Courses & Conferences

Bioconductor provides training in computational and statistical methods for the analysis of genomic data. You are welcome to use material from previous courses. However, you may not include these in separately published works (articles, books, websites). When using all or parts of the Bioconductor course materials (slides, vignettes, scripts) please cite the authors and refer your audience to the Bioconductor website.

Upcoming events are advertised 6 to 8 weeks in advance.

Keyword Course Title Materials Date Bioc/R Version
eQTL Epigenomics

eQTL analysis – an approach with Bioconductor, Vincent Carey

pdf

2014‑08‑24 2.14/3.1.1
Sequence Analysis Epigenomics

Introduction to Bioconductor for Sequence Analysis, Martin Morgan

html R Rmd

2014‑08‑24 2.14/3.1.1
RNASeq Epigenomics

Introduction to RNA-Seq data analysis, Benilton Carvalho

pdf

2014‑08‑24 2.14/3.1.1
RNASeq Epigenomics

Counting reads for RNA-seq in Bioconductor, Martin Morgan

pdf R Rnw

2014‑08‑24 2.14/3.1.1
R Epigenomics

Introduction to R (slides), Martin Morgan

html

2014‑08‑24 2.14/3.1.1
Methylation Epigenomics

Introduction to working with methylation arrays (slides), Martin Morgan

html

2014‑08‑24 2.14/3.1.1
Methylation Epigenomics

A short methylation analysis using minfi, Martin Morgan

html R Rmd

2014‑08‑24 2.14/3.1.1
Epigenomics Epigenomics

Introduction to Bioconductor for Epigneomics, Martin Morgan

pdf R Rnw

2014‑08‑24 2.14/3.1.1
Data Representation Epigenomics

Sequence data represenations in Bioconductor, Martin Morgan

html R Rmd

2014‑08‑24 2.14/3.1.1
eQTL BioC2014

Genetics of gene expression: computation and integrative prediction, Vincent Carey

html, Rmd, R

2014‑07‑30 2.14/3.1.1
Variants BioC2014

Variant calling with Bioconductor, Michael Lawrence

pdf, R, pkg

2014‑07‑30 2.14/3.1.1
Scalable Computing BioC2014

Parallel Computing with Bioconductor in the Amazon Cloud, Valerie Obenchain

pdf, R

2014‑07‑30 2.14/3.1.1
RNASeq BioC2014

Differential gene- and exon-level expression analyses for RNA-seq data using edgeR, voom and featureCounts, Mark Robinson

pdf, html, md

2014‑07‑30 2.14/3.1.1
RNASeq BioC2014

CRISPRseek: Design of target-specific guide RNAs in CRISPR-Cas9 genome-editing systems, Julie Zhu

pdf, html, Rmd, R

2014‑07‑30 2.14/3.1.1
RNASeq BioC2014

Analysis of RNA-Seq using the DESeq2 package, Michael Love

pdf, Rnw, R

2014‑07‑30 2.14/3.1.1
R/Bioconductor BioC2014

R / Bioconductor for everyone, Martin Morgan

slides, pdf, R

2014‑07‑30 2.14/3.1.1
Proteomics BioC2014

R / Bioconductor packages for Proteomics, Laurent Gatto

html, Rmd, R

2014‑07‑30 2.14/3.1.1
Pathway BioC2014

Integrated pathway analysis of multiple omics datasets, Aedin Culhane

html, Rmd, R

2014‑07‑30 2.14/3.1.1
Methylation BioC2014

Analysis of 450k methylation data with the minfi package, Kasper Hansen

pdf, Rnw, R

2014‑07‑30 2.14/3.1.1
Meta-analysis BioC2014

Meta-analysis of genomics experiments using Bioconductor, Levi Waldron

Rpres, R

2014‑07‑30 2.14/3.1.1
Genomic Ranges BioC2014

Learn how to use Bioconductor to perform common tasks on your high-throughput sequencing data, Hervé Pagès

pdf

2014‑07‑30 2.14/3.1.1
Flow Cytometry BioC2014

Tutorial, Introduction to Flow Cytometry Data Analysis using OpenCyto and Bioconductor, Greg Finak

