ReviewComputational and structural biotechnology journal2016
Computational methods for predicting genomic islands in microbial genomes.
Review in Computational and structural biotechnology journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
33 citing papers in PubMed, 85 citations in OpenAlex.
- GIPSy2: high-performance and scalable genomic island prediction software.Scientific reports · 2026Article
- Current state and future prospects of Horizontal Gene Transfer detection.NAR genomics and bioinformatics · 2025Review
- Characterization and Molecular Insights of a Chromium-Reducing BacteriumMicroorganisms · 2024Article
- Computational approach to identify novel genomic features conferring high fitness in Bacillus atrophaeus CNY01 and Bacillus velezensis AK-0 associated with plant growth promotion (PGP) in apple.BMC plant biology · 2024Article
- Article
- Use of whole genome sequencing for surveillance and control of foodborne diseases:Frontiers in microbiology · 2024Review
- A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures.Nucleic acids research · 2023Article
- The complete and closed genome of the facultative generalist Candidatus Endoriftia persephone from deep-sea hydrothermal vents.Molecular ecology resources · 2022Article
- Genome sequencing of biocontrol strain Bacillus amyloliquefaciens Bam1 and further analysis of its heavy metal resistance mechanism.Bioresources and bioprocessing · 2022Article
- Discovery of archaeal fusexins homologous to eukaryotic HAP2/GCS1 gamete fusion proteins.Nature communications · 2022Article
- Comparative Genomics Revealed Wide Intra-Species Genetic Heterogeneity and Lineage-Specific Genes ofMicrobiology spectrum · 2022Article
- Complete genome sequence of biocontrol strain Paenibacillus peoriae HJ-2 and further analysis of its biocontrol mechanism.BMC genomics · 2022Article
- Identifying genomic islands with deep neural networks.BMC genomics · 2021Article
- Article
- Typing methods based on whole genome sequencing data.One health outlook · 2020Review
- Genomic Characteristics of InvasiveBioinformatics and biology insights · 2020Article
- Pathogenicity Factors of Genomic Islands in Intestinal and ExtraintestinalFrontiers in microbiology · 2020Review
- Serratia liquefaciens FG3 isolated from a metallophyte plant sheds light on the evolution and mechanisms of adaptive traits in extreme environments.Scientific reports · 2019Article
- VCGIDB: A Database and Web Resource for the Genomic Islands fromPathogens (Basel, Switzerland) · 2019Article
- Current and Promising Approaches to Identify Horizontal Gene Transfer Events in Metagenomes.Genome biology and evolution · 2019Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clusters of genes acquired by lateral gene transfer in microbial genomes, are broadly referred to as genomic islands (GIs). GIs often carry genes important for genome evolution and adaptation to niches, such as genes involved in pathogenesis and antibiotic resistance. Therefore, GI prediction has gradually become an important part of microbial genome analysis. Despite inherent difficulties in identifying GIs, many computational methods have been developed and show good performance. In this mini-review, we first summarize the general challenges in predicting GIs. Then we group existing GI detection methods by their input, briefly describe representative methods in each group, and discuss their advantages as well as limitations. Finally, we look into the potential improvements for better GI prediction.
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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.