ArticleBMC genomics2018
EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06924697 (Effect of Glucagon-like Peptide-1), which is not on this map. Cited by 3 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Effect of Glucagon-like Peptide-1 (GLP-1) Receptor Agonist Stimulation on Smoking Consumption in Type 2 Diabetes Patients: Study Protocol of a Randomized, Parallel -Controlled Clinical Trial
Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Widespread genomic islands are hotspots of genome variations and mosaicism in giant viruses.Nature communications · 2026Article
- Article
- Involvement of β-Carbonic Anhydrase Genes in Bacterial Genomic Islands and Their Horizontal Transfer to Protists.Applied and environmental microbiology · 2018Article
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
Abstract
backgroundGenomic islands (GIs) are inserts of foreign DNA that have potentially arisen through horizontal gene transfer (HGT). There are evidences that GIs can contribute significantly to the evolution of prokaryotes. The acquisition of GIs through HGT in eukaryotes has, however, been largely unexplored. In this study, the previously developed GI prediction tool, SeqWord Gene Island Sniffer (SWGIS), is modified to predict GIs in eukaryotic chromosomes. Artificial simulations are used to estimate ratios of predicting false positive and false negative GIs by inserting GIs into different test chromosomes and performing the SWGIS v2.0 algorithm. Using SWGIS v2.0, GIs are then identified in 36 fungal, 22 protozoan and 8 invertebrate genomes.
resultsSWGIS v2.0 predicts GIs in large eukaryotic chromosomes based on the atypical nucleotide composition of these regions. Averages for predicting false negative and false positive GIs were 20.1% and 11.01% respectively. A total of 10,550 GIs were identified in 66 eukaryotic species with 5299 of these GIs coding for at least one functional protein. The EuGI web-resource, freely accessible at http://eugi.bi.up.ac.za , was developed that allows browsing the database created from identified GIs and genes within GIs through an interactive and visual interface.
conclusionsSWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes.
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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.