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

Nucleic acids research | Nov 1, 2011

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.

Pubmed ID: 21646344 RIS Download

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

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Catholic University of the Sacred Heart; Milan; Italy

An Italian private research university that offers undergraduate, graduate and postdoctoral level degrees in both Italian and English.


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Software for a normalization scheme that corrects nucleotide composition bias, mappability variations and differential local DNA structural effects in deep sequencing data.


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