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Previous Workshops

View details about workshops taught over the last few years.
Application Open
Participants will gain practical experience and skills to be able to: Get more information about a gene list; Discover what pathways are enriched in a gene list (and use it for hypothesis generation); Find out how a set of genes is connected by e.g. protein interactions and identify pathways, systems and modules within this network; Predict gene function and extend a gene list; We will develop a unified analysis flow chart throughout the course that students will be able to follow after the workshop to conduct their own analysis.
Application Open
Participants will gain practical experience and skills to be able to: Extract features for biomarker discovery from -omics data (RNA-Seq, methylation, mutations, proteomics, chromatin profiling, etc) Navigate pharmacogenomics datasets and data preparation for pharmacogenomic analysis Identify univariable biomarkers from RNA-Seq data Perform meta-analysis to identify robust biomarkers Visualize and summarize the output of pharmacogenomic biomarker analysis
Application Open
Participants will gain practical experience and skills to be able to: Understand key differences in medical imaging modalities, Learn basic image processing techniques, Process raw clinical images into analysis ready formats, Locate and download publicly available imaging data sets, Extract imaging features and train machine learning models for clinical prediction, Familiarize yourself with auto-segmentation tools and build deep learning models for medical image segmentation.
Application Open
(2024) Proteomics: Laval, QC September 16-18, 2024
Participants will gain practical experience and skills to be able to: Design optimal proteomics experiments for protein identification across diverse sample sets Process proteomics datasets in a robust and reproducible manner Use multiple software platforms for data processing, analysis, and visualization Demonstrate best practices in proteomics data analysis Generate informative and reproducible visualizations Interpret and describe proteomics experimental findings
Application Open
(2024) Machine Learning: Virtual September 07-08, 2024
Students will gain experience in: Applications and Limitations of Machine Learning and Deep Learning Decision Trees and Random Forests – how they work, how they are coded in Python and R, and how they can be used in bioinformatic applications (biomarker discovery and modeling) Artificial Neural Networks (ANNs) – how they work, how data is encoded, how they are coded in Python and R, and how they can be used in bioinformatic applications (classification and secondary structure prediction) Large Language Models (LLMs) – how they work, and how they can be used in bioinformatics applications (text mining, information extraction) Using Machine Learning tools (Decision Trees, ANNs and HMMs) on the Web (SciKit Learn and Keras/Colab)
Application Open
Participants will gain practical experience and skills to be able to: Align ChIP-seq and WGBS sequence data to a reference genome (required) Identify narrow and broad peaks from ChIP-seq data Identify methylated levels from WGBS data Visualize and summarize the output of ChIP-Seq and WGBS analyses Explore integrative tools for epigenomic data sets Discover existing online resources to facilitate epigenomic data analysis
Application Open
Participants will gain practical experience and skills to be able to: Align ChIP-seq and WGBS sequence data to a reference genome (required) Identify narrow and broad peaks from ChIP-seq data Identify methylated levels from WGBS data Visualize and summarize the output of ChIP-Seq and WGBS analyses Explore integrative tools for epigenomic data sets Discover existing online resources to facilitate epigenomic data analysis
Application Open
Participants will gain practical experience and skills to be able to: Understand the challenges and opportunities presented by complex biological data. Apply fundamental techniques for exploring and summarizing biological datasets. Address missing values through sophisticated imputation methodologies.  Analyze relationships within high-dimensional data, both between variables and samples, employing regression methods, regularization techniques, and clustering methods. Integrate various statistical methods into a cohesive workflow to tackle a variety of common problems in bioinformatics from data exploration to interpretation.
Application Open
Participants will gain practical experience and skills to be able to: Appreciate the bench practices and workflow in preparation for optimum single molecular spatial experiments. Understand the guiding principles that influence panel design and will be able to design an optimum panel. Perform data clean-up and pre-processing (normalization, dimensional reduction) steps relevant and specific towards single molecular platforms. Understand the different non-spatial and spatial methods of analysis and will be able to apply some of these methods during the workshop, including the fundamental application of geo-spatial statistical analysis. Understand the principles behind non-segmentation and segmentation analysis and apply a basic non-seg/segmentation method over their analysis. At the end of the course, the registrant will be able to plan single-molecular experiments and direct their experiment through analysis.
