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342 results for “sorghum”
Sorghum SbGhd7 is a major regulator of floral transition and directly represses genes crucial for flowering activation
GEO Series GSE237989. Sorghum bicolor. 6 samples. Type: Expression profiling by high throughput sequencing.
Sorghum SbGhd7 is a major regulator of floral transition and directly represses genes crucial for flowering activation
GEO Series GSE238095. Sorghum bicolor. 10 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
The stem cell-type transcriptome of bioenergy sorghum reveals the spatial regulation of secondary cell wall networks
GEO Series GSE218642. Sorghum bicolor. 24 samples. Type: Expression profiling by high throughput sequencing.
Translational regulation in response to sulfur in Sorghum bicolor
GEO Series GSE184725. Sorghum bicolor. 12 samples. Type: Expression profiling by high throughput sequencing; Other.
Sorghum nitrogen stress tolerance
GEO Series GSE54705. Sorghum bicolor. 28 samples. Type: Expression profiling by high throughput sequencing.
Comparative transcriptome analysis reveals conserved defense responses in maize and sorghum to Setosphaeria turcica
GEO Series GSE156026. Sorghum bicolor; Zea mays. 36 samples. Type: Expression profiling by high throughput sequencing.
Sorghum gene expression using Agilent custom 4x44K Microarray
GEO Series GSE36689. Sorghum bicolor. 12 samples. Type: Expression profiling by array.
Characterization of growth and development of sorghum genotypes with differential susceptibility to Striga hermonthica
GEO Series GSE167101. Sorghum bicolor. 15 samples. Type: Expression profiling by high throughput sequencing.
Data from: Whole genome sequence accuracy is improved by replication in a population of mutagenized sorghum.
The accurate detection of induced mutations is critical for both forward and reverse genetics studies. Experimental chemical mutagenesis induces relatively few single base changes per individual. In a complex eukaryotic genome, false positive detection of mutations can occur at or above this mutagenesis rate. We demonstrate here, using a population of ethyl methanesulfonate (EMS) treated Sorghum bicolor BTx623 individuals, that using replication to detect false positive induced variants in next-generation sequencing data permits higher throughput variant detection with greater accuracy. We used a lower sequence coverage depth (average of 7X) from 586 independently mutagenized individuals and detected 5,399,493 homozygous SNPs. Of these, 76% originated from only 57,872 genomic positions prone to false positive variant calling. These positions are characterized by high copy number paralogs where the error-prone SNP positions are at copies containing a variant at the SNP position. The ability of short stretches of homology to generate these error prone positions suggests that incompletely assembled or poorly mapped repeated sequences are one driver of these error prone positions.. Removal of these false positives left 1,275,872 homozygous and 477,531 heterozygous EMS-induced SNPs which, congruent with the mutagenic mechanism of EMS, were greater than 98% G:C to A:T transitions. Through this analysis we generated a database of sequence indexed mutants of Sorghum. This collection contains 4,035 high impact homozygous mutations in 3,637 genes and 56,514 homozygous missense mutations in 23,227 genes. Each line contains, on average, 2,177 annotated homozygous SNPs per genome, including seven likely gene knockouts and 96 missense mutations. The number of mutations in a transcript was linearly correlated with the transcript length and also the G+C count, but not with the GC/AT ratio. Analysis of the detected mutagenized positions identified CG-rich patches, and flanking sequences strongly influenced EMS-induced mutation rates. Our method for detecting false-positive induced mutations is generally applicable to any organism, is independent of the choice of in silico variant-calling algorithm, and is most valuable when the true mutation rate is likely to be low, such as in laboratory induced mutations or somatic mutation detection in medicine.
S Table 1. Characteristics of all sorghum cultivars assessed for drought and salinity.
