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Genetics 760:
Genomic Methods for Genetic Analysis
Course Organizer:
Jim Noonan:
[email protected]
TAs:
Tim Johnstone:
Carina Gerveshi:
[email protected]
[email protected]
Course Wiki:
we will email this to you
Genetics 760:
Objectives
• Intro to genome analysis and interpretation
• How to address biological questions from a genomic perspective
• Analyze data from high throughput sequencing applications
- ChIP-seq
- RNA-seq
- Whole exome sequencing
- Metagenomics
- Big functional genomics datasets
Genetics 760:
Objectives
• You will learn how to:
- Work with massive datasets in a Linux HPC environment
- Write your own scripts in Python and R to parse files, run
pipelines, do basic statistical analyses
- Understand, design and interpret genomic analyses
- Use genomics data to gain biological insights
Genetics 760:
Components
• Lectures on topics in genomics
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Introduction to computational methods
Accessing genomes
High throughput sequencing technologies
Gene expression, regulation and epigenetics
Genetic variation in genes and regulatory elements
Metagenomics and proteomics
Large genome survey projects (ENCODE)
Genomics of human disease
Genetics 760:
Components
• Problem sets and Friday Discussion sessions
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PS#0 and PS#1: Intensive introduction to the Linux
environment and scripting, and working with genomic
data
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We will primarily use Python in this course. Your first
assignment is to take the ~13-hour intro to Python
course at Codecademy: www.codecademy.com. We
expect you to have done this by the time PS#1 is due.
Genetics 760:
Components
• Problem sets and Friday Discussion sessions
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PS#2-4: Applications of genomics datasets
• Analyzing regulomics data (ChIP-seq, epigenetics)
• Analyzing transcriptome data
• Discovery and interpretation of whole-exome
variation
Genetics 760:
Components
• Final Project:
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Team-based, collaborative analysis of an original
genomic question
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This will occupy the last month of the course (April –
early May)
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You will have access to the TAs, but will be working
largely independently
Genetics 760:
Advice
• If you have no experience, the learning curve in the
first month or so will be very steep: do not get
discouraged
• This course will take more time than you are used to
• Communicate effectively with your TAs
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Ask specific questions
Explain exactly what you are trying to do and what is
not working
Keep careful track of your workflow
Take the initiative and exclude obvious problems before
you ask the TAs to debug your code
Information we need from you
In a single email to [email protected]:
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Your name
Your netID
A non-Yale email account
Your Grad School year
Are you taking the course for Credit or are you an Auditor?
• Your level of experience with:
- Working in a UNIX/Linux environment
- High performance computing
- Scripting in Perl or Python
- R
- Any other programming language
- High-throughput sequencing apps or data