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Background: Copy number variations (CNVs) are the main genetic structural variations in cancer genome. Detecting CNVs in genetic exome region is efficient and cost-effective in identifying cancer associated genes. Many tools had been developed accordingly and yet these tools lack of reliability because of high false negative rate, which is intrinsically caused by genome exonic bias. Results: To provide an alternative option, here, we report Anaconda, a comprehensive pipeline that allows flexible integration of multiple CNV-calling methods and systematic annotation of CNVs in analyzing WES data. Just by one command, Anaconda can generate CNV detection result by up to four CNV detecting tools. Associated with comprehensive annotation analysis of genes involved in shared CNV regions, Anaconda is able to deliver a more reliable and useful report in assistance with CNV-associate cancer researches. Conclusion: Anaconda package and manual can be freely accessed at http://mcg.ustc.edu.cn/bsc/ANACONDA/.
Gao, J., Wan, C., Zhang, H., Li, A., Zang, Q., Ban, R., … Zhang, Y. (2017). Anaconda: AN automated pipeline for somatic COpy Number variation Detection and Annotation from tumor exome sequencing data. BMC Bioinformatics, 18(1). https://doi.org/10.1186/s12859-017-1833-3