DistAngsd: Fast and Accurate Inference of Genetic Distances for Next-Generation Sequencing Data
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Dokumenter
- Fulltext
Forlagets udgivne version, 1,52 MB, PDF-dokument
Commonly used methods for inferring phylogenies were designed before the emergence of high-Throughput sequencing and can generally not accommodate the challenges associated with noisy, diploid sequencing data. In many applications, diploid genomes are still treated as haploid through the use of ambiguity characters; while the uncertainty in genotype calling-Arising as a consequence of the sequencing technology-is ignored. In order to address this problem, we describe two new probabilistic approaches for estimating genetic distances: distAngsd-geno and distAngsd-nuc, both implemented in a software suite named distAngsd. These methods are specifically designed for next-generation sequencing data, utilize the full information from the data, and take uncertainty in genotype calling into account. Through extensive simulations, we show that these new methods are markedly more accurate and have more stable statistical behaviors than other currently available methods for estimating genetic distances-even for very low depth data with high error rates.
Originalsprog | Engelsk |
---|---|
Artikelnummer | msac119 |
Tidsskrift | Molecular Biology and Evolution |
Vol/bind | 39 |
Udgave nummer | 6 |
Antal sider | 14 |
ISSN | 0737-4038 |
DOI | |
Status | Udgivet - 2022 |
Bibliografisk note
Funding Information:
was funded by a Carlsberg Foundation Young Researcher Fellowship awarded by the Carlsberg Foundation in 2019 (CF19-0712)
Publisher Copyright:
© 2022 The Author(s) 2022. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.
Antal downloads er baseret på statistik fra Google Scholar og www.ku.dk
ID: 315860700