Titill: | popSTR2 enables clinical and population-scale genotyping of microsatellites |
Höfundur: |
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Útgáfa: | 2019-12-05 |
Tungumál: | Enska |
Umfang: | 2269-2271 |
Háskóli/Stofnun: | Reykjavík University (RU) Háskólinn í Reykjavík (HR) |
Svið: | Tæknisvið (HR) School of Technology (RU) |
Deild: | Verkfræðideild (HR) Department of Engineering (RU) |
Birtist í: | Bioinformatics;36(7) |
ISSN: | 1367-4803 1460-2059 |
DOI: | https://doi.org/10.1093/bioinformatics/btz913 |
Efnisorð: | Statistics and Probability; Computational Theory and Mathematics; Biochemistry; Molecular Biology; Computational Mathematics; Computer Science Applications; Líkindafræði; Tölfræði; Tölvunarfræði; Lífefnafræði; Sameindalíffræði |
URI: | https://hdl.handle.net/20.500.11815/2646 |
Tilvitnun:Kristmundsdottir, S., Eggertsson, H. P., Arnadottir, G. A. og Halldorsson, B. V. (2020). popSTR2 enables clinical and population-scale genotyping of microsatellites. Bioinformatics, 36(7), 2269–2271. https://doi.org/10.1093/bioinformatics/btz913
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Útdráttur:Summary: popSTR2 is an update and augmentation of our previous work ‘popSTR: a population-based microsatellite genotyper’. To make genotyping sensitive to inter-sample differences, we supply a kernel to estimate
sample-specific slippage rates. For clinical sequencing purposes, a panel of known pathogenic repeat expansions is
provided along with a script that scans and flags for manual inspection markers indicative of a pathogenic expansion. Like its predecessor, popSTR2 allows for joint genotyping of samples at a population scale. We now provide a
binning method that makes the microsatellite genotypes more amenable to analysis within standard association
pipelines and can increase association power.
Availability and implementation: https://github.com/DecodeGenetics/popSTR.
Contact: snaedisk@decode.is or bjarni.halldorsson@decode.is
Supplementary information: Supplementary data are available at Bioinformatics online.
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Leyfi:VC The Author(s) 2019. Published by Oxford University Press. 2269
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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