Interplay between Topology and Edge Weights in Real-World Graphs: Concepts, Patterns, and an Algorithm

What are the relations between the edge weights and the topology in real-world graphs? Given only the topology of a graph, how can we assign realistic weights to its edges based on the relations? Several trials have been done for edge-weight prediction where some unknown edge weights are predicted with most edge weights known. There are also existing works on generating both topology and edge weights of weighted graphs. Differently, we are interested in generating edge weights that are realistic in a macroscopic scope, merely from the topology, which is unexplored and challenging. To this end, we explore and exploit the patterns involving edge weights and topology in real-world graphs. Specifically, we divide each graph into layers where each layer consists of the edges with weights at least a threshold. We observe consistent and surprising patterns appearing in multiple layers: the similarity between being adjacent and having high weights, and the nearly-linear growth of the fraction of edges having high weights with the number of common neighbors. We also observe a power-law pattern that connects the layers. Based on the observations, we propose PEAR, an algorithm assigning realistic edge weights to a given topology. The algorithm relies on only two parameters, preserves all the observed patterns, and produces more realistic weights than the baseline methods with more parameters.Comment: ECML PKDD 2023 Journal Track (Data Mining and Knowledge Discovery journal

CRISPR-GRANT: a cross-platform graphical analysis tool for high-throughput CRISPR-based genome editing evaluation

Abstract Backgroud CRISPR/Cas is an efficient genome editing system that has been widely used for functional genetic studies and exhibits high potential in biomedical translational applications. Indel analysis has thus become one of the most common practices in the lab to evaluate DNA editing events generated by CRISPR/Cas. Several indel analysis tools have been reported, however, it is often required that users have certain bioinformatics training and basic command-line processing capability. Results Here, we developed CRISPR-GRANT, a stand-alone graphical CRISPR indel analysis tool, which could be easily installed for multi-platforms, including Linux, Windows, and macOS. CRISPR-GRANT offered a straightforward GUI by simple click-and-run for genome editing analysis of single or pooled amplicons and one-step analysis for whole-genome sequencing without the need of data pre-processing, making it ideal for novice lab scientists. Moreover, it also exhibited shorter run-time compared with tools currently available. Conclusion Therefore, CRISPR-GRANT is a valuable addition to the current CRISPR toolkits that significantly lower the barrier for wet-lab researchers to conduct indel analysis from large NGS datasets. CRISPR-GRANT binaries are freely available for Linux (above Ubuntu 16.04), macOS (above High Sierra 10.13) and Windows (above Windows 7) at https://github.com/fuhuancheng/CRISPR-GRANT . CRISPR-GRANT source code is licensed under the GPLv3 license and free to download and use