ArticleNucleic acids research2012
A cross-sample statistical model for SNP detection in short-read sequencing data.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Estimating error models for whole genome sequencing using mixtures of Dirichlet-multinomial distributions.Bioinformatics (Oxford, England) · 2017Article
- MultiGeMS: detection of SNVs from multiple samples using model selection on high-throughput sequencing data.Bioinformatics (Oxford, England) · 2016Article
- VarBin, a novel method for classifying true and false positive variants in NGS data.BMC bioinformatics · 2013Article
- Accuracy of Next Generation Sequencing Platforms.Next generation, sequencing & applicationsArticle
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Authors and funding
8 authors.
Funding
Abstract
Highly multiplex DNA sequencers have greatly expanded our ability to survey human genomes for previously unknown single nucleotide polymorphisms (SNPs). However, sequencing and mapping errors, though rare, contribute substantially to the number of false discoveries in current SNP callers. We demonstrate that we can significantly reduce the number of false positive SNP calls by pooling information across samples. Although many studies prepare and sequence multiple samples with the same protocol, most existing SNP callers ignore cross-sample information. In contrast, we propose an empirical Bayes method that uses cross-sample information to learn the error properties of the data. This error information lets us call SNPs with a lower false discovery rate than existing methods.
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Registered trials
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