README.md
In Bioconductor/UseBioconductor: Use R / Bioconductor for Sequence Analysis
Use R / Bioconductor for Sequence Analysis
Fred Hutchinson Cancer Research Center, Seattle, WA
6-7 April, 2015
Contact: Martin Morgan
(mtmorgan@fredhutch.org)
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.
Registration
Please register online.
Schedule (tentative)
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.
Day 2 (9:00 - 12:30; 1:30 - 5:00)
- D. Machine Learning.
- E. Gene Set Enrichment.
- F. ChIP-seq ChIP-seq with
csaw
- I. Large Data -- efficient, parallel, and cloud
programming with
BiocParallel,
GenomicFiles,
and other resources.
Bioconductor/UseBioconductor documentation built on May 6, 2019, 7:52 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Use R / Bioconductor for Sequence Analysis
Fred Hutchinson Cancer Research Center, Seattle, WA 6-7 April, 2015
Contact: Martin Morgan (mtmorgan@fredhutch.org)
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.
Registration
Please register online.
Schedule (tentative)
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.
Day 2 (9:00 - 12:30; 1:30 - 5:00)
- D. Machine Learning.
- E. Gene Set Enrichment.
- F. ChIP-seq ChIP-seq with csaw
- I. Large Data -- efficient, parallel, and cloud programming with BiocParallel, GenomicFiles, and other resources.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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