Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SR4R: An Integrative SNP Resource for Genomic Breeding and Population Research in Rice.

ABSTRACT: The information commons for rice (IC4R) database is a collection of 18 million single nucleotide polymorphisms (SNPs) identified by resequencing of 5152 rice accessions. Although IC4R offers ultra-high density rice variation map, these raw SNPs are not readily usable for the public. To satisfy different research utilizations of SNPs for population genetics, evolutionary analysis, association studies, and genomic breeding in rice, raw genotypic data of these 18 million SNPs were processed by unified bioinformatics pipelines. The outcomes were used to develop a daughter database of IC4R - SnpReady for Rice (SR4R). SR4R presents four reference SNP panels, including 2,097,405 hapmapSNPs after data filtration and genotype imputation, 156,502 tagSNPs selected from linkage disequilibrium-based redundancy removal, 1180 fixedSNPs selected from genes exhibiting selective sweep signatures, and 38 barcodeSNPs selected from DNA fingerprinting simulation. SR4R thus offers a highly efficient rice variation map that combines reduced SNP redundancy with extensive data describing the genetic diversity of rice populations. In addition, SR4R provides rice researchers with a web interface that enables them to browse all four SNP panels, use online toolkits, as well as retrieve the original data and scripts for a variety of population genetics analyses on local computers. SR4R is freely available to academic users at http://sr4r.ic4r.org/.

SUBMITTER: Yan J

PROVIDER: S-EPMC7646087 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

An SNP resource for rice genetics and breeding based on subspecies indica and japonica genome alignments.

Project description:Dense coverage of the rice genome with polymorphic DNA markers is an invaluable tool for DNA marker-assisted breeding, positional cloning, and a wide range of evolutionary studies. We have aligned drafts of two rice subspecies, indica and japonica, and analyzed levels and patterns of genetic diversity. After filtering multiple copy and low quality sequence, 408,898 candidate DNA polymorphisms (SNPs/INDELs) were discerned between the two subspecies. These filters have the consequence that our data set includes only a subset of the available SNPs (in particular excluding large numbers of SNPs that may occur between repetitive DNA alleles) but increase the likelihood that this subset is useful: Direct sequencing suggests that 79.8% +/- 7.5% of the in silico SNPs are real. The SNP sample in our database is not randomly distributed across the genome. In fact, 566 rice genomic regions had unusually high (328 contigs/48.6 Mb/13.6% of genome) or low (237 contigs/64.7 Mb/18.1% of genome) polymorphism rates. Many SNP-poor regions were substantially longer than most SNP-rich regions, covering up to 4 Mb, and possibly reflecting introgression between the respective gene pools that may have occurred hundreds of years ago. Although 46.2% +/- 8.3% of the SNPs differentiate other pairs of japonica and indica genotypes, SNP rates in rice were not predictive of evolutionary rates for corresponding genes in another grass species, sorghum. The data set is freely available at http://www.plantgenome.uga.edu/snp.

| S-EPMC515328 | biostudies-literature

Hybrid breeding of rice via genomic selection.

Project description:Hybrid breeding is the main strategy for improving productivity in many crops, especially in rice and maize. Genomic hybrid breeding is a technology that uses whole-genome markers to predict future hybrids. Predicted superior hybrids are then field evaluated and released as new hybrid cultivars after their superior performances are confirmed. This will increase the opportunity of selecting true superior hybrids with minimum costs. Here, we used genomic best linear unbiased prediction to perform hybrid performance prediction using an existing rice population of 1495 hybrids. Replicated 10-fold cross-validations showed that the prediction abilities on ten agronomic traits ranged from 0.35 to 0.92. Using the 1495 rice hybrids as a training sample, we predicted six agronomic traits of 100 hybrids derived from half diallel crosses involving 21 parents that are different from the parents of the hybrids in the training sample. The prediction abilities were relatively high, varying from 0.54 (yield) to 0.92 (grain length). We concluded that the current population of 1495 hybrids can be used to predict hybrids from seemingly unrelated parents. Eventually, we used this training population to predict all potential hybrids of cytoplasm male sterile lines from 3000 rice varieties from the 3K Rice Genome Project. Using a breeding index combining 10 traits, we identified the top and bottom 200 predicted hybrids. SNP genotypes of the training population and parameters estimated from this training population are available for general uses and further validation in genomic hybrid prediction of all potential hybrids generated from all varieties of rice.

| S-EPMC6920338 | biostudies-literature

Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding.

