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An example interface of flexible dynamic query forms of Tripal MegaSearch. The filters, pre-populated with values mapped to the underlying data source columns, can be added dynamically in this type of interface.
Source publication
Tripal MegaSearch is a Tripal module for querying and downloading biological data stored in Chado. This module allows site users to select data types, restrict the dataset by applying various filters and then customizing fields to view and download through a single interface. Set by site administrators, example data types include gene, germplasm, m...
Context in source publication
Citations
... Users have multiple ways to search for genes in the system, either using a gene locus (or a list of them), keywords, genomic coordinates powered by MegaSearch [38] or using the BLAST graphical interface searches from Sequenceserver [39] (Figure 2A). Results are connected to genome browsers [37] specific to each genome. ...
... To facilitate the utilization of cotton research data in basic discovery, translation, and crop improvement, CottonGen, over the last decade, has focused on integrating new whole genomic data with transcriptomic, genetic map, genetic marker, trait locus, phenotypic, and genotypic data. To accommodate the data mining needs that came with these new types and large volumes of data, various web interfaces were developed by the CottonGen team, such as MegaSearch [68], MapViewer [63], BIMS [13], Chado Loader, Chado Data Display, and Chado Search modules [69], or Tripal modules that other database teams developed such as the Synteny Viewer and Tripal BLAST [8]. The open-source database platform Tripal allows database teams to meet emerging demands for storage, querying, and the display of new data types more efficiently and quickly. ...
Over the last eight years, the volume of whole genome, gene expression, SNP genotyping, and phenotype data generated by the cotton research community has exponentially increased. The efficient utilization/re-utilization of these complex and large datasets for knowledge discovery, translation, and application in crop improvement requires them to be curated, integrated with other types of data, and made available for access and analysis through efficient online search tools. Initiated in 2012, CottonGen is an online community database providing access to integrated peer-reviewed cotton genomic, genetic, and breeding data, and analysis tools. Used by cotton researchers worldwide, and managed by experts with crop-specific knowledge, it continuous to be the logical choice to integrate new data and provide necessary interfaces for information retrieval. The repository in CottonGen contains colleague, gene, genome, genotype, germplasm, map, marker, metabolite, phenotype, publication, QTL, species, transcriptome, and trait data curated by the CottonGen team. The number of data entries housed in CottonGen has increased dramatically, for example, since 2014 there has been an 18-fold increase in genes/mRNAs, a 23-fold increase in whole genomes, and a 372-fold increase in genotype data. New tools include a genetic map viewer, a genome browser, a synteny viewer, a metabolite pathways browser, sequence retrieval, BLAST, and a breeding information management system (BIMS), as well as various search pages for new data types. CottonGen serves as the home to the International Cotton Genome Initiative, managing its elections and serving as a communication and coordination hub for the community. With its extensive curation and integration of data and online tools, CottonGen will continue to facilitate utilization of its critical resources to empower research for cotton crop improvement.
In cotton, most agronomic traits are controlled by polygenes. In this study, 110 F6 recombinant inbred lines (RILs), derived from Upland cotton cross NC05AZ06 x NC11‐2100, were used to develop a linkage map and to identify QTL for six fiber quality traits, three yield traits, and plant height. These RILs were genotyped using the CottonSNP63K array, and phenotyped for fiber quality, yield traits and plant height in two‐year field trials. Analysis of variance revealed significant (p < 0.05) differences among RILs for all traits studied, and the heritability estimates were moderate (30‐60%) to high (>60%). Both positive and negative correlations were observed for fiber quality and yield traits. A total of 3,774 polymorphic SNP markers were used to develop a genetic map with an average marker density of 1.54 SNP/cM. Thirty QTL for fiber quality traits, yield traits, and plant height were detected on 15 different chromosomes, explaining 6.80‐20.02% of the phenotypic variance (PVE). Of these, 14 were major QTL (PVE >10%) and three major QTL were detected in both years. Candidate gene analysis in the major QTL detected in both years and plant height QTL with PVE of 20.02% revealed five putative genes for fiber quality traits and one putative gene for plant height. The linkage map and identified QTL along with the candidate genes in the study could serve as additional breeding resources for Upland cotton genetic improvement. This article is protected by copyright. All rights reserved
CarrotOmics (https://carrotomics.org/) is a comprehensive database for carrot (Daucus carota L.) breeding and research. CarrotOmics was developed using resources available at the MainLab Bioinformatics core (https://www.bioinfo.wsu.edu/) and is implemented using Tripal with Drupal modules. The database delivers access to download or visualize the carrot reference genome with gene predictions, gene annotations and sequence assembly. Other genomic resources include information for 11 224 genetic markers from 73 linkage maps or genotyping-by-sequencing and descriptions of 371 mapped loci. There are records for 1601 Apiales species (or subspecies) and descriptions of 9408 accessions from 11 germplasm collections representing more than 600 of these species. Additionally, 204 Apiales species have phenotypic information, totaling 28 517 observations from 10 041 biological samples. Resources on CarrotOmics are freely available, search functions are provided to find data of interest and video tutorials are available to describe the search functions and genomic tools. CarrotOmics is a timely resource for the Apiaceae research community and for carrot geneticists developing improved cultivars with novel traits addressing challenges including an expanding acreage in tropical climates, an evolving consumer interested in sustainably grown vegetables and a dynamic environment due to climate change. Data from CarrotOmics can be applied in genomic-assisted selection and genetic research to improve basic research and carrot breeding efficiency.
Database URL
https://carrotomics.org/
In this era of big data, breeding programs are producing ever larger amounts of data. This necessitates access to efficient management systems to keep track of cross, performance, pedigree, geographical and image-based data, as well as genotyping data. In this article, we report the progress on the Breeding Information Management System (BIMS), a free, secure and online breeding management system that allows breeders to store, manage, archive and analyze their private breeding data. BIMS is the first publicly available database system that enables individual breeders to integrate their private phenotypic and genotypic data with public data and, at the same time, have complete control of their own breeding data along with access to tools such as data import/export, data analysis and data archiving. The integration of breeding data with publicly available genomic and genetic data enhances genetic understanding of important traits and maximizes the marker-assisted breeding utility for breeders and allied scientists. BIMS incorporates the use of the Android App Field Book, open-source phenotype data collection software for phones and tablets that allows breeders to replace hard copy field books, thus alleviating the possibility of transcription errors while providing faster access to the collected data. BIMS comes with training materials and support for individual or small group training and is currently implemented in the Genome Database for Rosaceae, CottonGEN, the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium.
Database URLs: (https://www.rosaceae.org/), (https://www.cottongen.org/), (https://www.citrusgenomedb.org/), (https://www.pulsedb.org/) and (https://www.vaccinium.org/)
