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Original link：Meta organisms - miRNA Sequencing
Small RNA is a general term for a class of non coding short RNAs in organisms that have important regulatory functions. Numerous studies have confirmed that small RNAs are involved in regulating almost all life processes in animals and plants, including cell proliferation, differentiation, apoptosis, and are closely related to the occurrence and development of human diseases. Small RNA sequencing is precisely the analysis of this important type of regulatory small RNA, especially microRNAs (miRNAs), using high-throughput sequencing technology to identify and analyze small RNA sequences ranging from 18 to 30 nucleotides in samples.
Technical advantages
Library optimization：Optimize the library preparation process based on the different miRNA sequences of animals and plants, and maximize the enrichment of sample small RNA sequences
Exclusive analysis software：We have independently developed data analysis software ACGT101 miR, whose reliability has been verified by hundreds of experimental projects; The generated charts can be directly used for paper writing.
Rich project experience：One of the earliest companies in China to provide small RNA sequencing services, users have published over a hundred papers related to small RNA sequencing.
technology roadmap
Total RNA
3 'and 5' connector connections
reverse transcription
PCR amplification
PAGE purification
Machine sequencing (HiSeq 2500, SE50)
Data analysis
Analysis content
1. Data quality control: distribution statistics of sequencing quality values; Quality control of sequencing bases; Sequencing data output statistics.
2. Obtaining comparable sequencing data
3. miRNA database and genome alignment of sequencing species
4. Comparison with other RNA databases
5. Predicting novel miRNAs
6. Correlation analysis of miRNA sequences themselves
7. Quantitative analysis of miRNA and differential expression analysis of miRNA in multiple samples; Differential expression miRNA screening; Differential expression miRNA statistics; Cluster analysis of miRNA expression patterns (limited to multiple sample projects).
8. Duplicate correlation testing (limited to biological duplicate samples only).
9. Prediction of Target Genes for Differentially Expressed miRNAs (Target Finder Software for Plants, Target Scan and miRanda Software for Animals)
10. Differential expression of miRNA target genes GO, KEGG annotation and GO, KEGG enrichment, pathway pathway pathway analysis, and pathway network analysis. (Multiple sample projects only)
Differential expression of miRNA target genes GO, KEGG annotation and GO, KEGG enrichment, pathway pathway pathway analysis, and pathway network analysis. (Multiple sample projects only)
Sample type
Cells, tissues, body fluids, serum, plasma, whole blood, total RNA, etc
Suggested total RNA starting amount: 5 μ g, minimum 2.5 μ g, concentration ≥ 120 ng/μ L
Sample type
1.Wu S, Li Y, Chen S, Liang S, Ren X, Guo W, Sun Q, Yang X. (2017) Effect of dietary Astragalus Polysaccharide supplements on testicular piRNA expression profiles of breeding cocks. International Journal of Biological Macromolecules 103(1), 957-964.
2.Tritten L, Tam M, Vargas M, Jardim A, Stevenson MM, Keiser J, Geary TG. (2017) Excretory/secretory products from the gastrointestinal nematode Trichuris muris. Experimental Parasitology 178(1), 30-36.
3.Sun J, Yao L, Chen T, Xi Q, Zhang Y. (2017) The effect of dietary ginseng polysaccharide supplementation on the immune responses involved in porcine milk-derived esRNAs. bioRxiv [Epub ahead of print].
4.Ghorecha V, Zheng Y, Liu L, Sunkar R, Krishnayya NSR. (2017) MicroRNA dynamics in a wild and cultivated species of Convolvulaceae exposed to drought stress. Physiology and Molecular Biology of Plants 23(2), 291-300.
5.Li H, Peng T, Wang Q, Wu Y, Chang J, Zhang M, Tang G, Li C. (2017) Development of Incompletely Fused Carpels in Maize Ovary Revealed by miRNA, Target Gene and Phytohormone Analysis. Frontiers in Plant Science 8(1), 463.
Case Presentation
Analysis and comparison of small RNAs during pollen development in homologous tetraploid and diploid rice
1. Research background
MicroRNAs (miRNAs) regulate the expression of plant genes by inhibiting their target genes and play an important role in plant reproduction. However, current research on miRNA profiling of homologous tetraploid rice is quite limited.
