Ontology highlight
ABSTRACT:
PROVIDER: PRJNA611871 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11434657_1.fastq.gz | Fastqsanger.gz | |||
| SRR11434657_2.fastq.gz | Fastqsanger.gz | |||
| SRR11434658_1.fastq.gz | Fastqsanger.gz | |||
| SRR11434658_2.fastq.gz | Fastqsanger.gz | |||
| SRR11434659_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 18 |
Similar Datasets
Project description:Neuropterida phylogeny using Anchored Phylogenomics
| PRJNA398561 | ENA
Project description:Genotyping studies suggest that there is genetic variability among P. gingivalis strains, however the extent of variability remains unclear, and the regions of variability have only partially been identified. We previously used heteroduplex analysis of the ribosomal operon intergenic spacer region (ISR) to type P. gingivalis strains in several diverse populations, identifying 6 predominant heteroduplex types and many minor ones. In addition we used ISR sequence analysis to determine the relatedness of P. gingivalis strains to one another, and demonstrated a link between ISR sequence phylogeny and the disease-associated phenotype of P. gingivalis strains. The availability of whole genome microarrays based on the genomic sequence of strain W83 has allowed a more comprehensive analysis of P. gingivalis strain variability, using the entire genome. The objectives of this study were to define the phylogeny of P. gingivalis strains using the entire genome, to compare the phylogeny based on genome content to the phylogeny based on a single locus (ISR), and to identify genes that are associated with the strongly disease-associated strain W83 that could be important for virulence. Keywords: Comparative genomic hybridization
2008-10-09 | GSE13128 | GEO
Project description:Genotyping studies suggest that there is genetic variability among P. gingivalis strains, however the extent of variability remains unclear, and the regions of variability have only partially been identified. We previously used heteroduplex analysis of the ribosomal operon intergenic spacer region (ISR) to type P. gingivalis strains in several diverse populations, identifying 6 predominant heteroduplex types and many minor ones. In addition we used ISR sequence analysis to determine the relatedness of P. gingivalis strains to one another, and demonstrated a link between ISR sequence phylogeny and the disease-associated phenotype of P. gingivalis strains. The availability of whole genome microarrays based on the genomic sequence of strain W83 has allowed a more comprehensive analysis of P. gingivalis strain variability, using the entire genome. The objectives of this study were to define the phylogeny of P. gingivalis strains using the entire genome, to compare the phylogeny based on genome content to the phylogeny based on a single locus (ISR), and to identify genes that are associated with the strongly disease-associated strain W83 that could be important for virulence. Keywords: Comparative genomic hybridization Comparative genomic analysis of 7 clinically prevalent P. gingivalis strains was performed, using whole genome microarrays based on the sequence of strain W83. Strain W83 was the reference strains and there were 6 test strains. Flip-dye replicates were performed.
2010-05-18 | E-GEOD-13128 | biostudies-arrayexpress
Project description:Streptococcus suis is an important zoonotic pathogen that can cause meningitis and sepsis in both pigs and humans. In this study,we evaluated the genetic difference of 40 Streptococcus suis strains belonging to various sequence types by comparative genomic hybridization to identify genes associated with the variation in pathogenicity using NimbleGen’s tilling microarray platform. Application of Comparative Phylogenomics to Identify Genetic Differences Relating to Pathogenicity of Streptococcus suis
2014-05-20 | GSE40035 | GEO
Project description:We extended the mathematical models of measuring biodiversity to estimate DNA methylation heterogeneity in a cell population. We propose a model-based approach (abundance-based, phylogeny-based and pairwise similarity-based heterogeneity) and consider similarity in DNA methylation patterns from individual cells to evaluate heterogeneity that overcomes biases due to missing data. We also applied commonly used non-model based method (methylation entropy) and other reported methods of estimating methylation heterogeneity such as single-cell based approach to evaluate methylation heterogeineity.
2017-09-15 | GSE103859 | GEO