CIRCNV: Detection of CNVs Based on a Circular Profile of Read Depth from Sequencing Data
| Autor: | Yue-Hui Chen, Xi-Guo Yuan, Qi Li, Tian Ye, Haque A.K. Alvi, Hai-Yong Zhao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
tumor purity QH301-705.5 Biology Genome Article General Biochemistry Genetics and Molecular Biology DNA sequencing Structural variation 03 medical and health sciences 0302 clinical medicine polar coordinate transformation Copy-number variation Biology (General) local outlier factor Local outlier factor General Immunology and Microbiology business.industry Pattern recognition 030104 developmental biology Transformation (function) copy number variations 030220 oncology & carcinogenesis Outlier Human genome next-generation sequencing Artificial intelligence General Agricultural and Biological Sciences business |
| Zdroj: | Biology Volume 10 Issue 7 Biology, Vol 10, Iss 584, p 584 (2021) |
| ISSN: | 2079-7737 |
| DOI: | 10.3390/biology10070584 |
| Popis: | Copy number variation (CNV) is a common type of structural variation in the human genome. Accurate detection of CNVs from tumor genomes can provide crucial information for the study of tumor genesis and cancer precision diagnosis. However, the contamination of normal genomes in tumor genomes and the crude profiles of the read depth make such a task difficult. In this paper, we propose an alternative approach, called CIRCNV, for the detection of CNVs from sequencing data. CIRCNV is an extension of our previously developed method CNV-LOF, which uses local outlier factors to predict CNVs. Comparatively, CIRCNV can be performed on individual tumor samples and has the following two new features: (1) it transfers the read depth profile from a line shape to a circular shape via a polar coordinate transformation, in order to improve the efficiency of the read depth (RD) profile for the detection of CNVs and (2) it performs a second round of CNV declaration based on the truth circular RD profile, which is recovered by estimating tumor purity. We test and validate the performance of CIRCNV based on simulation and real sequencing data and perform comparisons with several peer methods. The results demonstrate that CIRCNV can obtain superior performance in terms of sensitivity and precision. We expect that our proposed method will be a supplement to existing methods and become a routine tool in the field of variation analysis of tumor genomes. |
| Databáze: | OpenAIRE |
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