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MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites
MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites
Chengqi Wang
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Yibo Dong
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Chang Li
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Jenna Oberstaller
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Min Zhang
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Justin Gibbons
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Camilla Valente Pires
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Mianli Xiao
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Lei Zhu
(School of Biological Sciences, Nanyang Technological University)
Rays H. Y. Jiang
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Kami Kim
(Department of Internal Medicine, Morsani College of Medicine)
Jun Miao
(Department of Internal Medicine, Morsani College of Medicine)
Thomas D. Otto
(School of Infection & Immunity, MVLS, University of Glasgow)
Liwang Cui
(Department of Internal Medicine, Morsani College of Medicine)
John H. Adams
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Xiaoming Liu
(Center for Global Health and Infectious Diseases Research and USF Genomics Program, College of Public Health, University of South Florida)
Journal: Genome Biology
•
• BMC
• ISSN: 1474-760X
Abstract
Abstract Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning framework, MalariaSED, for predicting chromatin profiles in malaria parasites. The MalariaSED performance was validated by published ChIP-qPCR and TF motifs results. Applying MalariaSED to ~ 1.3 million variants shows that geographically differentiated noncoding variants are associated with parasite invasion and drug resistance. Further analysis reveals chromatin accessibility changes at Plasmodium falciparum rings are partly associated with artemisinin resistance. MalariaSED illuminates the potential functional roles of noncoding variants in malaria parasites.
Chengqi Wang, Yibo Dong, Chang Li, et al. (2023). "MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites". Genome Biology. https://doi.org/10.1186/s13059-023-03063-z