Bioconductor for Sequence Analysis

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Introduction

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.

Schedule (tentative)

Day 1 (9:00 - 12:30; 1:30 - 5:00)

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

Analysis and comprehension of high throughput genomic data

Sequencing, microarrays, proteomics, flow cytometry, imaging, ...
Recent publication: Huber et al., Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods 12:115-121

Packages

Software
Annotation -- static, versioned annotations, e.g., genome sequences, gene models, gene identifiers
Experiment data -- example experiments used to illustrate work flows and analysis
Recently: r Biocpkg("AnnotationHub") for easy web-based retrieval of genome-scale annotation