MicroRNAs (miRNAs) are a class of small endogenous non-coding RNAs of about 22 nt in length, with hairpin structures as precursors, which are widely found in animal, plant and viral organisms. Numerous studies have shown that miRNAs are involved in a wide range of biological processes, including cell proliferation, apoptosis, differentiation, metabolism, tumor and growth development, as well as in response to stress (including drought, high salt and nutrient stress).
Degradome sequencing, also referred to as parallel analysis of RNA ends (PARE) sequencing, is a technique that uses high-throughput sequencing combined with bioinformatics to identify these mRNA degradation fragments on a large scale and thereby identify miRNA regulatory target genes. By using degradome sequencing, we are able to identify microRNA target genes from experiments without the limitations of bioinformatics predictions, making the data much more accurate and convincing, and greatly simplifying the subsequent validation work, thus becoming a powerful tool for miRNA target gene identification.
Table 1 Degradome sequencing versus other methods for finding miRNA target genes
The AGO protein will cut the target gene mRNA sequence at the 10th base (counting from the 5' end of the miRNA) of the miRNA-mRNA complementary region. The target gene sequence is divided into a 5' fragment and a 3' fragment, where the 3 ' fragment will be ligated to the 5' Adaptor by RNAase due to the free 5' phosphate group, the 5' fragment and the uncut mRNA will not be sequenced due to the 5' end cap structure which cannot be added to the splice. The 5' Adaptor sequence contains an endonuclease MmeI that recognizes a stable site, this single cleaves 20-30 bp after the recognition binding site, and after cutting the synthesized double-stranded cDNA plus the 3' Adaptor for sequencing, the degradome sequence length is about 50 nt, and the sequencing yields sequences are sequenced with a 3' splice sequence.
The construction of degradome library. (German M A et al., 2009)
With the use of technologies such as miRNA sequencing, we can now quickly and accurately identify new miRNAs. miRNAs and their corresponding target genes can be identified and confirmed by reverse sequencing of the degradome from the shear target of the miRNA.
The primary application of degradome sequencing is the detection of miRNA splice genes. When we can detect which genes are cut by the miRNA, we can then learn which genes are affected downstream, which in combination with GO and KEGG annotation information helps us to further understand how the miRNA is regulated.
Different isoforms of the same miRNA family in an organism may cut two sites or a partially overlapping site on the same target gene. However, the use of RACE does not allow quantitative analysis of these miRNA-directed cleavage target genes, although the target genes can be verified, so it is not possible to know which site is the primary site of cleavage. Degradome sequencing has solved these problems by quantifying the target gene sequences.
Because the processing of miRNA is cleaved by DCL enzymes, RNA with a 5' phosphate group is also captured by the 5' adapter and entered into the sequencing library. Analysis of degradome sequencing data reveals that some pri-miRNAs are also likely to be cleaved and degraded.
Deeper mining of the degradome sequencing data helped to identify some miRNA precursor sequences that could be cleaved by their mature miRNAs, such as prior-miRNAs (At5g14545-miR398b, At2g38325-miR390a) that could be cleaved by miR398b and miR390a. After comparing degradome data between different species, feedback regulation was found between miRNAs and their precursors, explained as miRNA self-regulation.
Combined analysis using different sequencing data is a common approach to transcriptional regulation studies, and the results of different experiments can corroborate each other to explore the deeper secrets of gene expression regulation in organisms.
Now, many software can be used to process RNA degradation product sequencing data, usually to obtain biologically significant information from the raw data, such as known or newly discovered non-coding RNAs, regulatory networks of genes and gene functions. The following are a few commonly used software packages for analyzing degradome sequencing data:
1. CleaveLand: analyses sRNA-target RNA interactions and can predict information such as shear sites, target RNA sequences and miRNA sequences.
2. SeqTar: a prediction software, an enhanced version of CleaveLand
3. TAPIR: software for predicting miRNA-target RNA interactions, allowing prediction of target RNA and miRNA interactions from multiple species.
4. PAREsnip: for the prediction of sRNA target genes based on previous high-throughput sequencing and degradome sequencing data
5. TargetFinder: supports the prediction of miRNA and mRNA sequences, and miRNA-target RNA interactions in plant and animal genomes
6. SoMART is an online software service for miRNAs and trans small interfering RNAs (tasiRNAs), including analysis resources and tools.
The small RNA Target Database is a database of known and predicted miRNA and sRNA target genes. The following are some commonly used sRNA target databases:
1. StarBase: a comprehensive database with gene annotation and the ability to find miRNA-target interactions, containing microarray data and degradome sequencing data from six plants and animals, human, mouse, nematode, Arabidopsis, rice and grape.
2. miRTarBase: miRNA-target interaction database supporting the prediction of novel miRNA-target interactions.
3. TargetScan: a miRNA database for predicting miRNA interactions with target RNAs in the 3'UTR and 5'UTR regions.
4. PicTar: a allows prediction of miRNA-target RNA interactions in multiple species.