菜鸟电竞平台

Service hotline 400-065-1811

Product Brief

Transcriptome sequencing ( The object of RNA-Seq study is the sum of all mRNA that a specific cell can transcribe under a certain functional state. The next generation of high-throughput sequencing technology can obtain almost all transcript sequence information of a specific species or organ in a certain state, so as to accurately analyze the important issues of life science, such as gene expression difference, gene structure variation, and screening molecular markers (SNPs or SSR).


A workflow for RNA-seq

Ruairi J, Genomics Research, 2018


Our advantages

One Eight years of experience in transcriptome sequencing, and independently developed software in many biological domains, such as differential alternative splicing algorithms. The detection rate and accuracy of ASD, CASH and so on exceed that of similar software.

2. without relying on the information of existing species, we can study non model species, and develop multiple processes for different platform data.

3. integrating many recognized transcriptome related databases in academic circles, in essence, improves the breadth and accuracy of later analysis.


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 2mL;

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: ensure cell volume is Ten Six More than one, otherwise the risk should be built.

2. RNA extraction: classical kit rapid extraction;

3. RNA quality control: gel electrophoresis quality control > Nanodrop quality control > Agilent2200 quality control;

Four Library Construction: PolyA database;

5. on board sequencing: Strong ice recommended choice NovaSeq sequencing platform, dual terminal sequencing, large flux, high base precision, low cost and fast speed. Recommended data volume: 6Gb.



Data analysis process




Result example

1. Quality control of raw data
Taking the raw data as the research object, Fastp software was used to filtrate the low quality sequence, undetected sequence, and the joint sequence. The base weight, GC content, length distribution, joint retention and Duplication ratio before and after filtration were analyzed. Figure 1 shows the results of raw data quality control.

Base quality results map

Note: the abscissa of the left picture represents the base site, and the ordinate represents the base mass value. The different color curves represent the mass values of different bases on each read; the right coordinates represent the base sites, the ordinates represent the ratio of base content, and the different color curves represent the bases of different sites.



2. RNA genome alignment (RNA Mapping)
Hisat2/Mapsplice/Star/Tophat2 algorithm was used to compare genomic alignments, and bam files of genomic alignment were obtained. Based on BAM files, information statistics were obtained to obtain genomic comparison rate and reads distribution on gene structure and chromosomes. Figure 2 shows the results of RNA genome alignment.

Distribution of reads on gene structure and chromosomes

Miao et al., Mol Cell Endocrinol, 2015

Note: the left figure is the distribution of reads in different genetic structures (such as exons, Chi Ko, intergenic regions, 5 '-UTR and 3' -UTR); the right picture is the distribution of reads on chromosomes; the abscissa indicates chromosome numbers, and ordinates represent percentages. The gray columns represent the ratio of bases on each chromosome to the genome, and green pillars represent reads on chromosomes. The ratio of the base number to the genome.




3, expression Statistics (Expression)
Using HTSeq and genome annotation gff3 files, the gene expression quantity was selected according to the single or dual end sequencing type, and RPKM or FPKM were normalized. Based on the statistical results, the correlation among samples, RPKM/FPKM density and abundance were analyzed, reflecting the distribution and dispersion of gene expression level and the difference of gene expression level in different samples.

Gene expression analysis

Note: the left is the RPKM density map of different samples, the abscissa coordinates log10 (RPKM), the ordinate represents the ratio of the number of genes corresponding to each log10 (RPKM) value, the right picture is the box diagram of gene expression of different samples, the abscissa represents the name of different samples, and the ordinate represents the distribution of log10 (RPKM) of each gene in the sample.



4. Differential gene screening (Dif Gene Analysis)
DESeq2/DESeq/EBSeq/EdgeR/Limma and other algorithms were used for differential screening, and the differential genes satisfying Fold Change and FDR thresholds were obtained. Based on the results of differential screening and FPKM or RPKM of the samples, volcanic map analysis (Volcano Plot) and cluster analysis (Heatmap) were performed.

Volcanoes and cluster maps of different genes

Liu et al., Nature, 2016

Note: the left is the volcanic map of the differential gene, the red shows significant difference genes, the blue represents the non significant difference gene, the right picture is the gene expression clustering map, the abscissa is the sample group, the ordinate is the gene, the red indicates high expression, the green expression is low expression.



