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