Abstract
Since the Copy Number Alterations (CNAs) are discovered to be tightly associated with cancers, accurately detecting them is an important task in the genomic structural variants research. Although a series of CNAs calling algorithms have been proposed and several evaluations made attempts to reveal their performance, their comparisons are still limited by the amount and type of experimental data and the conclusions have poor consensus. In this work, we use a large-scale real dataset from CAGEKID consortium to evaluate total 12 commonly used CNAs detection methods. This large-scale dataset comprises of SNP array data on 94 samples and the whole genome sequencing data on 10 samples. Twelve compared methods comprehensively cover the current CNAs detection scenarios, which include using SNP array data, sequencing data with tumor and normal matched samples and single tumor sample.
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Luo, F., Zhu, Y. (2018). Compare Copy Number Alterations Detection Methods on Real Cancer Data. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_57
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DOI: https://doi.org/10.1007/978-3-319-95930-6_57
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