Informed and automated k-mer size selection for genome assembly

By Rayan Chikhi, Paul Medvedev. Bioinform., 2013

726 citations recorded by the source corpus.

MOTIVATION Genome assembly tools based on the de Bruijn graph framework rely on a parameter k, which represents a trade-off between several competing effects that are difficult to quantify. There is currently a lack of tools that would automatically estimate the best k to use and/or quickly generate histograms of k-mer abundances that would allow the user to make an informed decision. RESULTS We develop a fast and accurate sampling method that constructs approximate abundance histograms with several orders of magnitude performance improvement over traditional methods. We then present a fast heuristic that uses the generated abundance histograms for putative k values to estimate the best possible value of k. We test the effectiveness of our tool using diverse sequencing datasets and find that its choice of k leads to some of the best assemblies. AVAILABILITY Our tool KmerGenie is freely available at: http://kmergenie.bx.psu.edu/.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dada61b80069b6d22815089421b6cb18402336dc

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Citations in the corpus

GenomeScope: Fast reference-free genome profiling from short reads (2016, methodology): “A few computational approaches are now available for estimating the genome size from unassembled sequencing reads (Chikhi and Medvedev, 2014; Melsted and Halldorsson, 2014) and some genome assemblers internally compute related statistics to guide the algorithm (Bankevich et al.”
Using SPAdes De Novo Assembler (2020, methodology): “Also, the tools that do automatic selection of a single k -mer length, like KmerGenie (Chikhi & Medvedev, 2013) are not designed to be used with multi k -mer assemblers like SPAdes, and therefore their use is not advised.”
GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes (2020, methodology): “In the context of diploid organisms, several computational approaches have been developed to estimate genome characteristics directly from unassembled sequencing reads, including genome size and heterozygosity 14 – 16 or repetitiveness and heterozygosity 17 .”