eALPS: Estimating abundance levels in pooled sequencing using available genotyping data

Itamar Eskin, Farhad Hormozdiari*, Lucia Conde, Jacques Riby, Chris Skibola, Eleazar Eskin, Eran Halperin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 uncertainly 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.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 17th Annual International Conference, RECOMB 2013, Proceedings
Pages32-44
Number of pages13
DOIs
StatePublished - 2013
Event17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013 - Beijing, China
Duration: 7 Apr 201310 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7821 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013
Country/TerritoryChina
CityBeijing
Period7/04/1310/04/13

Keywords

  • Expectation-Maximization
  • Metagenomics
  • Pooling
  • Relative Abundance

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