Dynamic control of gene expression is critical for blood stage development of malaria parasites. Here, we used multi-omic analyses to investigate transcriptional regulation by the chromatin-associated microrchidia protein, MORC, during asexual blood stage development of the human malaria parasite Plasmodium falciparum. We show that PfMORC (PF3D7_1468100) interacts with a suite of nuclear proteins, including APETALA2 (ApiAP2) transcription factors (PfAP2-G5, PfAP2-O5, PfAP2-I, PF3D7_0420300, PF3D7_0613800, PF3D7_1107800, and PF3D7_1239200), a DNA helicase DS60 (PF3D7_1227100), and other chromatin remodelers (PfCHD1 and PfEELM2). Transcriptomic analysis of PfMORC^HA-glmS knockdown parasites revealed 163 differentially expressed genes belonging to hypervariable multigene families, along with upregulation of genes mostly involved in host cell invasion. In vivo genome-wide chromatin occupancy analysis during both trophozoite and schizont stages of development demonstrates that PfMORC is recruited to repressed, multigene families, including the var genes in subtelomeric chromosomal regions. Collectively, we find that PfMORC is found in chromatin complexes that play a role in the epigenetic control of asexual blood stage transcriptional regulation and chromatin organization.

Variant calling is fundamental in bacterial genomics, underpinning the identification of disease transmission clusters, the construction of phylogenetic trees, and antimicrobial resistance detection. This study presents a comprehensive benchmarking of variant calling accuracy in bacterial genomes using Oxford Nanopore Technologies (ONT) sequencing data. We evaluated three ONT basecalling models and both simplex (single-strand) and duplex (dual-strand) read types across 14 diverse bacterial species. Our findings reveal that deep learning-based variant callers, particularly Clair3 and DeepVariant, significantly outperform traditional methods and even exceed the accuracy of Illumina sequencing, especially when applied to ONT’s super-high accuracy model. ONT’s superior performance is attributed to its ability to overcome Illumina’s errors, which often arise from difficulties in aligning reads in repetitive and variant-dense genomic regions. Moreover, the use of high-performing variant callers with ONT’s super-high accuracy data mitigates ONT’s traditional errors in homopolymers. We also investigated the impact of read depth on variant calling, demonstrating that 10× depth of ONT super-accuracy data can achieve precision and recall comparable to, or better than, full-depth Illumina sequencing. These results underscore the potential of ONT sequencing, combined with advanced variant calling algorithms, to replace traditional short-read sequencing methods in bacterial genomics, particularly in resource-limited settings.