Dataset documentation and intended uses. Recommended documentation frameworks include datasheets for datasets, dataset nutrition labels, data statements for NLP, and accountability frameworks. Author statement that they bear all responsibility in case of violation of rights, etc., and Links to access the dataset and its metadata. Simulation environments should link to (open source) code repositories. The dataset itself should ideally use an open and widely used data format.

This highlights an urgent need of contributions from the machine learning community.

Detecting chemical modifications on RNA molecules remains a key challenge in epitranscriptomics. Traditional reverse transcription-based sequencing methods introduce enzyme- and sequence-dependent biases and fragment RNA molecules, confounding the accurate mapping of modifications across the transcriptome. Nanopore direct RNA sequencing offers a powerful alternative by preserving native RNA molecules, enabling the detection of modifications at single-molecule resolution. However, current computational tools can identify only a limited subset of modification types within well-characterized sequence contexts for which ample training data exists. Here, we introduce a model-free computational method that reframes modification detection as an anomaly detection problem, requiring only canonical (unmodified) RNA reads without any other annotated data. For each nanopore read, our approach extracts robust, modification-sensitive features from the raw ionic current signal at a site using the signature transform, then computes an anomaly score by comparing the resulting feature vector to its nearest neighbors in an unmodified reference dataset. We convert anomaly scores into statistical p-values to enable anomaly detection at both individual read and site levels. Validation on densely-modified \textit{E. coli} rRNA demonstrates that our approach detects known sites harboring diverse modification types, without prior training on these modifications. We further applyied this framework to dengue virus (DENV) transcripts and mammalian mRNAs. For DENV sfRNA, it led to revealing a novel 2'-O-methylated site, which we validate orthogonally by qRT-PCR assays. These results demonstrate that our model-free approach operates robustly across different types of RNAs and datasets generated with different nanopore sequencing chemistries.

The ability to predict and interpret modifications of ribonucleic acid (RNA) has been a welcome advance in biochemistry research. However, existing predictive approaches have a key drawback--they can only predict a single type of RNA modification without supporting multiple types or providing insightful interpretation of their prediction results. Researchers from Xi'an Jiaotong-Liverpool University, led by Dr Jia Meng, have addressed this issue by developing a model that supports 12 RNA modification types, greatly expanding RNA research prediction and interpretation. "To the best of our knowledge, these 12 are the only widely occurring RNA modifications that can be profiled transcriptome-wide with existing base-resolution technologies. This makes them highly desirable for reliable large-scale prediction," Dr Meng said.