A new algorithm, PfAGSS, for predicting 3' splice sites in Plasmodium falciparum genomic sequences is described. Application of this program to the published P. falciparum chromosome 2 and 3 data suggests that existing programs result in a high error rate in assigning 3' intron boundaries.
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Epitranscriptomic modifications, particularly N6-methyladenosine (mA), are crucial regulators of gene expression, influencing processes such as RNA stability, splicing, and translation. Traditional computational methods for detecting mA from Nanopore direct RNA sequencing (DRS) data are constrained by their reliance on experimentally validated labels, often resulting in the underestimation of modification sites. Here, we introduce pum6a, an innovative attention-based framework that integrates positive and unlabeled multi-instance learning (MIL) to address the challenges of incomplete labeling and missing read-level annotations.
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