Summary of WGS datasets. by McKenna Feltes (11242085)

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Mapping of the <i>zion</i> locus. by McKenna Feltes (11242085)

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WheresWalker pipeline utilizes WGS data to identify segregating SNPs and indels. by McKenna Feltes (11242085)

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Indel markers generated by WheresWalker for each mutant. by McKenna Feltes (11242085)

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SRA accession numbers for BioProject PRJNA1187516. by McKenna Feltes (11242085)

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Mapping indices: WheresWalker SNP index, Het^wt, and homozygosity values for zebrafish and maize datasets. by McKenna Feltes (11242085)

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WheresWalker outperforms homozygosity density mapping for Maize mutant. by McKenna Feltes (11242085)

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Phenotype frequencies for each mutant and for complementation testing. by McKenna Feltes (11242085)

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SNP Index profile for all chromosomes corresponding to regional plots in Figs 3 and 4. by McKenna Feltes (11242085)

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Phenotype frequency and complementation for additional novel DY alleles. by McKenna Feltes (11242085)

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Bulk Segregant Analysis (BSA) homozygosity mapping mirrors WheresWalker output. by McKenna Feltes (11242085)

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Candidate lists generated by WheresWalker for each mutant. by McKenna Feltes (11242085)

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<i>slc3a2a</i>^<i>zion</i> is a novel regulator of B-lp metabolism. by McKenna Feltes (11242085)

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Oligo sequences. by McKenna Feltes (11242085)

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<i>zion</i> WGS summary. by McKenna Feltes (11242085)

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WheresWalker identifies the correct chromosomal region for three dark yolk loci. by McKenna Feltes (11242085)

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Comparison curve of objective function (J). by Wanjun Zhang (496858)

Boxplots showcasing variability of additional key topological metrics obtained after 50 algorithm runs for the generation of a) chorionic vessels and b) villous vessels. by Diana C. de Oliveira (9220729)

PATH has state-of-the-art performance versus previous binding affinity prediction algorithms. by Yuxi Long (11024307)

“…The benchmarked algorithms include physics-based and deep learning algorithms from the famous AutoDock framework (scoring function of AutoDock4 implemented in the AutoDockFR package [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref068" target="_blank">68</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref077" target="_blank">77</a>], Vinardo [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref069" target="_blank">69</a>], GNINA [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref070" target="_blank">70</a>]), empirical (AA-Score [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref071" target="_blank">71</a>]), knowledge-based (SMoG2016 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref072" target="_blank">72</a>]), and deep learning-based scoring functions (OnionNet [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref073" target="_blank">73</a>], PLANET [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref074" target="_blank">74</a>]). …”

Risk and density functions for standard relapse from remission condition to disease <i>b</i> condition (similar shapes for standard relapse to disease <i>a</i>). by Benoîte de Saporta (20454662)