Biology subjects

Haynes, S.

Publications and source records attributed to Haynes, S..

3 recordsLinked to original sources

bioRxiv2026

A Systematic Benchmark of Antibiotic Resistance Gene Detection Tools for Shotgun Metagenomic Datasets

1.Accurate detection of antimicrobial resistance genes (ARGs) from metagenomic data is essential for understanding resistance dissemination within microbial communities, yet tool performance remains influenced by sequencing coverage, community complexity, and dataset variability. In this study, we systematically benchmarked five widely used read-based ARG detection tools (ARGprofiler, KARGA, ARIBA, GROOT, and SRST2) across simulated metagenomic datasets representing varying sequencing coverages, microbial complexities, and approximate realistic metagenomic dataset. The results demonstrated that sequencing coverage is a major determinant of ARG detection accuracy, with reliable detection achieved at 10x coverage and performance stabilizing between 20x and 30x. ARGprofiler exhibited the highest overall F1-score (0.891 at [≥]10x), whereas KARGA showed higher recall at low coverage levels, but lower precision compared to ARGprofiler. Increasing community complexity led to a decline in accuracy across all tools, and under realistic uneven coverage, performance variability increased substantially, with KARGA achieving the highest mean F1- score (0.122 {+/-} 0.067). Runtime evaluation further revealed substantial differences in computational efficiency, with ARGprofiler, SRST2, and GROOT being the most resource-efficient, while KARGA imposed the highest computational burden. Collectively, these findings highlight that both sequencing coverage and community complexity profoundly shape ARG detection outcomes and that tool selection should balance accuracy with computational efficiency. The study also emphasizes the need for standardized benchmarking datasets that reflect true metagenomic complexity to ensure robust and comparable ARG surveillance across analytical pipelines.

bioinformatics↗

bioRxiv2023

Comprehensive chromatome profiling identifies metabolic enzymes on chromatin in healthy and cancer cells

Metabolic and epigenetic rewiring are widely considered hallmarks of cancer, with emerging evidence of crosstalk between them. Anecdotal evidence of metabolic enzymes moonlighting in the chromatin environment has suggested how this crosstalk might be facilitated, but the extent of nuclear relocalization of metabolic enzymes remains elusive. Here, we provide a comprehensive chromatin proteomics resource across cancer lineages as well as healthy samples and demonstrate that metabolic enzyme moonlighting on chromatin is widespread across tissues and pathways. We show that the abundance of metabolic enzymes on chromatin is tissue-specific, with oxidative phosphorylation proteins depleted in lung cancer samples, perhaps suggesting an interplay between cell identity and nuclear metabolism. Finally, we explore metabolic functions in the chromatin environment and show that one-carbon folate enzymes are associated with DNA damage and repair processes, providing an approach to explore non-canonical functions of metabolic enzymes.

cancer biology↗

bioRxiv2023

Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data

The tremendous rate with which data is generated and analysis methods emerge makes it increasingly difficult to keep track of their domain of applicability, assumptions, and limitations and consequently, of the efficacy and precision with which they solve specific tasks. Therefore, there is an increasing need for benchmarks, and for the provision of infrastructure for continuous method evaluation. APAeval is an international community effort, organized by the RNA Society in 2021, to benchmark tools for the identification and quantification of the usage of alternative polyadenylation (APA) sites from short-read, bulk RNA-sequencing (RNA-seq) data. Here, we reviewed 17 tools and benchmarked eight on their ability to perform APA identification and quantification, using a comprehensive set of RNA-seq experiments comprising real, synthetic, and matched 3'-end sequencing data. To support continuous benchmarking, we have incorporated the results into the OpenEBench online platform, which allows for seamless extension of the set of methods, metrics, and challenges. We envisage that our analyses will assist researchers in selecting the appropriate tools for their studies. Furthermore, the containers and reproducible workflows generated in the course of this project can be seamlessly deployed and extended in the future to evaluate new methods or datasets.

bioinformatics↗