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Empirical evidence suggests that the malaria parasite  Plasmodium falciparum  employs a broad range of mechanisms to regulate gene transcription throughout the organism’s complex life cycle. To better understand this regulatory machinery, we assembled a rich collection of genomic and epigenomic data sets, including information about transcription factor (TF) binding motifs, patterns of covalent histone modifications, nucleosome occupancy, GC content, and global 3D genome architecture. We used these data to train machine learning models to discriminate between high-expression and low-expression genes, focusing on three distinct stages of the red blood cell phase of the  Plasmodium  life cycle. Our results highlight the importance of histone modifications and 3D chromatin architecture in  Plasmodium  transcriptional regulation and suggest that AP2 transcription factors may play a limited regulatory role, perhaps operating in conjunction with epigenetic factors.

plos computational biology/plos computational biology

Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features