Application Open
Participants will gain practical experience and skills to be able to: Appreciate the bench practices and workflow in preparation for optimum single molecular spatial experiments. Understand the guiding principles that influence panel design and will be able to design an optimum panel. Perform data clean-up and pre-processing (normalization, dimensional reduction) steps relevant and specific towards single molecular platforms. Understand the different non-spatial and spatial methods of analysis and will be able to apply some of these methods during the workshop, including the fundamental application of geo-spatial statistical analysis. Understand the principles behind non-segmentation and segmentation analysis and apply a basic non-seg/segmentation method over their analysis. At the end of the course, the registrant will be able to plan single-molecular experiments and direct their experiment through analysis.
Application Open
Participants will gain practical experience and skills to be able to: Get more information about a gene list; Discover what pathways are enriched in a gene list (and use it for hypothesis generation); Find out how a set of genes is connected by e.g. protein interactions and identify pathways, systems and modules within this network; Predict gene function and extend a gene list; We will develop a unified analysis flow chart throughout the course that students will be able to follow after the workshop to conduct their own analysis.
Registration Closed
Participants will gain practical experience and skills to be able to: Perform basic bioinformatics tasks such as tool installation Perform read alignment and transcript quantification Perform quality control Visualize and interpret scRNA-seq data Perform clustering and differential expression analysis  Annotate cell clusters Integrate scRNA-seq data sets
Registration Closed
Participants will gain practical experience and skills to be able to: Perform command-line Linux based analysis on the cloud (Amazon AWS) Perform basic bioinformatics tasks such as tool installation Understand reference genome and transcriptome annotations Assess quality of RNA-seq data and perform read trimming Align RNA-seq data to a reference genome Visualize RNA-seq alignments, splicing patterns and sequence variants Estimate known gene and transcript expression using multiple approaches Perform differential expression analysis Visualize and summarize the output of RNA-seq analyses in R Perform principal component analysis (PCA) and batch correction Perform pathway analysis Perform alignment free expression estimation and DE analysis
Registration Closed
Participants will gain practical experience and skills to be able to use R to visualize and investigate patterns in their data.
Registration Closed
Participants will gain practical experience and skills to be able to: Meet the challenges of data handling Break down problems into structured parts Use R syntax, functions and packages
Cancelled
Participants will gain practical experience and skills to be able to: Design appropriate metabolome-focused experiments Understand the advantages and limitations of metabolomic data analysis Devise an appropriate bioinformatics workflow for processing and analyzing metabolomic data Apply appropriate statistics to undertake rigorous data analysis Visualize datasets to gain intuitive insights into the composition and/or activity of their metabolome  
Cancelled
Participants will gain practical experience and skills to be able to: Design appropriate metabolome-focused experiments Understand the advantages and limitations of metabolomic data analysis Devise an appropriate bioinformatics workflow for processing and analyzing metabolomic data Apply appropriate statistics to undertake rigorous data analysis Visualize datasets to gain intuitive insights into the composition and/or activity of their metabolome  
Registration Closed
Participants will gain practical experience and skills to be able to: Understand the advantages and limitations of metagenomic data analysis Devise an appropriate bioinformatics workflow for processing and analyzing microbiome shotgun metagenomic sequence data Perform both read-based and assembly-based analyses of shotgun metagenomic sequence data Apply appropriate statistics to undertake rigorous data analysis
Registration Closed
Participants will gain practical experience and skills to be able to: Design appropriate microbiome-focused experiments Understand the advantages and limitations of marker gene data analysis Devise an appropriate bioinformatics workflow for processing and analyzing microbiome marker-gene sequence data Apply appropriate statistics to undertake rigorous data analysis
Registration Closed
