ChIP-seq analysis using R - Quality Control

ChIP-seq analysis using R - Quality Control

Keywords

ChIP-Seq, RNA-Seq, QC, Data-format, Experimental-design

Authors

  • Anna Poetsch

Type

  • Practical

Description

This practical illustrates steps that can be undertaken to assess the quality of the sequencing data. We will start from the fastq files and assess their quality in respect to potential contamination and technical artifacts.

Aims

The aim of this practical is to get familiar with performing quality control on sequencing data both for RNA-Seq and for ChIP-Seq.

Prerequisites

  • HTS-introduction
  • Unix

Target audience

Biologist, Computational biologist

Learning objectives

  • Being able to perform Contamination screens
  • Being able to perform basic quality control on fastQ files

Materials

  • Day1-1OIST-HTSA-Worksho-October-2014QC-practical
  • Day1-5OIST-HTSA-Workshop-October-2014QCpracticalwalkthrough

Data

Timing

2 hours

Content stability

Stable. There might be small updates in the future.

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, Pag├Ęs H and Gentleman R (2009) ShortRead: a Bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25:2607-2608.

Scientific topics: RNA-Seq

Keywords: ChIP-Seq, RNA-Seq, QC, Data-format, Experimental-design

Authors: Anna Poetsch

ChIP-seq analysis using R - Quality Control https://tess.elixir-europe.org/materials/chip-seq-analysis-using-r-quality-control This practical illustrates steps that can be undertaken to assess the quality of the sequencing data. We will start from the fastq files and assess their quality in respect to potential contamination and technical artifacts. RNA-Seq ChIP-Seq, RNA-Seq, QC, Data-format, Experimental-design