Calendar

DESCRIPTION: This lecture introduces the fundamental concepts of an associative network in neural computation. Studying a simple network architecture allows analyzing the process of associating one memory to another through tuned synaptic connections. The discussion combines mathematical and computational study of this system, setting the foundation for further study in neural networks and machine learning. This course will be 50% lecture and 50% lab. The lab will be hands-on, with students able to work interactively at the computer they use for the Zoom session.

TEACHERS: Lyle Muller (Western University, OBI Centre for Analytics) and Roberto Budzinski (University of Lethbridge, OBI Centre for Analytics)

LEVEL: Intermediate

FORMAT: Lecture + Hands-on.

CERTIFICATES: Attendance and Completion

PREREQUISITES:

* Basic linear algebra (vectors, matrices, matrix multiplication) and programming (functions, variables, loops).
* Basic Python knowledge and know-how.

NOTE: THIS COURSE HAS LIMITED ENROLMENT. IF ENROLLED AND YOU WILL NOT BE ABLE TO ATTEND, THEN KINDLY UNENROL SO ANOTHER PERSON CAN ENROL.

DESCRIPTION: Are you not sure which workshops to sign up for in this Summer School? In this session, we will give an overview of the program of the Compute Ontario Summer School to help you decide. We'll also show you what other training opportunity in Advanced Research Computing and Research Data Management are available for you in Canada after the summer school.

TEACHER: Ramses van Zon (SciNet, University of Toronto)

LEVEL: Introductory

FORMAT: Webinar

CERTIFICATE: Attendance

PREREQUISITES: None

DESCRIPTION: Jupyter Notebook is commonly used for interactive computing in Python. This session provides the options and features for working with Jupyter on the Digital Research Alliance of Canada's remote computing clusters and demonstrates several use case examples on the clusters.

TEACHER: Jinhui Qin (SHARCNET, Western University)

LEVEL: Introductory

FORMAT: Lecture + Demonstration

CERTIFICATE: Attendance

PREREQUISITES: Basic Python and Linux command line experience.

DESCRIPTION: This lecture introduces the fundamental concepts of an associative network in neural computation. Studying a simple network architecture allows analyzing the process of associating one memory to another through tuned synaptic connections. The discussion combines mathematical and computational study of this system, setting the foundation for further study in neural networks and machine learning. This course will be 50% lecture and 50% lab. The lab will be hands-on, with students able to work interactively at the computer they use for the Zoom session.

TEACHERS: Lyle Muller (Western University, OBI Centre for Analytics) and Roberto Budzinski (University of Lethbridge, OBI Centre for Analytics)

LEVEL: Intermediate

FORMAT: Lecture + Hands-on.

CERTIFICATES: Attendance and Completion

PREREQUISITES:

* Basic linear algebra (vectors, matrices, matrix multiplication) and programming (functions, variables, loops).
* Basic Python knowledge and know-how.

NOTE: THIS COURSE HAS LIMITED ENROLMENT. IF ENROLLED AND YOU WILL NOT BE ABLE TO ATTEND, THEN KINDLY UNENROL SO ANOTHER PERSON CAN ENROL.

DESCRIPTION: Using version control for your scripts, codes, documents, papers, and even data, allows you to track changes, keep backups, and facilitate collaboration. This introductory workshop will teach you the basics of version control with the popular distributed version control software GIT. This workshop assumes that students have a basic understanding of Linux shell commands.

TEACHER: James Willis (SciNet, University of Toronto)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITE: Basic understanding of Linux shell commands.

DESCRIPTION: Plotting and data visualization are essential for effectively communicating bioinformatics findings, yet they are often treated as trivial tasks. In this course, we will showcase the power of a well-designed plot! We will cover key principles of effective visualization, work through examples ranging from basic to complex, and conclude with a hands-on workshop. By the end of the course, you will be able to create publication- or presentation-ready plots for your own research using R and ggplot2.

TEACHER: Rachel Edgar (Bioinformatics.ca, UHN)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITES:

* Basic knowledge of R.
* Basic knowledge of working in R-Studio.

DESCRIPTION: This workshop is a primer for those largely new to supercomputing, i.e., to computing on shared, remote resources. It is intended to demystify the somewhat intimidating term "High-Performance Computing" (HPC), and to serve as a foundation upon which to build over the coming days. Topics will include motivation for HPC, available resources, essential issues, and a high level overview of parallel programming models commonly used on these systems.

TEACHER: Ramses van Zon (SciNet, University of Toronto)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATES: Attendance and Completion

PREREQUISITE: Basic Linux (e.g., "Introduction to Linux Shell" course)

DESCRIPTION: This half-day session offers a brief introduction to R, with a focus on data analysis and statistics. We will discuss the following topics: the R interface, primitive data types, lists, vectors, matrices, and data frames - a crucial data type in data analysis and the trademark of the R language. Advanced topics to be covered include: basics statistics and function creation; and the basics of scripting.

TEACHER: Alexey Fedoseev (SciNet, University of Toronto)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITES: Some programming experience in another programming language

DESCRIPTION: This course will provide an overview of the analytical process for working with neurophysiological data and deriving insights from it. We will explore preprocessing, feature extraction, and downstream analysis (e.g., machine learning) end-to-end with discussions throughout on considerations for analysis based on theory and empirical observations.

