ArticlePLoS computational biology2019
Where did you come from, where did you go: Refining metagenomic analysis tools for horizontal gene transfer characterisation.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Kente: A Graph-based Pangenomic Approach for Horizontal Gene Transfer Detection in Microbiomes.bioRxiv : the preprint server for biology · 2026Article
- A survey of computational approaches for characterizing microbial interactions in microbial mats.Genome biology · 2025Review
- Current state and future prospects of Horizontal Gene Transfer detection.NAR genomics and bioinformatics · 2025Review
- Profiling lateral gene transfer events in the human microbiome using WAAFLE.Nature microbiology · 2025Article
- Enhancing insights into diseases through horizontal gene transfer event detection from gut microbiome.Nucleic acids research · 2024Article
- hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer.International journal of molecular sciences · 2022Article
- Inferring microbiota functions from taxonomic genes: a review.GigaScience · 2022Review
- Examining horizontal gene transfer in microbial communities.Nature reviews. Microbiology · 2021Review
- Experimental approaches to tracking mobile genetic elements in microbial communities.FEMS microbiology reviews · 2020Review
- Diversity within species: interpreting strains in microbiomes.Nature reviews. Microbiology · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Horizontal gene transfer (HGT) has changed the way we regard evolution. Instead of waiting for the next generation to establish new traits, especially bacteria are able to take a shortcut via HGT that enables them to pass on genes from one individual to another, even across species boundaries. The tool Daisy offers the first HGT detection approach based on read mapping that provides complementary evidence compared to existing methods. However, Daisy relies on the acceptor and donor organism involved in the HGT being known. We introduce DaisyGPS, a mapping-based pipeline that is able to identify acceptor and donor reference candidates of an HGT event based on sequencing reads. Acceptor and donor identification is akin to species identification in metagenomic samples based on sequencing reads, a problem addressed by metagenomic profiling tools. However, acceptor and donor references have certain properties such that these methods cannot be directly applied. DaisyGPS uses MicrobeGPS, a metagenomic profiling tool tailored towards estimating the genomic distance between organisms in the sample and the reference database. We enhance the underlying scoring system of MicrobeGPS to account for the sequence patterns in terms of mapping coverage of an acceptor and donor involved in an HGT event, and report a ranked list of reference candidates. These candidates can then be further evaluated by tools like Daisy to establish HGT regions. We successfully validated our approach on both simulated and real data, and show its benefits in an investigation of an outbreak involving Methicillin-resistant Staphylococcus aureus data.
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