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 </hidden>Link to [[https://www.nature.com/articles/s41586-021-03819-2|PDF]]</WRAP> </hidden>Link to [[https://www.nature.com/articles/s41586-021-03819-2|PDF]]</WRAP>
 {{ :asa:seminar:2024:flash:videoasa_juelidekosar_reemmajjani.mp4 |}} {{ :asa:seminar:2024:flash:videoasa_juelidekosar_reemmajjani.mp4 |}}
-**Team:** Juli Kosar and Reem Majjani+**Team:** Jüli Kosar and Reem Majjani
 </WRAP> </WRAP>
 <WRAP round box>Informed and automated k-mer size selection for genome assembly. Chikhi et al. Bioinformatics 2014, 30(1):31-7 <WRAP><hidden 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|http://kmergenie.bx.psu.edu]]. </hidden> Link to {{:wiki:2019:kmergenie_chikhi2013.bioinformatics.pdf|PDF}} </WRAP> <WRAP round box>Informed and automated k-mer size selection for genome assembly. Chikhi et al. Bioinformatics 2014, 30(1):31-7 <WRAP><hidden 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|http://kmergenie.bx.psu.edu]]. </hidden> Link to {{:wiki:2019:kmergenie_chikhi2013.bioinformatics.pdf|PDF}} </WRAP>