ArticleBMC bioinformatics2011
Genotyping common and rare variation using overlapping pool sequencing.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Using feedback in pooled experiments augmented with imputation for high genotyping accuracy at reduced cost.G3 (Bethesda, Md.) · 2025Article
- A joint use of pooling and imputation for genotyping SNPs.BMC bioinformatics · 2022Article
- Rare variant discovery and calling by sequencing pooled samples with overlaps.Bioinformatics (Oxford, England) · 2013Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundRecent advances in sequencing technologies set the stage for large, population based studies, in which the ANA or RNA of thousands of individuals will be sequenced. Currently, however, such studies are still infeasible using a straightforward sequencing approach; as a result, recently a few multiplexing schemes have been suggested, in which a small number of ANA pools are sequenced, and the results are then deconvoluted using compressed sensing or similar approaches. These methods, however, are limited to the detection of rare variants.
resultsIn this paper we provide a new algorithm for the deconvolution of DNA pools multiplexing schemes. The presented algorithm utilizes a likelihood model and linear programming. The approach allows for the addition of external data, particularly imputation data, resulting in a flexible environment that is suitable for different applications.
conclusionsParticularly, we demonstrate that both low and high allele frequency SNPs can be accurately genotyped when the DNA pooling scheme is performed in conjunction with microarray genotyping and imputation. Additionally, we demonstrate the use of our framework for the detection of cancer fusion genes from RNA sequences.
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Registered trials
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