Bioinformatics of Genomic Medicine

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Course Objectives

Genomic medicine is the practice of utilizing multi-omic (genomic, transcriptomic, epigenomic) data to improve the diagnosis and treatment of patients. The CBW has developed a 2-day course that will explore various aspects of genomic medicine, covering and teaching popular tools and methods in the field. The course will start with topics that are important to the analysis of genetic disorders, including phenotyping and the annotation of genetic variants. Next, we will cover multi-omic approaches that can be used to identify homogenous clusters of patients, build patient trajectories to identify likely outcomes, and improve these outcomes through better selection of therapies. This workshop will not cover cancer.

Participants will gain practical experience and skills to be able to:

  • Identify disease variants:
    • Conduct basic exome analysis to identify disease-causing mutations
    • Perform deep phenotyping of patients using the Human Phenotype Ontology (HPO)
    • Conduct detailed variant annotation and prioritization
  • Perform patient classification:
    • Understand and select appropriate epigenomic datasets for patient classification
    • Conduct data fusion to identify homogenous patient subgroups
    • Identify potential therapies based on molecular profiles

Target Audience

Graduates, postgraduates, PIs, and clinician-researchers working or about to embark on an analysis of omics (genomes, transcriptomes, epigenomes) for analysis of patient samples. Attendees may be familiar with some aspect of genome data analysis (e.g. exome analysis or gene expression analysis) or have no direct experience.

Prerequisites: Basic familiarity with Linux environment and S, R, or Matlab. Must be able to complete and understand the following simple Linux and R tutorials (up to and including “Descriptive Statistics”) before attending:

You will also require your own laptop computer. Minimum requirements: 1024x768 screen resolution, 1.5GHz CPU, 2GB RAM, 10GB free disk space, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements).If you do not have access to your own computer, you may loan one from the CBW. Please contact for more information.

Pre-work and pre-readings can be found at

Course Outline

Day 1

Module 1: Introduction and patient phenotyping in genetic disease (Michael Brudno)

  • Basic overview of rare and complex genetic disorders
  • Introduction to Human Phenotype Ontology (HPO)
  • Deep Phenotyping with the HPO and PhenoTips
  • Cohort visualization with PhenoStacks
  • Patient confidentiality and patient privacy

Lab Practical:

  • "Phenotype" a set of patients based on provided descriptions

Module 2: Introduction to tools, computing infrastructure, and data (Mathieu Bourgey)

  • Introduction to cluster computing concepts
  • Introduction to GenAp

Lab Practical:

  • Learn to configure, launch, and connect computing jobs
  • Learn to launch GenAp pipelines
  • Introduction to the test data
  • Examine and understand the format of various levels of data: FASTQ, BAM, VCF
  • Run a basic exome pipeline using GenAp

Module 3: Variant annotation (Sergey Naumenko)

  • Variant scoring and relevant datasets (coding versus non-coding, conservation, functional annotation, actionable variants)
  • Variant prioritization
  • Variant reporting
  • Identification of disease causing mutations using Exomizer
  • Data sharing for Rare Disease: Matchmaker Exchange

Lab Practical:

  • Perform variant annotation
  • Explore variant lists
  • Run Exomizer on a set of exomes to identify causative mutations
  • Perform “class matchmaking” to solve disorders

Module 4: Translating research workflows into clinical tests (Natalie Stickle)

  • Overview of the types of documentation you need to provide for a test using a bioinformatics workflow to certify it for use in a clinically accredited setting (CAP/CLIA, OLA, etc.)
  • Methods for testing bioinformatics analyses using laboratory standards and in-silico generated testing data
  • A real-world example for a panel based genetic test, including examples of common problems and pitfalls
  • E-health records

Day 2

Module 5: Available epigenetics data and resources (Guillaume Bourque)

  • Overview of epigenetic technologies and analysis: ChIP-Seq, RNA-seq, RRBS, arrays, etc
  • IHEC
  • GTex

Lab Practical:

  • Explore IHEC
  • Run GenAp RRBS or Methylation array pipeline

Module 6: Epigenetic profiling in disease (Andrei Turinsky)

  • What is the right tissue? Issues with contamination
  • Experiment design & batch effect correction
  • Clustering and diagnosis of patients based on methylation profiles

Lab Practical:

  • Perform batch correction on a set of patient epigenomes.
  • Perform clustering to identify homogeneous disease groups.
  • Bump hunting to discover differentially methylated regions.

Module 7: Patient similarity fusion (Anna Goldenberg)

  • Prediction of drug response with focus on subtyping

Lab Practical:

  • Building classifiers for cluster identification for clinical practice