SUMMARY: Protein domain segmentation is a crucial aspect of understanding protein functions and interactions, and it is vital for protein modelling exercises and evolutionary studies. Current segmentation methods often rely on predefined classification schemes, leading to inconsistencies and biases. AFragmenter provides a schema-free and tuneable approach to protein domain segmentation based on network analysis of AlphaFold-predicted structures. Utilizing Predicted Aligned Error values, AFragmenter constructs a fully connected network of protein residues and identifies distinct structural domains by using Leiden clustering. This method empowers users to adjust parameters including contrast threshold and resolution, providing control over the segmentation process. AVAILABILITY AND IMPLEMENTATION: AFragmenter is implemented in Python3 and freely available under an MIT license. It can be found as a Python library and command line tool at https://github.com/sverwimp/AFragmenter, pip, and Conda.

SUMMARY: Missing regions in short-read assemblies of prokaryote genomes are often attributed to biases in sequencing technologies and to repetitive elements, the former resulting in low sequencing coverage of certain loci and the latter to unresolved loops in the de novo assembly graph. We developed SASpector, a command-line tool that compares short-read assemblies (draft genomes) to their corresponding closed assemblies and extracts missing regions to analyze them at the sequence and functional level. SASpector allows to benchmark the need for resolved genomes, can be integrated into pipelines to control the quality of assemblies, and could be used for comparative investigations of missingness in assemblies for which both short-read and long-read data are available in the public databases. AVAILABILITY AND IMPLEMENTATION: SASpector is available at https://github.com/LoGT-KULeuven/SASpector. The tool is implemented in Python3 and available through pip and Docker (0mician/saspector). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.