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Product Brief

The whole transcriptome refers to all the specific cells that can be transcribed under certain conditions. The sum of RNA includes mRNA and non coding RNA (non-coding RNA). Research on non coding RNA mainly focuses on miRNA, lncRNA and circRNA with regulatory roles. Based on the whole transcriptome sequencing study based on the two generation sequencing technology, the mRNA, lncRNA, circRNA and miRNA in the same sample were analyzed simultaneously, and the research contents were systematized by 22 correlation analysis, three element association analysis and multivariate association analysis, aiming to dig out the transcriptional regulation problems behind the life phenomenon.



Schematic diagram of ceRNA (competitive endogenous RNA) mechanism

Wang Y et al., Trends Genet, 2016

Our advantages

1. double Library Construction: Small RNA library and chain specific library of ribosomes. Strong ice 8 years of experience in building a database to ensure the quality of the database.

2.4 kinds of RNA omnidirectional analysis: not only can quantitative analysis of known lncRNA and miRNA, but also predict the new lncRNA through Stringtie reconstruction transcript, and predict circRNA Target analysis So as to get MiRNA-mRNA , The targeting relationship of lncRNA-miRNA and circRNA-miRNA;

Three Full database integration: integrating and updating regularly recognized databases and target gene prediction algorithms in the field of biology, such as NP Inter, miRBase, RNAhybrid, etc., to ensure that the analysis results closely follow the forefront of the industry;

Four Upstream sequencing + downstream validation: Customers only need to provide cells, tissues or total. RNA, Li Bing will complete the whole service process from computer sequencing to data analysis, and follow up qPCR verification.



Sample requirements

Tissue samples:

One Animal tissue More than 1G;

Two plant tissue More than 2G;

Three Cell sample More than 1 x 10 Six One;

Four Whole Blood More than 5mL;

Five Mycelium More than 10 Six One or More than 30mg.

RNA samples:

One Sample demand: RNA is more than 10 g.

Two Sample concentration: RNA sample is more than 100 ng/ L;

Three Sample purity: OD260/OD280 is between 1.8-2.2, OD260/OD230 is more than 2, 28S/18S is more than 1, animal sample RIN is more than 7, plant sample RIN is more than 6.5, RNA has no obvious degradation.


Experiment flow


One Customer samples: cell volume Ten Six Above;

Two total RNA extraction and quality control: gel electrophoresis quality control > Nanodrop quality control > Agilent 2200 quality control;

3. the construction of small RNA Library: 10-50bp, single end sequencing SE50, library molecular 18-30bp;

Four Construction of ribosomal Library: after reverse transcription RNase was used to remove rRNA.

5. on board sequencing: Strong ice recommended choice NovaSeq, dual terminal sequencing, has high flux, high base precision, low cost and fast speed.



Data analysis process

Result example

1, mRNA data analysis results are shown in "transcriptome sequencing".

2, miRNA data analysis results are shown in "small RNA sequencing".

3. LncRNA identification and differential lncRNA analysis.
Taking the counts obtained by RNA mapping as the research object, we used the Genetype annotation information of NCBI Gene/Ensembl Biomart/NONCODE database to identify the known lncRNA/ pseudogenes / other long chain non coding RNA. Subsequently, taking the counts of these lncRNAs as the object of study, DESeq2/DESeq/EBSeq/EdgeR/Limma and other algorithms were used to conduct differential screening, and the differential gene (Dif-lncRNA) that met the difference multiple and FDR threshold was obtained. Based on the results of differential screening, volcanic map and cluster analysis were carried out to obtain Volcano Plot and Cluster Heatmap.

difference LncRNAs volcano analysis and cluster analysis

Yang F et al., Gene, 2016

Note: (A) difference LncRNA The red map shows significant differences in volcanic map analysis. LncRNA Blue indicates non significant difference. LncRNA ; (B) difference lncRNA Cluster analysis Heat map The deeper the red, the more. LncRNA The up regulation is more significant. The deeper the blue, the more. The downregulation of lncRNA was more significant.



