HBeeID: A molecular tool that identifies honey bee subspecies from different geographic populations.
Ontology highlight
ABSTRACT:
INSTRUMENT(S): -
ORGANISM(S): Apis Mellifera
SUBMITTER: Puerto Rico Science, Technology and Research Trust
PROVIDER: PRJEB74317 | EVA | 2024-03-30
REPOSITORIES: EVA
Dataset's files
Items per page: 5 1 - 4 of 4 |
Similar Datasets
Project description:BackgroundHoney bees are the principal commercial pollinators. Along with other arthropods, they are increasingly under threat from anthropogenic factors such as the incursion of invasive honey bee subspecies, pathogens and parasites. Better tools are needed to identify bee subspecies. Genomic data for economic and ecologically important organisms is increasing, but in its basic form its practical application to address ecological problems is limited.ResultsWe introduce HBeeID a means to identify honey bees. The tool utilizes a knowledge-based network and diagnostic SNPs identified by discriminant analysis of principle components and hierarchical agglomerative clustering. Tests of HBeeID showed that it identifies African, Americas-Africanized, Asian, and European honey bees with a high degree of certainty even when samples lack the full 272 SNPs of HBeeID. Its prediction capacity decreases with highly admixed samples.ConclusionHBeeID is a high-resolution genomic, SNP based tool, that can be used to identify honey bees and screen species that are invasive. Its flexible design allows for future improvements via sample data additions from other localities.
| S-EPMC11348773 | biostudies-literature
Project description:Recent breakthroughs in spatial transcriptomics technologies have enhanced our understanding of diverse cellular identities, spatial organizations, and functions. Yet existing spatial transcriptomics tools are still limited in either transcriptomic coverage or spatial resolution, hindering unbiased, hypothesis-free transcriptomic analyses at high spatial resolution. Here we develop Reverse-padlock Amplicon Encoding FISH (RAEFISH), an image-based spatial transcriptomics method with whole-genome coverage and single-molecule resolution in intact tissues. We demonstrate spatial profiling of 23,000 human or 22,000 mouse transcripts in single cells and tissue sections. Our analyses reveal transcript-specific subcellular localization, cell-type-specific and cell-type-invariant zonation-dependent transcriptomes, and gene programs underlying preferential cell-cell interactions. Finally, we further develop our technology for direct spatial readout of gRNAs in an image-based high-content CRISPR screen. Overall, these developments provide the research community with a broadly applicable technology that enables high-coverage, high-resolution spatial profiling of both long and short, native and engineered RNA species in many biomedical contexts.
2025-07-22 | GSE302564 | GEO
Project description:Microarrays have become a powerful tool for DNA-based molecular diagnostics and identification of pathogens. However, most of them target a limited range of organisms and are generally based on only one or very few genes for organism identification. Although such microarrays are proven tools for species identification, they suffer from the fact that identification is only possible for organisms for which probes were specifically pre-developed. Furthermore, this approach often leads to problems with taxonomic-level resolution with insufficient diagnostic differences between closely related taxa found in the commonly used DNA sequences. An alternative strategy is to use the hybridisation pattern generated by many different anonymous markers distributed over the entire genome for identification based on comparison to a type database. We realised this strategy using a high density microarray containing 95,000 different 13-mer probes. Here, we demonstrate the specificity of our microarray based on results obtained with nine different bacterial species and strains. The hybridisation patterns allowed clear differentiation at the strain and even variant level. The reproducibility of our system was high as shown by high correlation coefficients between replicates, despite the occurrence of mismatch hybridisation. The results indicate the potential for identification of all bacterial taxa at the subspecies level using our universal microarray.
2010-03-16 | GSE15391 | GEO
Project description:High resolution HiC libraries are usually lightly sequenced before investing in a deep sequencing. We modeled HiC resolution in function of the sequencing depth to predict accurately the resolution of any high resolution HiC library given a small sequnecing batch of the library. To test our tool, we used public datasets as well as a newly generated dataset using Arima kit on mouse purified rods photoreceptors.
2021-11-30 | GSE152491 | GEO
Project description:Microarrays have become a powerful tool for DNA-based molecular diagnostics and identification of pathogens. However, most of them target a limited range of organisms and are generally based on only one or very few genes for organism identification. Although such microarrays are proven tools for species identification, they suffer from the fact that identification is only possible for organisms for which probes were specifically pre-developed. Furthermore, this approach often leads to problems with taxonomic-level resolution with insufficient diagnostic differences between closely related taxa found in the commonly used DNA sequences. An alternative strategy is to use the hybridisation pattern generated by many different anonymous markers distributed over the entire genome for identification based on comparison to a type database. We realised this strategy using a high density microarray containing 95,000 different 13-mer probes. Here, we demonstrate the specificity of our microarray based on results obtained with nine different bacterial species and strains. The hybridisation patterns allowed clear differentiation at the strain and even variant level. The reproducibility of our system was high as shown by high correlation coefficients between replicates, despite the occurrence of mismatch hybridisation. The results indicate the potential for identification of all bacterial taxa at the subspecies level using our universal microarray. Hybridisation patterns of DNA from bacterial type strains (E. coli strains K12 and B, Pantoea agglomerans strains ATCC27155T and C9-1, Pantoea stewartii pv stewartii strain DC283, Salmonella Typhimurium strains LT2 and DT204 and Micrococcus luteus) were compared to each other. Using GeneSpring v7.3.1, cluster analyses were performed as well as ANOVA in order to determine the more discriminative probes out of our 95,000-probe panel.
