6. Discussions
6.2 Improving completeness of regulation role of small non-coding RNAs by adding other kind
Next-generation sequencing technology is the good way for analyzing small non-coding RNAs. To deeply investigate the regulation role of small RNAs, multiple information such as gene expression data from cDNA microarray, gene function (GO) and pathways are combined in previous studies or my study.
However, it is still not enough to completely decipher the pathway of small non-coding RNAs. Several kinds of NGS data can be merged for doing more comprehensive analysis.
The first is RNA-seq which is the application of NGS. It is used to detect the expression level of transcripts. Unlike cDNA microarray, RNA-seq can detect novel alternative splicing events and more correctly monitor the expression of transcripts. The probe sets of cDNA microarray are designed based on whole known isoforms. So, RNA-seq can more sensitively detect the expression level of each isoform. In miRNAs and siRNAs target site prediction, the more correct mRNA expression profile is helpful for reducing the false positive rate of prediction due to the miRNAs and siRNAs down-regulating the expression level of mRNAs. Moreover, the mRNA expression profile of RNA-seq can also reduce the false positive rate of predicting transcriptional cis-regulatory elements in the promoter regions. For example, E2F3, a transcription factor, is predicted that induces a set of miRNAs by binding their promoter regions. If E2F3 is not detected in the mRNA expression profile, it is not the candidate for regulating these miRNAs. If the set of miRNAs are up-regulated but E2F3 is down-regulated, E2F3 is not the regulator of these miRNAs.
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The second is chip (chromatin immunoprecipitation)-seq which is another application of NGS. Chip-seq is applied for high-throughput screening the DNA sequences which are bound by selected proteins. These proteins can be transcription factors (TFs) or other important proteins in biological processes.
After mapping the sequencing reads of chip-seq of selected transcription factor to genomes, the TFBSs (transcription factor binding sites) are identified in the sequence abundant regions. These experimental evidences can be used to build the relationship between miRNAs and the TFs which regulate them. For example, the sequence abundant regions locate the promoter region of miRNAs. The TFs have high possibility regulating miRNAs. The complete miRNA regulatory network from transcription to translation level can be generated by combing RNA-seq and chip-seq.
The third is CLIP (crosslinking immunoprecipitation)-seq which is the novel application of NGS. CLIP-seq is the method for high-throughput screening the RNA sequences which are bound by selected proteins. Recent studies use AGO (Argonaute) protein to do CLIP-seq [180-181]. AGO protein is required for miRNAs and siRNAs targeting mRNAs. So, the miRNA and mRNA interaction sites can be detected by AGO CLIP-seq. AGO CLIP-seq can directly give the evidence of miRNA and mRNA interactions. But these interaction sites need to be further analyzed. This is because AGO CLIP-seq is global screening the miRNA and mRNA interaction sequences. Identifying what miRNAs interact with these sequences is required. For example, the interaction region is chr1: 156770-156798 [+] in human. All known miRNAs are used to find the interaction in this region or not by current target site prediction guideline.
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The fourth is Degradome-Seq (degradome sequencing) which is designed for identifying the miRNA cleavage sites by using a modified 5’-rapid amplification of cDNA ends (RACE) with next-generation sequencing technology. Degradome-Seq first is used to find miRNA target sites in plants [71, 182-185]. Recently, some studies are purposed that they used Degradome-Seq to identify miRNA-derived cleavage sites [186-187]. Therefore, Degradome-Seq can give the direct evidence of miRNA and mRNA interactions in plants and animals.
The final is high-throughput sequencing of DNA methylation. There are three high-throughput sequencing methods for DNA methylation such as bisulfate-sequencing (BS-seq), methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methyl-binding protein sequencing (MBD-seq) [188]. The degree of DNA methylation affects the transcription processes. In plants, the function of hc-siRNAs regulates gene expression by triggering DNA methylation.
Therefore, the DNA methylation profile can be applied for identifying relationship between hc-siRNAs and their target genes.
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