ArticleMolecular biology and evolution2010
Inference and characterization of horizontally transferred gene families using stochastic mapping.
Article in Molecular biology and evolution, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed, 91 citations in OpenAlex.
- Home is where the host is: evolutionary history of geographic spread, host switching and adaptive genomic signatures in two generalist group BMicrobial genomics · 2026Article
- Co-occurrence is associated with horizontal gene transfer across marine bacteria independent of phylogeny.The ISME journal · 2026Article
- Co-occurrence is associated with horizontal gene transfer across marine bacteria independent of phylogeny.bioRxiv : the preprint server for biology · 2025Article
- Timescale and genetic linkage explain the variable impact of defense systems on horizontal gene transfer.Genome research · 2025Article
- Persistent, Private, and Mobile Genes: A Model for Gene Dynamics in Evolving Pangenomes.Molecular biology and evolution · 2025Article
- A global survey of prokaryotic genomes reveals the eco-evolutionary pressures driving horizontal gene transfer.Nature ecology & evolution · 2024Article
- Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses.Genome biology · 2023Article
- Robust analysis of prokaryotic pangenome gene gain and loss rates with Panstripe.Genome research · 2023Article
- Differences in genetic flux in invasiveHeliyon · 2022Article
- Evolution of the connectivity and indispensability of a transferable gene: the simplicity hypothesis.BMC ecology and evolution · 2022Article
- Realistic Gene Transfer to Gene Duplication Ratios Identify Different Roots in the Bacterial Phylogeny Using a Tree Reconciliation Method.Life (Basel, Switzerland) · 2022Article
- Mirage: estimation of ancestral gene-copy numbers by considering different evolutionary patterns among gene families.Bioinformatics advances · 2021Article
- Molecular Evolution ofFrontiers in plant science · 2019Article
- Avoiding Regions Symptomatic of Conformational and Functional Flexibility to Identify Antiviral Targets in Current and Future Coronaviruses.Genome biology and evolution · 2016Article
- Article
- The Landscape of A-to-I RNA Editome Is Shaped by Both Positive and Purifying Selection.PLoS genetics · 2016Article
- Evolutionary assembly patterns of prokaryotic genomes.Genome research · 2016Article
- Regulation of genetic flux between bacteria by restriction-modification systems.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Genomic analysis of 38 Legionella species identifies large and diverse effector repertoires.Nature genetics · 2016Article
- SICLE: a high-throughput tool for extracting evolutionary relationships from phylogenetic trees.PeerJ · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Macrogenomic events, in which genes are gained and lost, play a pivotal evolutionary role in microbial evolution. Nevertheless, probabilistic-evolutionary models describing such events and methods for their robust inference are considerably less developed than existing methodologies for analyzing site-specific sequence evolution. Here, we present a novel method for the inference of gains and losses of gene families. First, we develop probabilistic-evolutionary models describing the dynamics of gene-family content, which are more biologically realistic than previously suggested models. In our likelihood-based models, gains and losses are represented by transitions between presence and absence, given an underlying phylogeny. We employ a mixture-model approach in which we allow both the gain rate and the loss rate to vary among gene families. Second, we use these models together with the analytic implementation of stochastic mapping to infer branch-specific events. Our novel methodology allows us to infer and quantify horizontal gene transfer (HGT) events. This enables us to rank various gene families and lineages according to their propensity to undergo gains and losses. Applying our methodology to 4,873 gene families shows that: 1) the novel mixture models describe the observed variability in gene-family content among microbes significantly better than previous models; 2) The stochastic mapping approach enables accurate inference of gain and loss events based on simulations; 3) At least 34% of the gene families analyzed are inferred to have experienced HGT at least once during their evolution; and 4) Gene families that were inferred to experience HGT are both enriched and depleted with respect to specific functional categories.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.