University of Cambridge The Gurdon Institute


BEADS: Bias Elimination Algorithm for Deep Sequencing

Systematic bias in deep sequencing data and its correction by BEADS. (Publication link available soon)

Ming-Sin Cheung, Thomas A. Down and Julie Ahringer.


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

In high-throughput sequencing data, the recovery of sequenced DNA fragments is not uniform along the genome. In particular, GC-rich sequences are often over-represented and AT-rich sequences under-represented in sequencing data. In addition, the read mapping procedure also generates regional bias. Sequence reads that can be mapped to multiple sites in the genome are usually discarded. Genomic regions with high degeneracy therefore show lower mapped read coverage than unique portions of the genome. Mappability varies along the genome and thus creates systematic bias. Futhermore, local DNA or chromatin structural effects can lead to coverage inhomogeneity of sequencing data.

The above biases impede data interpretation. Here, we present BEADS, a normalization method that corrects each of these biases and suggest the procedure to be done routinely on deep sequencing data prior to downstream analysis.


[11-05-2010]    BEADS v1.0 is available.

[29-03-2011]    BEADS v1.1 released. GFF to WIG and SAM to GFF conversions are available.


Nicole Cheung - msc51 -at-

Thomas Down - tad26 -at-

Julie Ahringer - ja219 -at-

Last updated 29 Mar 2011 by Nicole Cheung.

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Copyright © 2010 Nicole Cheung (The Gurdon Institute, University of Cambridge)