5. Functional analysis (GO Analysis)
In order to identify the related functions of different genes, we often need to analyze the differential genes by GO enrichment. The NovelBio team invested a lot of time and manpower in the database, using NCBI/UNIPROT/SWISSPROT/AMIGO and other GO databases to perform functional analysis of the differentially expressed genes, so as to get the functional items (GO Term) that were significantly enriched by different genes.

Gene function analysis

He et al., Cancer Sci, 2017

Note: the map shows the first 15 functional items with significant difference from the 3 levels of Biological Process (BP), Molecular Function (MF) and cell components (Cellular Component, CC). The abscissa is -Log2 (P-value) /-Log10 (P-value) and the ordinate is the name of the Go-Term entry.



6. Signal pathway analysis (Pathway Analysis)
Pathway enrichment analysis of differentially expressed genes can help us to find out the signal pathways which are not related to the same genes. The NovelBio team integrates a series of recognized general databases (KEGG, NCBI, EMBL, etc.) in the field of biology, optimizes the algorithm needed, and analyzes signal transduction pathways of differential genes, so as to get signalling pathway entries that are significantly enriched by differential genes.

Enrichment analysis of Pathway

He et al., Cancer Sci, 2017

Note: the map shows 25 Pathway entries enriched by differential genes. The abscissa is the name of the Pathway entry, the ordinate is the enrichment degree (Enrichment), the red represents the salient item, and the blue represents the non significant item.



7, GO-Tree analysis
By using the subordinate relationship of GO-term's upper and lower levels in GO database, GO-Tree was drawn to get significant functional groups and hierarchical affiliation.

GO Tree Miao et al., Mol Cell Endocrinol, 2015

Note: the map shows the inherent affiliation of GO Terms, which is significantly enriched in differentially expressed genes. Red represents functional items that significantly increase gene enrichment; green represents functional entries that are significantly enriched in genes downregulated, and yellow represents functional items that are significantly enriched and downregulated.



8, Path-Act-Network analysis
The relationship between upstream and downstream signaling pathways was recorded by KEGG database, and Path-Act-Network was plotted to obtain the upstream and downstream regulatory relationship of signalling pathways.

Path-Act-Network Miao et al., Mol Cell Endocrinol, 2015

Note: the map shows the upstream and downstream regulatory relationships between the differentially expressed genes and the pathway enrichment. Red indicated that pathway was significantly up-regulated; green indicated that pathway was significantly enriched in downregulated genes.



Advanced data analysis
1. Co expression of network analysis (Co-Exp-Network Analysis)
After in-depth analysis and mining of known annotation information, researchers often hope to find more innovative points. The NovelBio team is based on GO Analysis and Pathway. The saliency entries obtained by Analysis, as well as the items of interest, were analyzed by co expression network and K-Core analysis based on the gene expression values in these items, so as to get the correlation between genes and the core degree of gene, and then to Co- Expression.txt With K-Core as the research object, Cytoscape is used for graphic display to get Co-Expression-Network.

Co expression network

Miao X et al., Scientific reports, 2016

Note: dots with the same color indicate genes with similar co expression ability. The size of dots indicates the degree of K-core of the gene.



2. Gene-Act-Network Analysis
In the study, it is often found that there are too many differential genes, and the signaling pathways are complex. It is difficult to link the related genes and find the "core" genes. Based on the significant entries obtained from GO Analysis and Pathway Analysis, the NovelBio team used the KEGG related gene expression annotation to help researchers draw Gene-Act-Network and quickly locate the "core" gene.

Gene interaction network

Sun L et al, Sci Rep. 2016

Note: red dot indicates mRNAs up, green dot indicates mRNAs down.



3, Wayne analysis
The typical feature of Wayne diagram is that it uses some overlapping parts to show the possible relationship between sets. Using the Wayne mapping analysis method, we can find out the common or specific differentially expressed genes among different groups and conduct in-depth analysis.

Wien analysis

Chen et al., BMC Genomics, 2014

Note: the map shows the Wayne analysis map of the up regulation gene (left) and down regulated gene (below), and the number represents the number of genes in different intersections.



4, trend analysis
In trend oriented results, researchers often want to analyze the differences in the time / logic trend of the differential genes, and the comparison between the 22 is obviously insufficient to meet this requirement. The NovelBio team provided a customized trend analysis process for researchers. The FPKM value of Wayne gene among different groups was taken as the research object. STEM algorithm was used to carry out trend analysis and get the trend according to the logical sequence of samples.

trend analysis

Chen et al., BMC Genomics, 2014

Note: Based on the numerous results of trend analysis, the researchers concluded and integrated several types of change trends, and then carried out follow-up work in a more targeted way. In the study, 6 significant trends were finally concluded. The researchers chose two trends with the largest number of genes, and carried out in-depth analysis of these genes by GO.