html, Rmd, R

2014‑07‑30 2.14/3.1.1
ChIPSeq BioC2014

Visualisation and assessment of ChIP-seq quality using ChIPQC and Diffbind packages, Tom Carroll

pdf, R

2014‑07‑30 2.14/3.1.1
Annotation BioC2014

Bioconductor annotations: using and sharing resources, Marc Carlson

workflow, vignette

2014‑07‑30 2.14/3.1.1
Scalable Computing ISMB2014

Scalable Integrative Bioinformatics with Bioconductor, Vincent Carey

pptx

2014‑07‑15 2.14/3.1.1
RNASeq ISMB2014

Analysis of RNA-Seq Data, Mike Love

html

2014‑07‑15 2.14/3.1.1
R/Bioconductor ISMB2014

Trends in Genomic Data Analysis in R, Levi Waldron

pdf

2014‑07‑15 2.14/3.1.1
Annotation ISMB2014

Accessing Annotation Resources, Martin Morgan

pdf, R, R

2014‑07‑15 2.14/3.1.1
RNASeq useR2014

Work flows : RNA-Seq, Martin Morgan

html, R

2014‑06‑30 2.14/3.1.0
R/Bioconductor useR2014

Introduction to R / Bioconductor, Martin Morgan

html, R

2014‑06‑30 2.14/3.1.0
Data Representation useR2014

Sequence Data Representation, Martin Morgan

html, R

2014‑06‑30 2.14/3.1.0
Annotation useR2014

Annotations, Martin Morgan

html, R

2014‑06‑30 2.14/3.1.0
eQTL / molecular-QTL CSAMA2014

eQTL / molecular-QTL analyses, Vincent Carey

pdf

2014‑06‑22 2.14/3.1.1
Visualization CSAMA2014

Visualisation in Statistical Genomics, Vincent Carey

pdf

2014‑06‑22 2.14/3.1.1
Visualization CSAMA2014

Image Analysis, Susan Holmes, Wolfgang Huber, Trevor Martin

pdf

2014‑06‑22 2.14/3.1.1
Variants CSAMA2014

Variant tallies, visualisation, HDF5, Paul Theodor Pyl

pdf, R

2014‑06‑22 2.14/3.1.1
Statistics CSAMA2014

Elements of statistics 5: experimental design, Simon Anders

pdf

2014‑06‑22 2.14/3.1.1
Statistics CSAMA2014

Elements of statistics 4: regularisation & kernels, Wolfgang Huber

pdf

2014‑06‑22 2.14/3.1.1
Statistics CSAMA2014

Elements of statistics 3: Classification and clustering - basic concepts, Unknown

html

2014‑06‑22 2.14/3.1.1
Statistics CSAMA2014

Elements of statistics 2: multiple testing, false discovery rates, independent filtering, Wolfgang Huber

pdf

2014‑06‑22 2.14/3.1.1
Statistics CSAMA2014

Elements of statistics 1: t-test and linear model, Robert Gentleman

pdf

2014‑06‑22 2.14/3.1.1
Reporting CSAMA2014

Reporting your analysis - authoring knitr/Rmarkdown, ReportingTools, shiny, Laurent Gatto

pkg

2014‑06‑22 2.14/3.1.1
RNASeq CSAMA2014

RNA-Seq 3: alternative exon usage, Alejandro Reyes

pdf

2014‑06‑22 2.14/3.1.1
RNASeq CSAMA2014

RNA-Seq 1: differential expression analysis - GLMs and testing, Simon Anders

pdf

2014‑06‑22 2.14/3.1.1
RNASeq CSAMA2014

High-throughput sequencing: Alignment and related topic, Simon Anders

pdf

2014‑06‑22 2.14/3.1.1
RNASeq CSAMA2014

A complete RNA-Seq differential expression workflow, Michael Love, Simon Anders, Wolfgang Huber

pdf, R

2014‑06‑22 2.14/3.1.1
R/Bioconductor CSAMA2014

Accessing resources - packages, classes, methods, and efficient code, Martin Morgan

