title: Systematic bias in high-throughput sequencing data and its correction by BEADS.
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.
authors.affiliation: The Gurdon Institute and Department of Genetics, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK.
authors.forename: Ming-Sin
authors.initials: MS
authors.lastname: Cheung
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Down
authors.forename: Isabel
authors.initials: I
authors.lastname: Latorre
authors.forename: Julie
authors.initials: J
authors.lastname: Ahringer
mesh_terms: D000465:Algorithms; D000818:Animals; D001482:Base Composition; D017173:Caenorhabditis elegans / Q000235:genetics; D047369:Chromatin Immunoprecipitation; D018509:DNA, Helminth / Q000737:chemistry; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D018509:DNA, Helminth