5. Weighted gene co expression network analysis (WGCNA) analysis.
WGCNA analysis is a system biology method used to describe the pattern of gene association among different samples. Based on weighted expression correlation, hierarchical clustering analysis is performed, and according to the set criteria, the clustering results are segmented, and different gene modules are obtained. If phenotypic information is available, we can also calculate the correlation between gene modules and phenotypes, identify modules related to traits, and identify candidate biomarker genes or therapeutic targets according to the association of gene sets and the association between gene sets and phenotypes.

WGCNA analysis Wan et al., Exp Eye Res. 2018

Note: the left picture shows the corresponding relationship between gene clustering and module identification. The highly co expressed gene cluster is in the similar branch in clustering. The right graph shows the module and the phenotype correlation thermograph. The upper figure in the box is the correlation between module ME and phenotypic data, and the numbers in brackets are the P values of correlation.



Examples of literature

[1] Ju L, Han J, Zhang X, et al. Obesity-associated inflammation, al. 1., 2019, 11, 10 (2): (121.)


[2] He H, Chen E, Lei L, et al. Alteration of, al. 2019, 29.,


[3] Zhang C, Wang JJ, He X, et al. Characterization and Characterization, 2018 C 14, 51 (6):


[4] Chen E, Yang F, He H, et al. Alteration of Alteration, 2018, 20; 17 (1):


[5] Ge X, Chen J, Li L, et al. Midostaurin potentiates, al. 2018, 18; 10 (1):


[6] Miao N, Bian S, Lee T, et al. Opposite Roles, al., N, Bian, S, and S.


[7] Heng S, Yan W, Zongyou P, et al. Gefitinib for Gefitinib, 2018, 11;


[8] He C, et al. Phosphorylation of ETS-1 is a critical a 2017 C 14.


[9] Wei, J. et al. The GARP Complex Is Involved in Involved 2017 J.; 19 (13):


[10] Wu, W. et al. CASH: a constructing comprehensive splice site splice 2017 W. Doi:10.1093/bib/bbx034 (IF=5.134)


[11] Chen J, et al. Network analysis-based approach for exploring the exploring 2016, 9;


[12] Liu Z, et Al.Autism -like behaviours and germline transmission in transgenic monkeys overexpressing MeCP2. Nature. 2016; 4; 530 (7588): (())


[13] Hu, Y. et al. Interactions of OsMADS1 with floral homeotic floral 2015 Y., 8 (9): (1366-1384)


[14] Wang F, et al. Alternative splicing of the androgen receptor androgen 2015 F 14, 112 (15):


亚博体育【yabo7.in】←输入域名访问独家合作伙伴 _光明网亚博体育【yabo7.in】-海外商业版图-中新网亚博体育【yabo7.in】← 输入域名 _ 新浪财经_新浪网亚博体育【yabo9.in】-新华网365体育【yabo9.in】_体育_腾讯网365体育【yabo9.in】←输入域名-长城网365体育【yabo9.in】_凤凰网365体育【yabo9.in】←输入域名--浙江频道--人民网365体育【yabo9.in】_游戏_腾讯网uedbet体育(yabo7.in)_凤凰网资讯_凤凰网uedbet体育(yabo7.in)←输入域名_凤凰资讯亚博体育【yabo7.in】←输入域名-成为俱乐部全球合作伙伴|曼联_新浪财经_新浪网亚博体育【yabo7.in】←输入域名访问独家合作伙伴 _光明网亚博体育【yabo7.in】-海外商业版图-中新网亚博体育【yabo7.in】← 输入域名 _ 新浪财经_新浪网亚博体育【yabo9.in】-新华网365体育【yabo9.in】_体育_腾讯网365体育【yabo9.in】←输入域名-长城网365体育【yabo9.in】_凤凰网365体育【yabo9.in】←输入域名--浙江频道--人民网365体育【yabo9.in】_游戏_腾讯网uedbet体育(yabo7.in)_凤凰网资讯_凤凰网uedbet体育(yabo7.in)←输入域名_凤凰资讯