Using deep learning to identify recent positive selection in malaria parasite sequence data.

Journal: Malaria journal
PMID: 34126997

Abstract

BACKGROUND: Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated.

Authors

  • Wouter Deelder
    London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
  • Ernest Diez Benavente
    Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; Department of Experimental Cardiology, University Medical Center Utrecht, Netherlands.
  • Jody Phelan
    London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
  • Emilia Manko
    London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
  • Susana Campino
    London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
  • Luigi Palla
    London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
  • Taane G Clark
    Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.

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