MOTIVATION: Identifying meaningful patterns in complex biological data necessitates correlation coefficients capable of capturing diverse relationship types beyond simple linearity. Furthermore, efficient computational tools are crucial for handling the ever-increasing scale of biological datasets. RESULTS: We introduce CCC-GPU, a high-performance, GPU-accelerated implementation of the Clustermatch Correlation Coefficient (CCC). CCC-GPU computes correlation coefficients for mixed data types, effectively detects nonlinear relationships, and offers significant speed improvements over its predecessor. AVAILABILITY AND IMPLEMENTATION: The source code of CCC-GPU is openly available on GitHub (https://github.com/pivlab/ccc-gpu) and archived on Zenodo (https://doi.org/10.5281/zenodo.18310318), distributed under the BSD-2-Clause Plus Patent License.

MOTIVATION: UMI-4C, a technique that combines chromosome conformation capture (4C) and unique molecular identifiers (UMI), is widely used to profile and quantitatively compare targeted chromosomal contact profiles. The analysis of UMI-4C experiments presents several computational challenges, including the removal of the PCR duplication bias and the identification of differential chromatin contacts. RESULTS: We have developed UMI4Cats (UMI-4C Analysis Turned Simple), an R package that facilitates processing, analyzing and visualizing of data obtained by UMI-4C experiments. AVAILABILITY AND IMPLEMENTATION: UMI4Cats is implemented as an R package supported on Linux, MacOS and MS Windows. UMI4Cats is available from Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/UMI4Cats.html) and GitHub (https://github.com/Pasquali-lab/UMI4Cats). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.