: An Extension to the Pipeline Enabling Analysis of Protein-RNA Cross-Linking/Mass Spectrometry Data.

Cross-linking and mass spectrometry (XL-MS) workflows are increasingly popular techniques for generating low-resolution structural information about interacting biomolecules. is an established software package for analysis of protein-protein XL-MS data, supporting stable isotope-labeled cross-linking reagents. Resultant paired peaks in mass spectra aid sensitivity and specificity of data analysis. The recently developed cross-linking of isotope-labeled RNA and mass spectrometry (CLIR-MS) approach extends the XL-MS concept to protein-RNA interactions, also employing isotope-labeled cross-link (XL) species to facilitate data analysis. Data from CLIR-MS experiments are broadly compatible with core functionality, but the required analysis approach for this novel data type presents several technical challenges not optimally served by the original package. Here we introduce , a Python package extension for , which automates the analysis approach required for CLIR-MS data, providing bespoke, state-of-the-art processing and visualization functionality for this novel data type. Using functions included with , we evaluate three false discovery rate control approaches for CLIR-MS data. We demonstrate the versatility of the -enabled data analysis pipeline by also reanalyzing published protein-RNA XL-MS data sets that lack isotope-labeled RNA. This study demonstrates that provides a sensitive and specific data analysis pipeline for detection of isotope-labeled XLs in protein-RNA XL-MS experiments.