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Hypersensitivity to DNaseI digestion is a hallmark of open chromatin, and DNaseI-seq allows the genome-wide identification of regions of open chromatin. Interpreting these data is challenging, largely because of inherent variation in signal-to-noise ratio between datasets. We have developed PeaKDEck, a peak calling program that distinguishes signal from noise by randomly sampling read densities and using kernel density estimation to generate a dataset-specific probability distribution of random background signal. PeaKDEck uses this probability distribution to select an appropriate read density threshold for peak calling in each dataset. We benchmark PeaKDEck using published ENCODE DNaseI-seq data and other peak calling programs, and demonstrate superior performance in low signal-to-noise ratio datasets.

Original publication

DOI

10.1093/bioinformatics/btt774

Type

Journal article

Journal

Bioinformatics

Publication Date

01/05/2014

Volume

30

Pages

1302 - 1304

Keywords

Algorithms, Genome, High-Throughput Nucleotide Sequencing, Probability, Sequence Analysis, DNA, Software Design