Abstract
BackgroundRecently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors’ complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer.ResultsIn this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly.ConclusionsThe limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.
Highlights
Copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility
Real data Gene-based comparison The average sensitivity, specificity and false discovery rate (FDR) of the 5 copy number variation (CNV) detection tools on real breast cancer whole exome sequencing (WES) data are shown in Table 4 (The CNV results of the tools for the real samples are given in Additional files 2, 3, 4, 5 and 6)
ExomeCNV employs a minimum power/specificity parameter, and it makes a call on a specific exon if the desired power/ specificity is achieved by the coverage of that exon
Summary
Copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. CNV is one of the most important somatic aberrations in cancer [13, 17,18,19], since oncogene activation is often attributed to chromosomal copy number amplification, and tumor suppressor gene inactivation is often caused by either heterozygous deletion associated with mutation or by homozygous deletion. With the arrival of generation sequencing (NGS) technologies [22], sequence-based CNV detection has rapidly emerged as a viable option to identify CNVs with higher resolution and accuracy [14, 23, 24]. Recently whole-genome sequencing (WGS) and whole-exome sequencing (WES) have become primary strategies for NGS technologies in CNV detection and for studying of human diseases. Exome represents a highly function-enriched subset of the human genome, and CNVs in exome are more likely to be disease-causing aberrations than those in nongenic regions [28, 29]
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