Limits...
cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate.

Klambauer G, Schwarzbauer K, Mayr A, Clevert DA, Mitterecker A, Bodenhofer U, Hochreiter S - Nucleic Acids Res. (2012)

Bottom Line: Using a Bayesian approach, cn.MOPS decomposes variations in the depth of coverage across samples into integer copy numbers and noise by means of its mixture components and Poisson distributions, respectively.The noise estimate allows for reducing the FDR by filtering out detections having high noise that are likely to be false detections.We compared cn.MOPS with the five most popular methods for CNV detection in NGS data using four benchmark datasets: (i) simulated data, (ii) NGS data from a male HapMap individual with implanted CNVs from the X chromosome, (iii) data from HapMap individuals with known CNVs, (iv) high coverage data from the 1000 Genomes Project. cn.MOPS outperformed its five competitors in terms of precision (1-FDR) and recall for both gains and losses in all benchmark data sets.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioinformatics, Johannes Kepler University, A-4040 Linz, Austria.

ABSTRACT
Quantitative analyses of next-generation sequencing (NGS) data, such as the detection of copy number variations (CNVs), remain challenging. Current methods detect CNVs as changes in the depth of coverage along chromosomes. Technological or genomic variations in the depth of coverage thus lead to a high false discovery rate (FDR), even upon correction for GC content. In the context of association studies between CNVs and disease, a high FDR means many false CNVs, thereby decreasing the discovery power of the study after correction for multiple testing. We propose 'Copy Number estimation by a Mixture Of PoissonS' (cn.MOPS), a data processing pipeline for CNV detection in NGS data. In contrast to previous approaches, cn.MOPS incorporates modeling of depths of coverage across samples at each genomic position. Therefore, cn.MOPS is not affected by read count variations along chromosomes. Using a Bayesian approach, cn.MOPS decomposes variations in the depth of coverage across samples into integer copy numbers and noise by means of its mixture components and Poisson distributions, respectively. The noise estimate allows for reducing the FDR by filtering out detections having high noise that are likely to be false detections. We compared cn.MOPS with the five most popular methods for CNV detection in NGS data using four benchmark datasets: (i) simulated data, (ii) NGS data from a male HapMap individual with implanted CNVs from the X chromosome, (iii) data from HapMap individuals with known CNVs, (iv) high coverage data from the 1000 Genomes Project. cn.MOPS outperformed its five competitors in terms of precision (1-FDR) and recall for both gains and losses in all benchmark data sets. The software cn.MOPS is publicly available as an R package at http://www.bioinf.jku.at/software/cnmops/ and at Bioconductor.

Show MeSH
The processing pipelines for CNV detection in NGS data. Left column: modeling across samples and integer copy number estimation are unique to cn.MOPS. Right column: either GC correction [class (a) methods] or read count ratios [class (b) methods] are required for previous pipelines.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3351174&req=5

gks003-F1: The processing pipelines for CNV detection in NGS data. Left column: modeling across samples and integer copy number estimation are unique to cn.MOPS. Right column: either GC correction [class (a) methods] or read count ratios [class (b) methods] are required for previous pipelines.

Mentions: The cn.MOPS processing pipeline is depicted in Figure 1. The left column shows modeling across samples and integer copy number estimation that are unique to the cn.MOPS pipeline. On the right-hand side, GC correction is unique to some previous analysis pipelines; however, this step is not necessary for cn.MOPS, as the local model automatically captures GC content effects. The steps of the cn.MOPS processing pipeline and the central cn.MOPS model are described in the following subsections.Figure 1.


cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate.

Klambauer G, Schwarzbauer K, Mayr A, Clevert DA, Mitterecker A, Bodenhofer U, Hochreiter S - Nucleic Acids Res. (2012)

The processing pipelines for CNV detection in NGS data. Left column: modeling across samples and integer copy number estimation are unique to cn.MOPS. Right column: either GC correction [class (a) methods] or read count ratios [class (b) methods] are required for previous pipelines.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351174&req=5

gks003-F1: The processing pipelines for CNV detection in NGS data. Left column: modeling across samples and integer copy number estimation are unique to cn.MOPS. Right column: either GC correction [class (a) methods] or read count ratios [class (b) methods] are required for previous pipelines.
Mentions: The cn.MOPS processing pipeline is depicted in Figure 1. The left column shows modeling across samples and integer copy number estimation that are unique to the cn.MOPS pipeline. On the right-hand side, GC correction is unique to some previous analysis pipelines; however, this step is not necessary for cn.MOPS, as the local model automatically captures GC content effects. The steps of the cn.MOPS processing pipeline and the central cn.MOPS model are described in the following subsections.Figure 1.

Bottom Line: Using a Bayesian approach, cn.MOPS decomposes variations in the depth of coverage across samples into integer copy numbers and noise by means of its mixture components and Poisson distributions, respectively.The noise estimate allows for reducing the FDR by filtering out detections having high noise that are likely to be false detections.We compared cn.MOPS with the five most popular methods for CNV detection in NGS data using four benchmark datasets: (i) simulated data, (ii) NGS data from a male HapMap individual with implanted CNVs from the X chromosome, (iii) data from HapMap individuals with known CNVs, (iv) high coverage data from the 1000 Genomes Project. cn.MOPS outperformed its five competitors in terms of precision (1-FDR) and recall for both gains and losses in all benchmark data sets.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioinformatics, Johannes Kepler University, A-4040 Linz, Austria.

ABSTRACT
Quantitative analyses of next-generation sequencing (NGS) data, such as the detection of copy number variations (CNVs), remain challenging. Current methods detect CNVs as changes in the depth of coverage along chromosomes. Technological or genomic variations in the depth of coverage thus lead to a high false discovery rate (FDR), even upon correction for GC content. In the context of association studies between CNVs and disease, a high FDR means many false CNVs, thereby decreasing the discovery power of the study after correction for multiple testing. We propose 'Copy Number estimation by a Mixture Of PoissonS' (cn.MOPS), a data processing pipeline for CNV detection in NGS data. In contrast to previous approaches, cn.MOPS incorporates modeling of depths of coverage across samples at each genomic position. Therefore, cn.MOPS is not affected by read count variations along chromosomes. Using a Bayesian approach, cn.MOPS decomposes variations in the depth of coverage across samples into integer copy numbers and noise by means of its mixture components and Poisson distributions, respectively. The noise estimate allows for reducing the FDR by filtering out detections having high noise that are likely to be false detections. We compared cn.MOPS with the five most popular methods for CNV detection in NGS data using four benchmark datasets: (i) simulated data, (ii) NGS data from a male HapMap individual with implanted CNVs from the X chromosome, (iii) data from HapMap individuals with known CNVs, (iv) high coverage data from the 1000 Genomes Project. cn.MOPS outperformed its five competitors in terms of precision (1-FDR) and recall for both gains and losses in all benchmark data sets. The software cn.MOPS is publicly available as an R package at http://www.bioinf.jku.at/software/cnmops/ and at Bioconductor.

Show MeSH