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VisCap: inference and visualization of germ-line copy-number variants from targeted clinical sequencing data.

Pugh TJ, Amr SS, Bowser MJ, Gowrisankar S, Hynes E, Mahanta LM, Rehm HL, Funke B, Lebo MS - Genet. Med. (2015)

Bottom Line: VisCap calculates the fraction of overall sequence coverage assigned to genomic intervals and computes log2 ratios of these values to the median of reference samples profiled using the same test configuration.Candidate CNVs are called when log2 ratios exceed user-defined thresholds.VisCap is a sensitive method for inferring CNVs from targeted sequence data from targeted gene panels.

View Article: PubMed Central - PubMed

Affiliation: Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.

ABSTRACT

Purpose: To develop and validate VisCap, a software program targeted to clinical laboratories for inference and visualization of germ-line copy-number variants (CNVs) from targeted next-generation sequencing data.

Methods: VisCap calculates the fraction of overall sequence coverage assigned to genomic intervals and computes log2 ratios of these values to the median of reference samples profiled using the same test configuration. Candidate CNVs are called when log2 ratios exceed user-defined thresholds.

Results: We optimized VisCap using 14 cases with known CNVs, followed by prospective analysis of 1,104 cases referred for diagnostic DNA sequencing. To verify calls in the prospective cohort, we used droplet digital polymerase chain reaction (PCR) to confirm 10/27 candidate CNVs and 72/72 copy-neutral genomic regions scored by VisCap. We also used a genome-wide bead array to confirm the absence of CNV calls across panels applied to 10 cases. To improve specificity, we instituted a visual scoring system that enabled experienced reviewers to differentiate true-positive from false-positive calls with minimal impact on laboratory workflow.

Conclusions: VisCap is a sensitive method for inferring CNVs from targeted sequence data from targeted gene panels. Visual scoring of data underlying CNV calls is a critical step to reduce false-positive calls for follow-up testing.Genet Med 18 7, 712-719.Genetics in Medicine (2016); 18 7, 712-719. doi:10.1038/gim.2015.156.

No MeSH data available.


Related in: MedlinePlus

Normalization of log2 ratios from probes on the X chromosome. Distribution of log2 ratios from probes across all samples in a sequencing batch. Upper panels depict log2 ratios from probes on the X chromosome before (panel a) and after (panel b) inference and correction for sex composition within the sequencing batch used as a reference set. Lower panels depict log2 ratios from all probes from all samples run on a panel, including a sample that failed automated QC (panel c, case H) and was removed for an iterative run. Each boxplot depicts a 5-number summary dependent on the interquartile multiplier (x) set in the configuration file: 1) lower whisker is the lowest value to exceed the Q1 - x times the interquartile range; 2) lower hinge is the first quartile value (Q1); 3) middle line is the median; 4) upper hinge is the third quartile (Q3) value; 5) upper whisker is the lowest value to exceed Q3 + x times the interquartile range.
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fig2: Normalization of log2 ratios from probes on the X chromosome. Distribution of log2 ratios from probes across all samples in a sequencing batch. Upper panels depict log2 ratios from probes on the X chromosome before (panel a) and after (panel b) inference and correction for sex composition within the sequencing batch used as a reference set. Lower panels depict log2 ratios from all probes from all samples run on a panel, including a sample that failed automated QC (panel c, case H) and was removed for an iterative run. Each boxplot depicts a 5-number summary dependent on the interquartile multiplier (x) set in the configuration file: 1) lower whisker is the lowest value to exceed the Q1 - x times the interquartile range; 2) lower hinge is the first quartile value (Q1); 3) middle line is the median; 4) upper hinge is the third quartile (Q3) value; 5) upper whisker is the lowest value to exceed Q3 + x times the interquartile range.

Mentions: The X-chromosome requires further normalization because there are significant fractional coverage differences between males and females. Depending on the balance of males and females in the batch, males may display single-copy loss of chromosome X or females may display a single-copy gain. These patterns are evident from the presence of two clusters of boxplots depicting fractional coverage values across targets on the X-chromosome for each sample (Figure 2). These clusters are detected computationally by removing outlier probes and then partitioning all samples around two medoids, a more robust alternative to K-means clustering.19 To enable consistent visualization of CNVs from male and females in the same batch, the log2 ratios for each sample are normalized toward zero through subtraction or addition of the cluster median. To facilitate review of this procedure, boxplots of log2 ratios are generated before and after subtraction/addition of the cluster medians (Figure 2a,b, respectively). The program also outputs predicted sex for each case as an additional source of quality control (QC) data.


