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The CNVrd2 package: measurement of copy number at complex loci using high-throughput sequencing data.

Nguyen HT, Merriman TR, Black MA - Front Genet (2014)

Bottom Line: The CNVrd2 method first uses observed read-count ratios to refine segmentation results in one population.The performance of CNVrd2 was compared to that of two other read depth-based methods (CNVnator, cn.mops) at the CCL3L1 and DEFB103A loci.The highest concordance with the paralog ratio test method was observed for CNVrd2 (77.8/90.4% for CNVrd2, 36.7/4.8% for cn.mops and 7.2/1% for CNVnator at CCL3L1 and DEF103A).

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, University of Otago Dunedin, New Zealand ; Department of Mathematics and Statistics, University of Otago Dunedin, New Zealand ; Department of Biochemistry, Virtual Institute of Statistical Genetics, University of Otago Dunedin, New Zealand.

ABSTRACT
Recent advances in high-throughout sequencing technologies have made it possible to accurately assign copy number (CN) at CN variable loci. However, current analytic methods often perform poorly in regions in which complex CN variation is observed. Here we report the development of a read depth-based approach, CNVrd2, for investigation of CN variation using high-throughput sequencing data. This methodology was developed using data from the 1000 Genomes Project from the CCL3L1 locus, and tested using data from the DEFB103A locus. In both cases, samples were selected for which paralog ratio test data were also available for comparison. The CNVrd2 method first uses observed read-count ratios to refine segmentation results in one population. Then a linear regression model is applied to adjust the results across multiple populations, in combination with a Bayesian normal mixture model to cluster segmentation scores into groups for individual CN counts. The performance of CNVrd2 was compared to that of two other read depth-based methods (CNVnator, cn.mops) at the CCL3L1 and DEFB103A loci. The highest concordance with the paralog ratio test method was observed for CNVrd2 (77.8/90.4% for CNVrd2, 36.7/4.8% for cn.mops and 7.2/1% for CNVnator at CCL3L1 and DEF103A). CNVrd2 is available as an R package as part of the Bioconductor project: http://www.bioconductor.org/packages/release/bioc/html/CNVrd2.html.

No MeSH data available.


Related in: MedlinePlus

Read lengths and mapping qualities (top), mapping qualities (middle) and average read depth (bottom). Data for the 2 MB CCL3L1 region are on the left and the 2 Mb DEFB103A region on the right.
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Figure 4: Read lengths and mapping qualities (top), mapping qualities (middle) and average read depth (bottom). Data for the 2 MB CCL3L1 region are on the left and the 2 Mb DEFB103A region on the right.

Mentions: Alignment results are presented in Figure 4. At CCL3L1 a total of 395,078,047 reads across all samples were aligned to a 2 Mb region around the gene (Chr17:33670000-35670000). These reads had lengths ranging from 36 to 160 bp (median of 91.5 bp and the highest frequency, 44.9%, was 100 bp), and mapping qualities from 0 to 70 (median of 33.5 and the highest frequency, 73.4%, was 60). The majority of reads (73.5% and 72.5%) aligning to the CCL3L1-containing region (chr17:34617501-3465201) and CCL3L1 gene (Chr17:34623842-34625730) had a mapping quality of 0, presumably reflecting multiple alignments to the paralogs within the locus.


The CNVrd2 package: measurement of copy number at complex loci using high-throughput sequencing data.

Nguyen HT, Merriman TR, Black MA - Front Genet (2014)

Read lengths and mapping qualities (top), mapping qualities (middle) and average read depth (bottom). Data for the 2 MB CCL3L1 region are on the left and the 2 Mb DEFB103A region on the right.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4117933&req=5

Figure 4: Read lengths and mapping qualities (top), mapping qualities (middle) and average read depth (bottom). Data for the 2 MB CCL3L1 region are on the left and the 2 Mb DEFB103A region on the right.
Mentions: Alignment results are presented in Figure 4. At CCL3L1 a total of 395,078,047 reads across all samples were aligned to a 2 Mb region around the gene (Chr17:33670000-35670000). These reads had lengths ranging from 36 to 160 bp (median of 91.5 bp and the highest frequency, 44.9%, was 100 bp), and mapping qualities from 0 to 70 (median of 33.5 and the highest frequency, 73.4%, was 60). The majority of reads (73.5% and 72.5%) aligning to the CCL3L1-containing region (chr17:34617501-3465201) and CCL3L1 gene (Chr17:34623842-34625730) had a mapping quality of 0, presumably reflecting multiple alignments to the paralogs within the locus.

Bottom Line: The CNVrd2 method first uses observed read-count ratios to refine segmentation results in one population.The performance of CNVrd2 was compared to that of two other read depth-based methods (CNVnator, cn.mops) at the CCL3L1 and DEFB103A loci.The highest concordance with the paralog ratio test method was observed for CNVrd2 (77.8/90.4% for CNVrd2, 36.7/4.8% for cn.mops and 7.2/1% for CNVnator at CCL3L1 and DEF103A).

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, University of Otago Dunedin, New Zealand ; Department of Mathematics and Statistics, University of Otago Dunedin, New Zealand ; Department of Biochemistry, Virtual Institute of Statistical Genetics, University of Otago Dunedin, New Zealand.

ABSTRACT
Recent advances in high-throughout sequencing technologies have made it possible to accurately assign copy number (CN) at CN variable loci. However, current analytic methods often perform poorly in regions in which complex CN variation is observed. Here we report the development of a read depth-based approach, CNVrd2, for investigation of CN variation using high-throughput sequencing data. This methodology was developed using data from the 1000 Genomes Project from the CCL3L1 locus, and tested using data from the DEFB103A locus. In both cases, samples were selected for which paralog ratio test data were also available for comparison. The CNVrd2 method first uses observed read-count ratios to refine segmentation results in one population. Then a linear regression model is applied to adjust the results across multiple populations, in combination with a Bayesian normal mixture model to cluster segmentation scores into groups for individual CN counts. The performance of CNVrd2 was compared to that of two other read depth-based methods (CNVnator, cn.mops) at the CCL3L1 and DEFB103A loci. The highest concordance with the paralog ratio test method was observed for CNVrd2 (77.8/90.4% for CNVrd2, 36.7/4.8% for cn.mops and 7.2/1% for CNVnator at CCL3L1 and DEF103A). CNVrd2 is available as an R package as part of the Bioconductor project: http://www.bioconductor.org/packages/release/bioc/html/CNVrd2.html.

No MeSH data available.


Related in: MedlinePlus