Dostmohammadi, A. | bioRxiv Science

bioRxiv2025

namiRa: A Comprehensive, Manually Curated Database for MicroRNA Expression, Function, and Deregulation in Cancer

MicroRNAs (miRNAs) have the potential to serve as oncogenes or tumor suppressors, playing important roles in the pathogenesis of human cancers. Despite the growing recognition of miRNA significance, the contributed information remains scattered in the publications. This fragmentation underscores the need for a centralized and comprehensive database that consolidates miRNA expression patterns, functional roles, and regulatory interactions across diverse cancer types. So far, several miRNA databases have been developed, but neither of them enables in-depth functional analysis or comparative visualization of miRNA data. Here, we present namiRa, a manually curated database, to offer a comprehensive resource for miRNA expression and functional significance in various types of cancer, describing miRNA-cancer associations based on a thorough review of the literature. namiRa provides an extensive collection of miRNA expression profiles, detection methods, functional analyses for miRNAs in vitro and in vivo, and visualized regulatory networks across different cancer types. The current version of namiRa documents curated relationships between 1,077 human miRNAs and 33 types of human cancers, based on data from 9,884 published papers. namiRa is accessible at https://www.namira-db.com.

cancer biology↗

bioRxiv2025

TrackRefiner: A tool for refinement of bacillus cell tracking data

MotivationSingle-cell resolution time-lapse microscopy of bacterial populations is a powerful tool for assessing cellular behavior and interaction dynamics. Realizing the full potential of this approach requires accurate image analysis: segmentation of individual cell objects, tracking of persistent cells from frame to frame, and connecting of mother cells to daughters when division events occur. In particular, accurate tracking is needed to produce longitudinal datasets for analysis of interactions and features that develop through time. Tracking is challenging when populations are densely packed or when cells undergo significant motion between frames. The leading software packages struggle to provide accurate data in such cases. ResultTo address this problem, we present TrackRefiner, a tool for refinement of bacillus cell tracking data. This package was specifically designed to refine the tracking outputs of CellProfiler, a commonly used image processing tool. TrackRefiner is built with a modular and publicly accessible structure, making it adaptable for integration with other image processing software. To assess the packages performance, we manually determined ground truth tracking results for eight datasets from four research groups, comprising a total of 159,349 object links. This curated dataset, the first of its kind, serves as a valuable benchmark for assessing the performance of bacillus cell tracking algorithms. For timelapses with frequent imaging, TrackRefiner consistently achieved, with one exception, over 98% detection accuracy and corrected 57-100% of tracking errors. Accuracy was reduced for images sampled at lower frequency. Availability and implementationFor easy access, TrackRefiner has been published on PyPI and Anaconda. Source code and user manuals can be accessed via Github and OSF. The manually curated benchmark dataset is also posted at these sites.

microbiology↗