Genome-wide scans for selective sweeps using convolutional neural networks.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Recent methods for selective sweep detection cast the problem as a classification task and use summary statistics as features to capture region characteristics that are indicative of a selective sweep, thereby being sensitive to confounding factors. Furthermore, they are not designed to perform whole-genome scans or to estimate the extent of the genomic region that was affected by positive selection; both are required for identifying candidate genes and the time and strength of selection.

Authors

  • Hanqing Zhao
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Matthijs Souilljee
    Faculty of EEMCS, University of Twente, Enschede, The Netherlands.
  • Pavlos Pavlidis
    Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece.
  • Nikolaos Alachiotis
    Faculty of EEMCS, University of Twente, Enschede, The Netherlands.