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The development of cost-effective next-generation sequencing methods has spurred the development of high-throughput bioinformatics tools for detection of sequence variation. With many disparate variant-calling algorithms available, investigators must ask, 'Which method is best for my data?' Machine learning research has shown that so-called ensemble methods that combine the output of multiple models can dramatically improve classifier performance. Here we describe a novel variant-calling approach based on an ensemble of variant-calling algorithms, which we term the Consensus Genotyper for Exome Sequencing (CGES). CGES uses a two-stage voting scheme among four algorithm implementations. While our ensemble method can accept variants generated by any variant-calling algorithm, we used GATK2.8, SAMtools, FreeBayes and Atlas-SNP2 in building CGES because of their performance, widespread adoption and diverse but complementary algorithms.

Consensus Genotyper for Exome Sequencing (CGES): improving the quality of exome variant genotypes