ArticleBioinformatics (Oxford, England)2017
Estimating error models for whole genome sequencing using mixtures of Dirichlet-multinomial distributions.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Statistical modeling of immunoprecipitation efficiency of MeRIP-seq data enabled accurate detection and quantification of epitranscriptome.Computational and structural biotechnology journal · 2025Article
- sccomp: Robust differential composition and variability analysis for single-cell data.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- SUP: a probabilistic framework to propagate genome sequence uncertainty, with applications.NAR genomics and bioinformatics · 2023Article
- Sampling Variation of RAD-Seq Data from Diploid and Tetraploid Potato (Plants (Basel, Switzerland) · 2021Article
- The presence and impact of reference bias on population genomic studies of prehistoric human populations.PLoS genetics · 2019Article
- HaploJuice : accurate haplotype assembly from a pool of sequences with known relative concentrations.BMC bioinformatics · 2018Article
- Haplosaurus computes protein haplotypes for use in precision drug design.Nature communications · 2018Article
- accuMUlate: a mutation caller designed for mutation accumulation experiments.Bioinformatics (Oxford, England) · 2018Article
- Article
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Authors and funding
5 authors.
Funding
Abstract
motivationAccurate identification of genotypes is an essential part of the analysis of genomic data, including in identification of sequence polymorphisms, linking mutations with disease and determining mutation rates. Biological and technical processes that adversely affect genotyping include copy-number-variation, paralogous sequences, library preparation, sequencing error and reference-mapping biases, among others.
resultsWe modeled the read depth for all data as a mixture of Dirichlet-multinomial distributions, resulting in significant improvements over previously used models. In most cases the best model was comprised of two distributions. The major-component distribution is similar to a binomial distribution with low error and low reference bias. The minor-component distribution is overdispersed with higher error and reference bias. We also found that sites fitting the minor component are enriched for copy number variants and low complexity regions, which can produce erroneous genotype calls. By removing sites that do not fit the major component, we can improve the accuracy of genotype calls. AVAILABILITY AND IMPLEMENTATION: Methods and data files are available at https://github.com/CartwrightLab/WuEtAl2017/ (doi:10.5281/zenodo.256858). CONTACT: cartwright@asu.edu. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.
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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.