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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.

Show MeSH
A comparison of the conceptual steps in aCGH and CNV-seq methods. 1. Starting material in both cases is genomic fragments from two genomes. 2. In CNV-seq the fragments are samples and sequenced. 3. Genomic fragments are directly hybridized on to an array. In CNV-seq the mapping is performed by sequence alignment. 4. In microarray the light intensities reflect the number of hybridized fragments. In CNV-seq the number of mapped reads are counted directly. 5. Data analysis, including estimation of copy number ratios, confidence values, etc. 6. Output of the results.
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Figure 1: A comparison of the conceptual steps in aCGH and CNV-seq methods. 1. Starting material in both cases is genomic fragments from two genomes. 2. In CNV-seq the fragments are samples and sequenced. 3. Genomic fragments are directly hybridized on to an array. In CNV-seq the mapping is performed by sequence alignment. 4. In microarray the light intensities reflect the number of hybridized fragments. In CNV-seq the number of mapped reads are counted directly. 5. Data analysis, including estimation of copy number ratios, confidence values, etc. 6. Output of the results.

Mentions: We have developed a method to detect CNV by shotgun sequencing, CNV-seq. The method is based on a robust statistical model that allows confidence assessment of observed copy number ratios and is conceptually derived from aCGH (Figure 1). The microarray-based procedure, aCGH involves a whole genome microarray where two sets of labeled genomic fragments are hybridized. Instead of a microarray, CNV-seq uses a sequence as a template and two sets of shotgun reads, one set from each target individual, X and Y (Figure 1). The two sets of shotgun reads are mapped by sequence alignment on a template genome. We use a sliding window approach to analyze the mapped regions and CNVs are detected by computing the number of reads for each individual in each of the windows, yielding ratios. These observed ratios are assessed by the computation of a probability of a random occurrence, given no copy number variation.


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

Xie C, Tammi MT - BMC Bioinformatics (2009)

A comparison of the conceptual steps in aCGH and CNV-seq methods. 1. Starting material in both cases is genomic fragments from two genomes. 2. In CNV-seq the fragments are samples and sequenced. 3. Genomic fragments are directly hybridized on to an array. In CNV-seq the mapping is performed by sequence alignment. 4. In microarray the light intensities reflect the number of hybridized fragments. In CNV-seq the number of mapped reads are counted directly. 5. Data analysis, including estimation of copy number ratios, confidence values, etc. 6. Output of the results.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: A comparison of the conceptual steps in aCGH and CNV-seq methods. 1. Starting material in both cases is genomic fragments from two genomes. 2. In CNV-seq the fragments are samples and sequenced. 3. Genomic fragments are directly hybridized on to an array. In CNV-seq the mapping is performed by sequence alignment. 4. In microarray the light intensities reflect the number of hybridized fragments. In CNV-seq the number of mapped reads are counted directly. 5. Data analysis, including estimation of copy number ratios, confidence values, etc. 6. Output of the results.
Mentions: We have developed a method to detect CNV by shotgun sequencing, CNV-seq. The method is based on a robust statistical model that allows confidence assessment of observed copy number ratios and is conceptually derived from aCGH (Figure 1). The microarray-based procedure, aCGH involves a whole genome microarray where two sets of labeled genomic fragments are hybridized. Instead of a microarray, CNV-seq uses a sequence as a template and two sets of shotgun reads, one set from each target individual, X and Y (Figure 1). The two sets of shotgun reads are mapped by sequence alignment on a template genome. We use a sliding window approach to analyze the mapped regions and CNVs are detected by computing the number of reads for each individual in each of the windows, yielding ratios. These observed ratios are assessed by the computation of a probability of a random occurrence, given no copy number variation.

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