SNP calling by sequencing pooled samples

BMC Bioinformatics

Methodology Article

Anna Esteve-Codina¹  Luca Ferretti¹  Bruno Nevado¹  Miguel Pérez-Enciso²  Emanuele Raineri³  Simon Heath³ 

[1] Centre for Research in Agricultural Genomics (CRAG), Universitat Autònonoma de Barcelona, 08193, Bellaterra, Spain;Centre for Research in Agricultural Genomics (CRAG), Universitat Autònonoma de Barcelona, 08193, Bellaterra, Spain;Institut Català de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, 08010, Barcelona, Spain;Centro Nacional de Análisis Genómico (CNAG), Parc Científic de Barcelona, 08028, Barcelona, Spain;

关键词: False Discovery Rate;    Minor Allele Frequency;    Sequencing Error;    Rare Allele;    Read Depth;   

DOI  :  10.1186/1471-2105-13-239

 received in 2011-11-21, accepted in 2012-09-06,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundPerforming high throughput sequencing on samples pooled from different individuals is a strategy to characterize genetic variability at a small fraction of the cost required for individual sequencing. In certain circumstances some variability estimators have even lower variance than those obtained with individual sequencing. SNP calling and estimating the frequency of the minor allele from pooled samples, though, is a subtle exercise for at least three reasons. First, sequencing errors may have a much larger relevance than in individual SNP calling: while their impact in individual sequencing can be reduced by setting a restriction on a minimum number of reads per allele, this would have a strong and undesired effect in pools because it is unlikely that alleles at low frequency in the pool will be read many times. Second, the prior allele frequency for heterozygous sites in individuals is usually 0.5 (assuming one is not analyzing sequences coming from, e.g. cancer tissues), but this is not true in pools: in fact, under the standard neutral model, singletons (i.e. alleles of minimum frequency) are the most common class of variants because P(f) ∝ 1/f and they occur more often as the sample size increases. Third, an allele appearing only once in the reads from a pool does not necessarily correspond to a singleton in the set of individuals making up the pool, and vice versa, there can be more than one read – or, more likely, none – from a true singleton.ResultsTo improve upon existing theory and software packages, we have developed a Bayesian approach for minor allele frequency (MAF) computation and SNP calling in pools (and implemented it in a program called snape): the approach takes into account sequencing errors and allows users to choose different priors. We also set up a pipeline which can simulate the coalescence process giving rise to the SNPs, the pooling procedure and the sequencing. We used it to compare the performance of snape to that of other packages.ConclusionsWe present a software which helps in calling SNPs in pooled samples: it has good power while retaining a low false discovery rate (FDR). The method also provides the posterior probability that a SNP is segregating and the full posterior distribution of f for every SNP. In order to test the behaviour of our software, we generated (through simulated coalescence) artificial genomes and computed the effect of a pooled sequencing protocol, followed by SNP calling. In this setting, snape has better power and False Discovery Rate (FDR) than the comparable packages samtools, PoPoolation, Varscan : for N = 50 chromosomes, snape has power ≈ 35% and FDR ≈ 2.5%. snape is available athttp://code.google.com/p/snape-pooled/ (source code and precompiled binaries).

【 授权许可】

CC BY   
© Raineri et al.; licensee BioMed Central Ltd. 2012