2010-05-06 | E-GEOD-15391 | biostudies-arrayexpress
Project description:Development of high resolution DNA barcode for Dioscorea Species Discrimination based on chloroplast genomes
| PRJEB29179 | ENA
Project description:The increasing application of RNA-seq to study non-model species demands easy-to-use and efficient bioinformatics tools to help researchers quickly uncover biological and functional insights. We developed ExpressAnalyst (www.expressanalyst.ca), a web-based tool for processing, analyzing, and interpreting RNA-seq data from any eukaryotic species. ExpressAnalyst contains a series of modules that enable raw data processing and annotation of FASTQ files, and statistical and functional analysis of counts tables and gene lists. All modules are integrated with EcoOmicsDB, an ortholog database that enables comprehensive analysis for species without a reference transcriptome. By coupling ultra-fast read mapping algorithms with high-resolution ortholog databases through a user-friendly web interface, ExpressAnalyst enables researchers to obtain global expression profiles and gene-level insights from raw RNA-seq reads within 24 hours. Here, we present ExpressAnalyst and demonstrate its utility with a case study of RNA-seq data from multiple non-model salamander species, including two that do not have a reference transcriptome.
2023-04-27 | GSE225876 | GEO
Project description:Aspergillus flavus and A. oryzae represent two unique species predicted to have spent centuries in vastly different environments. A. flavus is an important opportunistic plant pathogen known for contaminating crops with the carcinogenic mycotoxin, aflatoxin and A. oryzae is a domesticated fungus used in food fermentations. Remarkably, the genomes of these two species are still nearly identical. We have used the recently sequenced genomes of A. oryzae RIB40 and A. flavus NRRL3357 along with array based comparative genome hybridization (CGH) as a tool to compare genomes across several strains of these two species. A comparison of three strains from each species by CGH revealed only 42 and 129 genes unique to A. flavus and A. oryzae, respectively. Further, only 709 genes were identified as being polymorphic between the species. Despite the high degree of similarity between these two species, correlation analysis among all data from the CGH arrays for all strains used in this study reveals a species split. However, this view of species demarcation becomes muddled when focused on only those genes for secondary metabolism.
2010-04-10 | GSE15650 | GEO
Project description:The effect of nanomaterials (NMs) is less understood in light of the implemented and existing methodologies for regular chemicals. To understand the mode of action of NMs is one of the alternatives to improve predictions and environmental risk assessment (ERA). In the present work the high-throughput gene expression tool (4x44K microarray for Enchytraeus crypticus) was used to investigate the mechanisms activated by Ni exposure. Ni nanoparticles (Ni-NPs) were investigated together with Ni-salt (NiNO3). Testing was done based on reproduction effect concentrations (EC20, EC50) using 3 and 7 days exposure periods.
2017-06-13 | GSE69793 | GEO
Project description:The full complement of molecular pathways contributing to Parkinson’s disease (PD) pathogenesis remains unknown. Here, to address this issue, we began by using a high-resolution variant of functional magnetic resonance imaging (fMRI) to pinpoint brainstem regions differentially affected by, and resistant to, the disease. Then, relying on the imaging information as a guide, we profiled gene expression levels of postmortem brain samples and used a factorial statistical model to identify a disease related decrease in the expression of the polyamine enzyme spermidine/spermine N1-acetyltransferase 1 (SAT1). Next, a series of studies were performed to confirm the pathogenic relevance of this finding. First, to test for a causal link between polyamines and α-synuclein toxicity, we investigated a yeast model expressing α-synuclein. Polyamines were found to enhance the toxicity of α-synuclein, and an unbiased genome-wide screen for modifiers of α-synuclein toxicity identified Tpo4, a member of a family of proteins responsible for polyamine transport. Second, to test for a causal link between SAT1 activity and PD histopathology we investigated a mouse model expressing α-synuclein. DENSPM (N1, N11-diethylnorspermine), a polyamine analog that increases SAT1 activity, was found to reduce PD histopathology, while Berenil (diminazene aceturate), a pharmacological agent that reduces SAT1 activity, worsened the histopathology. Third, we genotyped PD patients and controls and isolated a rare but novel variant in the SAT1 gene, although the functional significance of this genetic variant was not identified. Taken together, the results suggest that the polyamine pathway contributes to PD pathogenesis. Imaging-guided microarray In principle, gene expression profiling techniques like microarray are well suited to identify molecular pathways contributing to the pathogenesis of complex diseases. In practice, however, microarray applied to diseases of the brain present a number of analytic challenges. By identifying regions within the same brain structure that are differentially targeted by and resistant to a disease, imaging-guided microarray is an approach designed to address these limitations. Specifically, guided by the spatial information generated from high resolution functional imaging, a 2x2 factorial analysis-of-variance can be designed, including both within and between group factors, and this “double subtraction” model is effective in improving signal-to-noise in a microarray experiment. Relying on imaging findings, we harvested the DMNV from 6 postmortem brains with evidence of PD and from 5 control brains. The postmortem PD cases were evaluated for pathological changes (Lewy body-containing neurons and Lewy neurites evidenced with antibodies directed against α-synuclein aggregates) that matched the pattern proposed by Braak. We relied on the imaging results to identify a neighboring medullary region relatively unaffected by the disease to be used as a within-brain control. We decided on the inferior olivary nucleus (ION), because it is histologically identifiable, and harvested the ION from each of the 6 PD cases and 5 controls. Microarray techniques were used to generate gene expression profiles for each of the 22 tissue samples. A repeated-measures 2x2 factorial ANOVA model constructed for the imaging study was applied to the expression dataset, in which expression levels from two regions of the medulla (DMNV vs. ION) were included as the first within group factor, diagnosis (PD vs. controls) was the between group factor, and age and sex were included as covariates. Based on current literature, one of the top hits (SAT1) was investigated further to determine if it played a role in PD pathogenesis.
2010-08-19 | E-GEOD-19587 | biostudies-arrayexpress