Participants will gain practical experience and skills to be able to: Understand high-throughput sequencing (HTS) platforms as applied to pathogen genomics and metagenomics sequencing Understand the value of data sharing and data curation in pathogen surveillance Analyze HTS data for pathogen surveillance and outbreak investigations Analyze antimicrobial resistance genes Detect emerging pathogens in metagenomics data Perform phylodynamic analysis Use different visualization tools for genomic epidemiology analysis
Registration Closed
(2023) Epigenomics Analysis October 11-13, 2023
Participants will gain practical experience and skills to be able to: Align ChIP-seq and WGBS sequence data to a reference genome (required) Identify narrow and broad peaks from ChIP-seq data Identify methylated levels from WGBS data Visualize and summarize the output of ChIP-Seq and WGBS analyses Explore integrative tools for epigenomic data sets
Registration Closed
(2023) Machine Learning August 16-17, 2023
Students will gain experience in: Applications and Limitations of Machine Learning and Deep Learning Decision Trees and Random Forests – how they work, how they are coded in Python and R, and how they can be used in bioinformatic applications (biomarker discovery and modeling) Artificial Neural Networks (ANNs) – how they work, how data is encoded, how they are coded in Python and R, and how they can be used in bioinformatic applications (classification and secondary structure prediction) Hidden Markov Models (HMMs) – how they work, how they are coded in Python and R and how they can be used in bioinformatics applications (gene finding) Using Machine Learning tools (Decision Trees, ANNs and HMMs) on the Web (SciKit Learn and Keras/Colab)
Registration Closed
Participants will gain practical experience and skills to be able to: Perform basic bioinformatics tasks such as tool installation Perform read alignment and transcript quantification Perform quality control Visualize and interpret scRNA-seq data Perform clustering and differential expression analysis  Annotate cell clusters Integrate scRNA-seq data sets
Registration Closed
(2023) RNA-seq Analysis July 17-19, 2023
Participants will gain practical experience and skills to be able to: Perform command-line Linux based analysis on the cloud (Amazon AWS) Perform basic bioinformatics tasks such as tool installation Understand reference genome and transcriptome annotations Assess quality of RNA-seq data and perform trimming Align RNA-seq data to a reference genome Visualize RNA-seq alignments, splicing patterns and sequence variants Estimate known gene and transcript expression using multiple approaches Perform differential expression analysis Visualize and summarize the output of RNA-seq analyses in R Perform batch correction Perform pathway analysis Alignment free expression estimation
Registration Closed
Participants will gain practical experience and skills to be able to: Design appropriate metabolome-focused experiments Understand the advantages and limitations of metabolomic data analysis Devise an appropriate bioinformatics workflow for processing and analyzing metabolomic data Apply appropriate statistics to undertake rigorous data analysis Visualize datasets to gain intuitive insights into the composition and/or activity of their metabolome
Registration Closed
Participants will gain practical experience and skills to be able to: Design appropriate microbiome-focused experiments Understand the advantages and limitations of metagenomic data analysis Devise an appropriate bioinformatics workflow for processing and analyzing microbiome sequence data (marker-gene, shotgun metagenomic, and metatranscriptomic data) Apply appropriate statistics to undertake rigorous data analysis
Registration Closed
(2023) Analysis Using R June 28-29, 2023
Participants will gain practical experience and skills to be able to use R to visualize and investigate patterns in their data.
Registration Closed
(2023) Introduction to R June 26-27, 2023
Participants will gain practical experience and skills to be able to: Meet the challenges of data handling Break down problems into structured parts Use R syntax, functions and packages
Registration Closed
Participants will gain practical experience and skills to be able to: Get more information about a gene list; Discover what pathways are enriched in a gene list (and use it for hypothesis generation); Find out how a set of genes is connected by e.g. protein interactions and identify pathways, systems and modules within this network; Predict gene function and extend a gene list; We will develop a unified analysis flow chart throughout the course that students will be able to follow after the workshop to conduct their own analysis.
