We validate SweeD via simulations and by scanning the first chromosome from the 1000 human Genomes project for selective sweeps. Thus, the ability to analyze substantially larger sample sizes by using SweeD leads to more accurate sweep detection. We show that an increase of sample size results in more precise detection of positive selection. Therefore, (in contrast to SweepFinder) the neutral site frequencies can optionally be directly calculated from a given demographic model. In addition, the user can specify various demographic models via the command-line to calculate their theoretically expected site frequency spectra. Furthermore, we implemented a checkpointing mechanism that allows to deploy SweeD on cluster systems with queue execution time restrictions, as well as to resume long-running analyses after processor failures. We also provide a parallel implementation of SweeD for multi-core processors. The sequential version of SweeD is up to 22 times faster than SweepFinder and, more importantly, is able to analyze thousands of sequences. It analyzes site frequency spectra and represents a substantial extension of the widely used SweepFinder program. We present SweeD (Sweep Detector), an open-source tool for the rapid detection of selective sweeps in whole genomes. However, the majority of these algorithms have not been designed for analyzing whole-genome data. In the last decade, a plethora of algorithms for identifying selective sweeps have been developed. Based on selective sweep theory, beneficial loci can be detected by examining the single nucleotide polymorphism patterns in intraspecific genome alignments. The advent of modern DNA sequencing technology is the driving force in obtaining complete intra-specific genomes that can be used to detect loci that have been subject to positive selection in the recent past.
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