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 Title Course Materials Date Bioc/R Version
Workflows Common Sequence Analysis Work Flows, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Visualization Visualization, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Reproducibility Reproducible Research, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
RNASeq RNA-Seq Lab: Workflow – gene-level exploratory analysis and differential expression, Michael Love et al. SeattleOct2014 html 2014‑10‑27 3.0/3.1.1
RNASeq RNA-Seq, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
R Introduction to R, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Packages Organizing Code in Functions, Files, and Packages, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Machine Learning Machine Learning, Sonali Arora SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Large Data Working with Large Data, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Intro Introduction, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Gene set enrichment Gene set enrichment, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Copy Number Copy Number, Sonali Arora SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Bioconductor Introduction to Bioconductor, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Appendix Appendix: Install IGV, Martin Morgan SeattleOct2014 html, R, Rmd hg19_alias.tab 2014‑10‑27 3.0/3.1.1
Annotation Annotating Genes, Genomes, and Variants, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
eQTL eQTL analysis – an approach with Bioconductor, Vincent Carey Epigenomics pdf 2014‑08‑24 2.14/3.1.1
Sequence Analysis Introduction to Bioconductor for Sequence Analysis, Martin Morgan Epigenomics html R Rmd 2014‑08‑24 2.14/3.1.1
RNASeq Introduction to RNA-Seq data analysis, Benilton Carvalho Epigenomics pdf 2014‑08‑24 2.14/3.1.1
RNASeq Counting reads for RNA-seq in Bioconductor, Martin Morgan Epigenomics pdf R Rnw 2014‑08‑24 2.14/3.1.1
R Introduction to R (slides), Martin Morgan Epigenomics html 2014‑08‑24 2.14/3.1.1
Methylation Introduction to working with methylation arrays (slides), Martin Morgan Epigenomics html 2014‑08‑24 2.14/3.1.1
Methylation A short methylation analysis using minfi, Martin Morgan Epigenomics html R Rmd 2014‑08‑24 2.14/3.1.1
Epigenomics Introduction to Bioconductor for Epigneomics, Martin Morgan Epigenomics pdf R Rnw 2014‑08‑24 2.14/3.1.1
Data Representation Sequence data represenations in Bioconductor, Martin Morgan Epigenomics html R Rmd 2014‑08‑24 2.14/3.1.1
eQTL Genetics of gene expression: computation and integrative prediction, Vincent Carey BioC2014 html, Rmd, R 2014‑07‑30 2.14/3.1.1
Variants Variant calling with Bioconductor, Michael Lawrence BioC2014 pdf, R, pkg 2014‑07‑30 2.14/3.1.1
Scalable Computing Parallel Computing with Bioconductor in the Amazon Cloud, Valerie Obenchain BioC2014 pdf, R 2014‑07‑30 2.14/3.1.1
RNASeq Differential gene- and exon-level expression analyses for RNA-seq data using edgeR, voom and featureCounts, Mark Robinson BioC2014 pdf, html, md 2014‑07‑30 2.14/3.1.1
RNASeq CRISPRseek: Design of target-specific guide RNAs in CRISPR-Cas9 genome-editing systems, Julie Zhu BioC2014 pdf, html, Rmd, R 2014‑07‑30 2.14/3.1.1
RNASeq Analysis of RNA-Seq using the DESeq2 package, Michael Love BioC2014 pdf, Rnw, R 2014‑07‑30 2.14/3.1.1
R/Bioconductor R / Bioconductor for everyone, Martin Morgan BioC2014 slides, pdf, R 2014‑07‑30 2.14/3.1.1
Proteomics R / Bioconductor packages for Proteomics, Laurent Gatto BioC2014 html, Rmd, R 2014‑07‑30 2.14/3.1.1
Pathway Integrated pathway analysis of multiple omics datasets, Aedin Culhane BioC2014 html, Rmd, R 2014‑07‑30 2.14/3.1.1
Methylation Analysis of 450k methylation data with the minfi package, Kasper Hansen BioC2014 pdf, Rnw, R 2014‑07‑30 2.14/3.1.1
Meta-analysis Meta-analysis of genomics experiments using Bioconductor, Levi Waldron BioC2014 Rpres, R 2014‑07‑30 2.14/3.1.1
Genomic Ranges Learn how to use Bioconductor to perform common tasks on your high-throughput sequencing data, Hervé Pagès BioC2014 pdf 2014‑07‑30 2.14/3.1.1
Flow Cytometry Tutorial, Introduction to Flow Cytometry Data Analysis using OpenCyto and Bioconductor, Greg Finak BioC2014 html, Rmd, R 2014‑07‑30 2.14/3.1.1
ChIPSeq Visualisation and assessment of ChIP-seq quality using ChIPQC and Diffbind packages, Tom Carroll BioC2014 pdf, R 2014‑07‑30 2.14/3.1.1
Annotation Bioconductor annotations: using and sharing resources, Marc Carlson BioC2014 workflow, vignette 2014‑07‑30 2.14/3.1.1
Scalable Computing Scalable Integrative Bioinformatics with Bioconductor, Vincent Carey ISMB2014 pptx 2014‑07‑15 2.14/3.1.1
RNASeq Analysis of RNA-Seq Data, Mike Love ISMB2014 html 2014‑07‑15 2.14/3.1.1
R/Bioconductor Trends in Genomic Data Analysis in R, Levi Waldron ISMB2014 pdf 2014‑07‑15 2.14/3.1.1
Annotation Accessing Annotation Resources, Martin Morgan ISMB2014 pdf, R, R 2014‑07‑15 2.14/3.1.1
