TY - JOUR
T1 - EALPS
T2 - Estimating abundance levels in pooled sequencing using available genotyping data
AU - Eskin, Itamar
AU - Hormozdiari, Farhad
AU - Conde, Lucia
AU - Riby, Jacques
AU - Skibola, Christine F.
AU - Eskin, Eleazar
AU - Halperin, Eran
PY - 2013/11/1
Y1 - 2013/11/1
N2 - The recent advances in high-throughput sequencing technologies bring the potential of a better characterization of the genetic variation in humans and other organisms. In many occasions, either by design or by necessity, the sequencing procedure is performed on a pool of DNA samples with different abundances, where the abundance of each sample is unknown. Such a scenario is naturally occurring in the case of metagenomics analysis where a pool of bacteria is sequenced, or in the case of population studies involving DNA pools by design. Particularly, various pooling designs were recently suggested that can identify carriers of rare alleles in large cohorts, dramatically reducing the cost of such large-scale sequencing projects. A fundamental problem with such approaches for population studies is that the uncertainty of DNA proportions from different individuals in the pools might lead to spurious associations. Fortunately, it is often the case that the genotype data of at least some of the individuals in the pool is known. Here, we propose a method (eALPS) that uses the genotype data in conjunction with the pooled sequence data in order to accurately estimate the proportions of the samples in the pool, even in cases where not all individuals in the pool were genotyped (eALPS-LD). Using real data from a sequencing pooling study of non-Hodgkin's lymphoma, we demonstrate that the estimation of the proportions is crucial, since otherwise there is a risk for false discoveries. Additionally, we demonstrate that our approach is also applicable to the problem of quantification of species in metagenomics samples (eALPS-BCR) and is particularly suitable for metagenomic quantification of closely related species.
AB - The recent advances in high-throughput sequencing technologies bring the potential of a better characterization of the genetic variation in humans and other organisms. In many occasions, either by design or by necessity, the sequencing procedure is performed on a pool of DNA samples with different abundances, where the abundance of each sample is unknown. Such a scenario is naturally occurring in the case of metagenomics analysis where a pool of bacteria is sequenced, or in the case of population studies involving DNA pools by design. Particularly, various pooling designs were recently suggested that can identify carriers of rare alleles in large cohorts, dramatically reducing the cost of such large-scale sequencing projects. A fundamental problem with such approaches for population studies is that the uncertainty of DNA proportions from different individuals in the pools might lead to spurious associations. Fortunately, it is often the case that the genotype data of at least some of the individuals in the pool is known. Here, we propose a method (eALPS) that uses the genotype data in conjunction with the pooled sequence data in order to accurately estimate the proportions of the samples in the pool, even in cases where not all individuals in the pool were genotyped (eALPS-LD). Using real data from a sequencing pooling study of non-Hodgkin's lymphoma, we demonstrate that the estimation of the proportions is crucial, since otherwise there is a risk for false discoveries. Additionally, we demonstrate that our approach is also applicable to the problem of quantification of species in metagenomics samples (eALPS-BCR) and is particularly suitable for metagenomic quantification of closely related species.
KW - NP-completeness
KW - algorithms
KW - alignment
KW - cancer genomics
UR - http://www.scopus.com/inward/record.url?scp=84887415303&partnerID=8YFLogxK
U2 - 10.1089/cmb.2013.0105
DO - 10.1089/cmb.2013.0105
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AN - SCOPUS:84887415303
VL - 20
SP - 861
EP - 877
JO - Journal of Computational Biology
JF - Journal of Computational Biology
SN - 1066-5277
IS - 11
ER -