Registration Closed
Participants will gain practical experience and skills to be able to: Understand high-throughput sequencing (HTS) platforms as applied to pathogen genomics and metagenomics sequencing Understand the value of data sharing and data curation in pathogen surveillance Analyze HTS data for pathogen surveillance and outbreak investigations Analyze antimicrobial resistance genes Detect emerging pathogens in metagenomics data Perform phylodynamic analysis Use different visualization tools for genomic epidemiology analysis
Registration Closed
Participants will gain practical experience and skills to be able to: Understand next generation sequencing (NGS) platforms as applied to pathogen genomics and metagenomics sequencing Analyze NGS data for pathogen surveillance and outbreak investigations Analyze antimicrobial resistance genes Detect emerging pathogens in metagenomics data Perform phylogeographic analysis Use different visualization tools for genomic epidemiology analysis
Registration Closed
Beginning with an understanding of the workflow involved to move from platform images to sequence generation, participants will gain practical experience and skills to be able to: Assess sequence quality Map sequence data onto a reference genome Perform de novo assembly tasks Quantify sequence data Integrate biological context with sequence information
Cancelled
As the cost of collecting transcriptomics data continues to drop, researchers in the environmental life sciences are increasingly seeking to use these data as part of their investigations. In many cases, this means using non-model organisms that have few or no genomics and bioinformatics resources for comprehensive data analysis and interpretation. The objective of this workshop is to equip researchers in the environmental life sciences with easy-to-use tools to process and analyze transcriptomics data from non-model organisms, and strategies for leveraging databases and statistical methods originally designed for model organisms.
Registration Closed
(2021) Epigenomics Analysis September 13-15, 2021
Participants will gain practical experience and skills to be able to: Align ChIP-seq and WGBS sequence data to a reference genome (required) Identify narrow and broad peaks from ChIP-seq data Identify methylated levels from WGBS data Visualize and summarize the output of ChIP-Seq and WGBS analyses Explore integrative tools for epigenomic data sets
Registration Closed
(2021) RNA-Seq Analysis September 08-10, 2021
Participants will gain practical experience and skills to be able to: Perform command-line Linux based analysis on the cloud (Amazon AWS) Perform basic bioinformatics tasks such as tool installation Assess quality of RNA-seq data and perform trimming Align RNA-seq data to a reference genome Visualize RNA-seq alignments and variants Estimate known gene and transcript expression using multiple approaches Perform differential expression analysis Visualize and summarize the output of RNA-seq analyses in R Perform batch correction Perform pathway analysis Alignment free expression estimation
Registration Closed
(2021) Microbiome Analysis September 01-03, 2021
Participants will gain practical experience and skills to be able to: Design appropriate microbiome-focused experiments Understand the advantages and limitations of metagenomic data analysis Devise an appropriate bioinformatics workflow for processing and analyzing metagenomic sequence data (marker-gene, shotgun metagenomic, and metatranscriptomic data) Apply appropriate statistics to undertake rigorous data analysis Visualize datasets to gain intuitive insights into the composition and/or activity of their data set
Registration Closed
(2021) Analysis Using R June 28-29, 2021
Participants will gain practical experience and skills to be able to use R to visualize and investigate patterns in their data.
Registration Closed
(2021) Introduction to R June 21-22, 2021
Participants will gain practical experience and skills to be able to: Meet the challenges of data handling Break down problems into structured parts Use R syntax, functions and packages
Registration Closed
Participants will gain practical experience and skills to be able to: Design appropriate metabolome-focused experiments Understand the advantages and limitations of metabolomic data analysis Devise an appropriate bioinformatics workflow for processing and analyzing metabolomic data Apply appropriate statistics to undertake rigorous data analysis Visualize datasets to gain intuitive insights into the composition and/or activity of their metabolome
Registration Closed
(2021) Cancer Analysis June 07-11, 2021
This 5-day workshop will cover the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the Cloud. Participants will gain practical experience and skills to: Visualize genomic data; Analyze cancer –omic data for gene expression, genome rearrangement, somatic mutations, and copy number variation; Analyze and conduct pathway analysis on the resultant cancer gene list; Integrate clinical data; Launch, configure, customize, and scale virtual machines (VM); Navigate and work with data sets from Cloud repositories; and Follow best practices in data and workflow management.
