SUMMARY: The development of third-generation sequencing technologies enables the detection of RNA modifications at single-molecule resolution. Specifically for direct RNA sequencing on the ONT platform, we have previously developed an m6A detection algorithm called mAFiA. Here, we present the updated method, now covering all 18 DRACH m6A contexts as well as the identification of pseudouridine sites (Ψ). Our modification level predictions compare favorably with orthogonal methods and respond to knockdown or knock out of writer proteins. The simultaneous detection of multiple modifications on a single RNA molecule opens up the possibility to study cross-modification interactions. AVAILABILITY AND IMPLEMENTATION: Ψ-co-mAFiA is available at https://github.com/dieterich-lab/psi-co-mAFiA and licensed under GPLv3.0. An archived version of the software is available on Zenodo at https://doi.org/10.5281/zenodo.16797676.

We present the Manuscripts of Handwritten Arabic (Muharaf) dataset, which is a machine learning dataset consisting of more than 1,600 historic handwritten page images transcribed by experts in archival Arabic. Each document image is accompanied by spatial polygonal coordinates of its text lines as well as basic page elements. This dataset was compiled to advance the state of the art in handwritten text recognition (HTR), not only for Arabic manuscripts but also for cursive text in general. The Muharaf dataset includes diverse handwriting styles and a wide range of document types, including personal letters, diaries, notes, poems, church records, and legal correspondences. In this paper, we describe the data acquisition pipeline, notable dataset features, and statistics. We also provide a preliminary baseline result achieved by training convolutional neural networks using this data.