Computational Molecular Biology 2025, Vol.15, No.6, 299-306 http://bioscipublisher.com/index.php/cmb 299 Case Study Open Access Case Study: Computational Detection of Horizontal Gene Transfer in Soil Microbiomes Jun Wang, Qikun Huang Microbial Resources Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author: qikun.huang@cuixi.org Computational Molecular Biology, 2025, Vol.15, No.6 doi: 10.5376/cmb.2025.15.0030 Received: 20 Nov., 2025 Accepted: 12 Dec., 2025 Published: 25 Dec., 2025 Copyright © 2025 Zhou and Huang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang J., and Huang Q.K., 2025, Case study: computational detection of horizontal gene transfer in soil microbiomes, Computational Molecular Biology, 15(6): 299-306 (doi: 10.5376/cmb.2025.15.0030) Abstract Horizontal gene transfer (HGT) plays a crucial role in microbial evolution and the diversification of soil ecosystem functions. This study employed computational methods to detect and analyze HGT events in agricultural soil microbiomes, aiming to reveal how gene transfer shapes microbial community dynamics and ecological functions. Metagenomic data were generated from soil samples collected from conventional and organic farming systems, then assembled, binned, and annotated to identify potential donor and recipient species. Combining sequence-based comparative genomics, composition-based analysis, and machine learning models, common mobile genetic elements and gene families associated with environmental adaptation and antibiotic resistance were detected. The results indicate that HGT significantly promotes microbial resilience and nutrient cycling in soil ecosystems and is influenced by environmental parameters such as pH and nutrient availability. This study provides a methodological framework for computational HGT detection and offers new insights into microbial evolution, soil health, and sustainable agricultural management. Future research integrating multi-omics data and standardized benchmarks will improve the accuracy and ecological interpretability of HGT studies. Keywords Horizontal gene transfer; Soil microbiome; Metagenomics; Computational biology; Microbial ecology 1 Introduction When discussing how microorganisms acquire new capabilities, the concept of horizontal gene transfer (HGT) is always unavoidable. It is not the kind of traditional inheritance passed from parents to offspring, but rather more like a "casual exchange of genes" behavior among microorganisms. Such exchanges occur intermittently in many ecological environments, and sometimes even cause certain bacteria to suddenly possess characteristics that do not originally exist, such as a significantly enhanced tolerance to antibiotics (Arnold et al., 2021). However, such changes are not always easily observable directly, and many transfer events are often hidden in the details of the genome. Nevertheless, once these genes are successfully transferred, they will quietly alter the genomic structure of the recipient bacteria and may also cause them to behave differently in the environment. Therefore, when studying how microorganisms adapt to their surrounding environment, it is almost inevitable to take HGT into account. In a structurally complex environment like soil, HGT is often regarded as an important force for maintaining the vitality of microbial communities. The rhizosphere, mycorrhizal sphere and other regions are particularly lively, and genes are more likely to be exchanged in these places (Nielsen & Van Elsas, 2019). These exchanges are not necessarily all beneficial, but they do make soil microorganisms more malleable, constantly reshaping metabolic functions, resistance characteristics and ecological relationships, and thus influencing important processes such as nutrient cycling. It is worth noting that external factors such as manure input or pollution can also stir up this transfer activity, sometimes enhancing and sometimes suppressing it, making the occurrence frequency of HGT in the soil not fixed. This study attempts to use bioinformatics processes such as MetaCHIP to calculate and identify HGT events in soil microbial communities without the need for a reference genome. MetaCHIP combines optimal matching and phylogenetic judgment methods, capable of capturing recent and earlier gene transfers, and thus is suitable for studying genetic communication at the community scale. By adopting these methods, we hope to gain a more
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