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

By Günter Klambauer, Karin Schwarzbauer, Andreas Mayr, Djork-Arné Clevert, Andreas Mitterecker, Ulrich Bodenhofer, Sepp Hochreiter. Nucleic Acids Research, 2012

445 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Genetics

View Paper: https://www.semanticscholar.org/paper/3ba96f0cc9d122a549e06e71457d79e75b027474

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In Cancer Research

1. Elaine R. Mardis
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1. Richard K. Wilson
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Citations in the corpus

• Next-Generation Sequencing Technology: Current Trends and Advancements (2023, unknown): “MultiQC [92] Whole-Genome Sequencing/Whole-Exome Sequencing/Targeted Panel Removal of duplicate reads Picard [99], Sambamba [100] Variant calling (single-nucleotide polymorphisms and indels) GATK [101], freeBayes [102], Platypus [103], VarScan [104], DeepVariant [105], Illumina Dragen [106] Filter a”
• SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution (2014, background): “MOPS [35–37], or VarScan 2 [38]), whole-genome sequencing (WGS, e.”
• Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives (2013, methodology): “MOPS proposed a data processing pipeline using a mixture of Poisson models to reduce the FDR in CNV detection [46].”
• CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing (2016, methodology): “…copy number analysis of these datasets, as well, including CNVer [6], ExomeCNV [7], exomeCopy [8], CONTRA [9], CoNIFER [10], ExomeDepth [11], VarScan 2 [12], XHMM [13], ngCGH [14], EXCAVATOR [15], CANOES [16], PatternCNV [17], CODEX [18], and recent versions of Control-FREEC [19] and cn.MOPS [20].”