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CNV-seq, a new method to detect copy number variation using high-throughput sequencing.

Xie C, Tammi MT - BMC Bioinformatics (2009)

Bottom Line: Our results show that the number of reads, not the length of the reads is the key factor determining the resolution of detection.Simulation of various sequencing methods with coverage between 0.1x to 8x show overall specificity between 91.7 - 99.9%, and sensitivity between 72.2 - 96.5%.We also show the results for assessment of CNV between two individual human genomes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences, National University of Singapore, Singapore. xie@nus.edu.sg

ABSTRACT

Background: DNA copy number variation (CNV) has been recognized as an important source of genetic variation. Array comparative genomic hybridization (aCGH) is commonly used for CNV detection, but the microarray platform has a number of inherent limitations.

Results: Here, we describe a method to detect copy number variation using shotgun sequencing, CNV-seq. The method is based on a robust statistical model that describes the complete analysis procedure and allows the computation of essential confidence values for detection of CNV. Our results show that the number of reads, not the length of the reads is the key factor determining the resolution of detection. This favors the next-generation sequencing methods that rapidly produce large amount of short reads.

Conclusion: Simulation of various sequencing methods with coverage between 0.1x to 8x show overall specificity between 91.7 - 99.9%, and sensitivity between 72.2 - 96.5%. We also show the results for assessment of CNV between two individual human genomes.

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Copy number variation between two human individuals. Copy number variation detected by CNV-seq using shotgun sequence data from two individuals, Venter and Watson. The top panel shows a genome level log2 ratio plot. The middle panel shows the plot for chromosome 10. The bottom panel shows detailed view of a CNV region in chromosome 10. The red color gradient in the middle and bottom sections represents log10 p calculated on each of ratios.
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Figure 5: Copy number variation between two human individuals. Copy number variation detected by CNV-seq using shotgun sequence data from two individuals, Venter and Watson. The top panel shows a genome level log2 ratio plot. The middle panel shows the plot for chromosome 10. The bottom panel shows detailed view of a CNV region in chromosome 10. The red color gradient in the middle and bottom sections represents log10 p calculated on each of ratios.

Mentions: The genomes of two individuals, Dr. Craig J. Venter and Dr. J. Watson were recently sequenced on 7.5× and 7.4× coverage respectively [21,24]. The genome of Dr. Craig J. Venter is sequenced using Sanger method and Dr. J. Watson's genome using 454 technology. We compared the two genomes using CNV-seq (Figure 5 and Additional File 1). The thresholds p' = 10-5 and log2(r') = 0.6 yield sliding window size of 26,481 bases for autosomal chromosomes. The sex chromosomes have a lower sequencing coverage than autosomal chromosomes, therefore larger window sizes are used: 72,044 bases for chromosome X and 269,032 bases for chromosome Y. We identified 174 contiguous regions covered by four or more consecutive windows. The sizes of these regions range from 66,202 bases to 941,612 bases.


CNV-seq, a new method to detect copy number variation using high-throughput sequencing.

Xie C, Tammi MT - BMC Bioinformatics (2009)

Copy number variation between two human individuals. Copy number variation detected by CNV-seq using shotgun sequence data from two individuals, Venter and Watson. The top panel shows a genome level log2 ratio plot. The middle panel shows the plot for chromosome 10. The bottom panel shows detailed view of a CNV region in chromosome 10. The red color gradient in the middle and bottom sections represents log10 p calculated on each of ratios.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2667514&req=5

Figure 5: Copy number variation between two human individuals. Copy number variation detected by CNV-seq using shotgun sequence data from two individuals, Venter and Watson. The top panel shows a genome level log2 ratio plot. The middle panel shows the plot for chromosome 10. The bottom panel shows detailed view of a CNV region in chromosome 10. The red color gradient in the middle and bottom sections represents log10 p calculated on each of ratios.
Mentions: The genomes of two individuals, Dr. Craig J. Venter and Dr. J. Watson were recently sequenced on 7.5× and 7.4× coverage respectively [21,24]. The genome of Dr. Craig J. Venter is sequenced using Sanger method and Dr. J. Watson's genome using 454 technology. We compared the two genomes using CNV-seq (Figure 5 and Additional File 1). The thresholds p' = 10-5 and log2(r') = 0.6 yield sliding window size of 26,481 bases for autosomal chromosomes. The sex chromosomes have a lower sequencing coverage than autosomal chromosomes, therefore larger window sizes are used: 72,044 bases for chromosome X and 269,032 bases for chromosome Y. We identified 174 contiguous regions covered by four or more consecutive windows. The sizes of these regions range from 66,202 bases to 941,612 bases.

Bottom Line: Our results show that the number of reads, not the length of the reads is the key factor determining the resolution of detection.Simulation of various sequencing methods with coverage between 0.1x to 8x show overall specificity between 91.7 - 99.9%, and sensitivity between 72.2 - 96.5%.We also show the results for assessment of CNV between two individual human genomes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences, National University of Singapore, Singapore. xie@nus.edu.sg

ABSTRACT

Background: DNA copy number variation (CNV) has been recognized as an important source of genetic variation. Array comparative genomic hybridization (aCGH) is commonly used for CNV detection, but the microarray platform has a number of inherent limitations.

Results: Here, we describe a method to detect copy number variation using shotgun sequencing, CNV-seq. The method is based on a robust statistical model that describes the complete analysis procedure and allows the computation of essential confidence values for detection of CNV. Our results show that the number of reads, not the length of the reads is the key factor determining the resolution of detection. This favors the next-generation sequencing methods that rapidly produce large amount of short reads.

Conclusion: Simulation of various sequencing methods with coverage between 0.1x to 8x show overall specificity between 91.7 - 99.9%, and sensitivity between 72.2 - 96.5%. We also show the results for assessment of CNV between two individual human genomes.

Show MeSH