DescriptionKmerGenie estimates the best k-mer length for genome de novo assembly. Given a set of reads, KmerGenie first computes the k-mer abundance histogram for many values of k. Then, for each value of k, it predicts the number of distinct genomic k-mers in the dataset, and returns the k-mer length which maximizes this number. Experiments show that KmerGenie's choices lead to assemblies that are close to the best possible over all k-mer lengths. KmerGenie predictions can be applied to single-k genome assemblers (e.g. Velvet, SOAPdenovo 2, ABySS, Minia). However, multi-k genome assemblers (e.g. SPAdes, IDBA) generally perform better with default parameters (using multiple k values), rather than the single best k predicted by KmerGenie.
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