Highly accurate protein structure prediction with AlphaFold Jumper et al. Nature volume 596, pages 583–589 (2021)
Abstract
Abstract
Motivation: Genome assembly tools based on the de Bruijn graph framework rely on a parameter k, which represents a trade-off be- tween 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 con- structs approximate abundance histograms with several orders of magnitude performance improvement over traditional methods. We then present a fast heuristic that uses the generated abundance histo- grams for putative k values to estimate the best possible value of k. We test the effectiveness of our tool using diverse sequencing data- sets 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.
