Triticum aestivum
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA307989 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR3090580_1.fastq.gz | Fastqsanger.gz | |||
| SRR3090580_2.fastq.gz | Fastqsanger.gz | |||
| SRR3090690_1.fastq.gz | Fastqsanger.gz | |||
| SRR3090690_2.fastq.gz | Fastqsanger.gz | |||
| SRR3090691_1.fastq.gz | Fastqsanger.gz |
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Project description:We identified the long non-coding RNAs (lncRNAs) in Triticum aestivum infected with Fusarium graminearum by high-throughput RNA sequencing. More than 393 million clean reads were obtained from Illumina Hiseq 4000 system and 126,391 transcripts was identified as high-confidence lncRNAs in T. aestivum against F. graminearum by an integrated approach. Already well over 4,130 of the total 4,276 differentially expressed lncRNAs were specifically expressed at 12 h post-inoculation (hpi), but only 89 of these were specifically expressed at 24 hpi, indicating that the initial stage was the crucial stage for lncRNA-mediated gene regulation of wheat defense against F. graminearum. Target analysis showed the lncRNAs participated in various biological stress processes.
2023-09-18 | GSE158213 | GEO
Project description:Fusarium graminearum (F.g) is responsible for Fusarium head blight (FHB), which is a destructive disease of wheat that accumulates mycotoxin such as deoxynivalenol (DON) and makes its quality unsuitable for end use. Several FHB resistant varieties development is going on world-wide. However the complete understanding of wheat defence response, pathogen (Fusarium graminearum) disease development mechanism and the gene crosstalk between organisms is still unclear. In our study focused to analyse pathogen (F. graminearum) molecular action in different Fusarium head blight resistance cultivars during the disease development. To understand the Fusarium graminearum pathogen molecular reaction, microarray gene expression analysis was carried out by using Fusarium graminearum (8 x 15k) Agilent arrays at two time points (3 & 7 days after infection) on three wheat genotypes (Japanese landrace cv. Nobeokabouzu-komugi - highly resistant, Chinese cv. Sumai 3 - resistant and Australian cv. Gamenya - susceptible), which spikes infected by Fusarium graminearum ‘H-3’strain. During the disease development the pathogen biomass as well as the expression of Trichothecene biosynthesis involved genes (Tri genes) in three wheat cultivars was determined. In our material no relation between fungus biomass and the disease symptoms were observed, however, it showed relation with fungus virulence factors expression (Tri genes). For the first time, we report the nature of Fusarium graminearum gene expression in the FHB-highly resistant cv. Nobeokabouzu-komugi during the disease development stage and the possible underlying molecular response.
2015-03-14 | GSE66772 | GEO
Project description:Bread wheat (Triticum aestivum L., cv. Fielder) plants were grown under iron (Fe) deficient hydroponic conditions to analyise transcriptomic changes in leaf and root tissue.
2024-12-11 | GSE283676 | GEO
Project description:Triticum aestivum cultivar:Fielder Raw sequence reads
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Project description:Small RNAs (21-24 nt) are pivotal regulators of gene expression that guide both transcriptional and post-transcriptional silencing mechanisms in diverse eukaryotes, including most if not all plants. MicroRNAs (miRNAs) and short interfering RNAs (siRNAs) are the two major types, both of which have a demonstrated and important role in plant development, stress responses and pathogen resistance. In this work, we used a deep sequencing approach (Sequencing-By-Synthesis, or SBS) to develop sequence resources of small RNAs from Triticum aestivum tissues (including leaves and spiklets infected with Fusarium and control). The high depth of the resulting datasets enabled us to examine in detail critical small RNA features as size distribution, tissue-specific regulation and sequence conservation between different organs in this species. We also developed database resources and a dedicated website (http://smallrna.udel.edu/) with computational tools for allowing other users to identify new miRNAs or siRNAs involved in specific regulatory pathways, verify the degree of conservation of these sequences in other plant species and map small RNAs on genes or larger regions of the genome under study. Small RNA libraries were derived from leaves and spiklets control and Fusarium-infected of Triticum aestivum. Total RNA from leaves was isolated usingTriReagent (Molecular Research Center)and from spikelets using the Plant RNA Purification Reagent (Invitrogen), and submitted to Illumina (Hayward, CA, http://www.illumina.com) for small RNA library construction using approaches described in (Lu et al., 2007) with minor modifications. The small RNA libraries were sequenced with the Sequencing-By-Synthesis (SBS) technology by Illumina. PERL scripts were designed to remove the adapter sequences and determine the abundance of each distinct small RNA. We thank Yang Yen for providing the plant material as well as Kan Nobuta and Gayathri Mahalingam for assistance with the computational methods.
2011-09-29 | E-GEOD-32476 | biostudies-arrayexpress
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived Triticum aestivum transcriptome (RNA-seq) profiling methods and to evaluate genotypes associated with resistance against the Wheat dwarf virus. Methods: Triticum aestivum mRNA profiles of genotypes associated with resistance against the Wheat dwarf virus were generated by deep sequencing, in four replicates, using Illumina. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays. Conclusions: Our study represents the first detailed analysis of Triticum aestivum transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA and miRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
2023-04-04 | GSE192518 | GEO
Project description:Small RNAs (21-24 nt) are pivotal regulators of gene expression that guide both transcriptional and post-transcriptional silencing mechanisms in diverse eukaryotes, including most if not all plants. MicroRNAs (miRNAs) and short interfering RNAs (siRNAs) are the two major types, both of which have a demonstrated and important role in plant development, stress responses and pathogen resistance. In this work, we used a deep sequencing approach (Sequencing-By-Synthesis, or SBS) to develop sequence resources of small RNAs from Triticum aestivum tissues (including leaves and spiklets infected with Fusarium and control). The high depth of the resulting datasets enabled us to examine in detail critical small RNA features as size distribution, tissue-specific regulation and sequence conservation between different organs in this species. We also developed database resources and a dedicated website (http://smallrna.udel.edu/) with computational tools for allowing other users to identify new miRNAs or siRNAs involved in specific regulatory pathways, verify the degree of conservation of these sequences in other plant species and map small RNAs on genes or larger regions of the genome under study.
2011-09-30 | GSE32476 | GEO
Project description:Triticum aestivum cultivar:Fielder (NAC1-OX) Raw sequence reads
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Project description:Triticum aestivum var. aureum cultivar:Fielder Raw sequence reads
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Project description:MS2 data for two Indian varieties of Triticum aestivum L. (Wheat). P1 to P5 files represent MS2 data in positive ESI mode whereas N1 to N5 files represent MS2 data in negative ESI mode.
2023-04-23 | MSV000091783 | MassIVE