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wuHMM: a robust algorithm to detect DNA copy number variation using long oligonucleotide microarray data.

Patrick Cahan1, Laura E Godfrey, Peggy S Eis

  • 1Department of Internal Medicine and Department of Genetics, Division of Oncology, Stem Cell Biology Section, Washington University, St Louis, MO, USA.

Nucleic Acids Research
|March 13, 2008
PubMed
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We developed wuHMM, an algorithm to detect copy number variants (CNVs) using array comparative genomic hybridization (aCGH) data. It improves accuracy by using sequence divergence, achieving high resolution in mouse genome comparisons.

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Copy number variants (CNVs) are genomic alterations impacting traits and diseases.
  • High-resolution array comparative genomic hybridization (aCGH) platforms offer detailed CNV detection.
  • Existing analytical tools struggle with the lower signal-to-noise ratio of oligonucleotide-based aCGH data.

Purpose of the Study:

  • To develop and evaluate wuHMM, a novel algorithm for accurate CNV detection from high-density aCGH data.
  • To assess wuHMM's performance across different probe densities (385K, 2.1M, 3.1M).
  • To leverage sequence divergence information for reducing false positive rates in CNV identification.

Main Methods:

  • Development of the wuHMM algorithm, incorporating sequence divergence for CNV mapping.
  • Application of wuHMM to aCGH experiments comparing 129X1/SvJ and C57BL/6J mouse genomes.

Related Experiment Videos

  • Performance assessment by comparing results from 385K, 2.1M, and 3.1M probe platforms and independent validation of 10 CNVs.

    • Main Results:

    • wuHMM effectively maps CNVs from aCGH platforms with 385K to over 3M probes.
    • The algorithm demonstrates robustness and requires no training data.
    • At a <10% false positive rate, wuHMM achieves resolutions of 24 kb (385K), 2-5 kb (2.1M), and 1 kb (3.1M).

    Conclusions:

    • wuHMM significantly enhances CNV detection accuracy from high-resolution aCGH data.
    • The algorithm's ability to use sequence divergence improves reliability.
    • wuHMM provides a powerful tool for genomic research, particularly in comparative genomics.