A statistical approach to detection of copy number variations in PCR-enriched targeted sequencing data

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Demidov, German, 1990-ca
• dc.contributor.author Simakova, Tamaraca
• dc.contributor.author Vnuchkova, Juliaca
• dc.contributor.author Bragin, Antonca
• dc.date.accessioned 2017-01-12T11:18:56Z
• dc.date.available 2017-01-12T11:18:56Z
• dc.date.issued 2016ca
• dc.description.abstract Background: Multiplex polymerase chain reaction (PCR) is a common enrichment technique for targeted massive parallel sequencing (MPS) protocols. MPS is widely used in biomedical research and clinical diagnostics as the fast and accurate tool for the detection of short genetic variations. However, identification of larger variations such as structure variants and copy number variations (CNV) is still being a challenge for targeted MPS. Some approaches and tools for structural variants detection were proposed, but they have limitations and often require datasets of certain type, size and expected number of amplicons affected by CNVs. In the paper, we describe novel algorithm for high-resolution germinal CNV detection in the PCR-enriched targeted sequencing data and present accompanying tool. Results: We have developed a machine learning algorithm for the detection of large duplications and deletions in the targeted sequencing data generated with PCR-based enrichment step. We have performed verification studies and established the algorithm’s sensitivity and specificity. We have compared developed tool with other available methods applicable for the described data and revealed its higher performance. Conclusion: We showed that our method has high specificity and sensitivity for high-resolution copy number detection in targeted sequencing data using large cohort of samples.
• dc.format.mimetype application/pdfca
• dc.identifier.citation Demidov G, Simakova T, Vnuchkova J, Bragin A. A statistical approach to detection of copy number variations in PCR-enriched targeted sequencing data. BMC Bioinformatics. 2016; 17(1): 429. DOI: 10.1186/s12859-016-1272-6ca
• dc.identifier.doi http://dx.doi.org/10.1186/s12859-016-1272-6
• dc.identifier.issn 1471-2105ca
• dc.identifier.uri http://hdl.handle.net/10230/27875
• dc.language.iso engca
• dc.publisher BioMed Centralca
• dc.relation.ispartof BMC Bioinformatics. 2016; 17(1): 429
• dc.rights © The Author(s). 2016. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.ca
• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Machine learning
• dc.subject.keyword MPS
• dc.subject.keyword Germline CNV
• dc.subject.keyword Multiplex PCR
• dc.subject.keyword Targeted amplification
• dc.title A statistical approach to detection of copy number variations in PCR-enriched targeted sequencing dataca
• dc.type info:eu-repo/semantics/articleca
• dc.type.version info:eu-repo/semantics/publishedVersionca