LITERATURE UPDATE
further point out remaining challenges for the field such as the integration of methylation signals in sub-strain analysis and the lack of benchmarks.
The selection of software and database for metagenomics sequence analysis impacts the outcome of microbial profiling and pathogen detection
Xu R, Rajeev S, Salvador LCM. PLoS One. 2023 Apr 7;18(4):e0284031. doi: 10.1371/journal.pone.0284031. eCollection 2023.
Copper-sensitive Escherichia coli has been used in metagenomic testing, as covered in Sequence- based Functional Metagenomics Reveals Novel Natural Diversity of Functional CopA in Environmental Microbiomes (Scanning electron micrograph [SEM]).
Benchmarking short-read metagenomics tools for removing host contamination Gao Y, Luo H, Lyu H et al. Gigascience. 2025 Jan 6;14:giaf004.
doi: 10.1093/gigascience/giaf004.
The rapid evolution of metagenomic sequencing technology offers remarkable opportunities to explore the intricate roles of microbiome in host health and disease, as well as to uncover the unknown structure and functions of microbial communities. However, the swift accumulation of metagenomic data poses substantial challenges for data analysis. Contamination from host DNA can substantially compromise result accuracy and increase additional computational resources by including non-target sequences. In this study, the authors assessed the impact of computational host DNA decontamination on downstream analyses, highlighting its importance in producing accurate results efficiently. They also evaluated the performance of conventional tools like KneadData, Bowtie2, BWA, KMCP, Kraken2, and KrakenUniq, each offering unique advantages for different applications. Furthermore, they highlighted the importance of an accurate host reference genome, noting that its absence negatively affected the decontamination performance across all tools.
These findings underscore the need for careful selection of decontamination tools and reference genomes to enhance the accuracy of
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metagenomic analyses. These insights provide valuable guidance for improving the reliability and reproducibility of microbiome research.
Unveiling microbial diversity: harnessing long-read sequencing technology Agustinho DP, Fu Y, Menon VK, Metcalf GA, Treangen TJ, Sedlazeck FJ. Nat Methods. 2024 Jun;21(6):954-966. doi: 10.1038/s41592-024-02262-1.
Long-read sequencing has recently transformed metagenomics, enhancing strain-level pathogen characterization, enabling accurate and complete metagenome-assembled genomes, and improving microbiome taxonomic classification and profiling. These advancements are not only due to improvements in sequencing accuracy, but also happening across rapidly changing analysis methods. In this review, the authors explore
long-read sequencing’s profound impact on metagenomics, focusing on computational pipelines for genome assembly, taxonomic characterisation and variant detection, to summarise recent advancements in the field and provide an overview of available analytical methods to fully leverage long reads.
They provide insights into the advantages and disadvantages of long reads over short reads and their evolution from the early days of long-read sequencing to their recent impact on metagenomics and clinical diagnostics. In addition, the authors
Shotgun metagenomic sequencing analysis is widely used for microbial profiling of biological specimens and pathogen detection. However, very little is known about the technical biases caused by the choice of analysis software and databases on the biological specimen. In this study, the authors evaluated different direct read shotgun metagenomics taxonomic profiling software to characterise the microbial compositions of simulated mice gut microbiome samples and of biological samples collected from wild rodents across multiple taxonomic levels. Using 10 of the most widely used metagenomics software and four different databases, the authors demonstrated that obtaining an accurate species-level microbial profile using the current direct read metagenomics profiling software is still a challenging task. They also showed that the discrepancies in results when different databases and software were used could lead to significant variations in the distinct microbial taxa classified, in the characterisations of the microbial communities, and in the differentially abundant taxa identified. Differences in database contents and read profiling algorithms are the main contributors for these discrepancies.
The inclusion of host genomes and of genomes of the interested taxa in the databases is important for increasing the accuracy of profiling. The analysis also showed that software included in this study differed in their ability to detect the presence of Leptospira, a major zoonotic pathogen of One Health importance, especially at the species level resolution.
The authors concluded that
using different databases and software combinations can result in confounding biological conclusions in microbial profiling. This study warrants that software and database selection must be based on the purpose of the study.
JUNE 2025
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Photo by fkfkrErbe, digital colorization by Christopher Pooley, USDA, ARS, EMU. Public domain Wikimedia Commons
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