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dc.contributor.authorLi, Xiao
dc.contributor.authorMuñoz, José F
dc.contributor.authorGade, Lalitha
dc.contributor.authorArgimon, Silvia
dc.contributor.authorBougnoux, Marie-Elisabeth
dc.contributor.authorBowers, Jolene R
dc.contributor.authorChow, Nancy A
dc.contributor.authorCuesta de la Plaza, Isabel 
dc.contributor.authorFarrer, Rhys A
dc.contributor.authorMaufrais, Corinne
dc.contributor.authorMonroy-Nieto, Juan
dc.contributor.authorPradhan, Dibyabhaba
dc.contributor.authorUehling, Jessie
dc.contributor.authorVu, Duong
dc.contributor.authorYeats, Corin A
dc.contributor.authorAanensen, David M
dc.contributor.authord'Enfert, Christophe
dc.contributor.authorEngelthaler, David M
dc.contributor.authorEyre, David W
dc.contributor.authorFisher, Matthew C
dc.contributor.authorHagen, Ferry
dc.contributor.authorMeyer, Wieland
dc.contributor.authorSingh, Gagandeep
dc.contributor.authorAlastruey-Izquierdo, Ana 
dc.contributor.authorLitvintseva, Anastasia P
dc.contributor.authorCuomo, Christina A
dc.date.accessioned2023-07-17T07:20:59Z
dc.date.available2023-07-17T07:20:59Z
dc.date.issued2023-04
dc.identifier.citationMicrob Genom. 2023 Apr;9(4):mgen000979.es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/16251
dc.description.abstractGenomic analyses are widely applied to epidemiological, population genetic and experimental studies of pathogenic fungi. A wide range of methods are employed to carry out these analyses, typically without including controls that gauge the accuracy of variant prediction. The importance of tracking outbreaks at a global scale has raised the urgency of establishing high-accuracy pipelines that generate consistent results between research groups. To evaluate currently employed methods for whole-genome variant detection and elaborate best practices for fungal pathogens, we compared how 14 independent variant calling pipelines performed across 35 Candida auris isolates from 4 distinct clades and evaluated the performance of variant calling, single-nucleotide polymorphism (SNP) counts and phylogenetic inference results. Although these pipelines used different variant callers and filtering criteria, we found high overall agreement of SNPs from each pipeline. This concordance correlated with site quality, as SNPs discovered by a few pipelines tended to show lower mapping quality scores and depth of coverage than those recovered by all pipelines. We observed that the major differences between pipelines were due to variation in read trimming strategies, SNP calling methods and parameters, and downstream filtration criteria. We calculated specificity and sensitivity for each pipeline by aligning three isolates with chromosomal level assemblies and found that the GATK-based pipelines were well balanced between these metrics. Selection of trimming methods had a greater impact on SAMtools-based pipelines than those using GATK. Phylogenetic trees inferred by each pipeline showed high consistency at the clade level, but there was more variability between isolates from a single outbreak, with pipelines that used more stringent cutoffs having lower resolution. This project generated two truth datasets useful for routine benchmarking of C. auris variant calling, a consensus VCF of genotypes discovered by 10 or more pipelines across these 35 diverse isolates and variants for 2 samples identified from whole-genome alignments. This study provides a foundation for evaluating SNP calling pipelines and developing best practices for future fungal genomic studies.es_ES
dc.description.sponsorshipThis project has been funded in part with US federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under award U19AI110818 to the Broad Institute. C.A.C. and M.C.F. are CIFAR fellows in the Fungal Kingdom Programme. C.d'E. is supported by the French Government’s Investissement d’Avenir programme [Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases [ANR10-LABX-62-IBEID)].es_ES
dc.language.isoenges_ES
dc.publisherMicrobiology Society es_ES
dc.type.hasVersionVoRes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCandidaes_ES
dc.subjectBenchmarkinges_ES
dc.subjectFungal genomicses_ES
dc.subjectVariant calling pipelineses_ES
dc.subjectWhole-genome sequencinges_ES
dc.subject.meshCandida aurises_ES
dc.subject.meshGenome, Fungal es_ES
dc.subject.meshPhylogeny es_ES
dc.subject.meshPolymorphism, Single Nucleotidees_ES
dc.subject.meshHumans es_ES
dc.subject.meshCandidiasis es_ES
dc.subject.meshDisease Outbreaks es_ES
dc.subject.meshDrug Resistance, Fungal es_ES
dc.titleComparing genomic variant identification protocols for Candida aurises_ES
dc.typeresearch articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID37043380es_ES
dc.format.volume9es_ES
dc.format.number4es_ES
dc.format.pagemgen000979es_ES
dc.identifier.doi10.1099/mgen.0.000979es_ES
dc.contributor.funderNIH - National Institute of Allergy and Infectious Diseases (NIAID) (Estados Unidos) es_ES
dc.contributor.funderUnited States Department of Health and Human Services es_ES
dc.contributor.funderBroad Institute es_ES
dc.contributor.funderCanadian Institute for Advanced Researches_ES
dc.contributor.funderAgence Nationale de la Recherche (Francia) es_ES
dc.description.peerreviewedes_ES
dc.identifier.e-issn2057-5858es_ES
dc.relation.publisherversionhttps://doi.org/10.1099/mgen.0.000979es_ES
dc.identifier.journalMicrobial genomicses_ES
dc.repisalud.centroISCIII::Centro Nacional de Microbiologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.rights.accessRightsopen accesses_ES


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Atribución 4.0 Internacional
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