Advanced ChIP-seq analyses

1. ChIP-seq Review

Congratulations on completing this course! You have learned how to load ChIP-seq data into R, how to prepare them for analysis and how to carry out a detailed analysis of this type of data. Let's briefly review the steps involved before discussing where to go from here.

2. Review: Importing data

The firs step in any ChIP-seq analysis is to import the data into R. You've learned how to do this using the *rtracklayer* and *GenomicAlignment* packages.

3. Review: Visualising ChIP-seq data

It is always a good idea to visualise your data and you should now know how to plot read coverage, peak locations and transcript annotations for selected regions of the genome using the *Gviz* package.

4. Review: Quality Control

As with any complex dataset it is necessary to subject ChIP-seq data to quality control procedures. You have learned about complicating factors like repetitive regions and how to use blacklisting to deal with them. You should also know how to use the *ChIPQC* package to to create a QC report for your data.

5. Review: Differential Binding

After all the data processing and quality control the data are finally ready for analysis. You have learned how to use the *DiffBind* package to determine differentially bound peaks and how to assess clustering of samples using tools like heatmaps and PCA plots.

6. Review: Enrichment analysis

Finally, you learned how to annotate peaks with nearby genes and how to use this information to interpret ChIP-seq results with the help of gene set enrichment analysis, using the *chipenrich* package.

7. Moving Ahead

With everything you have learned here you have a good foundation to carry out your own ChIP-seq analyses. The best thing to do now is to get your hands on some ChIP-seq data to put your newly acquired knowledge into practice. Datasets are freely available from public databases like GEO, including the one you worked with during this course. Take a look at the Bioconductor website. Bioconductor has many other packages that you may find useful. If you get stuck the Bioconductor community is there to help you along the way. The support forum is a great place to find answers to your questions.

8. Well done!

This is the end of the course *ChIP-seq workflows in R*. I hope you found it useful and that you'll enjoy analysing some exciting ChIP-seq datasets of your own.

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