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Conference Paper: Comparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data

TitleComparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data
Authors
KeywordsCancer genome
Ion torrent deep sequencing
Somatic variant calling
Methods evaluation
Read depth
Issue Date2019
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcmedgenomics/
Citation
Proceedings of the 30th Joint International Conference on Genome Informatics (GIW) & Australian Bioinformatics and Computational Biology Society (ABACBS) Annual Conference: medical genomics, Sydney, Australia, 9-11 December 2019. In BMC Medical Genomics, 2019, v. 12 n. suppl. 9, p. article no. 181 How to Cite?
AbstractBackground: The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only samples. Most of these methods have been developed and evaluated for the identification of somatic variation using Illumina sequencing datasets of moderate coverage. However, a comprehensive evaluation of somatic variant detection algorithms on Ion Torrent targeted deep sequencing data has not been performed. Methods: We have applied three somatic detection algorithms, Torrent Variant Caller, MuTect2 and VarScan2, on a large cohort of ovarian cancer patients comprising of 208 paired tumour-blood samples and 253 tumour-only samples sequenced deeply on Ion Torrent Proton platform across 330 amplicons. Subsequently, the concordance and performance of the three somatic variant callers were assessed. Results: We have observed low concordance across the algorithms with only 0.5% of SNV and 0.02% of INDEL calls in common across all three methods. The intersection of all methods showed better performance when assessed using correlation with known mutational signatures, overlap with COSMIC variation and by examining the variant characteristics. The Torrent Variant Caller also performed well with the advantage of not eliminating a high number of variants that could lead to high type II error. Conclusions: Our results suggest that caution should be taken when applying state-of-the-art somatic variant algorithms to Ion Torrent targeted deep sequencing data. Better quality control procedures and strategies that combine results from multiple methods should ensure that higher accuracy is achieved. This is essential to ensure that results from bioinformatics pipelines using Ion Torrent deep sequencing can be robustly applied in cancer research and in the clinic.
Persistent Identifierhttp://hdl.handle.net/10722/281996
ISSN
2023 Impact Factor: 2.1
2023 SCImago Journal Rankings: 0.703
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Q-
dc.contributor.authorKotoula, V-
dc.contributor.authorHsu, P-C-
dc.contributor.authorPapadopoulou, K-
dc.contributor.authorHo, JWK-
dc.contributor.authorFountzilas, G-
dc.contributor.authorGiannoulatou, E-
dc.date.accessioned2020-04-19T03:33:54Z-
dc.date.available2020-04-19T03:33:54Z-
dc.date.issued2019-
dc.identifier.citationProceedings of the 30th Joint International Conference on Genome Informatics (GIW) & Australian Bioinformatics and Computational Biology Society (ABACBS) Annual Conference: medical genomics, Sydney, Australia, 9-11 December 2019. In BMC Medical Genomics, 2019, v. 12 n. suppl. 9, p. article no. 181-
dc.identifier.issn1755-8794-
dc.identifier.urihttp://hdl.handle.net/10722/281996-
dc.description.abstractBackground: The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only samples. Most of these methods have been developed and evaluated for the identification of somatic variation using Illumina sequencing datasets of moderate coverage. However, a comprehensive evaluation of somatic variant detection algorithms on Ion Torrent targeted deep sequencing data has not been performed. Methods: We have applied three somatic detection algorithms, Torrent Variant Caller, MuTect2 and VarScan2, on a large cohort of ovarian cancer patients comprising of 208 paired tumour-blood samples and 253 tumour-only samples sequenced deeply on Ion Torrent Proton platform across 330 amplicons. Subsequently, the concordance and performance of the three somatic variant callers were assessed. Results: We have observed low concordance across the algorithms with only 0.5% of SNV and 0.02% of INDEL calls in common across all three methods. The intersection of all methods showed better performance when assessed using correlation with known mutational signatures, overlap with COSMIC variation and by examining the variant characteristics. The Torrent Variant Caller also performed well with the advantage of not eliminating a high number of variants that could lead to high type II error. Conclusions: Our results suggest that caution should be taken when applying state-of-the-art somatic variant algorithms to Ion Torrent targeted deep sequencing data. Better quality control procedures and strategies that combine results from multiple methods should ensure that higher accuracy is achieved. This is essential to ensure that results from bioinformatics pipelines using Ion Torrent deep sequencing can be robustly applied in cancer research and in the clinic.-
dc.languageeng-
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcmedgenomics/-
dc.relation.ispartofBMC Medical Genomics-
dc.rightsBMC Medical Genomics. Copyright © BioMed Central Ltd.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCancer genome-
dc.subjectIon torrent deep sequencing-
dc.subjectSomatic variant calling-
dc.subjectMethods evaluation-
dc.subjectRead depth-
dc.titleComparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data-
dc.typeConference_Paper-
dc.identifier.emailHo, JWK: jwkho@hku.hk-
dc.identifier.authorityHo, JWK=rp02436-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s12920-019-0636-y-
dc.identifier.pmid31874647-
dc.identifier.pmcidPMC6929331-
dc.identifier.scopuseid_2-s2.0-85077059201-
dc.identifier.hkuros309734-
dc.identifier.volume12-
dc.identifier.issuesuppl. 9-
dc.identifier.spagearticle no. 181-
dc.identifier.epagearticle no. 181-
dc.identifier.isiWOS:000505257300002-
dc.publisher.placeUnited Kingdom-
dc.customcontrol.immutablecsl 200421-
dc.identifier.issnl1755-8794-

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