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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays.

Eric L Seiser1, Federico Innocenti2

  • 1Center for Pharmacogenomics and Individualized Therapy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Cancer Informatics
|February 7, 2015
PubMed
Summary

Germline DNA copy number variation (CNV) in cancer is an emerging area. Hidden Markov Model (HMM) algorithms show promise for CNV detection from genotyping microarrays but require further improvement for sensitivity.

Keywords:
copy number variationgenotyping microarrayhidden Markov model

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Area of Science:

  • Genomics
  • Cancer Research
  • Bioinformatics

Background:

  • Somatic DNA copy number alterations are well-studied in cancer.
  • The role of germline DNA copy number variation (CNV) in cancer is an emerging field.
  • Genotyping microarrays can infer DNA copy number alongside genotype determination.

Purpose of the Study:

  • To review computational approaches for germline CNV discovery from genotyping microarray data.
  • To evaluate the performance of commonly used algorithms, particularly those based on Hidden Markov Models (HMMs).

Main Methods:

  • Analysis of Illumina genotype microarray data.
  • Utilizing computational approaches, including Hidden Markov Models (HMMs).
  • Comparison of algorithms like QuantiSNP, PennCNV, and GenoCN.

Main Results:

  • HMM-based algorithms are commonly used for CNV discovery and generally outperform other methods.
  • Performance of CNV detection algorithms can vary across genotyping platforms and datasets.
  • A prevalent issue with HMM-based algorithms is low sensitivity.

Conclusions:

  • While HMMs are valuable for germline CNV detection, their low sensitivity necessitates further methodological improvements.
  • Continued research is needed to enhance the accuracy and sensitivity of CNV detection methodologies in cancer genomics.