Project description:Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (FST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.

| S-EPMC4551487 | biostudies-literature

Proteome of Douglas-fir: a resource for genomic research

Project description:First Douglas fir proteomes by nLC-MS/MS from 12 different organs : root, stem, xylem, needle, bud, female and male flowers, immature and mature seed, immature and mature somatic embryos and callus.

2023-10-24 | PXD037815 | Pride

Exploiting the Genomic Diversity of Rice (<i>Oryza sativa</i> L.): SNP-Typing in 11 Early-Backcross Introgression-Breeding Populations.

Project description:This study demonstrates genotyping-by-sequencing-based single-nucleotide polymorphism (SNP)-typing in 11 early-backcross introgression populations of rice (at BC1F5), comprising a set of 564 diverse introgression lines and 12 parents. Sequencing using 10 Ion Proton runs generated a total of ∼943.4 million raw reads, out of which ∼881.6 million reads remained after trimming for low-quality bases. After alignment, 794,297 polymorphic SNPs were identified, and filtering resulted in LMD50 SNPs (low missing data, with each SNP, genotyped in at least 50% of the samples) for each sub-population. Every data point was supported by actual sequencing data without any imputation, eliminating imputation-induced errors in SNP calling. Genotyping substantiated the impacts of novel breeding strategy revealing: (a) the donor introgression patterns in ILs were characteristic with variable introgression frequency in different genomic regions, attributed mainly to stringent selection under abiotic stress and (b) considerably lower heterozygosity was observed in ILs. Functional annotation revealed 426 non-synonymous deleterious SNPs present in 102 loci with a range of 1-4 SNPs per locus and 120 novel SNPs. SNP-typing this diversity panel will further assist in the development of markers supporting genomic applications in molecular breeding programs.

| S-EPMC6024854 | biostudies-literature

NanoMiner - integrative human transcriptomics data resource for nanoparticle research.

Project description:The potential impact of nanoparticles on the environment and on human health has attracted considerable interest worldwide. The amount of transcriptomics data, in which tissues and cell lines are exposed to nanoparticles, increases year by year. In addition to the importance of the original findings, this data can have value in broader context when combined with other previously acquired and published results. In order to facilitate the efficient usage of the data, we have developed the NanoMiner web resource (http://nanominer.cs.tut.fi/), which contains 404 human transcriptome samples exposed to various types of nanoparticles. All the samples in NanoMiner have been annotated, preprocessed and normalized using standard methods that ensure the quality of the data analyses and enable the users to utilize the database systematically across the different experimental setups and platforms. With NanoMiner it is possible to 1) search and plot the expression profiles of one or several genes of interest, 2) cluster the samples within the datasets, 3) find differentially expressed genes in various nanoparticle studies, 4) detect the nanoparticles causing differential expression of selected genes, 5) analyze enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms for the detected genes and 6) search the expression values and differential expressions of the genes belonging to a specific KEGG pathway or Gene Ontology. In sum, NanoMiner database is a valuable collection of microarray data which can be also used as a data repository for future analyses.

| S-EPMC3709991 | biostudies-literature

Genetic gains in IRRI's rice salinity breeding and elite panel development as a future breeding resource.

Project description:Key messageEstimating genetic gains and formulating a future salinity elite breeding panel for rice pave the way for developing better high-yielding salinity tolerant lines with enhanced genetic gains. Genetic gain is a crucial parameter to check the breeding program's success and help optimize future breeding strategies for enhanced genetic gains. To estimate the genetic gains in IRRI's salinity breeding program and identify the best genotypes based on high breeding values for grain yield (kg/ha), we analyzed the historical data from the trials conducted in the IRRI, Philippines and Bangladesh. A two-stage mixed-model approach accounting for experimental design factors and a relationship matrix was fitted to obtain the breeding values for grain yield and estimate genetic trends. A positive genetic trend of 0.1% per annum with a yield advantage of 1.52 kg/ha was observed in IRRI, Philippines. In Bangladesh, we observed a genetic gain of 0.31% per annum with a yield advantage of 14.02 kg/ha. In the released varieties, we observed a genetic gain of 0.12% per annum with a 2.2 kg/ha/year yield advantage in the IRRI, Philippines. For the Bangladesh dataset, a genetic gain of 0.14% per annum with a yield advantage of 5.9 kg/ha/year was observed in the released varieties. Based on breeding values for grain yield, a core set of the top 145 genotypes with higher breeding values of > 2400 kg/ha in the IRRI, Philippines, and > 3500 kg/ha in Bangladesh with a reliability of > 0.4 were selected to develop the elite breeding panel. Conclusively, a recurrent selection breeding strategy integrated with novel technologies like genomic selection and speed breeding is highly required to achieve higher genetic gains in IRRI's salinity breeding programs.

