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Original link:Metaorganisms - Eukaryotic Transcriptome Sequencing
brief introduction
Transcriptome broadly refers to all transcripts of a specific cell in a certain functional state, including mRNA and non coding RNA (ncRNA). It is an inevitable link between genomic genetic information and biological functions. Transcriptome sequencing in eukaryotes is based on high-throughput sequencing, which can quickly obtain a collection of all transcripts of a specific cell or tissue in a certain state of a species, used for studying gene structure and function, variable splicing, and predicting new transcripts.ranscriptome research can study gene function and structure at the overall level, and has become a priority research method to reveal the mechanisms of biological growth and development regulation and adaptation to stress, biological evolution laws, important mechanisms of disease occurrence and development, and discover key targets of pathogenic gene regulation. Currently, it has been widely applied in various fields such as basic research, clinical diagnosis and drug development, animal and plant breeding, etc.
characteristic
Ability to handle complex samples
Rich experience in library construction, incorporating library homogenization technology
Assist clients in quickly and accurately conducting bioinformatics analysis
Can flexibly customize information analysis based on customer needs
Initial sample size and sample delivery recommendations
Sample type | initiation mass |
Animal and clinical organ tissues/brain tissues, etc | >20mg |
Animal and clinical skin/bone/blood vessels/adipose tissue, etc | >100mg |
Plant leaf tissue/flowers | >200mg |
Plant roots/stems/fruits/seeds | >500mg |
Primary cells/cell lines | >5 x 106 pieces |
Neutrophils/eosinophils/basophils | >5 x 107 pieces |
Total RNA | >1 μ g and RIN>7.0 |
matters needing attention:
① It is recommended to store the tissue samples in relevant tissue preservation solutions such as RNAlate, RNAOld, RNAProtect, etc., and then store them at -80 ℃ or send them on dry ice;
② After sufficient lysis using TRIzol or other lysis solutions, the cell samples are stored at -80 ℃ or sent on dry ice
③ For more detailed sample preparation guidelines, please contact online customer service
Bioinformatics analysis process and content
Referenced transcriptome | Analysis content | remarks |
Sequencing data quality control | Remove connector sequences, contaminated sequences, and low-quality error sequences from the original offline data | |
Data volume statistics and quality evaluation | ||
Sequence alignment and transcript reconstruction | Reference genome alignment | Compare the proportion of offline data to the upper genome |
Distribution of reference genome alignment regions | Statistical comparison of the proportion of exons and introns in the genome sequence | |
Reference sequence chromosome density distribution | Statistical distribution of alignment sequences on chromosomes | |
Transcriptome reconstruction | Contains sequence merged. fa and. gtf files | |
Gene/transcript overall expression analysis | Gene expression summary table | |
Transcript expression summary table | ||
Box plot of gene and transcript expression distribution | ||
Statistical distribution of gene and transcript expression intervals | ||
Transcript coverage depth statistics | ||
Distribution density map of gene and transcript expression levels | ||
Differential expression gene/transcriptome analysis (sample size ≥ 2) | Differential expression genes and transcripts statistical bar chart | |
Differential expression genes and transcriptome expression profiles | ||
Differential expression genes and transcriptome volcano map | ||
Cluster heatmap of differentially expressed genes and transcripts | ||
GO enrichment analysis of differentially expressed genes | Including GO enrichment bar charts, scatter plots, radar charts, etc | |
Enrichment analysis of differentially expressed genes KEGG | Including KEGG pathway enrichment scatter plots, pathway maps, radar maps, etc | |
Enrichment analysis of differentially expressed genes Reactome (including only 19 common species) | Including Reactome enrichment scatter plots, bar charts, etc | |
Enrichment analysis of differentially expressed genes DO (disease annotation database) (including only human species) | Including scatter plots, bar charts, etc. for DO database enrichment | |
structural analysis | Variable splicing analysis | By default, ASprofile variable shear analysis results are provided, and rMATS differential variable shear analysis results can be provided for free after sales |
SNP/InDel analysis | ||
Sample correlation (sample size ≥ 2) | Correlation coefficient chart and PCA (principal component analysis) chart |
Application scenarios and cases
Application Scenario 1：Differential gene screening and functional analysis
Applicable scope：Any direction including clinical medicine, basic medicine, biochemistry, animal and plant and fungal research
By using eukaryotic transcriptome sequencing, differentially expressed genes can be screened by comparing the gene expression levels between the experimental group and the control group. Then, further locking of differentially expressed genes is carried out, such as GO, KEGG enrichment analysis and GSEA analysis, combined with published literature in Pubmed and some star molecules accumulated in the research group to annotate the functions of differentially expressed genes, and further analyze the functional genes of interest. After entering the experimental validation stage, qPCR, Northern, Western Blot, FISH validation, gene knockout, and overexpression can be performed on the screened differentially expressed genes.
Application Scenario 2：Time series analysis or concentration gradient analysis
Applicable scope：Clinical samples, cell samples, animal and plant samples with multiple time periods, or samples treated with different drug concentrations
In the process of transcriptome data analysis, there is a special type of experimental design. Collect experimental samples from different time periods or test samples with different concentration gradients of drugs, reagents, etc. Subsequently, studying the expression patterns of different genes at different time periods or concentration gradients is commonly referred to as "time series analysis"
Application Scenario 3：Discovery of upstream regulatory genes such as transcription factors/regulatory factors/splicing factors
Applicable scope：Any research direction including clinical medicine, basic medicine, biochemistry, animal and plant research, etc
Conventional transcriptome differential analysis is highly likely to yield a large number of differentially expressed genes, which poses a challenge for target localization in later experimental validation. Transcription factors are a great entry point without specific pathways of interest or star molecules. Transcription factors can regulate genomic DNA openness, recruit RNA polymerase for transcription processes, recruit cofactors to regulate specific transcription stages, and regulate various life processes such as immune response and developmental patterns. Therefore, analyzing the expression and regulatory activity of transcription factors is of great significance for deciphering complex life activities. Other regulatory factors, including variable splicing and other regulatory genes, can also participate in upstream regulation.
Application scenario 4: Large sample research
Applicable scope：Animal and plant breeding, genetic populations and species origins, population cohorts and biomarker mining
With the rapid development of sequencing technology, transcriptome sequencing studies with a small number of samples are no longer able to explain complex biological problems. Researchers have begun to use large sample sizes of transcriptome samples, combined with statistical and machine learning methods, to identify core genes that conform to specific patterns and research objectives. Using methods such as Mendelian randomization, correlation analysis, linear regression, LASSO regression, Cox regression, etc., to analyze gene or genomic diversity in different samples and explore deeper and more comprehensive biological significance.
Project Process
Transcriptome sequencing with eukaryotic involvement in metaorganisms can provide researchers with a complete range of service processes, including sample extraction, library sequencing, and data analysis, providing high-quality data results and providing strong reference for subsequent researchAt the same time, the cloud analysis of metabiotic transcriptome has been fully upgraded, providing customers with a variety of analysis content to meet the standard and personalized analysis needs of researchers.
