Sequentia Biotech, Carrer de Valencia, Barcelona, Spain.
Departamento de Matemáticas, Escuela Técnica Superior de Ingeniería Industrial de Barcelona (ETSEIB), Universitat Politècnica de Catalunya (UPC), Diagonal 647, Barcelona, Spain.
BMC Bioinformatics. 2021 Oct 29;22(1):530. doi: 10.1186/s12859-021-04452-6.
Accurate copy number variant (CNV) detection is especially challenging for both targeted sequencing (TS) and whole-exome sequencing (WES) data. To maximize the performance, the parameters of the CNV calling algorithms should be optimized for each specific dataset. This requires obtaining validated CNV information using either multiplex ligation-dependent probe amplification (MLPA) or array comparative genomic hybridization (aCGH). They are gold standard but time-consuming and costly approaches.
We present isoCNV which optimizes the parameters of DECoN algorithm using only NGS data. The parameter optimization process is performed using an in silico CNV validated dataset obtained from the overlapping calls of three algorithms: CNVkit, panelcn.MOPS and DECoN. We evaluated the performance of our tool and showed that increases the sensitivity in both TS and WES real datasets.
isoCNV provides an easy-to-use pipeline to optimize DECoN that allows the detection of analysis-ready CNV from a set of DNA alignments obtained under the same conditions. It increases the sensitivity of DECoN without the need for orthogonal methods. isoCNV is available at https://gitlab.com/sequentiateampublic/isocnv .
靶向测序（TS）和全外显子组测序（WES）数据的精确拷贝数变异（CNV）检测特别具有挑战性。为了最大限度地提高性能，应该针对每个特定数据集优化 CNV 调用算法的参数。这需要使用多重连接依赖性探针扩增（MLPA）或阵列比较基因组杂交（aCGH）来获得经过验证的 CNV 信息。它们是金标准，但耗时且昂贵。
我们提出了 isoCNV，它仅使用 NGS 数据优化 DECoN 算法的参数。参数优化过程是使用通过三个算法的重叠调用获得的虚拟 CNV 验证数据集来执行的：CNVkit、panelcn.MOPS 和 DECoN。我们评估了我们的工具的性能，并表明它提高了 TS 和 WES 真实数据集的灵敏度。
isoCNV 提供了一个易于使用的管道来优化 DECoN，允许从在相同条件下获得的一组 DNA 比对中检测到可分析的 CNV。它在不需要正交方法的情况下提高了 DECoN 的灵敏度。isoCNV 可在 https://gitlab.com/sequentiateampublic/isocnv 获得。