| S-EPMC10830834 | biostudies-literature

The Genomic Medicine Integrative Research Framework: A Conceptual Framework for Conducting Genomic Medicine Research.

Project description:Conceptual frameworks are useful in research because they can highlight priority research domains, inform decisions about interventions, identify outcomes and factors to measure, and display how factors might relate to each other to generate and test hypotheses. Discovery, translational, and implementation research are all critical to the overall mission of genomic medicine and prevention, but they have yet to be organized into a unified conceptual framework. To fill this gap, our diverse team collaborated to develop the Genomic Medicine Integrative Research (GMIR) Framework, a simple but comprehensive tool to aid the genomics community in developing research questions, strategies, and measures and in integrating genomic medicine and prevention into clinical practice. Here we present the GMIR Framework and its development, along with examples of its use for research development, demonstrating how we applied it to select and harmonize measures for use across diverse genomic medicine implementation projects. Researchers can utilize the GMIR Framework for their own research, collaborative investigations, and clinical implementation efforts; clinicians can use it to establish and evaluate programs; and all stakeholders can use it to help allocate resources and make sure that the full complexity of etiology is included in research and program design, development, and evaluation.

| S-EPMC6556906 | biostudies-literature

StaphyloBase: a specialized genomic resource for the staphylococcal research community.

Project description:With the advent of high-throughput sequencing technologies, many staphylococcal genomes have been sequenced. Comparative analysis of these strains will provide better understanding of their biology, phylogeny, virulence and taxonomy, which may contribute to better management of diseases caused by staphylococcal pathogens. We developed StaphyloBase with the goal of having a one-stop genomic resource platform for the scientific community to access, retrieve, download, browse, search, visualize and analyse the staphylococcal genomic data and annotations. We anticipate this resource platform will facilitate the analysis of staphylococcal genomic data, particularly in comparative analyses. StaphyloBase currently has a collection of 754 032 protein-coding sequences (CDSs), 19 258 rRNAs and 15 965 tRNAs from 292 genomes of different staphylococcal species. Information about these features is also included, such as putative functions, subcellular localizations and gene/protein sequences. Our web implementation supports diverse query types and the exploration of CDS- and RNA-type information in detail using an AJAX-based real-time search system. JBrowse has also been incorporated to allow rapid and seamless browsing of staphylococcal genomes. The Pairwise Genome Comparison tool is designed for comparative genomic analysis, for example, to reveal the relationships between two user-defined staphylococcal genomes. A newly designed Pathogenomics Profiling Tool (PathoProT) is also included in this platform to facilitate comparative pathogenomics analysis of staphylococcal strains. In conclusion, StaphyloBase offers access to a range of staphylococcal genomic resources as well as analysis tools for comparative analyses. Database URL: http://staphylococcus.um.edu.my/.

| S-EPMC5630900 | biostudies-other

Integrative genomic analysis in HCC samples [SNP]

Project description:We integrated the copy number data with gene expression data from the same HCC samples, and identified fifteen putative driver genes with recurrently genomic aberrations and their associated modules in HCC. We further confirmed empirically that three putative driver genes (MYH10, CEP104 and RRS1) play significant roles in tumor initiation and progression of HCC. Notably, we demonstrated that RRS1 regulates the MDM2-P53 pathway and promotes tumor progression by retaining RPL11 in the nucleolus in HCC. Altogether, these data provide insights into novel cancer driver genes and suggested molecular targets for treatment for HCC.

2018-01-19 | GSE109360 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data