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Motivation: Computational methods are essential to extract actionableinformation from raw sequencing data, and to thus fulfill the promise ofnext-generation sequencing technology. Unfortunately, computational toolsdeveloped to call variants from human sequencing data disagree on many of theirpredictions, and current methods to evaluate accuracy and computationalperformance are ad-hoc and incomplete. Agreement on benchmarking variantcalling methods would stimulate development of genomic processing tools andfacilitate communication among researchers.  Results: We propose SMaSH, a benchmarking methodology for evaluating humangenome variant calling algorithms. We generate synthetic datasets, organize andinterpret a wide range of existing benchmarking data for real genomes, andpropose a set of accuracy and computational performance metrics for evaluatingvariant calling methods on this benchmarking data. Moreover, we illustrate theutility of SMaSH to evaluate the performance of some leading single nucleotidepolymorphism (SNP), indel, and structural variant calling algorithms.  Availability: We provide free and open access online to the SMaSH toolkit,along with detailed documentation, at smash.cs.berkeley.edu.

SMaSH: A Benchmarking Toolkit for Human Genome Variant Calling