Inner stem tissue of V2 seedlings and husks (maize B73)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA369198 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR5217070.fastq.gz | Fastqsanger.gz | |||
| SRR5217080.fastq.gz | Fastqsanger.gz | |||
| SRR5217090.fastq.gz | Fastqsanger.gz | |||
| SRR5217071.fastq.gz | Fastqsanger.gz | |||
| SRR5217081.fastq.gz | Fastqsanger.gz |
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Project description:Most cells in a multicellular organism carry the same genetic information; however, they can still differentiate to take up different functions in the organism. This differentiation process involves tight regulation of gene expressions by regulatory sequences including transcriptional enhancers. Enhancers can be located distantly from their target genes; this characteristic makes it difficult to identify enhancers only using experimental approaches. Importantly, enhancers carry characteristic chromatin signatures such as low DNA methylation, high chromatin accessibility and histone modifications such as H3K9ac and H3K27ac. Computational enhancer identification approaches often use machine learning methods in combination with a number of experimental genome-wide datasets to identify key enhancers features and scans the whole genome. Limited knowledge on plant enhancers restricts us in available approaches we can use to perform genome-wide enhancer prediction in plant species. In this work, we have integrated genome-wide low DNA methylation, chromatin accessibility and H3K9ac data from two tissues, inner stem tissue (V2-IST) from seedlings at the V2 stage and husk tissue, in the crop plant Zea mays. This study identified approximately 1,400 candidate enhancers. By comparing data from two different tissues, we determined tissue-specificity of the candidate enhancers. We also have predicted putative target genes using gene expression data taking an advantage of having data from two tissues.
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Project description:Most cells in a multicellular organism carry the same genetic information; however, they can still differentiate to take up different functions in the organism. This differentiation process involves tight regulation of gene expressions by regulatory sequences including transcriptional enhancers. Enhancers can be located distantly from their target genes; this characteristic makes it difficult to identify enhancers only using experimental approaches. Importantly, enhancers carry characteristic chromatin signatures such as low DNA methylation, high chromatin accessibility and histone modifications such as H3K9ac and H3K27ac. Computational enhancer identification approaches often use machine learning methods in combination with a number of experimental genome-wide datasets to identify key enhancers features and scans the whole genome. Limited knowledge on plant enhancers restricts us in available approaches we can use to perform genome-wide enhancer prediction in plant species. In this work, we have integrated genome-wide low DNA methylation (Regulski et al., 2013, GSE39232), chromatin accessibility and H3K9ac data from two tissues, inner stem tissue in seedlings and husk tissue, in a crop plant Zea mays. This study identified approximately 1,400 candidate enhancers.
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