Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1090234 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Pangenome arrays contain DNA oligomers targeting several sequenced reference genomes from the same species. In microbiology these can be employed to investigate the often high genetic variability within a species by comparative genome hybridization (CGH). The biological interpretation of pangenome CGH data depends on the ability to compare strains at a functional level, particularly by comparing the presence or absence of orthologous genes. Due to the high genetic variability, available genotype-calling algorithms can not be applied to pangenome CGH data. Therefore, we have developed the algorithm PanCGH that incorporates orthology information about genes to predict the presence or absence of orthologous genes in a query organism using CGH arrays that target the genomes of sequenced representatives of a group of microorganisms. PanCGH was tested and applied in the analysis of genetic diversity among 39 Lactococcus lactis strains from three different subspecies (lactis, cremoris, hordniae) and isolated from two different niches (dairy and plant). Clustering of these strains using the presence/absence data of gene orthologs revealed a clear separation between different subspecies and reflected the niche of the strains. Keywords: CGH, CGH analysis, orthology, Lactococcus lactis
2008-12-31 | GSE12638 | GEO
Project description:Pangenome arrays contain DNA oligomers targeting several sequenced reference genomes from the same species. In microbiology these can be employed to investigate the often high genetic variability within a species by comparative genome hybridization (CGH). The biological interpretation of pangenome CGH data depends on the ability to compare strains at a functional level, particularly by comparing the presence or absence of orthologous genes. Due to the high genetic variability, available genotype-calling algorithms can not be applied to pangenome CGH data. Therefore, we have developed the algorithm PanCGH that incorporates orthology information about genes to predict the presence or absence of orthologous genes in a query organism using CGH arrays that target the genomes of sequenced representatives of a group of microorganisms. PanCGH was tested and applied in the analysis of genetic diversity among 39 Lactococcus lactis strains from three different subspecies (lactis, cremoris, hordniae) and isolated from two different niches (dairy and plant). Clustering of these strains using the presence/absence data of gene orthologs revealed a clear separation between different subspecies and reflected the niche of the strains. Keywords: CGH, CGH analysis, orthology, Lactococcus lactis We analyzed 39 CGH arrays, where on each array different strain of L. lactis was hybridized.
2010-06-24 | E-GEOD-12638 | biostudies-arrayexpress
Project description:A goat graph-based pangenome reveals structural variations involved in domestication and adaptation
| PRJNA1097515 | ENA
Project description:Over the last century, rainbow trout (RBT) has been widely used as a biological model in many biological disciplines and has become one of the best-studied fish. For example, RBT is an excellent model to study gene evolution as it is a partially tetraploid organism that underwent a whole-genome duplication (salmonid-specific 4th WGD) followed by a partial re-diploidization and significant genome rearrangements. In the meantime, RBT domestication spread quickly and globally to six continents for aquaculture and recreation. Efforts to sequence a pangenome reference have begun, and genome sequence is available for at least three rainbow trout clonal lines. However, epigenome reference annotations are needed to understand the functional genomic basis of RBT's phenotypic, environmental, and evolutional variations. This study provides a comprehensive catalog and epigenome annotation tracks of the rainbow trout. Gene regulatory elements, including chromatin histone modifications, chromatin accessibility, and DNA methylation, were identified by integrating data from ChIP-seq, ATAC-seq, and Methyl Mini-seq across RBT tissues together with RNA-seq gene expression data sets.
2024-10-03 | GSE245212 | GEO