Our Courses

The ITN has developed a library of courses covering a variety of topics relevant to cancer informatics and beyond!

Who are our courses for:

Our courses are designed for a range of audiences from those are new to data science to those who actively develop scientific software. We also have courses designed for scientific research leaders.

Researchers

People who:

Do (or are starting to do) informatics research
Need resources to get started or continue to develop

Software Developers

People who:

Create or maintain scripts for data analysis
Create or maintain full-blown software tools that enable research

Leaders

People who:

Lead a team
Lead a project
Act as a mentor
Make key decisions

Current Courses

Search for courses that are relevant for you by typing in key words in the search box or the search for each column name. Click on filter arrows next to column names to sort classes.

Note that all of our courses are open source, so all the source material is available on GitHub. All content is licensed with CC-BY 4.0 and therefore reusable with attribution.

Affiliated ITCR course Materials

CoGAPS on SciServer

This introductory course will provide a quick overview of how the Bayesian NMF algorithm, CoGAPS (Coordinated Gene Activity across Pattern Subsets), can provide new insights into single cell datasets. Through the exercises you will analyze a real dataset using the SciServer compute platform.

Links:

Introduction to pVactools

The course is intended for anyone seeking a better understanding of current best practices in neoantigen identification and prioritization using pVACtools. It assumes that the learner is familiar with basic biology, genetics and immunology concepts. This course will teach learners to:

Understand key concepts of immunogenomics and neoantigen identification
How to run the pVACtools software suite
How to visualize and prioritize neoantigen candidates with pVACview

Links:

Introduction to Immuno on Terra

This course is an introduction to the immuno workflow on Terra, which leverages a collection of modular WDL workflows for analysis of genomic sequencing data. These enable primary analysis of DNA and RNA-seq data, including for quality control, somatic and germline variant calling, expression analysis, fusion detection, HLA typing, somatic variant allele expression, and immunogenomics approaches for cancer vaccine development (including neoantigen prediction with pVACtools).

Links:

Introduction to Clinical Interpretation of Somatic Cancer Variants

This course will introduce background concepts, tools, resources, and relevant standard operating procedures and guidelines for the clinical interpretation of somatic cancer variants.

The course will teach learners to:

Understand key concepts of somatic cancer variant interpretation
Introduce key SOPs and guidelines for classifying the clinical relevance and oncogenicity of somatic cancer variants
Introduce ClinGen Somatic Cancer efforts
Describe somatic variant knowledgebases
Introduce CIViC as a curation platform for somatic variant interpretation

Additional Helpful Resources

IntroductioN to R PRogramming

Here you will find all the resources to a course taught through Johns Hopkins: https://jhudatascience.org/intro_to_r/

Recordings of past lectures can be found at the bottom of this page: https://jhudatascience.org/intro_to_r/resources.html

Open Case Studies (OCS) for Intermediate R Programming and Statistics

https://www.opencasestudies.org/

Google Sites

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