ExCNVSS: A Noise-Robust Method for Copy Number Variation Detection in Whole Exome Sequencing Data
| العنوان: | ExCNVSS: A Noise-Robust Method for Copy Number Variation Detection in Whole Exome Sequencing Data |
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| المؤلفون: | Jinhwa Kong, Keonbae Lee, Jung-Im Won, Unjoo Lee, Jeehee Yoon, Jaemoon Shin |
| المصدر: | BioMed Research International BIOMED RESEARCH INTERNATIONAL BioMed Research International, Vol 2017 (2017) |
| بيانات النشر: | Hindawi Limited, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 0301 basic medicine, Article Subject, DNA Copy Number Variations, lcsh:Medicine, Biology, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Exome, Copy-number variation, International HapMap Project, Exome sequencing, Genetics, Models, Genetic, General Immunology and Microbiology, business.industry, Matched control, lcsh:R, High-Throughput Nucleotide Sequencing, Pattern recognition, General Medicine, 030104 developmental biology, Simulated data, Artificial intelligence, Noise (video), business, Research Article, Test data |
| الوصف: | Copy number variations (CNVs) are structural variants associated with human diseases. Recent studies verified that disease-related genes are based on the extraction of rare de novo and transmitted CNVs from exome sequencing data. The need for more efficient and accurate methods has increased, which still remains a challenging problem due to coverage biases, as well as the sparse, small-sized, and noncontinuous nature of exome sequencing. In this study, we developed a new CNV detection method, ExCNVSS, based on read coverage depth evaluation and scale-space filtering to resolve these problems. We also developed the method ExCNVSS_noRatio, which is a version of ExCNVSS, for applying to cases with an input of test data only without the need to consider the availability of a matched control. To evaluate the performance of our method, we tested it with 11 different simulated data sets and 10 real HapMap samples’ data. The results demonstrated that ExCNVSS outperformed three other state-of-the-art methods and that our method corrected for coverage biases and detected all-sized CNVs even without matched control data. |
| وصف الملف: | text/xhtml |
| تدمد: | 2314-6141 2314-6133 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56190cefe34f0bab0229ff7eeab80a26 https://doi.org/10.1155/2017/9631282 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....56190cefe34f0bab0229ff7eeab80a26 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23146141 23146133 |
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