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Title: Reference genome assessment from a population scale perspective: an accurate profile of variability and noise.
Authors: Carbonell-Caballero, José
Amadoz, Alicia
Alonso, Roberto
Hidalgo, Marta R
Çubuk, Cankut
Conesa, David
López-Quílez, Antonio
Dopazo, Joaquín
metadata.dc.subject.mesh: Animals
Genetic Variation
Models, Statistical
Quality Control
Reproducibility of Results
Issue Date: 2017
Abstract: Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome. The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples. This tool is freely available at or Supplementary data are available at Bioinformatics online.
metadata.dc.identifier.doi: 10.1093/bioinformatics/btx482
Appears in Collections:Producción 2020

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