title: Genotyping common and rare variation using overlapping pool sequencing.
abstract: BACKGROUND
  Recent 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.
  
  
  RESULTS
  In 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.
  
  
  CONCLUSIONS
  Particularly, 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.
authors: Department of Computer Science, University of California, Los Angeles, CA 90095, USA.
authors: Dan
authors: D
authors: He
authors: Noah
authors: N
authors: Zaitlen
authors: Bogdan
authors: B
authors: Pasaniuc
authors: Eleazar
authors: E
authors: Eskin
authors: Eran
authors: E
authors: Halperin
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D005787:Gene Frequency; D050939:Gene Fusion; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D009369:Neoplasms / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D011336:Probability; D017422:Sequence Analysis, DNA