Loriini
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA498485 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Strains 2-22 (S. agalactiae ST261 isolated from fish) and A909 (ST7) were grown in TH medium, at 30C and harvested at OD 0.3-0.4. Please note: ST261 and ST7 refer to MLST types commonly used in S.agalactiae as a first approach for phylogenomic relationships (MLST is based on the sequence of 7 genes).
2013-03-01 | E-MEXP-3828 | biostudies-arrayexpress
Project description:Strains A909 (ST7 strain isolated from human) and CF01173 (ST7 strain isolated from fish) were grown in TH medium at 37C and harvested at OD 0.3-0.4. Please note: ST7 refers to MLST types commonly used in S.agalactiae as a first approach for phylogenomic relationships (MLST is based on the sequence of 7 genes).
2013-03-01 | E-MEXP-3830 | biostudies-arrayexpress
Project description:Molecular phylogenomics investigates evolutionary relationships based on genomic data. However, despite genomic sequence conservation, changes in protein interactions can occur relatively rapidly and may cause strong functional diversification. To investigate such functional evolution, we here combine phylogenomics with interaction proteomics. We develop this concept by investigating the molecular evolution of the shelterin complex, which protects telomeres, across 16 vertebrate species from zebrafish to humans covering 450 million years of evolution. Our phylointeractomics screen discovers previously unknown telomere-associated proteins and reveals how homologous proteins undergo functional evolution. For instance, we show that TERF1 evolved as a telomere-binding protein in the common stem lineage of marsupial and placental mammals. Phylointeractomics is a versatile and scalable approach to investigate evolutionary changes in protein function and thus can provide experimental evidence for phylogenomic relationships.
2017-02-15 | PXD005517 | Pride
Project description:Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship estimation could provide valuable information for missing persons cases. The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a robust machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.
2022-07-27 | GSE209804 | GEO
Project description:HybSeq solves relationships within Euphorbia balsamifera
| PRJNA415769 | ENA