Data integration in biodiversity - A principal investigation on three liverwort species of Riccia integrating metabolomics, sequencing and phenotypic data for use in integrative taxonomy
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PROVIDER: PRJEB57912 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Riccia fluitans isolate:Rf_01 Genome sequencing
| PRJNA1158334 | ENA
Project description:Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-based method that integrates data transformation in PCA and finds an appropriate data transformation using the maximum profile likelihood. Extensions of the method to handle functional data and missing values are also developed. Several numerical algorithms are provided for efficient computation. The proposed method is illustrated using simulated and real-world data examples.
| S-EPMC4408558 | biostudies-literature
Project description:BackgroundToday's species names are entry points into a web of publicly available knowledge and are integral parts of legislation concerning biological conservation and consumer safety. Species information usually is fragmented, can be misleading due to the existence of different names and might even be biased because of an identical name that is used for a different species. Safely navigating through the name space is one of the most challenging tasks when associating names with data and when decisions are made which name to include in legislation. Integrating publicly available dynamic data to characterise plant genetic resources of botanic gardens and other facilities will significantly increase the efficiency of recovering relevant information for research projects, identifying potentially invasive taxa, constructing priority lists and developing DNA-based specimen authentication.New informationTo demonstrate information availability and discuss integration into botanic collections, scientific names derived from botanic gardens were evaluated using the Encyclopedia of Life, The Catalogue of Life and The Plant List. 98.5% of the names could be verified by the combined use of these providers. Comparing taxonomic status information 13 % of the cases were in disagreement. About 7 % of the verified names were found to be included in the International Union for Conservation of Nature Red List, including one extinct taxon and three taxa with the status "extinct in the wild". As second most important factor for biodiversity loss, potential invasiveness was determined. Approximately 4 % of the verified names were detected using the Global Invasive Species Information Network, including 208 invasive taxa. According to Delivering Alien Invasive Species Inventories for Europe around 20 % of the verified names are European alien taxa including 15 of the worst European invasive taxa. Considering alternative names in the data recovery process, success increased up to 18 %.
| S-EPMC4910503 | biostudies-literature
Project description:Characterization of Riccia fluitans environmental adaptation using direct RNA
| PRJEB72691 | ENA
Project description:Characterization of Riccia fluitans environmental adaptation using RNA-seq method
| PRJEB72692 | ENA
Project description:Purpose: The goal of this study is to integrate NGS-derived parotid salivary gland transcriptome profiling (RNA-seq) to metabolomics (UPLC-MS/MS) methods and identify mechanisms driving radiation-induced xerostomia, with potential clinical application to head and neck cancer patients. Methods: Parotid salvary gland mRNA profiles of 28-day-old wild-type (WT) and 5 Gy radiation treated mice were generated by deep sequencing, in triplicate, using Illumina TruSeq. The sequenced reads that passed quality filters were trimmed with Trimmomatic, mapped to mm10 genome with HISAT2, and summarized at the gene level with HTSeq-count and analyzed with DESeq2. Results: Using an optimized data analysis workflow, we mapped sequenced reads per sample to the mouse genome (build mm10) and identified 25422 genes with at least 1 read in the parotid glands of WT and radiation treated mice with HISTA2 workflow. RNA-seq data QA/QC by Principal Component Analysis showed a clear separation between conditions. 155 genes showed significant differential expression between the WT and radiation treated salivary gland, with an adjsuted p-value < 0.05. Pre ranked Gene Set Enrichment Analysis against the CPDB database led to 859 altered pathways with a p-value < 0.05. Integrative analysis pointed to 103 RNA-Seq/Metabolite joint enriched pathways with a p-value < 0.05.
2021-03-31 | GSE155902 | GEO
Project description:Chemophenetic study involving 4 species of Riccia with 3 biological replications each, acquired in positive and negative ion mode
2022-04-20 | MSV000089284 | MassIVE
Project description:Tibetan sheep microbiome and metabolomics
| PRJEB36249 | ENA
Project description:The interplay between pathogens and hosts has been studied for decades using targeted approaches such as the analysis of mutants and host immunological responses. Although much has been learned from such studies, they focus on individual pathways and fail to reveal the global effects of infection on the host. To alleviate this issue, high-throughput methods such as transcriptomics and proteomics have been used to study host-pathogen interactions. Recently, metabolomics was established as a new method to study changes in the biochemical composition of host tissues. We report a metabolomics study of Salmonella enterica serovar Typhimurium infection. We used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Direct Infusion to reveal that dozens of host metabolic pathways are affected by Salmonella in a murine infection model. In particular, multiple host hormone pathways are disrupted. Our results identify unappreciated effects of infection on host metabolism and shed light on mechanisms used by Salmonella to cause disease, and by the host to counter infection. Female C57BL/6 mice were infected with Salmonella enterica serovar Typhimurium SL1344 cells by oral gavage. Feces and livers were collected and metabolites extracted using acetonitrile. For experiments with feces, samples were collected from 4 mice before and after infection. For liver experiments, 11 uninfected and 11 infected mice were used. Samples were combined into 3 groups of 3-4 mice each, resulting in the analysis of 3 group samples of uninfected and 3 of infected mice. Extracts were infused into a 12-T Apex-Qe hybrid quadrupole-FT-ICR mass spectrometer equipped with an Apollo II electrospray ionization source, a quadrupole mass filter and a hexapole collision cell. Raw mass spectrometry data were processed as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140 [PMID 19081807]). To identify differences in metabolite composition between uninfected and infected samples, we filtered the list of masses for metabolites which were present on one set of samples but not the other. Additionally, we calculated the ratios between averaged intensities of metabolites from uninfected and infected mice. To assign possible metabolite identities, monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org). Masses were searched against the Mus musculus database within a mass error of 3 ppm. Data were analyzed by unpaired t tests with 95% confidence intervals.
2011-02-08 | E-GEOD-21720 | biostudies-arrayexpress