2. Research results
In this study, researchers used small RNA sequencing to analyze the miRNA profiles during pollen development in diploid and polyploid rice. The research results showed that compared with diploid rice, 172 differentially expressed miRNAs (DEMs) were detected in tetraploid rice, and 57 miRNAs were specifically expressed in homologous tetraploid rice. Among these 172 DEMs, 115 miRNAs were upregulated and 61 miRNAs were downregulated. GO analysis of target genes upregulated in DEM showed that their functions were enriched in membrane transport during the pre meiotic interphase, meiotic reproduction, and nucleotide binding during the single microspore stage. Osa-miR5788 and osa-miR1432-5pR+1 are upregulated during meiosis, and their target genes reveal the interaction of meiosis related genes and suggest that they may be involved in gene regulation related to chromosome behavior. In addition, during the pollen development of homologous tetraploid rice, abundant 24 nt siRNA related to transposable elements were discovered; However, their content significantly decreased in diploid rice, indicating that 24 nt siRNA may play a role in pollen development.
Venn diagram analysis of miRNA expression at different stages during pollen development
The results of this study provide new insights into the role of miRNA in pollen development in homologous tetraploid rice and its relationship with pollen sterility, laying the foundation for understanding the impact of small RNA expression profiles on polyploidy.
reference
Li, et al. (2016) Comparative Small RNA Analysis of Pollen Development in Autotetraploid and Diploid Rice. International
Journal of Molecular Sciences. 17, 499; doi:10.3390/ijms17040499
Result Display
1. Statistical analysis of differential miRNA upregulation and downregulation
The frequency statistics of upregulation and downregulation of differentially expressed genes are used to determine the number of differentially expressed miRNAs under different experimental conditions. The horizontal axis represents the comparison group information, the vertical axis represents the number of upregulated and downregulated miRNAs, red represents upregulated miRNAs, blue represents downregulated miRNAs, and the number represents the number of upregulated and downregulated miRNAs.
2. Cluster analysis of differentially expressed miRNAs
Differential miRNA clustering analysis is used to determine the clustering patterns of miRNA regulation under different experimental conditions. Based on the similarity of miRNA expression profiles in the samples, cluster analysis is performed to visually display the expression of miRNAs in different samples (or treatments), thereby obtaining biologically relevant information. Different colors represent different levels of miRNA expression, with colors ranging from blue to white to red indicating low to high levels of expression. Red represents high expression genes, while dark blue represents low expression genes.
3. Differential miRNA Venn diagram
The differential gene Venn diagram between different comparison groups can intuitively display the number of common and unique differentially expressed miRNAs between different comparison groups. The differential gene Venn diagram has obvious biological significance, such as the experimental design of the same control but different treatments, which can compare the differential genes under different treatments.
4. GO Enrichment Bar Chart
GO enrichment bar chart of miRNA target genes: used to reflect the distribution of differentially expressed genes on GO terms enriched in biological processes, cellular components, and molecular functions.
5. GO enrichment scatter plot
Perform GO enrichment analysis on differentially expressed miRNAs and display them in a scatter plot. The Rich factor represents the number of differentially expressed genes located in the GO divided by the total number of genes located in the GO. The smaller the P-value, the higher the degree of GO enrichment.
common problem
1.What is the naming convention for miRNA?
Regarding miRNA naming, it is based on the naming rules of miRBase: the Latin name of the species is abbreviated with 3 letters - miR/MIR - number (plant), miR represents the mature microRNA, and MIR is used as the precursor for plants. Refer to the official website of miRBase: Currently, * is no longer used to label complementary sequences of microRNAs and their hairpin precursors. Instead, "-3p" and "-5p" are used as suffixes to distinguish these two sequences, replacing the old naming convention. Please refer to the blog article on the miRBase database when version 17.0 was released http://www.mirbase.org/blog/2011/04/mirbase-17-released/
2. How to screen for differentially expressed genes?
When filtering, it may be necessary to refer to this parameter. Generally, if the focus is on discovery, all copies should be retained. If the focus is on differential expression, only high and middle copies can be retained. Log2 (Treat/Control)>0 indicates upregulation, 1 indicates 2-fold upregulation, 0 indicates upregulation, and 1 indicates 2-fold upregulation,
3.What are the advantages of the ACGT101 program?
The advantages are as follows:
1) Analyzing the entire process while considering sequencing quality scoring (phred score);
2) Before comparing the mature body with the reported precursor sequence, in addition to discovering a completely new miRNA sequence, the mature body sequence at the other end and the flanking miRNA sequence of the reported mature body can also be discovered;
3) The comparison and filtering with other RNA databases is based on the miRBase database, which avoids filtering out a small number of reported miRNAs with coding regions on the comparison;
4) Table 5 has rich and comprehensive data information, and all results are traceable;
5) 100 customer references.