Registration Closed
(2021) Machine Learning May 25-26, 2021
Students will gain experience in: Applications and Limitations of Machine Learning and Deep Learning Decision Trees and Random Forests – how they work, how they are coded in Python and R, and how they can be used in bioinformatic applications (biomarker discovery and modeling) Artificial Neural Networks (ANNs) – how they work, how data is encoded, how they are coded in Python and R, and how they can be used in bioinformatic applications (classification and secondary structure prediction) Hidden Markov Models (HMMs) – how they work, how they are coded in Python and R and how they can be used in bioinformatics applications (gene finding) Using Machine Learning tools (Decision Trees, ANNs and HMMs) on the Web (SciKit Learn and Keras/Colab)
Registration Closed
Participants will gain practical experience and skills to be able to: Get more information about a gene list; Discover what pathways are enriched in a gene list (and use it for hypothesis generation); Find out how a set of genes is connected by e.g. protein interactions and identify pathways, systems and modules within this network; Predict gene function and extend a gene list; Identify master regulators, such as transcription factors, active in the experiment. We will develop a unified analysis flow chart throughout the course that students will be able to follow after the workshop to conduct their own analysis.
Registration Closed
Participants will gain practical experience and skills to be able to: Align ChIP-seq and WGBS sequence data to a reference genome (required) Identify narrow and broad peaks from ChIP-seq data Identify methylated levels from WGBS data Visualize and summarize the output of ChIP-Seq and WGBS analyses Explore integrative tools for epigenomic data sets
Registration Closed
(2020) Machine Learning September 21-22, 2020
Students will gain experience in: Applications and Limitations of Machine Learning and Deep Learning Data encoding for Machine Learning Artificial Neural Networks (ANNs) – how they work and how they can be used in bioinformatic applications (secondary structure prediction) ANNs – how to program a useful ANN for bioinformatics in Python Hidden Markov Models (HMMs) – how they work and how they can be used in bioinformatics applications (gene finding) HMMs – how to program a useful HMM for bioinformatics in Python Support Vector Machines, Decision Trees an Random Forests – how they work and how they can be used in bioinformatic applications (biomarker discovery and modeling) Using Machine Learning tools on the Web (WEKA) Using Machine Learning Apps (TENSORFLOW)
Registration Closed
Participants will gain practical experience and skills to be able to: Get more information about a gene list; Discover what pathways are enriched in a gene list (and use it for hypothesis generation); Find out how a set of genes is connected by e.g. protein interactions and identify pathways, systems and modules within this network; Predict gene function and extend a gene list; Identify master regulators, such as transcription factors, active in the experiment. We will develop a unified analysis flow chart throughout the course that students will be able to follow after the workshop to conduct their own analysis.
Registration Closed
Beginning with an understanding of the workflow involved to move from platform images to sequence generation, participants will gain practical experience and skills to be able to: Assess sequence quality Map sequence data onto a reference genome Perform de novo assembly tasks Quantify sequence data Integrate biological context with sequence information
Registration Closed
Participants will gain practical experience and skills to be able to: Perform command-line Linux based analysis on the cloud Assess quality of RNA-seq data Align RNA-seq data to a reference genome Estimate known gene and transcript expression Perform differential expression analysis Discover novel isoforms Visualize and summarize the output of RNA-seq analyses in R Assemble transcripts from RNA-Seq data.