VisCap: inference and visualization of germ-line copy-number variants from targeted clinical sequencing data.

Pugh TJ, Amr SS, Bowser MJ, Gowrisankar S, Hynes E, Mahanta LM, Rehm HL, Funke B, Lebo MS - Genet. Med. (2015)

Normalization of log2 ratios from probes on the X chromosome. Distribution of log2 ratios from probes across all samples in a sequencing batch. Upper panels depict log2 ratios from probes on the X chromosome before (panel a) and after (panel b) inference and correction for sex composition within the sequencing batch used as a reference set. Lower panels depict log2 ratios from all probes from all samples run on a panel, including a sample that failed automated QC (panel c, case H) and was removed for an iterative run. Each boxplot depicts a 5-number summary dependent on the interquartile multiplier (x) set in the configuration file: 1) lower whisker is the lowest value to exceed the Q1 - x times the interquartile range; 2) lower hinge is the first quartile value (Q1); 3) middle line is the median; 4) upper hinge is the third quartile (Q3) value; 5) upper whisker is the lowest value to exceed Q3 + x times the interquartile range.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940431&req=5

fig2: Normalization of log2 ratios from probes on the X chromosome. Distribution of log2 ratios from probes across all samples in a sequencing batch. Upper panels depict log2 ratios from probes on the X chromosome before (panel a) and after (panel b) inference and correction for sex composition within the sequencing batch used as a reference set. Lower panels depict log2 ratios from all probes from all samples run on a panel, including a sample that failed automated QC (panel c, case H) and was removed for an iterative run. Each boxplot depicts a 5-number summary dependent on the interquartile multiplier (x) set in the configuration file: 1) lower whisker is the lowest value to exceed the Q1 - x times the interquartile range; 2) lower hinge is the first quartile value (Q1); 3) middle line is the median; 4) upper hinge is the third quartile (Q3) value; 5) upper whisker is the lowest value to exceed Q3 + x times the interquartile range.
Mentions: The X-chromosome requires further normalization because there are significant fractional coverage differences between males and females. Depending on the balance of males and females in the batch, males may display single-copy loss of chromosome X or females may display a single-copy gain. These patterns are evident from the presence of two clusters of boxplots depicting fractional coverage values across targets on the X-chromosome for each sample (Figure 2). These clusters are detected computationally by removing outlier probes and then partitioning all samples around two medoids, a more robust alternative to K-means clustering.19 To enable consistent visualization of CNVs from male and females in the same batch, the log2 ratios for each sample are normalized toward zero through subtraction or addition of the cluster median. To facilitate review of this procedure, boxplots of log2 ratios are generated before and after subtraction/addition of the cluster medians (Figure 2a,b, respectively). The program also outputs predicted sex for each case as an additional source of quality control (QC) data.

Bottom Line: VisCap calculates the fraction of overall sequence coverage assigned to genomic intervals and computes log2 ratios of these values to the median of reference samples profiled using the same test configuration.Candidate CNVs are called when log2 ratios exceed user-defined thresholds.VisCap is a sensitive method for inferring CNVs from targeted sequence data from targeted gene panels.

View Article: PubMed Central - PubMed

Affiliation: Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.

ABSTRACT

Purpose: To develop and validate VisCap, a software program targeted to clinical laboratories for inference and visualization of germ-line copy-number variants (CNVs) from targeted next-generation sequencing data.

Methods: VisCap calculates the fraction of overall sequence coverage assigned to genomic intervals and computes log2 ratios of these values to the median of reference samples profiled using the same test configuration. Candidate CNVs are called when log2 ratios exceed user-defined thresholds.

Results: We optimized VisCap using 14 cases with known CNVs, followed by prospective analysis of 1,104 cases referred for diagnostic DNA sequencing. To verify calls in the prospective cohort, we used droplet digital polymerase chain reaction (PCR) to confirm 10/27 candidate CNVs and 72/72 copy-neutral genomic regions scored by VisCap. We also used a genome-wide bead array to confirm the absence of CNV calls across panels applied to 10 cases. To improve specificity, we instituted a visual scoring system that enabled experienced reviewers to differentiate true-positive from false-positive calls with minimal impact on laboratory workflow.

Conclusions: VisCap is a sensitive method for inferring CNVs from targeted sequence data from targeted gene panels. Visual scoring of data underlying CNV calls is a critical step to reduce false-positive calls for follow-up testing.Genet Med 18 7, 712-719.Genetics in Medicine (2016); 18 7, 712-719. doi:10.1038/gim.2015.156.

No MeSH data available.


Related in: MedlinePlus