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Systematic bias in high-throughput sequencing data and its correction by BEADS.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Cheung, Ming-Sin 
Down, Thomas A 
Latorre, Isabel 

Abstract

Genomic sequences obtained through high-throughput sequencing are not uniformly distributed across the genome. For example, sequencing data of total genomic DNA show significant, yet unexpected enrichments on promoters and exons. This systematic bias is a particular problem for techniques such as chromatin immunoprecipitation, where the signal for a target factor is plotted across genomic features. We have focused on data obtained from Illumina's Genome Analyser platform, where at least three factors contribute to sequence bias: GC content, mappability of sequencing reads, and regional biases that might be generated by local structure. We show that relying on input control as a normalizer is not generally appropriate due to sample to sample variation in bias. To correct sequence bias, we present BEADS (bias elimination algorithm for deep sequencing), a simple three-step normalization scheme that successfully unmasks real binding patterns in ChIP-seq data. We suggest that this procedure be done routinely prior to data interpretation and downstream analyses.

Description

Keywords

Algorithms, Animals, Base Composition, Caenorhabditis elegans, Chromatin Immunoprecipitation, DNA, Helminth, High-Throughput Nucleotide Sequencing, Sequence Analysis, DNA

Journal Title

Nucleic Acids Res

Conference Name

Journal ISSN

0305-1048
1362-4962

Volume Title

39

Publisher

Oxford University Press (OUP)

Rights

Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Sponsorship
Wellcome Trust (054523/Z/98/C)