Reference genome assessment from a population scale perspective: an accurate profile of variability and noise.

TitleReference genome assessment from a population scale perspective: an accurate profile of variability and noise.
Publication TypeJournal Article
Year of Publication2017
AuthorsCarbonell-Caballero, J, Amadoz, A, Alonso, R, Hidalgo, MR, Cubuk, C, Conesa, D, López-Quílez, A, Dopazo, J
Date Published2017 Nov 15
KeywordsAnimals; Genetic Variation; Genome; Genomics; Genotype; Humans; Models, Statistical; Quality Control; Reproducibility of Results; Software

Motivation: 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.Results: 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.Availability and implementation: This tool is freely available at or information: Supplementary data are available at Bioinformatics online.

Alternate JournalBioinformatics
PubMed ID28961772
PubMed Central IDPMC5870781