Detecting single-nucleotide polymorphism (SNP) in pooled sequencing data is more challenging than in individual sequencing because of sampling variations across pools. To effectively differentiate SNP signal from sequencing error, appropriate estimation of the sequencing error is necessary. In this article, we propose an empirical Bayes mixture (EBM) model for SNP detection and allele frequency estimation in pooled sequencing data.