Genotyping common and rare variation using overlapping pool sequencing

BMC Bioinformatics

Proceedings

Dan He¹  Eleazar Eskin¹  Bogdan Pasaniuc²  Noah Zaitlen²  Eran Halperin³ 

[1] Department of Computer Science, University of California, 90095, Los Angeles, CA, USA;Department of Epidemiology, Harvard School of Public Health, Harvard University, 02115, Boston, MA, USA;Program in Medical and Population Genetics, Broad Institute, 02142, Cambridge, MA, USA;The Blavatnik School of Computer Science, and the Molecular Microbiology and Biotechnology Department, Tel-Aviv University, 69978, Tel-Aviv, Israel;International Computer Science Institute, 1947 Center St., 94704, Berkeley, AC, USA;

关键词: Fusion Gene;    Read Pair;    Imputation Accuracy;    Decode Scheme;    Wellcome Trust Case Control Consortium;   

DOI  :  10.1186/1471-2105-12-S6-S2

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© He et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.