<p>The data is presented in a table (S Table 1. Characteristics of all sorghum cultivars assessed for drought and salinity) as a supplement of a manuscript in MDPI <i>Agronomy | </i>Special Issue<i> "Novel Insights into Abiotic Stress Tolerance of Crops" </i>journal. </p>
Identification of beneficial and detrimental bacteria that impact sorghum responses to drought using multi-scale and multi-system microbiome comparisons
<p><strong>Background: </strong>Drought is a major abiotic stress that limits agricultural productivity. Previous field-level experiments have demonstrated that drought decreases microbiome diversity in the root and rhizosphere and may lead to enrichment of specific groups of microbes, such as <em>Actinobacteria</em>. How these changes ultimately affect plant health is not well understood. In parallel, model systems have been used to tease apart the specific interactions between plants and single, or small groups of microbes. However, translating this work into crop species and achieving increased crop yields within noisy field settings remains a challenge. Thus, the next scientific leap forward in microbiome research must cross the great lab-to-field divide. Toward this end, we combined reductionist, transitional and ecological approaches, applied to the staple cereal crop sorghum to identify key beneficial and detrimental, root associated microbes that robustly affect drought stressed plant phenotypes.</p> <p> </p> <p><strong>Results: </strong>Fifty-three bacterial strains, originally characterized for association with <em>Arabidopsis</em>, were applied to sorghum seeds and their effect on root growth was monitored for seven days. Two <em>Arthrobacter </em>strains, members of the <em>Actinobacteria </em>phylum, caused root growth inhibition (RGI) in <em>Arabidopsis</em> and sorghum. In the context of synthetic communities, strains of <em>Variovorax</em> were able to protect both <em>Arabidopsis </em>and sorghum from the RGI caused by <em>Arthrobacter</em>. As a transitional system, we tested the synthetic communities through a 24-day high-throughput sorghum phenotyping assay and found that during drought stress, plants colonized by <em>Arthrobacter</em> were significantly smaller and had reduced leaf water content as compared to control plants. However, plants colonized by both <em>Arthrobacter</em> and <em>Variovorax</em> performed as well or better than control plants. In parallel, we performed a field trial wherein sorghum was evaluated across well-watered and drought conditions. Drought responsive microbes were identified, including an enrichment in <em>Actinobacteria</em>, consistent with previous findings. By incorporating data on soil properties into the microbiome analysis, we accounted for experimental noise with a newly developed method and were then able to observe that the abundance of <em>Arthrobacter</em> strains negatively correlated with plant growth. Having validated this approach, we cross-referenced datasets from the high-throughput phenotyping and field experiments and report a list of high confidence bacterial taxa that positively associated with plant growth under drought stress.</p> <p> </p> <p><strong>Conclusions: </strong>A three-tiered experimental system connected reductionist and ecological approaches and identified beneficial and deleterious bacterial strains for sorghum under drought stress.</p>
K-State - ARPA-E SMARTFARM Grain Sorghum 2021 Kansas site comprehensive sensor modalities data set.
<p>Comprehensive Year 1 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Kansas site of the project. </p>
VALORIZATION OF DIFFERENT LANDRACE AND COMMERCIAL SORGHUM (Sorghum bicolor (L.) Moench) STRAW VARIETIES BY ANAEROBIC DIGESTION
<p>The provided data are the raw or primary data used to create the figures and tables in the article: VALORIZATION OF DIFFERENT LANDRACE AND COMMERCIAL SORGHUM (Sorghum bicolor (L.) Moench) STRAW VARIETIES BY ANAEROBIC DIGESTION.</p> <p> </p>
Impact of Sorghum Rice on Beta-Cell Function and Insulin Resistance in Prediabetes
ClinicalTrials.gov study NCT07298304. IPD Sharing: NO. Countries: 1. Publications: 0.
Investigating the Effect of Onyx Sorghum on Blood Glucose in Individuals With Type 2 Diabetes
ClinicalTrials.gov study NCT03714451. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Polyphenols in Sorghum and Iron Absorption
ClinicalTrials.gov study NCT01162616. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Molecular and physiological characterization of brassinosteroid receptor BRI1 mutants in Sorghum bicolor
GEO Series GSE265868. Sorghum bicolor. 12 samples. Type: Expression profiling by high throughput sequencing.
Data from: An individual-based model of seed and rhizome propagated perennial plant species and sustainable management of Sorghum halepense in soybean production systems in Argentina
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Data from: Dissecting genome-wide association signals for loss-of-function phenotypes in sorghum flavonoid pigmentation traits
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Sorghum wax chemistry data
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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