| URL: | http://eugi.bi.up.ac.za |
| Full name: | EuGI |
| Description: | SWGIS v2.0 predicts GIs in large eukaryotic chromosomes based on the atypical nucleotide composition of these regions. Along with SWGIS, the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web |
| Year founded: | 2018 |
| Last update: | 2018.09.21 |
| Version: | v2.0 |
| Accessibility: |
Accessible
|
| Country/Region: | South Africa |
| Data type: | |
| Data object: |
NA
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| Database category: | |
| Major species: |
NA
|
| Keywords: |
| University/Institution: | University of Pretoria |
| Address: | Centre for Bioinformatics and Computational Biology; Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria 0002, Private Bag X20, Hatfield, 0028 South Africa |
| City: | Hatfield |
| Province/State: | |
| Country/Region: | South Africa |
| Contact name (PI/Team): | Frederick Johannes Clasen |
| Contact email (PI/Helpdesk): | edohan.clasen@fabi.up.ac.za |
|
EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes. [PMID: 29724163]
BACKGROUND: Genomic 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. |