Flex-sweep 2.0: more flexible and faster selective sweeps detection
Murga-Moreno, J.; Enard, D.
Show abstract
Flex-sweep is a convolutional neural network-based method able to detect a wide range of selective sweeps, including those thousands of generations old, from single population genomic data, while robust to background selection. Here we present a substantial update that streamlines the entire workflow. The new version vastly reduces memory needs and vastly speeds up summary-statistic computation over fully customizable statistics combinations and genomic regions, relaxes CNN constraints by supporting custom architectures and haplotype matrix sorting methods. Domain-Adaptive Neural Network (DANN) training is now supported, as well as ancestral-state polarization and a robust, clustering and confounder-aware gene set sweep enrichment pipeline robust for downstream analysis. Flex-sweep 2.0 scales to hundreds of thousands of training simulations, and enables genome-wide inference on a standard workstation.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.