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A novel workflow to improve multi-locus genotyping of wildlife species: an experimental set-up with a known model systemuse asterix (*) to get italics
Gillingham, Mark A. F., Montero, B. Karina, Wilhelm, Kerstin, Grudzus, Kara, Sommer, Simone and Santos, Pablo S. C.Please use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
2020
<p>Genotyping novel complex multigene systems is particularly challenging in non-model organisms. Target primers frequently amplify simultaneously multiple loci leading to high PCR and sequencing artefacts such as chimeras and allele amplification bias. Most next-generation sequencing genotyping pipelines have been validated in non-model systems whereby the real genotype is unknown and the generation of artefacts may be highly repeatable. Further hindering accurate genotyping, the relationship between artefacts and copy number variation (CNV) within a PCR remains poorly described. Here we investigate the latter by experimentally combining multiple known major histocompatibility complex (MHC) haplotypes of a model organism (chicken, Gallus gallus, 43 artificial genotypes with 2-13 alleles per amplicon). In addition to well defined “optimal” primers, we simulated a non-model species situation by designing “naive” primers, with sequence data from closely related Galliform species. We applied a novel open-source genotyping pipeline (ACACIA) to the data, and compared its performance with another, previously published, pipeline. ACACIA yielded very high allele calling accuracy (&gt;98%). Non-chimeric artefacts increased linearly with increasing CNV but chimeric artefacts leveled when amplifying more than 4-6 alleles. As expected, we found heterogeneous amplification efficiency of allelic variants when co-amplifying multiple loci. Using our validated ACACIA pipeline and the example data of this study, we discuss in detail the pitfalls researchers should avoid in order to reliably genotype complex multigene systems. ACACIA and the datasets used in this study are publicly available at GitLab and FigShare (https://gitlab.com/psc_santos/ACACIA and https://figshare.com/projects/ACACIA/66485).</p>
https://figshare.com/projects/ACACIA/66485You should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://
https://gitlab.com/psc_santos/ACACIAYou should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://
open-source genotyping pipeline, ACACIA, next generation sequencing, amplicon genotyping, allele dropout, PCR amplification bias, sequencing bias, multigene family, MHC
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Bioinformatics & Computational Biology, Evolutionary Ecology, Genome Evolution, Molecular Evolution
No need for them to be recommenders of PCIEvolBiol. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe [john@doe.com]
2019-05-15 17:30:44
François Rousset
Helena Westerdahl, Sebastian Ernesto Ramos-Onsins, Paul J. McMurdie , Arnaud Estoup, Vincent Segura, Jacek Radwan , Torbjørn Rognes , William Stutz , Kevin Vanneste , Thomas Bigot, Jill A. Hollenbach , Wieslaw Babik , Marie-Christine Le Paslier , A. Murat Eren , Miguel Alcaide , Morgan G. I. Langille , Melissa Gymrek