Crosbie, N. D. | bioRxiv Science

bioRxiv2025

grepq: A Rust application that quickly filters FASTQ files by matching sequences to a set of regular expressions

Regular expressions (regex) (Kleene 1951) have been an important tool for finding patterns in biological codes for decades (Hodgman 2000 and citations therein), and unlike fuzzy-finding approaches, do not result in approximate matches. The performance of regular expressions can be slow, however, especially when searching for matching patterns in large files. grepq is a Rust application that quickly filters FASTQ files by matching sequences to a set of regular expressions. grepq is designed with a focus on performance and scalability, is easy to install and easy to use, enabling users to quickly filter large FASTQ files, to enumerate named and unnamed variants, to update the order in which patterns are matched against sequences through in-built tune and summarise commands, and optionally, to output a SQLite file for further sequence analysis. grepq is open-source and available on GitHub and Crates.io.

bioinformatics↗

bioRxiv2024

Machine learning driven image segmentation and shape clustering of algal microscopic images obtained from various water types

Algae and cyanobacteria are microorganisms found in almost all fresh and marine waters, where they can pose environmental and public health risks when they grow excessively and produce blooms. Accurate identification and quantification of these microorganisms are vital for ecological research, water quality monitoring, and public health safety. However, traditional methods of manually counting and morphologically identifying these microorganisms are time-consuming and prone to human error. Application of the machine learning-driven Fast Segment Anything Model (FastSAM), an image segmentation model, automates and potentially enhances the accuracy and efficiency of cell identification and enumeration from microscopic images. We assessed FastSAM for algal cell image segmentation, and three clustering evaluation metrics. Segmentation of microscopic images of algal and cyanobacterial cells in water and treated wastewater samples using the Convolutional Neural Network based FastSAM algorithm demonstrated benefits and challenges of this machine learning-driven image processing. Notably, the pre-trained algorithm segmented entire elements in all microscopic images used in this study. Depending on the shape, 50-100% similarity was observed between machine-based segmentation and manual validation of all segmented elements, with 100% of single cells being correctly segmented by FastSAM. The performance of clustering metrics varied between 57-94% with the Spectral Angle Mapper achieving the most accurate performance, 84-94%, compared to the manually chosen clustering benchmarks. Cyanobacterial and algal communities are biologically diverse and have ecological significance. The application of image clustering techniques in studying their cell shapes marks an important advancement in microbial ecology and environmental monitoring. As technology progresses, these methods will become increasingly utilised to decipher the complex roles that algae and cyanobacteria play in our ecosystems supporting mitigation and public health protection measures.

microbiology↗