene list derived from -omics data. This method helps to contextualize large gene lists by linking them to known biological processes, functional modules, and disease mechanisms. While highly informative, pathway enrichment analysis requires careful interpretation and an understanding of statistical methodologies, reference databases, and potential biases in gene-set analysis. In this session, we will explore key concepts and methods for pathway enrichment analysis, and we will discuss different enrichment approaches, including over-representation analysis of a defined gene list and gene set enrichment analysis (GSEA). Participants will be offered hands-on practice in which they will use RStudio to run R/BioConductor scripts for pathway enrichment analysis as well as the Cytoscape software to visualize the results of enrichment analysis on their personal computers. Basic familiarity with R will be beneficial.

TEACHERS: Ruth Isserlin (Bioinformatics.ca, University of Toronto) and Veronique Voisin (Bioinformatics.ca, UHN)

LEVEL: Intermediate

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITES:

* Knowing how to open R or R-Studio and install packages.
* Basic knowledge of R (recommended).
* General knowledge of differential expression of RNA-seq or scRNA-seq data.