FAANG chicken additional data
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB53920 | ENA |
REPOSITORIES: ENA
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Project description:The Functional Annotation of ANimal Genomes (FAANG) project aims, through a coordinated international effort, to provide high quality functional annotation of animal genomes with an initial focus on farmed and companion animals. A key goal of the initiative is to ensure high quality and rich supporting metadata to describe the project's animals, specimens, cell cultures and experimental assays. By defining rich sample and experimental metadata standards and promoting best practices in data descriptions, deposition and openness, FAANG champions higher quality and reusability of published datasets. FAANG has established a Data Coordination Centre, which sits at the heart of the Metadata and Data Sharing Committee. It continues to evolve the metadata standards, support submissions and, crucially, create powerful and accessible tools to support deposition and validation of metadata. FAANG conforms to the findable, accessible, interoperable, and reusable (FAIR) data principles, with high quality, open access and functionally interlinked data. In addition to data generated by FAANG members and specific FAANG projects, existing datasets that meet the main-or more permissive legacy-standards are incorporated into a central, focused, functional data resource portal for the entire farmed and companion animal community. Through clear and effective metadata standards, validation and conversion software, combined with promotion of best practices in metadata implementation, FAANG aims to maximise effectiveness and inter-comparability of assay data. This supports the community to create a rich genome-to-phenotype resource and promotes continuing improvements in animal data standards as a whole.
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Project description:Carbon (C) and nitrogen (N) contents of grain-filling stage are keys item that determined the growth of rice, and also the quality of seed. Therefore, to elucidating the mechanism of C/N signaling in a seed is an important problem for crops whose seed is used as food like rice. The DNA microarray analysis with the rice seed which was performed the additional fertilizer at the time of heading, in order to clarify how C/N signal change of the rhizosphere in seed production stage affects a seed component on a gene expression level.
2014-08-04 | GSE49818 | GEO
Project description:BACKGROUND: Modeling results from chicken microarray studies is challenging for researchers due to little functional annotation associated with these arrays. The Affymetrix GenChip chicken genome array, one of the biggest arrays that serve as a key research tool for the study of chicken functional genomics, is among the few arrays that link gene products to Gene Ontology (GO). However the GO annotation data presented by Affymetrix is incomplete, for example, they do not show references linked to manually annotated functions. In addition, there is no tool that facilitates microarray researchers to directly retrieve functional annotations for their datasets from the annotated arrays. This costs researchers amount of time in searching multiple GO databases for functional information. RESULTS: We have improved the breadth of functional annotations of the gene products associated with probesets on the Affymetrix chicken genome array by 45% and the quality of annotation by 14%. We have also identified the most significant diseases and disorders, different types of genes, and known drug targets represented on Affymetrix chicken genome array. To facilitate functional annotation of other arrays and microarray experimental datasets we developed an Array GO Mapper (AGOM) tool to help researchers to quickly retrieve corresponding functional information for their dataset. CONCLUSION: Results from this study will directly facilitate annotation of other chicken arrays and microarray experimental datasets. Researchers will be able to quickly model their microarray dataset into more reliable biological functional information by using AGOM tool. The disease, disorders, gene types and drug targets revealed in the study will allow researchers to learn more about how genes function in complex biological systems and may lead to new drug discovery and development of therapies. The GO annotation data generated will be available for public use via AgBase website and will be updated on regular basis.
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Project description:Carbon (C) and nitrogen (N) contents of grain-filling stage are keys item that determined the growth of rice, and also the quality of seed. Therefore, to elucidating the mechanism of C/N signaling in a seed is an important problem for crops whose seed is used as food like rice. The DNA microarray analysis with the rice seed which was performed the additional fertilizer at the time of heading, in order to clarify how C/N signal change of the rhizosphere in seed production stage affects a seed component on a gene expression level. Fertilization was supplied at the same time of plantation and performed middle fertilizing 37 days after germination. 400 mg of ammonium chloride (NH4 Cl) was supplied at the time of heading to the “ + NH4 Cl ” group. Rice seeds were selected from 6 plants for RNA extraction, and hybridization on Affymetrix microarrays. We sought to understand the change of gene expression by the additional fertilization.
2014-08-04 | E-GEOD-49818 | biostudies-arrayexpress
Project description:BackgroundWhile high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature.ResultsWe suggest an intuitive permutation-based testing procedure for assessing the additional predictive value of high-dimensional molecular data. Our method combines two well-known statistical tools: logistic regression and boosting regression. We give clear advice for the choice of the only method parameter (the number of boosting iterations). In simulations, our novel approach is found to have very good power in different settings, e.g. few strong predictors or many weak predictors. For illustrative purpose, it is applied to the two publicly available cancer data sets.ConclusionsOur simple and computationally efficient approach can be used to globally assess the additional predictive power of a large number of candidate predictors given that a few clinical covariates or a known prognostic index are already available. It is implemented in the R package "globalboosttest" which is publicly available from R-forge and will be sent to the CRAN as soon as possible.
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