SUMMARY: Splitpea takes skipped exon event data at the sample or differential expression level from SUPPA2 and rMATS and maps potential changes to protein-protein interaction (PPI) network rewiring events. It handles a variety of input formats via an easy-to-install Python package, including percent spliced in values comparing two conditions, skipped exon counts, or precalculated exon usage statistics between experimental conditions. In each case, Splitpea produces rewired network graphs, edge and gene-level summary statistics, and Cytoscape- or Gephi-ready files for easy visualization, allowing users to find PPIs potentially disrupted or increased by alternative splicing. AVAILABILITY AND IMPLEMENTATION: Source code and accompanying documentation can be found on Github (https://github.com/ylaboratory/splitpea-package), released under a BSD 3-clause license for open-source use, and the Splitpea package is installable via PyPI.

Many of the most commonly explored natural language processing (NLP) information extraction tasks can be thought of as evaluations of declarative knowledge, or fact-based information extraction. Procedural knowledge extraction, i.e., breaking down a described process into a series of steps, has received much less attention, perhaps in part due to the lack of structured datasets that capture the knowledge extraction process from end-to-end. To address this unmet need, we present FlaMBé (Flow annotations for Multiverse Biological entities), a collection of expert-curated datasets across a series of complementary tasks that capture procedural knowledge in biomedical texts. This dataset is inspired by the observation that one ubiquitous source of procedural knowledge that is described as unstructured text is within academic papers describing their methodology. The workflows annotated in FlaMBé are from texts in the burgeoning field of single cell research, a research area that has become notorious for the number of software tools and complexity of workflows used. Additionally, FlaMBé provides, to our knowledge, the largest manually curated named entity recognition (NER) and disambiguation (NED) datasets for tissue/cell type, a fundamental biological entity that is critical for knowledge extraction in the biomedical research domain. Beyond providing a valuable dataset to enable further development of NLP models for procedural knowledge extraction, automating the process of workflow mining also has important implications for advancing reproducibility in biomedical research.