TEACHERS: Irene Harmsen (Cove Neuro, OBI Centre for Analytics)

LEVEL: Intermediate

FORMAT: Webinar

CERTIFICATES: Attendance and Completion.

PREREQUISITES:

* Knowledge of time-series data analysis.
* Python programming experience.
* Basic understanding of statistics and machine learning.

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.

DESCRIPTION: This course introduces the fundamental concepts of programming such as conditional statement, Loops(while and for), Arrays, Pointers, Functions and Dynamic memory allocation. No programming experience will be assumed or required.

TEACHER: Rakesh Srirajaraghavaraju (CAC, Queen's University)

LEVEL: Introductory

FORMAT: Lecture

CERTIFICATE: Attendance

PREREQUISITES: None

DESCRIPTION: This course provides an introduction to machine learning that enables computers to learn AI models from data without being explicitly programmed. It comprises two parts:

* Part I covers the fundamentals of machine learning, and
* Part II demonstrates the applications of various machine methods in solving a real world problem.

Rather than presenting the key concepts and components of machine learning in an abstract way, this course introduces them with a small number of examples. By using plotting and animations, insight into some of the mechanics of machine learning can be had. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration.

TEACHER: Weiguang Guan (SHARCNET, McMaster University)

LEVEL: Introductory to Intermediate

FORMAT: Lecture

CERTIFICATE: Attendance

PREREQUISITES:

* Data preparation or equivalent knowledge.
* Basic Python knowledge and experience.
* Knowledge and experience with Tensorflow and Scikit-learn would also be helpful.

DESCRIPTION: This course introduces the fundamental concepts of programming such as conditional statement, Loops(while and for), Arrays, Pointers, Functions and Dynamic memory allocation. No programming experience will be assumed or required.

TEACHER: Rakesh Srirajaraghavaraju (CAC, Queen's University)

LEVEL: Introductory

FORMAT: Lecture

CERTIFICATE: Attendance

PREREQUISITES: None

DESCRIPTION: This course provides an introduction to machine learning that enables computers to learn AI models from data without being explicitly programmed. It comprises two parts:

* Part I covers the fundamentals of machine learning, and
* Part II demonstrates the applications of various machine methods in solving a real world problem.

Rather than presenting the key concepts and components of machine learning in an abstract way, this course introduces them with a small number of examples. By using plotting and animations, insight into some of the mechanics of machine learning can be had. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration.

TEACHER: Weiguang Guan (SHARCNET, McMaster University)

LEVEL: Introductory to Intermediate

FORMAT: Lecture

CERTIFICATE: Attendance

PREREQUISITES:

* Data preparation or equivalent knowledge.
* Basic Python knowledge and experience.
* Knowledge and experience with Tensorflow and Scikit-learn would also be helpful.

DESCRIPTION: The original high-level programming language, Fortran continues to be used today for high-performance computing in many fields. It has evolved over the years, and modern Fortran provides implicit parallelism (array expressions), explicit parallelism (coarrays), and object-oriented features, among other things. It supports the MPI, OpenMP, and OpenACC parallel programming standards. The primary aim of this course is to help you understand and modify existing Fortran code, but would also be useful if you wish to start a new project in Fortran. You should have prior experience with some other programming language, but this is otherwise a beginner-level course.

TEACHERS: Ross Dickson (ACENET, Dalhousie University) and Chris Geroux (ACENET, Dalhousie University)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance and Completion

PREREQUISITE: Prior experience with some other programming language

DESCRIPTION: Long-read sequencing technologies enable the sequencing of DNA fragments 10KB and longer. This read length greatly improves sequence mappability and assembly, providing an advantage over short-read sequences that are difficult to map uniquely to repetitive and GC-rich regions. Long-read sequencing has applications in a number of fields, including genome assembly, diagnosis of genetic diseases, and metagenomics. In this workshop, we will focus on PacBio HiFi sequences and introduce you to tools for haplotyping, calling and visualizing structural variants and repeat expansions, visualizing read methylation, and detection of novel isoforms from PacBio Iso-Seq.

TEACHERS: Madeline Couse (HPC4Health, SickKids) and Lauren Liang (HPC4Health, SickKids)

LEVEL: Intermediate

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITE: Basic knowledge about DNA/RNA sequencing.

DESCRIPTION: The original high-level programming language, Fortran continues to be used today for high-performance computing in many fields. It has evolved over the years, and modern Fortran provides implicit parallelism (array expressions), explicit parallelism (coarrays), and object-oriented features, among other things. It supports the MPI, OpenMP, and OpenACC parallel programming standards. The primary aim of this course is to help you understand and modify existing Fortran code, but would also be useful if you wish to start a new project in Fortran. You should have prior experience with some other programming language, but this is otherwise a beginner-level course.

TEACHERS: Ross Dickson (ACENET, Dalhousie University) and Chris Geroux (ACENET, Dalhousie University)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance and Completion

PREREQUISITE: Prior experience with some other programming language