Identical sequences found in distant genomes reveal frequent horizontal transfer across the bacterial domain
- Netherlands Cancer Institute, Netherlands
- Utrecht University, Netherlands
- Max Planck Institute for Molecular Genetics, Germany
- Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine,, Germany
Abstract
Horizontal Gene Transfer (HGT) is an essential force in microbial evolution. Despite detailed studies on a variety of systems, a global picture of HGT in the microbial world is still missing. Here, we exploit that HGT creates long identical DNA sequences in the genomes of distant species, which can be found efficiently using alignment-free methods. Our pairwise analysis of 93 481 bacterial genomes identified 138 273 HGT events. We developed a model to explain their statistical properties as well as estimate the transfer rate between pairs of taxa. This reveals that long-distance HGT is frequent: our results indicate that HGT between species from different phyla has occurred in at least 8% of the species. Finally, our results confirm that the function of sequences strongly impacts their transfer rate, which varies by more than 3 orders of magnitude between different functional categories. Overall, we provide a comprehensive view of HGT, illuminating a fundamental process driving bacterial evolution.
Data availability
Results of the analysis are provided as supplementary files
Article and author information
Author details
Funding
Netherlands Organisation for Scientific Research (Vidi grant 864.14.004)
- Ksenia Arkhipova
- Bas Dutilh
H2020 European Research Council (Consolidator Grant 865694: DiversiPHI)
- Bas Dutilh
Fondation pour la Recherche Médicale (SPE201803005264)
- Florian Massip
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Richard A Neher, University of Basel, Switzerland
Version history
- Received: September 3, 2020
- Accepted: June 13, 2021
- Accepted Manuscript published: June 14, 2021 (version 1)
- Version of Record published: July 9, 2021 (version 2)
Copyright
© 2021, Sheinman et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 3,431
- views
-
- 400
- downloads
-
- 26
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Identical sequences found in distant genomes reveal frequent horizontal transfer across the bacterial domain
eLife 10:e62719.
https://doi.org/10.7554/eLife.62719
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).
-
- Computational and Systems Biology
Transcriptomic profiling became a standard approach to quantify a cell state, which led to accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here we present Phantasus - a user-friendly web-application for interactive gene expression analysis which provides a streamlined access to more than 96000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed on-line at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under MIT license.
