English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/203109
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:

DC FieldValueLanguage
dc.contributor.authorGoig, Galo A.-
dc.contributor.authorBlanco, Silvia-
dc.contributor.authorGarcia-Basteiro, Alberto L.-
dc.contributor.authorComas, Iñaki-
dc.date.accessioned2020-03-08T07:01:09Z-
dc.date.available2020-03-08T07:01:09Z-
dc.date.issued2020-03-02-
dc.identifier.citationBMC Biology 18(1): 24 (2020)-
dc.identifier.urihttp://hdl.handle.net/10261/203109-
dc.description.abstract[Background] Contaminant DNA is a well-known confounding factor in molecular biology and in genomic repositories. Strikingly, analysis workflows for whole-genome sequencing (WGS) data commonly do not account for errors potentially introduced by contamination, which could lead to the wrong assessment of allele frequency both in basic and clinical research.-
dc.description.abstract[Results] We used a taxonomic filter to remove contaminant reads from more than 4000 bacterial samples from 20 different studies and performed a comprehensive evaluation of the extent and impact of contaminant DNA in WGS. We found that contamination is pervasive and can introduce large biases in variant analysis. We showed that these biases can result in hundreds of false positive and negative SNPs, even for samples with slight contamination. Studies investigating complex biological traits from sequencing data can be completely biased if contamination is neglected during the bioinformatic analysis, and we demonstrate that removing contaminant reads with a taxonomic classifier permits more accurate variant calling. We used both real and simulated data to evaluate and implement reliable, contamination-aware analysis pipelines.-
dc.description.abstract[Conclusion] As sequencing technologies consolidate as precision tools that are increasingly adopted in the research and clinical context, our results urge for the implementation of contamination-aware analysis pipelines. Taxonomic classifiers are a powerful tool to implement such pipelines.-
dc.description.sponsorshipThis work was funded by projects of the European Research Council (ERC) (638553-TB-ACCELERATE) and Ministerio de Economía y Competitividad (Spanish Government) research grant SAF2016-77346-R (to IC), SAF2017-92345-EXP (to IC), and BES-2014-071066 (to GAG).-
dc.publisherSpringer Nature-
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/638553-
dc.relationMINECO/ICTI2013-2016/SAF2016-77346-R-
dc.relationMICIU/ICTI2017-2020/SAF2017-92345-EXP-
dc.relationSAF2017-92345-EXP/AEI/10.13039/501100011033-
dc.relation.isversionofPublisher's version-
dc.titleContaminant DNA in bacterial sequencing experiments is a major source of false genetic variability-
dc.typeartículo-
dc.identifier.doi10.1186/s12915-020-0748-z-
dc.relation.publisherversionhttps://doi.org/10.1186/s12915-020-0748-z-
dc.identifier.e-issn1741-7007-
dc.date.updated2020-03-08T07:01:10Z-
dc.language.rfc3066en-
dc.rights.holderThe Author(s).-
dc.rights.licensehttp://creativecommons.org/licenses/by/4.0/-
dc.contributor.funderEuropean Research Council-
dc.contributor.funderMinisterio de Economía y Competitividad (España)-
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)-
dc.contributor.funderAgencia Estatal de Investigación (España)-
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000781es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100011033es_ES
Appears in Collections:(IBV) Artículos
Files in This Item:
File Description SizeFormat 
12915_2020_Article_748.pdf4,13 MBAdobe PDFThumbnail
View/Open
Show simple item record
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.