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. 2010 Feb;20(2):273-80.
doi: 10.1101/gr.096388.109. Epub 2009 Dec 17.

A SNP discovery method to assess variant allele probability from next-generation resequencing data

Yufeng Shen  1 , Zhengzheng Wan, Cristian Coarfa, Rafal Drabek, Lei Chen, Elizabeth A Ostrowski, Yue Liu, George M Weinstock, David A Wheeler, Richard A Gibbs, Fuli Yu
Affiliations

A SNP discovery method to assess variant allele probability from next-generation resequencing data

Yufeng Shen et al. Genome Res. 2010 Feb.

Abstract

Accurate identification of genetic variants from next-generation sequencing (NGS) data is essential for immediate large-scale genomic endeavors such as the 1000 Genomes Project, and is crucial for further genetic analysis based on the discoveries. The key challenge in single nucleotide polymorphism (SNP) discovery is to distinguish true individual variants (occurring at a low frequency) from sequencing errors (often occurring at frequencies orders of magnitude higher). Therefore, knowledge of the error probabilities of base calls is essential. We have developed Atlas-SNP2, a computational tool that detects and accounts for systematic sequencing errors caused by context-related variables in a logistic regression model learned from training data sets. Subsequently, it estimates the posterior error probability for each substitution through a Bayesian formula that integrates prior knowledge of the overall sequencing error probability and the estimated SNP rate with the results from the logistic regression model for the given substitutions. The estimated posterior SNP probability can be used to distinguish true SNPs from sequencing errors. Validation results show that Atlas-SNP2 achieves a false-positive rate of lower than 10%, with an approximately 5% or lower false-negative rate.

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Figures

Figure 1.
Figure 1.
The overall workflow of the Atlas-SNP2 package. The reference genomic sequence and reads undergo an initial data processing step, whereby the reference sequence is split into smaller pieces and the reads into smaller batches. A combined BLAT and Cross_Match analysis was used to anchor and align reads back to the reference positions. All of the single nucleotide mismatches are parsed and assessed for their probabilities of being SNPs using the Atlas-SNP2 core statistical methods.
Figure 2.
Figure 2.
An illustration of the mapped reads at positions found with single base substitutions. (Blue) Reads with the reference alleles (the bases match those of the reference genomic sequence); (yellow) the variant alleles (that are the mismatches). With a reasonable average sequencing coverage, true SNPs are likely to be covered with more variant reads than false positives caused by sequencing errors.
Figure 3.
Figure 3.
The validation results in S. aureus data with at least three variant reads when three different sets of priors were used for tuning purposes. We used three sets of priors (Supplemental Table S2) in Equation 5 for SNP probability assessment. (A) The false-positive rate (FP) and false-negative rate (FN) can be evaluated using our defined SNPs and errors (described in Methods). The results indicate that a 10% false-positive rate and a 5% false-negative rate can be achieved when using either the “set 1” or the “set 2” parameters, while “set 1” enables a smoother resolution. (B) The FP/[FP + true-positives (TP)] is plotted against the posterior SNP probability cutoff for results obtained using “set 1” priors.
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