Statistical issues in the analysis of DNA Copy Number Variations

Kennedy, Richard E.; Erickson, Stephen W.; Wojczynski, Mary K.; Bruder, Carl E.G.; Tiwari, Hemant K.

doi:10.1504/ijcbdd.2008.022208

Cited by 24 publications

(23 citation statements)

References 99 publications

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“…Statistical methods for analyzing aCGH data are readily available and are described in review articles such as Wineinger et al [10] and Medvedev et al [11]. However, aCGH is expensive and has limited resolution and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Copy number variation detection using next generation sequencing read counts

2014

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BackgroundA copy number variation (CNV) is a difference between genotypes in the number of copies of a genomic region. Next generation sequencing (NGS) technologies provide sensitive and accurate tools for detecting genomic variations that include CNVs. However, statistical approaches for CNV identification using NGS are limited. We propose a new methodology for detecting CNVs using NGS data. This method (henceforth denoted by m-HMM) is based on a hidden Markov model with emission probabilities that are governed by mixture distributions. We use the Expectation-Maximization (EM) algorithm to estimate the parameters in the model.ResultsA simulation study demonstrates that our proposed m-HMM approach has greater power for detecting copy number gains and losses relative to existing methods. Furthermore, application of our m-HMM to DNA sequencing data from the two maize inbred lines B73 and Mo17 to identify CNVs that may play a role in creating phenotypic differences between these inbred lines provides results concordant with previous array-based efforts to identify CNVs.ConclusionsThe new m-HMM method is a powerful and practical approach for identifying CNVs from NGS data.

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Section: Introductionmentioning

confidence: 99%

Copy number variation detection using next generation sequencing read counts

2014

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“…Despite tremendous improvement in the different technologies and analytical methods, CNV detection remains a difficult task (Wineinger et al, 2008; Curtis et al, 2009; Winchester et al, 2009; Eckel-Passow et al, 2011; Haraksingh et al, 2011; Pinto et al, 2011; Valsesia et al, 2011, 2012). Both DNA microarrays and NGS are prone to batch effects.…”

Section: High-throughput Cnv Discovery Platformsmentioning

confidence: 99%

The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation

Valsesia¹,

Macé²,

Jacquemont³

et al. 2013

Front. Genet.

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Differences between genomes can be due to single nucleotide variants, translocations, inversions, and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 500 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease. Hence there is a need for better-tailored and more robust tools for the detection and genome-wide analyses of CNVs. While a link between a given CNV and a disease may have often been established, the relative CNV contribution to disease progression and impact on drug response is not necessarily understood. In this review we discuss the progress, challenges, and limitations that occur at different stages of CNV analysis from the detection (using DNA microarrays and next-generation sequencing) and identification of recurrent CNVs to the association with phenotypes. We emphasize the importance of germline CNVs and propose strategies to aid clinicians to better interpret structural variations and assess their clinical implications.

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“…Copy number variation 'calls' involve using probe expression and that of adjacent probes in the genome to identify regions where there is allelic loss or amplification. 1 Similar to gene expression arrays, comparative genome hybridisation array data must be normalised, filtered and analysed critically in order to determine if variation in probe expression is due to normal variation or experimental error, or truly representative of copy number. The resolution of the array depends on how much 'space' is between each probe; for example, if a copy number variation occurs in between two regions of probed DNA, it will not be detected.…”

Section: Comparative Genome Hybridisation Arraysmentioning

confidence: 99%

Bioinformatics in otolaryngology research. Part two: other high-throughput platforms in genomics and epigenetics

Upadhyay

Belbin

et al. 2014

J. Laryngol. Otol.

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Objectives: This second segment of the two-part review summarises several modern high-throughput methods in genomics, epigenetics and molecular biology. Many principles from nucleotide sequencing and transcriptomics can be applied to other high-throughput molecular biology techniques. Specifically, this manuscript reviews: array comparative genome hybridisation; single nucleotide polymorphism arrays; microarray technology, used to study epigenetics; and methodology applied in proteomics. Finally, the review describes current methods for the integration of multiple molecular biology platforms.Conclusion: Progress in treating human disease in general will require close collaboration with experts in bioinformatics. Improved understanding, by clinicians and physician-scientists in our field, of the concepts presented in both parts of this review will advance diagnosis and therapy for diseases of the head and neck.

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Statistical issues in the analysis of DNA Copy Number Variations

Cited by 24 publications

References 99 publications

Copy number variation detection using next generation sequencing read counts

Copy number variation detection using next generation sequencing read counts

The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation

Bioinformatics in otolaryngology research. Part two: other high-throughput platforms in genomics and epigenetics

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