RNASeq Work flows : RNA-Seq, Martin Morgan useR2014 html, R 2014‑06‑30 2.14/3.1.0
R/Bioconductor Introduction to R / Bioconductor, Martin Morgan useR2014 html, R 2014‑06‑30 2.14/3.1.0
Data Representation Sequence Data Representation, Martin Morgan useR2014 html, R 2014‑06‑30 2.14/3.1.0
Annotation Annotations, Martin Morgan useR2014 html, R 2014‑06‑30 2.14/3.1.0
eQTL / molecular-QTL eQTL / molecular-QTL analyses, Vincent Carey CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Visualization Visualisation in Statistical Genomics, Vincent Carey CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Visualization Image Analysis, Susan Holmes, Wolfgang Huber, Trevor Martin CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Variants Variant tallies, visualisation, HDF5, Paul Theodor Pyl CSAMA2014 pdf, R 2014‑06‑22 2.14/3.1.1
Statistics Elements of statistics 5: experimental design, Simon Anders CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Statistics Elements of statistics 4: regularisation & kernels, Wolfgang Huber CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Statistics Elements of statistics 3: Classification and clustering - basic concepts, Unknown CSAMA2014 html 2014‑06‑22 2.14/3.1.1
Statistics Elements of statistics 2: multiple testing, false discovery rates, independent filtering, Wolfgang Huber CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Statistics Elements of statistics 1: t-test and linear model, Robert Gentleman CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Reporting Reporting your analysis - authoring knitr/Rmarkdown, ReportingTools, shiny, Laurent Gatto CSAMA2014 pkg 2014‑06‑22 2.14/3.1.1
RNASeq RNA-Seq 3: alternative exon usage, Alejandro Reyes CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
RNASeq RNA-Seq 1: differential expression analysis - GLMs and testing, Simon Anders CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
RNASeq High-throughput sequencing: Alignment and related topic, Simon Anders CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
RNASeq A complete RNA-Seq differential expression workflow, Michael Love, Simon Anders, Wolfgang Huber CSAMA2014 pdf, R 2014‑06‑22 2.14/3.1.1
R/Bioconductor Accessing resources - packages, classes, methods, and efficient code, Martin Morgan CSAMA2014 html, R 2014‑06‑22 2.14/3.1.1
Proteomics Proteomics, Laurent Gatto CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Introduction Introduction to R and Bioconductor, Martin Morgan CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Genomic Ranges Computing with genomic ranges, sequences and alignments, Michael Lawrence CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Gene set enrichment Gene set enrichment analysis, Robert Gentleman CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
DNASeq DNA-Seq 2: visualisation and quality assessment of variant calls, Paul Theodor Pyl CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
DNASeq DNA-Seq 1: Variant calling, Michael Lawrence CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
ChIPSeq ChIP-seq Analysis, Martin Morgan CSAMA2014 pdf, R 2014‑06‑22 2.14/3.1.1
Annotation Working with gene and genome annotations, Martin Morgan CSAMA2014 pdf, R 2014‑06‑22 2.14/3.1.1
Annotation Working with Ranges infrastructure: annotating and understanding regions, Martin Morgan CSAMA2014 pdf, R 2014‑06‑22 2.14/3.1.1
Visualization Working with Annotations, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
RNASeq Visualization of Genomic Data, Sonali Arora SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
R/Bioconductor Working with R, Martin Morgan SeattleFeb2014 pdf, R, Rnw 2014‑02‑27 2.14/3.1.0
R/Bioconductor Bioconductor - Slides, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Genomic Ranges Working with Genomic Ranges, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Genomic Ranges Working with FASTQ, BAM, and VCF files, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Data Representation Working with DNA Sequences, Sonali Arora SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Data Representation Genomic Ranges - Slides, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Annotation RNASeq Analysis, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Annotation Annotations, Martin Morgan SeattleFeb2014 pdf,R, Rnw 2014‑02‑27 2.14/3.1.0
Visualization Visualization., Martin Morgan summerx pdf, R, Rnw 2014‑01‑27 2.14/3.1.0
Variants Variants, Martin Morgan summerx pdf, Rnw 2014‑01‑27 2.14/3.1.0
R/Bioconductor Bioconductor, Martin Morgan summerx pdf, R, Rnw 2014‑01‑27 2.14/3.1.0
Genomic Ranges Ranges, Hervé Pagès summerx pdf, R, Rnw 2014‑01‑27 2.14/3.1.0
Best Practices Best Practices for Managing R / Bioconductor Scripts, Martin Morgan summerx pdf, Rnw 2014‑01‑27 2.14/3.1.0
Annotation Annotations, Martin Morgan summerx 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