MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites

Author:

Wang Chengqi,Dong Yibo,Li Chang,Oberstaller Jenna,Zhang Min,Gibbons Justin,Pires Camilla Valente,Xiao Mianli,Zhu Lei,Jiang Rays H. Y.,Kim Kami,Miao Jun,Otto Thomas D.,Cui Liwang,Adams John H.,Liu Xiaoming

Abstract

AbstractMalaria 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.

Funder

National Institute of Health

National Institutes of Health

Publisher

Springer Science and Business Media LLC

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