isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data
Abstract Background 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 o...
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| Format: | Article |
| Language: | English |
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BMC
2021-10-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-021-04452-6 |
| _version_ | 1818935315491979264 |
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| author | Rosa Barcelona-Cabeza Walter Sanseverino Riccardo Aiese Cigliano |
| author_facet | Rosa Barcelona-Cabeza Walter Sanseverino Riccardo Aiese Cigliano |
| author_sort | Rosa Barcelona-Cabeza |
| collection | DOAJ |
| description | Abstract Background 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. Results 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. Conclusions 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 . |
| first_indexed | 2024-12-20T05:18:13Z |
| format | Article |
| id | doaj.art-a0c45ff336f24384b42e8d318dc7242c |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-20T05:18:13Z |
| publishDate | 2021-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a0c45ff336f24384b42e8d318dc7242c2022-12-21T19:52:07ZengBMCBMC Bioinformatics1471-21052021-10-0122111310.1186/s12859-021-04452-6isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing dataRosa Barcelona-Cabeza0Walter Sanseverino1Riccardo Aiese Cigliano2Sequentia BiotechSequentia BiotechSequentia BiotechAbstract Background 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. Results 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. Conclusions 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 .https://doi.org/10.1186/s12859-021-04452-6Copy number variantsCNVOptimizationNGSWESTS |
| spellingShingle | Rosa Barcelona-Cabeza Walter Sanseverino Riccardo Aiese Cigliano isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data BMC Bioinformatics Copy number variants CNV Optimization NGS WES TS |
| title | isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data |
| title_full | isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data |
| title_fullStr | isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data |
| title_full_unstemmed | isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data |
| title_short | isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data |
| title_sort | isocnv in silico optimization of copy number variant detection from targeted or exome sequencing data |
| topic | Copy number variants CNV Optimization NGS WES TS |
| url | https://doi.org/10.1186/s12859-021-04452-6 |
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