ArticleGenes2020
ShadowCaster: Compositional Methods under the Shadow of Phylogenetic Models to Detect Horizontal Gene Transfers in Prokaryotes.
Article in Genes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- A machine learning framework for interpreting phylogenetic tree patterns in interkingdom horizontal gene transfer.Frontiers in bioinformatics · 2026Article
- Current state and future prospects of Horizontal Gene Transfer detection.NAR genomics and bioinformatics · 2025Review
- Detecting horizontal gene transfer among microbiota: an innovative pipeline for identifying co-shared genes within the mobilome through advanced comparative analysis.Microbiology spectrum · 2024Article
- HGTree v2.0: a comprehensive database update for horizontal gene transfer (HGT) events detected by the tree-reconciliation method.Nucleic acids research · 2023Article
- A complete theoretical framework for inferring horizontal gene transfers using partial order sets.PloS one · 2023Article
- hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer.International journal of molecular sciences · 2022Article
- Current Methods for Recombination Detection in Bacteria.International journal of molecular sciences · 2022Review
- Indirect identification of horizontal gene transfer.Journal of mathematical biology · 2021Article
- Selenium Nanoparticles as an Innovative Selenium Fertilizer Exert Less Disturbance to Soil Microorganisms.Frontiers in microbiology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Horizontal gene transfer (HGT) plays an important role for evolutionary innovations within prokaryotic communities and is a crucial event for their survival. Several computational approaches have arisen to identify HGT events in recipient genomes. However, this has been proven to be a complex task due to the generation of a great number of false positives and the prediction disagreement among the existing methods. Phylogenetic reconstruction methods turned out to be the most reliable ones, but they are not extensible to all genes/species and are computationally demanding when dealing with large datasets. In contrast, the so-called surrogate methods that use heuristic solutions either based on nucleotide composition patterns or phyletic distribution of BLAST hits can be applied easily to the genomic scale, but they fail in identifying common HGT events. Here, we present ShadowCaster, a hybrid approach that sequentially combines nucleotide composition-based predictions by support vector machines (SVMs) under the shadow of phylogenetic models independent of tree reconstruction, to improve the detection of HGT events in prokaryotes. ShadowCaster successfully predicted close and distant HGT events in both artificial and bacterial genomes. ShadowCaster detected HGT related to heavy metal resistance in the genome of
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Registered trials
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.