What is known about ATTEDII as a database to collect data of co-expression of genes?
Best insight from top research papers
ATTED-II is a gene coexpression database for plant species that provides standardized z-score coexpression data based on RNAseq and microarray data. It addresses the challenge of sampling bias by using principal component analysis and ensemble calculation to balance the samples and reduce noise . ATTED-II offers multispecies comparisons to detect gene relationships within an evolutionary context and provides reproducibility assessments of coexpression data . The database includes multiple coexpression data sets for various plant species, allowing for parallel views and network analysis tools . The latest version of ATTED-II expands its coverage to include agriculturally important plants and improves the quality of coexpression data through the inclusion of more gene expression data from microarray and RNA sequencing studies . It is recommended to explore multiple resources and consider different data sets, normalization approaches, and parameter settings to achieve robust analyses and reliable interpretations .
Answers from top 4 papers
More filters
| Papers (4) | Insight |
|---|---|
112 Citations | ATTED-II is a database that collects coexpression data of genes in Arabidopsis, rice, soybean, poplar, grape, alfalfa, and maize, with improved data quality and coverage. |
168 Citations | ATTED-II is a coexpression database for plant species that provides multiple coexpression data sets and network analysis tools. It includes microarray- and RNA sequencing-based coexpression data sets for various plant species, allowing users to find functional gene relationships and design experiments. The quality of the new coexpression data sets in ATTED-II is higher than previous data sets, and it now provides lineage-specific coexpression information. |
219 Citations | ATTED-II is a coexpression database for plant species that provides multiple coexpression platforms for nine plant species, including microarray- and RNA sequencing-based data. It allows for the assessment of the reproducibility of coexpression data and can be combined with external resources for plant biology insights. |
| ATTED-II is a gene coexpression database for nine plant species based on publicly available RNAseq and microarray data. It uses a sample balancing technique to manage sampling bias and provides standardized z-score coexpression data for integrated analysis. |
Related Questions
What are the best co-expression tools in plant biology?5 answersIn plant biology, several valuable co-expression tools have been developed to analyze gene interactions. One prominent tool is CoExpNetViz, available as a Cytoscape plug-in and web tool, facilitating comparative transcriptomics analysis for plant researchers without specialized bioinformatics knowledge. Additionally, PlantNexus offers global and tissue-specific gene co-expression networks for barley and sorghum, aiding in functional genomics research for these important cereal crops. Furthermore, the review highlights various gene co-expression network databases that store correlation values, gene descriptions, and expression profiles, supporting functional genomics advancements in crops and enabling robust interpretation of biologically relevant information. Overall, these tools play a crucial role in understanding gene regulatory networks and functional connectivity in plants.
Can transcriptomics be used to predict protein expression?4 answersTranscriptomics can be used to predict protein expression levels. Machine learning models combined with appropriate feature selection methods have shown excellent predictive performance in inferring protein expression levels using RNA expression profiles. The incorporation of additional trans-locus transcript abundance data as input features has been found to improve the ability to predict sample protein abundance. Furthermore, the predictive models have revealed that the abundance of a protein is not solely explained by its own cognate transcript level, but also by the transcript levels of its interaction partners. This suggests that protein-protein interactions exert post-transcriptional influence on proteome compositions, indicating the utility of mRNA and protein co-expression analysis in predicting expression changes in biological systems.
How to analysis gene data?5 answersGene data analysis involves several methods and platforms. One approach is to use genetic algorithms (GA) to select a subset of genes from microarray data for cancer classification. Another method involves using a heterogeneous scheduling platform to perform assembly line management on gene data analysis tasks, improving load balancing and resource allocation. The spark platform can be utilized to achieve gene data analysis through the execution of comparison with reference genes on an elastic distributed data set RDD, enhancing efficiency and flexibility. Additionally, gene data analysis can be performed using tools like SVM, KNN, and the R program, which can generate differentially expressed genes, gene clusters, and classify observed data. Furthermore, a gene analysis annotation method involves capturing gene data from existing databases, constructing an annotation gene database, and carrying out retrieval and annotation on the standard file to generate analysis reports.
What is the the database used?3 answersDatabases are used in various clinical and administrative settings for capturing patient data. They are valuable research options for studies of aetiology and prognosis, or when conducting trials is not feasible. High-quality databases can capture outcome data without the need for additional trial-related follow-up. They can also be used in hybrid models that combine randomization with outcome ascertainment using existing databases. Databases are also widely used in clinical research for data management automation. They play an important role in clinical research practice and have advancements such as reanalysis of randomized controlled trials (RCTs) and cloud computing techniques. Additionally, databases are used in the investigation of the possibility of wide deployment of floating wind power plants, specifically in the ocean around Japan.
What is the importance of clean omics datasets in the field of genomics?5 answersClean omics datasets are crucial in the field of genomics for accurate and meaningful analysis. Technical artefacts present in most omics datasets can introduce variations that hinder useful analyses. Batch effects, variations in sample preparation, platform settings, and other experimental procedures need to be adjusted for prior to analysis. The tunable median polish of ratio (TAMPOR) approach has been developed for batch effect correction and agglomeration of multi-batch datasets, resulting in a single analyte abundance data matrix suitable for systems biology analyses. Additionally, the generation of functional genomics datasets is increasing, providing insights into gene regulation and phenotypes. However, privacy concerns arise due to the use of next-generation sequencing. A data-sanitization procedure has been developed to share raw functional genomics reads while minimizing privacy leakage. Clean omics datasets are essential for personalized medicine, discovering cures for diseases, and understanding population diversity. The field of genomics is expanding rapidly, and the availability of inexpensive genome sequencing technology is driving this growth. However, the large amount of data produced requires efficient cleaning and harmonization methods.
Is RNA seq Big Data?10 answers