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Product Brief

Small RNA mainly includes miRNA, piRNA, tsRNA (tRF&tiRNA), snRNA and snoRNA. It is a class of RNA molecules that do not possess protein coding ability. They can regulate gene expression, play an important role in basic biological processes such as cell growth, development and metabolism, and even play a key role in the formation of cancer and other related diseases. High throughput sequencing technology eliminates the cumbersome Small RNA clone library construction process. It can generate millions of Small RNA sequences at one time. It can quickly identify the known Small RNA expressed under specific conditions and discover new Small RNA. It can also study the expression differences of Small RNA under different conditions, and can be combined with transcriptome sequencing and expression data for association analysis.

A workflow for Small RNA Sequencing

Jai PM et al., RNA Mapping, 2014

Our advantages

One For animals (including exocrine bodies) Small RNA (miRNA, piRNA, tsRNA) provides customized experimental analysis plan.

Two It can not only efficiently and accurately detect and annotate non model species. New MiRNA can also carry out the latest research such as piRNA, tiRNA and tRF.

Three Update in time MiRBase, miRanda , RNAhybrid and other professional miRNA databases and target gene prediction algorithms ensure that the analysis results are closely related to the forefront of the industry.

Sample requirements

Tissue samples:

One Animal tissues: >200mg;

Two Plant tissues: >200mg;

Three Cell culture: >2x10 Six One;

Four Whole Blood: >3ml;

Five Mycelium: >2x10 Six One or >300mg.

RNA samples:

One Please provide a volume of 15 L - 100 L Total RNA with a concentration of more than 10 mu g or 200ng/ L.

Two OD260/280 is between 1.8~2.2, OD260/230 is more than 2, RIN is more than 6.5, 28S:18S is more than 1, so RNA is not degraded.

Three Please mark the sample number clearly when sending the sample. Parafilm membrane seal;

Four Avoid repeated freezing and thawing during preservation.

Five Dry ice transport is required for sample delivery.

There are differences between different samples. Please refer to the ice cream for more details.

Experiment flow


One total RNA extraction and quality control: total RNA > 1 g; RIN> 6

Two Small RNA enrichment: cutting range 10-50bp

Three Small RNA quality control: Agilent 2200 quality control

Four Library Construction: Library molecules 18-30bp

5. on board sequencing: Small RNA sequencing NovaSeq, HiSeq, Ion Proton and other sequencer can be completed. Sequencing data volume 40M reads.

Data analysis process

"Animal little RNA






Plant little RNA


Result example

1, data filtering
The quality control of raw data, elimination of short sequences in the sequence, low quality sequences, undetected sequences, and removal of double ended connector sequences to ensure the quality and accuracy of the sequencing data, we will evaluate the quality of the sequencing data from the perspective of base quality and GC content. The filtered data is called Clean Data (filtered data).

Map of base mass results (after filtration)

Note: abscissa indicates base sites, and ordinates represent base mass.



2. Sequence alignment and length analysis
Since there are more members in Small RNA and each member has their own length range, we will first compare the data obtained from the sequencing to the miRNA database of the analysis species, then compare the unmapped reads to the NCBI database, and compare the length distribution statistics of small RNA (miRNA, piRNA and tsRNA) in different databases to observe various kinds of miRNA. The length distribution of RNA and the location of the main peak.

Length distribution of small RNA

Note: the left is the miRNA length distribution map and the right one is the piRNA length distribution map.



3. Base preference analysis
Mature miRNA often has U preference in the first place, and the piRNA from germ cells usually has U preference. Therefore, after sequence alignment, the length distribution statistics of small RNA sequences in different databases are compared, and the base preference of all small RNA is observed.

Base preference analysis of small RNA

Note: abscissa indicates sequence length, ordinate represents percentage, and different colors represent different nucleotides.


4, expression statistics
The most critical step in Small RNA sequencing is quantitative expression and differential screening of small RNA. After comparing the sequencing data to each small RNA corresponding database, the Counts number of each small RNA is standardized by using the expression statistics software.

Statistical analysis of miRNAs expression

An X et al., Theriogenology, 2016

Note: the table lists some differences. MiRNAs Name and sequence information, NE (normalized expression) represents the amount of miRNA expression after normalization.