html, R

2014‑06‑22 2.14/3.1.1
Proteomics CSAMA2014

Proteomics, Laurent Gatto

pdf

2014‑06‑22 2.14/3.1.1
Introduction CSAMA2014

Introduction to R and Bioconductor, Martin Morgan

pdf

2014‑06‑22 2.14/3.1.1
Genomic Ranges CSAMA2014

Computing with genomic ranges, sequences and alignments, Michael Lawrence

pdf

2014‑06‑22 2.14/3.1.1
Gene set enrichment CSAMA2014

Gene set enrichment analysis, Robert Gentleman

pdf

2014‑06‑22 2.14/3.1.1
DNASeq CSAMA2014

DNA-Seq 2: visualisation and quality assessment of variant calls, Paul Theodor Pyl

pdf

2014‑06‑22 2.14/3.1.1
DNASeq CSAMA2014

DNA-Seq 1: Variant calling, Michael Lawrence

pdf

2014‑06‑22 2.14/3.1.1
ChIPSeq CSAMA2014

ChIP-seq Analysis, Martin Morgan

pdf, R

2014‑06‑22 2.14/3.1.1
Annotation CSAMA2014

Working with gene and genome annotations, Martin Morgan

pdf, R

2014‑06‑22 2.14/3.1.1
Annotation CSAMA2014

Working with Ranges infrastructure: annotating and understanding regions, Martin Morgan

pdf, R

2014‑06‑22 2.14/3.1.1
Visualization SeattleFeb2014

Working with Annotations, Martin Morgan

pdf, Rnw, R

2014‑02‑27 2.14/3.1.0
RNASeq SeattleFeb2014

Visualization of Genomic Data, Sonali Arora

pdf, Rnw, R

2014‑02‑27 2.14/3.1.0
R/Bioconductor SeattleFeb2014

Working with R, Martin Morgan

pdf, R, Rnw

2014‑02‑27 2.14/3.1.0
R/Bioconductor SeattleFeb2014

Bioconductor - Slides, Martin Morgan

pdf, Rnw, R

2014‑02‑27 2.14/3.1.0
Genomic Ranges SeattleFeb2014

Working with Genomic Ranges, Martin Morgan

pdf, Rnw, R

2014‑02‑27 2.14/3.1.0
Genomic Ranges SeattleFeb2014

Working with FASTQ, BAM, and VCF files, Martin Morgan

pdf, Rnw, R

2014‑02‑27 2.14/3.1.0
Data Representation SeattleFeb2014

Working with DNA Sequences, Sonali Arora

pdf, Rnw, R

2014‑02‑27 2.14/3.1.0
Data Representation SeattleFeb2014

Genomic Ranges - Slides, Martin Morgan

pdf, Rnw, R

2014‑02‑27 2.14/3.1.0
Annotation SeattleFeb2014

RNASeq Analysis, Martin Morgan

pdf, Rnw, R

2014‑02‑27 2.14/3.1.0
Annotation SeattleFeb2014

Annotations, Martin Morgan

pdf,R, Rnw

2014‑02‑27 2.14/3.1.0
Visualization summerx

Visualization., Martin Morgan

pdf, R, Rnw

2014‑01‑27 2.14/3.1.0
Variants summerx

Variants, Martin Morgan

pdf, Rnw

2014‑01‑27 2.14/3.1.0
R/Bioconductor summerx

Bioconductor, Martin Morgan

pdf, R, Rnw

2014‑01‑27 2.14/3.1.0
Genomic Ranges summerx

Ranges, Hervé Pagès

pdf, R, Rnw

2014‑01‑27 2.14/3.1.0
Best Practices summerx

Best Practices for Managing R / Bioconductor Scripts, Martin Morgan

pdf, Rnw

2014‑01‑27 2.14/3.1.0
Annotation summerx

Annotations, Martin Morgan

pdf, R, Rnw

2014‑01‑27 2.14/3.1.0

 

Courses by year

Custom workshops

The Bioconductor project can provide customized workshops on statistical methods and software for the analysis of genomic data for different educational and industrial clients. Interested parties should contact Vincent Carey.

Fred Hutchinson Cancer Research Center