ArticleGenetics2023
GBScleanR: robust genotyping error correction using a hidden Markov model with error pattern recognition.
Article in Genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Article
- Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species.BMC genomics · 2025Article
- Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations.PloS one · 2025Article
- Construction of relatedness matrices in autopolyploid populations using low-depth high-throughput sequencing data.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison.DNA research : an international journal for rapid publication of reports on genes and genomes · 2024Article
- vcferr: Development, validation, and application of a single nucleotide polymorphism genotyping error simulation framework.F1000Research · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Reduced-representation sequencing (RRS) provides cost-effective and time-saving genotyping platforms. Despite the outstanding advantage of RRS in throughput, the obtained genotype data usually contain a large number of errors. Several error correction methods employing the hidden Markov model (HMM) have been developed to overcome these issues. These methods assume that markers have a uniform error rate with no bias in the allele read ratio. However, bias does occur because of uneven amplification of genomic fragments and read mismapping. In this paper, we introduce an error correction tool, GBScleanR, which enables robust and precise error correction for noisy RRS-based genotype data by incorporating marker-specific error rates into the HMM. The results indicate that GBScleanR improves the accuracy by more than 25 percentage points at maximum compared to the existing tools in simulation data sets and achieves the most reliable genotype estimation in real data even with error-prone markers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.