5. Differential Small RNA screening
Finding the difference small RNA is the basis of the whole research. The commonly used differential screening algorithms are DESeq and edgeR. The results of the expression statistics are screened differently, and the difference of the VS control group in the experimental group is calculated, and the difference multiplier, significance (P-Value) and false positive rate (FDR) are obtained.

difference MiRNAs thermogram

An J et al., Investigative Ophthalmology & Visual Science, 2015

Note: horizontal axis indicates group, vertical axis represents different miRNAs.


6. Target gene function analysis (GO Analysis) and signal pathway analysis (Pathway Analysis)
NCBI/UNIPROT/SWISSPROT/AMIGO and other GO databases and KEGG data were used to analyze functional genes and signal pathways of target genes. Functional items and Pathway entries that were significantly enriched in target genes were obtained, and network with target gene, function and pathway was plotted.

MiRNA Target gene function and Pathway network diagram

An J et al., Investigative Ophthalmology & Visual Science, 2015

Note: the figure shows MiRNA-204 Target genes and their target genes are significantly enriched. GO entries (right) and Pathway entries (left).


Advanced data analysis
1, Novel miRNA prediction
There are a lot of Novel miRNA which are not reported in the non model species. It is an important content to find and study them. NovelBio team helps researchers to extract reads from the genome, and then use the accepted algorithm in the industry, calculate the free energy of stem ring structure, and compare the miRBase database of other species, and finally get the predicted Novel miRNA.

Novel miRNAs Stem ring structure

An X Et al, Theriogenology. Two thousand and sixteen

Note: the graph shows further analysis and known. MiRNA mismatched sequencing reads predicted the new miRNA, and these 10 sequences all had significant stem loop clamp two level structure.



2. Target gene prediction of Small RNA
Taking the differential small RNA as the research object, we used Miranda algorithm and RNAHybrid algorithm to predict the target genes, and obtained the target gene binding sites of small RNA targeting to the 3 'UTR sequence of mRNA, and drew the target gene network map.

MiRNAs target gene network map

An J et al., Investigative Ophthalmology & Visual Science, 2015

Note: Green means downward. MiRNAs Red means up. MiRNAs.


3, Small RNA trend analysis
The miRNA of each group is TPM.txt The document is the object of study. STEM algorithm is used to analyze trend and get the trend according to the logical sequence of samples.

Expression of miRNAs during differentiation of DCs

Su XP et al., Nature Communications, 2013

Note: the graph shows DCs. Differentiation process MiRNAs Expression. H, I, M and R represent the four stages of differentiation respectively: H HSCs, I imDCs, M maDCs, R - maDCs. The number in the upper left corner represents Three hundred and ninety-one individual MiRNAs Divided into Twenty-six individual Clusters, the figure in the lower left corner represents miRNAs in each cluster. The number of colors. Clusters represents miRNAs enrichment.


Examples of literature

[1] Zheng L, Zhang X, Zhang H, et al. The miR164-dependent The, 2019, 3.,


[2] Xu C, Zhang H, Zhou W, et al. MicroRNA-10a, -210, MicroRNA-10a, C, Zhang, C, and H


[3] Wang Y, Zhang CY, Xia RH, et al. The MYB/miR-130a/NDRG2, al. 2018, 11, 9 (9): (917.)


[4] Chen X, Hao Z, Wei G, et al. The microRNA-10a/ID3/RUNX2, al. 2018, 15, 8 (1): (9225.)


[5] He Q, et al. Downregulation of miR-7116-5p in microglia by microglia 2017 Q; 65 (8):


[6] Xu C, et al. Integrated microRNA-mRNA analyses reveal OPLL specific OPLL 2016, 12;


[7] Liu R, et al. DNMT1-microRNA126 epigenetic circuit contributes to esophageal to 2015 R 15, 21 (4):


[8] Cheng T, et al. MeCP2 Suppresses Nuclear MicroRNA Processing and Processing 2014 T 10, 28 (5):


[9] Su X, et al. miRNomes of haematopoietic stem cells and cells 2013 X


[10] Xu C, et al. miRNA -100 Inhibits Human Bladder Urothelial Bladder 2013 C


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