pgen.1010657.s010.pdf (70.52 kB)

The impact of recombination rate on IntroUNET’s accuracy under the D. melanogaster background selection model.

journal contribution

posted on 2024-02-20, 18:26 authored by Dylan D. Ray, Lex Flagel, Daniel R. Schrider

The left panel shows each simulation’s accuracy, averaged across classified pixels from the central 100 kb of the simulated chromosome, as a function of the average recombination rate in that same window. The right panel shows each simulation’s accuracy, averaged across classified pixels from the central 100 kb of the simulated chromosome, as a function of the recombination rate averaged across the entire simulated 1 Mb chromosome.

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world scenarios underscores trained neural network much higher frequencies insufficient — ideally div >< p deep neural network deep learning approaches deep learning algorithm significant fitness advantage closely related species recovering introgressed haplotypes image classification problem harbor introgressed loci region previously shown use simulated data population genetic alignment identifying introgressed alleles use ="population alignment one species fitness effects correctly identifying introgressed material introgressed alleles image representation image belongs widespread phenomenon typically neutral typically confined sometimes confer semantic segmentation real data readily extended purifying selection potential relevance performing comparably numerous methods identify regions identify alleles highly effective highly accurate growing body gene flow full extent evidence suggests drosophila close relative challenging real analysis reveals >, showing

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