Beyond library size: a field guide to NGS normalization

Background: Next generation sequencing (NGS) is a widely used technology in both basic research and clinical settings and it will continue to have a major impact on biomedical sciences. However, the use of incorrect normalization methods can lead to systematic biases and spurious results, making the selection of an appropriate normalization strategy a crucial and often overlooked part of NGS analysis. Results: We present a basic introduction to the currently available normalization methods for differential expression and ChIP-seq applications, along with best use recommendations for different experimental techniques and datasets. We demonstrate that the choice of normalization technique can have a significant impact on the number of genes called as differentially expressed in an RNA-seq experiment or peaks called in a ChIP-seq experiment. Conclusions: The choice of the most adequate normalization method depends on both the distribution of signal in the dataset and the intended downstream applications. Depending on the design and purpose of the study, appropriate bias correction should also be considered.

• Publication year

  2014

• Venue

  bioRxiv

• Publication date

  2014-06-19

• Fields of study

  Biology, Computer Science

• Identifiers
  DOI 10.1101/006403
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• RNA-Seq Gene Profiling - A Systematic Empirical Comparison

  2014cited by this paper
• IVT-seq reveals extreme bias in RNA sequencing

  2014cited by this paper
• HTSeq—a Python framework to work with high-throughput sequencing data

  2014cited by this paper
• Identification of transcription factor binding sites from ChIP-seq data at high resolution

  2013cited by this paper
• Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells

  2013cited by this paper
• A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis

  2013cited by this paper
• Widespread Misinterpretable ChIP-seq Bias in Yeast

  2013cited by this paper
• Statistical Applications in Genetics and Molecular Biology Normalization , bias correction , and peak calling for ChIP-seq

  2012cited by this paper
• Normalization of ChIP-seq data with control

  2012influential reference
• Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

  2012cited by this paper
• Fast gapped-read alignment with Bowtie 2

  2012cited by this paper
• Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples

  2012cited by this paper
• ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions

  2011cited by this paper
• modMine: flexible access to modENCODE data

  2011cited by this paper
• Systematic bias in high-throughput sequencing data and its correction by BEADS

  2011cited by this paper
• Integrating diverse genomic data using gene sets

  2011cited by this paper
• Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE

  2010cited by this paper
• baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data

  2010cited by this paper
• The developmental transcriptome of Drosophila melanogaster

  2010cited by this paper
• A scaling normalization method for differential expression analysis of RNA-seq data

  2010influential reference
• Five-Vertebrate ChIP-seq Reveals the Evolutionary Dynamics of Transcription Factor Binding

  2010cited by this paper
• A signal-noise model for significance analysis of ChIP-seq with negative control

  2010influential reference
• Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

  2010cited by this paper
• PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls

  2009cited by this paper
• RNA-Seq: a revolutionary tool for transcriptomics

  2009cited by this paper
• Transcript length bias in RNA-seq data confounds systems biology

  2009cited by this paper
• Spontaneous mapping of number and space in adults and young children.

  2009cited by this paper
• The Sequence Alignment/Map format and SAMtools

  2009cited by this paper
• RNA-Seq gene expression estimation with read mapping uncertainty

  2009cited by this paper
• Design and analysis of ChIP-seq experiments for DNA-binding proteins

  2008cited by this paper
• Mapping and quantifying mammalian transcriptomes by RNA-Seq

  2008cited by this paper
• Microarray Technology in Practice

  2008cited by this paper
• Model-based Analysis of ChIP-Seq (MACS)

  2008cited by this paper
• An Integrated Software System for Analyzing Chip-chip and Chip-seq Data (supplementary Information)

  2008cited by this paper
• Substantial biases in ultra-short read data sets from high-throughput DNA sequencing

  2008cited by this paper
• Missing channels in two-colour microarray experiments: Combining single-channel and two-channel data

  2007cited by this paper
• Finishing a whole-genome shotgun: Release 3 of the Drosophila melanogaster euchromatic genome sequence

  2002cited by this paper
• Initial sequencing and analysis of the human genome

  2001cited by this paper
• BIOINFORMATICS APPLICATIONS NOTE

  2001cited by this paper
• The genome sequence of Drosophila melanogaster.

  2000cited by this paper

• Small RNAs, Big Diseases

  2020cites this paper
• Spatial statistical modelling of epigenomic variability

  2019cites this paper
• A cohabiting bacterium alters the spectrum of short RNAs secreted by Escherichia coli

  2018cites this paper
• ExCNVSS: A Noise-Robust Method for Copy Number Variation Detection in Whole Exome Sequencing Data

  2017cites this paper
• A fuzzy method for RNA-Seq differential expression analysis in presence of multireads

  2016cites this paper