ArticlePloS one2017
reGenotyper: Detecting mislabeled samples in genetic data.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A genomic approach for accurate identification of closely related species with next-generation sequencing samples.Scientific reports · 2026Article
- Reassessing Hybridisation in AustralianEcology and evolution · 2025Article
- The genetics of gene expression in a Caenorhabditis elegans multiparental recombinant inbred line population.G3 (Bethesda, Md.) · 2021Article
- Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation.Scientific reports · 2021Article
- iDEP Web Application for RNA-Seq Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Comparative analysis of transcriptomic profile, histology, and IDH mutation for classification of gliomas.Scientific reports · 2020Article
- The AncestralG3 (Bethesda, Md.) · 2020Article
- Dissecting the eQTL Micro-Architecture inFrontiers in genetics · 2020Article
- WormQTL2: an interactive platform for systems genetics in Caenorhabditis elegans.Database : the journal of biological databases and curation · 2020Article
- Assessment of kinship detection using RNA-seq data.Nucleic acids research · 2019Article
- Right data for right patient-a precisionFDA NCI-CPTAC Multi-omics Mislabeling Challenge.Nature medicine · 2018Article
- Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population.Frontiers in genetics · 2017Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In high-throughput molecular profiling studies, genotype labels can be wrongly assigned at various experimental steps; the resulting mislabeled samples seriously reduce the power to detect the genetic basis of phenotypic variation. We have developed an approach to detect potential mislabeling, recover the "ideal" genotype and identify "best-matched" labels for mislabeled samples. On average, we identified 4% of samples as mislabeled in eight published datasets, highlighting the necessity of applying a "data cleaning" step before standard data analysis.
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