AI Article Synopsis
- Malaria is a major infectious disease, and understanding the impact of noncoding genetic variants in malaria parasites is challenging.
- A new deep learning tool called MalariaSED has been developed to predict chromatin profiles in these parasites, with its accuracy confirmed by existing research.
- By analyzing about 1.3 million genetic variants, the study found that specific noncoding variants can influence parasite behavior, including invasion capabilities and resistance to drugs like artemisinin.
Article 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.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10577899 | PMC |
| http://dx.doi.org/10.1186/s13059-023-03063-z | DOI Listing |
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