Oryza sativa
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA271566 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR1740023.fastq.gz | Fastqsanger.gz | |||
| SRR1740024.fastq.gz | Fastqsanger.gz | |||
| SRR1740025.fastq.gz | Fastqsanger.gz |
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Project description:In order to identify new miRNAs, NAT-siRNAs and possibly abiotic-stress regulated small RNAs in rice, three small RNA libraries were constructed from control rice seedlings and seedlings exposed to drought or salt stress, and then subjected to pyrosequencing.
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Project description:The small RNAs presented here were produced as a preliminary exploration of small RNAs in rice, and as such, various tissues and stress conditions were sampled. Small RNAs present in these samples were all mapped to the rice genome TIGR version 5. The total number of distinct mapped sequences are 12879 for Run 1 and 88508 for Run 2. The total number of sequence reads were respectively 70406 and 191682. The datasets contain Oryza sativa var Nipponbar endogenous small RNA sequences in the size range 18 to 34 nt. Plants were grown in a Conviron Environmental Chamber at high light intensity using both high pressure sodium and metal halide lamps for 10.5 hr at 28 degrees C and for 13.5 hr at 26 degrees C in the dark. RNA was extracted from rice tissues at various stages of development and under different abiotic and biotic stresses. The small RNAs presented here were all mapped to the rice genome TIGR version 5. The total number of distinct mapped sequences are 12879 for Run 1 and 88508 for Run 2. The total number of sequence reads were respectively 70406 and 191682.
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Project description:In order to identify new miRNAs, NAT-siRNAs and possibly abiotic-stress regulated small RNAs in rice, three small RNA libraries were constructed from control rice seedlings and seedlings exposed to drought or salt stress, and then subjected to pyrosequencing. Totally three sets of small RNAs, which were obtained under normal condition as well as salt and drought stress conditions
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Project description:Abiotic environmental stresses cause serious economic losses in agriculture. These stresses include temperature extremes, high salinity and drought. To isolate drought-responsive novel coding and noncoding genes, we used the next generation sequencing method from three rice cultivars (wild type nipponbare, nipponbare AP2 transgenic plants, wild type vandana). 36 NGS data of mRNA-seq, small RNA-seq, riboZero-seq were analyzed. For the analyses of these data we constructed a TF-TG (Transcription Factor-Target Gene) network and an ap2 rooted cascading tree. Using these networks and tress we isolated lincRNAs, differentially expressed miRNAs and their targets. We identified several drought stress-related novel/function unknown coding transcripts (transcription factors and functional genes) and non-coding transcripts (small noncoding transcripts such as microRNA and long noncoding transcripts) from these database analyses and have constructed databases of drought stress-related coding and noncoding transcripts Identification of drought-responsive Regulatory Coding and Non-coding Transcripts from rice by deep RNA sequencing
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Project description:Abiotic environmental stresses cause serious economic losses in agriculture. These stresses include temperature extremes, high salinity and drought. To isolate drought-responsive novel coding and noncoding genes, we used the next generation sequencing method from three rice cultivars (wild type nipponbare, nipponbare AP2 transgenic plants, wild type vandana). 36 NGS data of mRNA-seq, small RNA-seq, riboZero-seq were analyzed. For the analyses of these data we constructed a TF-TG (Transcription Factor-Target Gene) network and an ap2 rooted cascading tree. Using these networks and tress we isolated lincRNAs, differentially expressed miRNAs and their targets. We identified several drought stress-related novel/function unknown coding transcripts (transcription factors and functional genes) and non-coding transcripts (small noncoding transcripts such as microRNA and long noncoding transcripts) from these database analyses and have constructed databases of drought stress-related coding and noncoding transcripts
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