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ChIP-seq analysis using R

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ChIP-seq analysis using R

Keywords

ChIP-Seq, Experimental-design, QC, Data-format, Alignment, Peak-calling, Differential-binding, Visualisation, Annotation

Authors

  • Anna Poetsch
  • based partially on material from Bori Mifsud and Ernest Turro

Top | Keywords | Authors | Description | Aims | Prerequisites | Target audience | Learning objectives | Materials | Data | Technical requirements | Literature references

Description

ChIP-seq is the most commonly used technique to study binding profiles of chromatin proteins, such as TFs or histone modification patterns. This course is an introduction to ChIP-seq data, and data analysis mainly using R, some command line based peak-callers and online software. It provides a theoretical background and the means to perform peak calling and differential binding analysis.

Aims

The aim of the course is to draw biologists' attention to the impact of experimental design, and the pitfalls of ChIP-seq data analysis, and to give them the tools to do their own preliminary data analysis.

Top | Keywords | Authors | Description | Aims | Prerequisites | Target audience | Learning objectives | Materials | Data | Technical requirements | Literature references

Prerequisites

  • R-programming
  • Unix
  • HTS-introduction
  • Preprocessing
  • Alignment

Target audience

  • Biologist
  • Programming experience

Learning objectives

  • Define approriate experimental design
  • Describe and perform steps of the ChIP-Seq workflow
  • Visualise raw and processed data
  • Annotate and interpret results

Top | Keywords | Authors | Description | Aims | Prerequisites | Target audience | Learning objectives | Materials | Data | Technical requirements | Literature references

Materials

Quality control of three sequencing data sets, two from ChiP-Seq experiments, one RNA-Seq.

Availability

Top | Keywords | Authors | Description | Aims | Prerequisites | Target audience | Learning objectives | Materials | Data | Technical requirements | Literature references

Technical requirements

Literature references

  • Hadfield J, Eldridge MD (2014) Multi-genome alignment for quality control and contamination screening of next-generation sequencing data. Frontiers in Genetics 5:31.
  • Morgan M, Anders S, Lawrence M, Aboyoun P, Pagès H and Gentleman R (2009) ShortRead: a Bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25:2607-2608.

Top | Keywords | Authors | Description | Aims | Prerequisites | Target audience | Learning objectives | Materials | Data | Technical requirements | Literature references

Keywords: ChIP-Seq, Experimental-design, QC, Data-format, Alignment, Peak-calling, Differential-binding, Visualisation, Annotation

Authors: Anna Poetsch, based partially on material from Bori Mifsud and Ernest Turro

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