Registration Closed
Participants will gain practical experience and skills to be able to: Design appropriate metabolome-focused experiments Understand the advantages and limitations of metabolomic data analysis Devise an appropriate bioinformatics workflow for processing and analyzing metabolomic data Apply appropriate statistics to undertake rigorous data analysis Visualize datasets to gain intuitive insights into the composition and/or activity of their metabolome
Registration Closed
Participants will gain practical experience and skills to be able to: Use R and its analysis tools, read and modify code, and explore protocols that can be adapted for their own research tasks. Write R functions and analysis scripts. Plot and visualize data using the elementary built-in routines via their (sometimes bewildering) array of parameters to sophisticated, publication-ready presentations.
Registration Closed
(2020) Introduction to R June 09-10, 2020
Participants will gain practical experience and skills to be able to: Meet the challenges of data handling Break down problems into structured parts Use R syntax, functions and packages Understand best practices for scientific computational work
Registration Closed
Participants will gain practical experience and skills to be able to: Perform command-line Linux based analysis on the cloud Assess quality of RNA-seq data Align RNA-seq data to a reference genome Estimate known gene and transcript expression Perform differential expression analysis Discover novel isoforms Visualize and summarize the output of RNA-seq analyses in R Assemble transcripts from RNA-Seq data.
Registration Closed
Participants will gain practical experience and skills to be able to: Get more information about a gene list; Discover what pathways are enriched in a gene list (and use it for hypothesis generation); Find out how a set of genes is connected by e.g. protein interactions and identify pathways, systems and modules within this network; Predict gene function and extend a gene list; Identify master regulators, such as transcription factors, active in the experiment. We will develop a unified analysis flow chart throughout the course that students will be able to follow after the workshop to conduct their own analysis.
Registration Closed
Beginning with an understanding of the workflow involved to move from platform images to sequence generation, participants will gain practical experience and skills to be able to: Assess sequence quality Map sequence data onto a reference genome Perform de novo assembly tasks Quantify sequence data Integrate biological context with sequence information
Registration Closed
Participants will gain practical experience and skills to be able to: Align ChIP-seq and WGBS sequence data to a reference genome (required) Identify narrow and broad peaks from ChIP-seq data Identify methylated levels from WGBS data Visualize and summarize the output of ChIP-Seq and WGBS analyses Explore integrative tools for epigenomic data sets
Registration Closed
Participants will gain practical experience and skills to: Visualize genomic data Analyze cancer –omic data for gene expression, genome rearrangement, somatic mutations, and copy number variation Analyze and conduct pathway analysis on the resultant cancer gene list Integrate clinical data Launch, configure, customize, and scale virtual machines (VM) Navigate and work with data sets from Cloud repositories Follow best practices in data and workflow management
Registration Closed
Participants will gain practical experience and skills to be able to: Design appropriate metabolome-focused experiments Understand the advantages and limitations of metabolomic data analysis Devise an appropriate bioinformatics workflow for processing and analyzing metabolomic data Apply appropriate statistics to undertake rigorous data analysis Visualize datasets to gain intuitive insights into the composition and/or activity of their metabolome
Registration Closed
Participants will gain practical experience and skills to be able to: Use R and its analysis tools, read and modify code, and explore protocols that can be adapted for their own research tasks. Write R functions and analysis scripts. Plot and visualize data using the elementary built-in routines via their (sometimes bewildering) array of parameters to sophisticated, publication-ready presentations.
Registration Closed
(2019) Introduction to R May 13-14, 2019
Participants will gain practical experience and skills to be able to: Meet the challenges of data handling Break down problems into structured parts Use R syntax, functions and packages Understand best practices for scientific computational work
Registration Closed
The course will begin with the workflow involved in moving from platform images to sequence generation, after which participants will gain practical skills for evaluating sequence read quality, mapping reads to a reference genome, and analyzing sequence reads for variation and expression level. The course will conclude with pathway and network analysis on the resultant ‘gene’ list. Participants will gain experience in cloud computing and data visualization tools. All class exercises will be self-contained units that include example data (e.g., Illumina paired-end data) as well as detailed instructions for installing all required bioinformatics tools.