MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites.
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
16 10 2023
16 10 2023
Historique:
received:
10
06
2022
accepted:
19
09
2023
medline:
2
11
2023
pubmed:
17
10
2023
entrez:
16
10
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37845769
doi: 10.1186/s13059-023-03063-z
pii: 10.1186/s13059-023-03063-z
pmc: PMC10577899
doi:
Substances chimiques
Chromatin
0
Antimalarials
0
Protozoan Proteins
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
231Subventions
Organisme : NIAID NIH HHS
ID : R01 AI117017
Pays : United States
Organisme : NIAID NIH HHS
ID : U19 AI089672
Pays : United States
Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
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