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- Use R / Bioconductor for Sequence Analysis (Intermediate Course)
Use R / Bioconductor for Sequence Analysis (Intermediate Course)
Seattle, USA
2015-04-06 ~ 2015-04-07
Instructors
- Sonali Arora
- Nathaniel Hayden
- Jim Hester
- Martin Morgan
- Hervé Pagès
- Marc Carlson
- Valerie Obenchain
- Dan Tenenbaum
- Paul Shannon
Description
This INTERMEDIATE course is designed for individuals comfortable using R, and with some familiarity with Bioconductor. It consists of approximately equal parts lecture and practical sessions addressing use of Bioconductor software for analysis and comprehension of high-throughput sequence and related data. Specific topics include use of central Bioconductor classes (e.g., GRanges, SummarizedExperiment), RNASeq gene differential expression, ChIP-seq and methylation work flows, approaches to management and integrative analysis of diverse high-throughput data types, and strategies for working with large data. Participants are required to bring a laptop with wireless internet access and a modern version of the Chrome or Safari web browser.
Materials
To launch an Amazon Machine Image (AMI) for this course:
- Create an Amazon Web Services (AWS) Account if you don’t already have one.
- Start the instance ami-705b6718; See the documentation for this. Make sure your security group has port 80 accessible.
- Paste the Public DNS name of your instance into a web browser.
- Log in to RStudio with username ubuntu and password bioc .
- Be sure and terminate your instance when you are done using it, in order to avoid excessive charges.
Use R / Bioconductor for Sequence Analysis
Fred Hutchinson Cancer Research Center, Seattle, WA
6-7 April, 2015
Schedule
Day 1 (9:00 - 12:30; 1:30 - 5:00)
- A. Introduction. Bioconductor and sequencing work flows
- B. Genomic Ranges. Working with Genomic Ranges and other Bioconductor data structures (e.g., in the GenomicRanges. package).
- C. Differential Gene Expression. RNA-Seq known gene differential expression with DESeq2 and edgeR.
- D. Machine Learning.
- E. Gene Set Enrichment.
Day 2 (9:00 - 12:30; 1:30 - 5:00)
- F. ChIP-seq ChIP-seq with csaw
- G. Methylation and regulatory work flows with minfi.
- H. Integrative Data Analysis – emerging approaches
- I. Large Data – efficient, parallel, and cloud programming with BiocParallel, GenomicFiles, and other resources.