4. LncRNA target analysis
Using differential lncRNAs and differential miRNAs as research objects, miRanda algorithm and RNAhybrid algorithm were used to predict target genes, and lncRNA-miRNA target relationship was obtained.

LncRNA-miRNA targeting relationship

Miao X et al., Sci Rep, 2016

Note: red triangle indicates upregulated. LncRNAs Green triangle means downgrading. LncRNAs Purple V triangle Express up MiRNAs Blue The V triangle indicates that the miRNAs is down.



5, ceRNA analysis
Taking lncRNA-miRNA targeting relationship as the research object, combined with the information of miRNA targeting genes, the ceRNA relationship of lncRNA-miRNA-mRNA was obtained by negative correlation analysis, and lncRNA-miRNA-mRNA Network was drawn.

LncRNA-miRNA-mRNA Network

Miao X et al., Sci Rep, 2016

Note: Purple triangle representation. LncRNA Red dot representation MRNA Yellow dots indicate the key. MRNA Blue The V triangle represents miRNA.



6. CeRNA gene function analysis (GO Analysis) and signal pathway analysis (Pathway Analysis)
Taking ceRNA as the research object, NCBI/UNIPROT/SWISSPROT/AMIGO GO database and KEGG database were used respectively for functional analysis and signal pathway analysis of differentially expressed genes, so that functional items and pathway entries were significantly enriched in ceRNA genes.

Differential gene GO analysis and Pathway analysis

Xu T et al., Oncogene. 2015

Note: A) lncRNA TINCR-siRNA VS scrambled siRNA differential gene cluster analysis map; (b) pathway enriched with differential genes. Entries; C) GO entries were significantly enriched in differentially expressed genes.



7, circRNA forecast
Ring RNA (circRNA) is a new type of RNA which is different from the traditional linear RNA. It has a closed ring structure. According to the special splicing form of circRNA in the process of expression, we adopted circexplorer/CIRI/ACFS/find_ CIRC and other algorithms can predict the reads of the reads obtained by sequencing, and we can find circRNA with two exons at the same time and the direction opposite to that of linear RNA, as well as some new circRNA from Intergenic or Intron region sources.

CircRNA predicts workflow and Prediction results (normal organization) VS cancer tissue)

Chen W et al., Nat Neurosci. 2015/Zheng Q et al., Nature Communications, 2016

Note: (a) abscissa coordinates circRNA reverse splicing reads Number ordinate representation CircRNA Number; (b) the distribution of circRNA in genomic structure; (c) abscissa indicates exonic circRNA Length, ordinate representation CircRNA quantity.



8, differential circRNA analysis
Taking the predicted circRNA as the research object, DESeq2/DESeq/EBSeq/EdgeR/Limma algorithm was adopted to screen the difference, and circRNA circRNA (Dif-circRNA) was obtained to meet the difference multiple (Log2FC>1 or <-1) and FDR threshold (FDR<0.05).

Results of differential circRNA analysis

Liu Q et al., Scientific Reports, 2016

Note: left picture is difference CircRNA cluster analysis chart (OA VS normal) Cartilage tissue The right picture is the difference circRNA. The volcanic map shows a marked difference in red dots. CircRNA.


9. CircRNA target analysis
Using differential circRNA as the research object, Miranda algorithm and RNAHybrid algorithm were used to predict the target regulatory relationship, and the relationship between miRNA and differential circRNA target regulation was obtained.

CircRNA-miRNA interaction

Zheng Q et al., Nature Communications, 2016

Note: the graph shows. CircHIPK3 Interacting The putative binding site of miRNAs.



10, ceRNA analysis
Taking circRNA-miRNA targeting relationship as the research object, combined with the information of miRNA targeting genes, the ceRNA relationship of circRNA-miRNA-mRNA was obtained by negative correlation analysis, and circRNA-miRNA-mRNA Network was drawn.

CircRNA-miRNA-mRNA Network

Liu Q et al., Scientific Reports, 2016

Note: green dots indicate CircRNA Yellow diamond representation MRNA Purple The V triangle represents miRNA.


