Genotyping by high throughput sequencing of mRNA from five different cultivars of Tulip
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB46050 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR6169800.fastq.gz | Fastqsanger.gz | |||
| ERR6169801.fastq.gz | Fastqsanger.gz | |||
| ERR6169802.fastq.gz | Fastqsanger.gz | |||
| ERR6169815.fastq.gz | Fastqsanger.gz | |||
| ERR6169799.fastq.gz | Fastqsanger.gz |
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Project description:Genotyping by high throughput sequencing of four different cultivars of Lilium
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Project description:The cDNA of 4 Lilium cultivars were sequenced in this study in order to develop SNP markers for mapping purpose. The four selected cultivars represent the four main hybrid groups in Lilium which are (Asiatic, Oriental, Longiflorum, and Trumpet). The SNP markers developed of this study will be implemented in mapping and association studies that aims to find SNP markers linked to virus resistance in Lilium. To achieve this target, SNP markers were generated from each cultivar separately, and of the four cultivars together trying to cover as much as variation in each cultivar and also to find SNP markers that can distinguish the four cultivars. Doing so, we hope to identify different genes resistance to virus in the four hybrid groups of Lilium.
2013-01-17 | E-MTAB-840 | biostudies-arrayexpress
Project description:Background: Grape cultivars and wines are distinguishable by their color, flavor and aroma profiles. Omic analyses (transcripts, proteins and metabolites) are powerful tools for assessing biochemical differences in biological systems. Results: Berry skins of red- (Cabernet Sauvignon, Merlot, Pinot Noir) and white-skinned (Chardonnay, Semillon) wine grapes were harvested near optimum maturity from the same experimental vineyard and ˚Brix-to-titratable acidity ratio. Identical sample aliquots were analyzed for transcripts by grapevine whole-genome oligonucleotide microarray and RNA-seq technologies, proteins by nano-liquid chromatography-mass spectroscopy, and metabolites by gas chromatography-mass spectroscopy and liquid chromatography-mass spectroscopy. Principal components analysis of each of five Omic technologies showed similar results across cultivars in all Omic datasets. Comparison of the processed data of genes mapped in RNA-seq and microarray data revealed a strong Pearson's correlation (0.80). The exclusion of probesets associated with genes with potential for cross-hybridization on the microarray improved the correlation to 0.93. The overall concordance of protein with transcript data was low with a Pearson's correlation of 0.27 and 0.24 for the RNA-seq and microarray data, respectively. Integration of metabolite with protein and transcript data produced an expected model of phenylpropanoid biosynthesis, which distinguished red from white grapes, yet provided detail of individual cultivar differences. Conclusions: The five Omic technologies were consistent in distinguishing cultivar variation. There was high concordance between transcriptomic technologies, but generally protein abundance did not correlate well with transcript abundance. The integration of multiple high-throughput Omic datasets revealed complex biochemical variation amongst five cultivars of an ancient and economically important crop species.
2015-11-12 | GSE72421 | GEO
Project description:Background: Grape cultivars and wines are distinguishable by their color, flavor and aroma profiles. Omic analyses (transcripts, proteins and metabolites) are powerful tools for assessing biochemical differences in biological systems. Results: Berry skins of red- (Cabernet Sauvignon, Merlot, Pinot Noir) and white-skinned (Chardonnay, Semillon) wine grapes were harvested near optimum maturity from the same experimental vineyard and Ë?Brix-to-titratable acidity ratio. Identical sample aliquots were analyzed for transcripts by grapevine whole-genome oligonucleotide microarray and RNA-seq technologies, proteins by nano-liquid chromatography-mass spectroscopy, and metabolites by gas chromatography-mass spectroscopy and liquid chromatography-mass spectroscopy. Principal components analysis of each of five Omic technologies showed similar results across cultivars in all Omic datasets. Comparison of the processed data of genes mapped in RNA-seq and microarray data revealed a strong Pearson's correlation (0.80). The exclusion of probesets associated with genes with potential for cross-hybridization on the microarray improved the correlation to 0.93. The overall concordance of protein with transcript data was low with a Pearson's correlation of 0.27 and 0.24 for the RNA-seq and microarray data, respectively. Integration of metabolite with protein and transcript data produced an expected model of phenylpropanoid biosynthesis, which distinguished red from white grapes, yet provided detail of individual cultivar differences. Conclusions: The five Omic technologies were consistent in distinguishing cultivar variation. There was high concordance between transcriptomic technologies, but generally protein abundance did not correlate well with transcript abundance. The integration of multiple high-throughput Omic datasets revealed complex biochemical variation amongst five cultivars of an ancient and economically important crop species. Vitis vinifera L. cv. Cabernet Sauvignon, Chardonnay, Merlot, Pinot Noir, Semillon berries were harvested from Nevada Agricultural Experiment Station Valley Road Vineyard, Reno, NV, USA. Whole-genome microarray analysis was used to assess the transcriptomic response of berry skins at harvest, approximately 24 °Brix (2011 vintage). Vines were grown under water deficit and well-watered conditions. At least two clusters harvested from non-adjacent vines were used for each of five experimental replicates.
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Project description:We report the transcriptome profile of different cultivars of Fusarium graminearum-infected wheat grains, aiming to search for some different expression genes and pathways to reveal the difference between wheat cultivars.
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Project description:<p><strong>BACKGROUND:</strong> Amaranth (<em>Amaranthus spp.</em>) has high nutritional quality, with edible grain and leaves, and many agronomic advantages, making it a promising part of the solution for global food insecurity. However, we lack comprehensive metabolomic and genome sequence data for many cultivars. To support the improvement of this versatile, sustainable crop, a detailed metabolome profiling of the edible grains and leaves and genome sequencing resources is required for the widely cultivated grain amaranth cultivars such as Coral Fountain (CF), Emerald Tassels (ET), Golden Giant (GG), Hopi Red Dye (HR), and New Mexico (NM).</p><p><strong>RESULTS:</strong> Through a non-targeted high-throughput metabolic profiling using ultra-performance liquid chromatography-tandem mass spectrometry, we precisely determined the whole-grain and leaf metabolites of these five cultivars. This analysis identified 426 and 420 metabolites with known chemical structures in the grain and leaf, respectively. The five amaranth cultivars differed significantly in the levels of several nutritionally valuable compounds in grains and leaves, including sulfur amino acids, vitamins, and chlorogenic acids, as well as potentially anti-nutritive compounds, such as oxalate and raffinose family oligosaccharides. On average, the cultivars CF and ET had more favorable levels of most identified health-promoting compounds compared to GG, HR, and NM. In addition, we provide high-quality reference genome sequences for the five cultivars using the PacBio Sequel II sequencing platform with an estimated genome size of 465-483 Mb comprising 46.9-48.7% repetitive elements. We generated an iso-seq library from different amaranth plant parts and utilized it to predict the amaranth genes and annotate their function into their respective gene ontology terms.</p><p><strong>CONCLUSIONS:</strong> These resources will assist in breeding improved amaranth varieties and identifying targeted genes for trait modification and advancement through genome editing and engineering technologies.</p><p><br></p><p><strong>Leaf assays</strong> are reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS3498' rel='noopener noreferrer' target='_blank'><strong>MTBLS3498</strong></a>.</p><p><strong>Seed assays</strong> are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS815' rel='noopener noreferrer' target='_blank'><strong>MTBLS815</strong></a>.</p>
2025-02-25 | MTBLS3498 | MetaboLights