Comparing genomic variant identification protocols for Candida auris

Author:

Li Xiao1,Muñoz José F.1,Gade Lalitha2,Argimon Silvia3,Bougnoux Marie-Elisabeth45,Bowers Jolene R.6,Chow Nancy A.2,Cuesta Isabel7,Farrer Rhys A.81,Maufrais Corinne95,Monroy-Nieto Juan6,Pradhan Dibyabhaba10,Uehling Jessie11,Vu Duong12,Yeats Corin A.3,Aanensen David M.3,d’Enfert Christophe5,Engelthaler David M.6,Eyre David W.13,Fisher Matthew C.14,Hagen Ferry151612,Meyer Wieland17,Singh Gagandeep10,Alastruey-Izquierdo Ana7,Litvintseva Anastasia P.2,Cuomo Christina A.1ORCID

Affiliation:

1. Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA

2. Mycotic Diseases Branch, Centers for Disease Control and Prevention, US Department of Health and Human Services, Atlanta, GA, 30329, USA

3. Centre for Genomic Pathogen Surveillance, Big Data Institute, University of Oxford, Oxford, UK

4. Université Paris Cité, Hôpital Necker-Enfants-Malades, Unité de Parasitologie-Mycologie, Assistance Publique des Hôpitaux de Paris, Paris, France

5. Institut Pasteur, Université Paris Cité, INRAE, USC2019, Unité Biologie et Pathogénicité Fongiques, Paris, France

6. Translational Genomics Research Institute, Pathogen and Microbiome Division, Flagstaff, AZ 86005, USA

7. Mycology Reference Laboratory, National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain

8. Medical Research Council Centre for Medical Mycology, University of Exeter, Exeter, EX4 4PY, UK

9. Institut Pasteur, Université Paris Cité, CNRS USR 3756, Hub de Bioinformatique et Biostatistique, Paris, France

10. All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India

11. Botany and Plant Pathology, Oregon State University, Corvallis, OR 97330, USA

12. Westerdijk Fungal Biodiversity Institute, Uppsalalaan 8, 3584CT, Utrecht, Netherlands

13. NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK

14. MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK

15. Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, Netherlands

16. Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, Netherlands

17. Sydney Medical School, University of Sydney, Sydney, NSW 2050, Australia

Abstract

Genomic 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.

Funder

Division of Intramural Research, National Institute of Allergy and Infectious Diseases

Commissariat Général à l'Investissement

Publisher

Microbiology Society

Subject

General Medicine

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