11, WGCNA function prediction
Weighted Gene Co-expression Network Analysis (WGCNA) is a method to construct gene co expression network based on gene expression data. The NovelBio team helps researchers use WGCNA to perform functional prediction, grouping genes according to gene expression patterns. According to the "similar functions of genes that can form co expressed and clustered together", it can be considered that circRNA and its cluster mRNA have similar functions, and can predict the phenotypic effects of circRNA through mRNA enrichment function and signaling pathway.

CircRNA And protein coding genes. WGCNA analysis

Wang Z et al. Frontiers in plant science, 2017

Note: The graph is The different module calculated by WGCNA, Highly correlated with different phenotypes Module, and visualized the moudle16 and two circRNA relational network diagrams respectively.


Advanced data analysis
1. Wayne analysis (Venn Analysis)
Taking the scientific problems that need to be solved in the experiment as the research objectives, taking the differences of mRNA, lncRNA, circRNA and miRNA between each two groups as the research objects, Wayne analysis method is used to get the mRNA, lncRNA, circRNA and miRNA in every two groups.


E50 VS E40 and E60 VS E50 difference MRNA/lncRNA Wayne diagram

Li Y et al., BioMed Research International. 2019

Note: left picture is E50 VS E40 difference MRNA and E60 VS E50 difference MRNA The result of Wayne analysis is that the right picture is E50 VS E40 difference LncRNA and E60 VS E50 difference LncRNA's Wayne analysis results.



2, lncRNA/circRNA gene enrichment analysis
In order to find out the regularity and study the biological function of lncRNA/circRNA from the complex data of omics, the NovelBio team customized Gene set enrichment analysis (GSEA) for researchers, and found a key biological pathway to further identify the biological mechanism of lncRNA/ circRNA and phenotype related.

intervene Gene enrichment analysis of LncRNA GAS5

Liu et al., Nat Commun, 2016

Note: the figure is LncRNA GAS5 After over expression and knocking down, HESCs The results of gene enrichment analysis. Among them, ES indicates enrichment score. NES indicates the value after ES normalization. The higher the score, the positive correlation between the gene category and the intervention.



Examples of literature

[1] Lei B, Zhou J, Xuan X, et al. Circular RNA, al. 2019, 4.,


[2] Li Y, Li GQ, Wang F, et al. Integrated Analysis Integrated, Y 2019, 23.


[3] Yu Y, Zhang M, Liu J, et al. Long Non-coding Long, Y 2018, 7


[4] Qu S, Hao X, Song W, et al. Circular RNA Circular, 2018 S 16, 11 (1):


[5] Yu Y, Zhang M, Wang N, et al. Epigenetic silencing Epigenetic, 2018, 3; 9 (7):


[6] Yin D, Lu X, Su J, et al. Long noncoding, al. 2018, 24, 17, 1 (92.)


[7] Liang Ding, et al. A novel stromal lncRNA signature reprograms, lncRNA, Carcinogenesis. 2018 Mar 8; 39 (3): 397-406. (IF=5.105)


[8] Sun D, et Al.LncRNA GAS5 inhibits microglial M2 polarization and exacerbates demyelination. EMBO Rep. 2017 Oct; 18 (10): 1801-1816. (IF=8.568)


[9] Lai, Z.Y. et al. Analysis of co-expression networks for circular for Circ_ 0047905, hsa_ Circ_ 0138960 and hascircRNA7690-15 are candidate oncogenes in gastric cancer. Cell Cycle. 2017 Oct 5:1-11. (IF=3.53)


[10] Zhang E, et al. H3K27 acetylation activated-long non-coding RNA CCAT1 RNA, E 2017, 7, 45, (6):


[11] Xu C, et al. Long non-coding RNA gas5 control human control Nat Commun. 2016 Nov 4; 7:13287. (IF=12.124)


[12] Zheng Q, et al. Circular RNA profiling reveals an abundant an 2016 Q 6


[13] Liu Q, et al. Circular RNA Related to the Chondrocyte the 2016, 2;


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