fastNGSadmix: admixture proportions and principal component analysis of a single NGS sample

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

fastNGSadmix : admixture proportions and principal component analysis of a single NGS sample. / Jørsboe, Emil; Hanghøj, Kristian Ebbesen; Albrechtsen, Anders.

In: Bioinformatics, Vol. 33, No. 19, 01.10.2017, p. 3148-3150.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Jørsboe, E, Hanghøj, KE & Albrechtsen, A 2017, 'fastNGSadmix: admixture proportions and principal component analysis of a single NGS sample', Bioinformatics, vol. 33, no. 19, pp. 3148-3150. https://doi.org/10.1093/bioinformatics/btx474

APA

Jørsboe, E., Hanghøj, K. E., & Albrechtsen, A. (2017). fastNGSadmix: admixture proportions and principal component analysis of a single NGS sample. Bioinformatics, 33(19), 3148-3150. https://doi.org/10.1093/bioinformatics/btx474

Vancouver

Jørsboe E, Hanghøj KE, Albrechtsen A. fastNGSadmix: admixture proportions and principal component analysis of a single NGS sample. Bioinformatics. 2017 Oct 1;33(19):3148-3150. https://doi.org/10.1093/bioinformatics/btx474

Author

Jørsboe, Emil ; Hanghøj, Kristian Ebbesen ; Albrechtsen, Anders. / fastNGSadmix : admixture proportions and principal component analysis of a single NGS sample. In: Bioinformatics. 2017 ; Vol. 33, No. 19. pp. 3148-3150.

Bibtex

@article{8f9959dd3b754a97971660253c255c83,
title = "fastNGSadmix: admixture proportions and principal component analysis of a single NGS sample",
abstract = "Motivation Estimation of admixture proportions and principal component analysis (PCA) are fundamental tools in populations genetics. However, applying these methods to low- or mid-depth sequencing data without taking genotype uncertainty into account can introduce biases. Results Here we present fastNGSadmix, a tool to fast and reliably estimate admixture proportions and perform PCA from next generation sequencing data of a single individual. The analyses are based on genotype likelihoods of the input sample and a set of predefined reference populations. The method has high accuracy, even at low sequencing depth and corrects for the biases introduced by small reference populations.",
author = "Emil J{\o}rsboe and Hangh{\o}j, {Kristian Ebbesen} and Anders Albrechtsen",
year = "2017",
month = oct,
day = "1",
doi = "10.1093/bioinformatics/btx474",
language = "English",
volume = "33",
pages = "3148--3150",
journal = "Computer Applications in the Biosciences",
issn = "1471-2105",
publisher = "Oxford University Press",
number = "19",

}

RIS

TY - JOUR

T1 - fastNGSadmix

T2 - admixture proportions and principal component analysis of a single NGS sample

AU - Jørsboe, Emil

AU - Hanghøj, Kristian Ebbesen

AU - Albrechtsen, Anders

PY - 2017/10/1

Y1 - 2017/10/1

N2 - Motivation Estimation of admixture proportions and principal component analysis (PCA) are fundamental tools in populations genetics. However, applying these methods to low- or mid-depth sequencing data without taking genotype uncertainty into account can introduce biases. Results Here we present fastNGSadmix, a tool to fast and reliably estimate admixture proportions and perform PCA from next generation sequencing data of a single individual. The analyses are based on genotype likelihoods of the input sample and a set of predefined reference populations. The method has high accuracy, even at low sequencing depth and corrects for the biases introduced by small reference populations.

AB - Motivation Estimation of admixture proportions and principal component analysis (PCA) are fundamental tools in populations genetics. However, applying these methods to low- or mid-depth sequencing data without taking genotype uncertainty into account can introduce biases. Results Here we present fastNGSadmix, a tool to fast and reliably estimate admixture proportions and perform PCA from next generation sequencing data of a single individual. The analyses are based on genotype likelihoods of the input sample and a set of predefined reference populations. The method has high accuracy, even at low sequencing depth and corrects for the biases introduced by small reference populations.

UR - http://www.scopus.com/inward/record.url?scp=85030685978&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btx474

DO - 10.1093/bioinformatics/btx474

M3 - Journal article

C2 - 28957500

AN - SCOPUS:85030685978

VL - 33

SP - 3148

EP - 3150

JO - Computer Applications in the Biosciences

JF - Computer Applications in the Biosciences

SN - 1471-2105

IS - 19

ER -

ID: 185476071