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- Title
Systematic bias in high-throughput sequencing data and its correction by BEADS.
- Authors
Cheung, Ming-Sin; Down, Thomas A; Latorre, Isabel; Ahringer, Julie
- 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.
- Publication
Nucleic acids research, 2011, Vol 39, Issue 15, pe103
- ISSN
1362-4962
- Publication type
Journal Article
- DOI
10.1093/nar/gkr425