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 |
Items per page: 5 1 - 5 of 5 |
Similar Datasets
Project description:Genotyping by high throughput sequencing of four different cultivars of Lilium
| PRJEB2814 | ENA
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.
2015-11-12 | E-GEOD-72421 | biostudies-arrayexpress
Project description:We performed a comprehensive high-throughput transcript sequencing of ten different grapevine cultivars, which resulted in the first high coverage atlas of the grape berry transcriptome.
2015-09-04 | E-MTAB-4350 | biostudies-arrayexpress
Project description:In this study, we sequenced small RNA content from three different rice cultivars employing Illumina technology. More than 15 million reads were generated using Illumina high-throughput sequencing platform. After pre-processing, distinct small RNA sequences were identified for each rice cultivars.
2015-12-31 | GSE64651 | GEO
Project description:For developing the more SNPs and new high-density genetic linkage map of tea plant, two parents and their 326 progenies and 147 registered tea cultivars was sequencing by newly developed Affymetrix Axiom genotyping technology
2022-03-09 | GSE182082 | GEO
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.
2021-11-18 | GSE188959 | GEO
Project description:Rice High-throughput genotyping by whole-genome resequencing
| PRJEB1981 | ENA
Project description:Soybean (Glycine max) breeding involves improving commercially grown varieties by introgressing important agronomic traits from poor yielding accessions and/or wild relatives of soybean while minimizing the associated yield drag. Molecular markers associated with these traits are instrumental in increasing the efficiency of producing such crosses and Single Nucleotide Polymorphisms (SNPs) are particularly well suited for this task, owing to high density in the non-genic regions and thus increased likelihood of finding a tightly linked marker to a given trait. A rapid method to develop SNP markers that can differentiate specific loci between any two parents in soybean is thus highly desirable. In this study we investigate such a protocol for developing SNP markers between multiple soybean accessions and the reference Williams 82 genome. To restrict sampling frequency reduced representation libraries (RRLs) of genomic DNA were generated by restriction digestion followed by library construction. We chose to sequence four accessions Dowling (PI 548663), Dwight (PI 597386), Komata (PI200492) and PI 594538A for their agronomic importance as well as Williams 82 as a control.MseI was chosen to digest genomic DNA based on predictions that it will cut sparingly in the mathematically defined high-copy-number regions of the genome. All RRLs were sequenced on the Illumina genome analyzer. Reads were aligned to the Glyma1 reference assembly and SNP calls made from the alignments. We identified from 4294 to 14550 SNPs between the four accessions and the Williams 82 reference. In addition a small number of SNPs (1142) were found by aligning Williams 82 reads to the reference assembly (Glyma1) suggesting limited genetic variation within the Williams 82 line. The SNP data allowed us to estimate genetic diversity between the four lines and Williams 82. Restriction digestion of soybean genomic DNA with MseI followed by high throughput sequencing provides a rapid and reproducible method for generating SNP markers.
| S-EPMC3176760 | biostudies-literature