As one of the providers of eukaryotic transcriptome with reference genome data analysis, CD Genomics uses bioinformatics to help you thoroughly analyze the genetic code of species quickly and accurately. Our unique data analysis skills can meet customers' personalized data analysis needs and provide easy-to-interpret data analysis reports.
Introduction of Eukaryotic Transcriptome Analysis
Transcriptome refers to the collection of all transcripts produced by a particular cell or tissue of a species in a certain state. It includes messenger RNA, ribosomal RNA, transport RNA and non-coding RNA. In a narrow sense, it refers to the collection of all mRNA, and transcriptome sequencing refers to the sequencing of all mRNAs.
Transcriptome sequencing (RNA-seq) of eukaryotes with reference genomes can quantitatively measure the change in the expression level of each transcript in a specific tissue or cell during growth or under different conditions. Through high-throughput next generation bioinformatic analysis of sequencing data, almost all transcripts of a specific tissue or organ of a species under a certain state can be obtained comprehensively and quickly. By comparing with the reference genome, we can analyze the mRNA sequence and obtain abundance information, but also analyze the gene structure, study SNP, variable splicing, gene structure optimization and new transcripts. Transcriptome sequencing has been widely used in animal and plant biological basic research, clinical diagnosis, and drug research and development.
Fig 1. Dendritic heat map. (Matthew K; Jason R. 2016)
Eukaryotic Transcriptome with Reference Genome sequencing data analysis can be used for, but not limited to, the following research:
Gene expression level research.
Gene structure level research.
CD Genomics Data Analysis Pipeline
CD Genomics provides eukaryotic transcriptome (with reference genome) sequencing data bioinformatic analysis service. We ensure reliability through strict data quality control.
Bioinformatics Analysis Content
Gene structure level analysis
Map to reference genome
New transcript prediction
New transcript annotation
Variable splicing prediction
Gene expression level analysis
Analysis of gene expression level
Differential gene analysis
GO enrichment analysis
KEGG enrichment analysis
Protein interaction network analysis
Transcription factor annotation
If you have any needs for eukaryotic transcriptome (with reference genome) sequencing data analysis, we will provide appropriate biological information analysis content accordingly. Please feel free to contact us for details.
How It Works
CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.
Combining rich project experience, professional project plan guidance and analysis process, CD Genomics has successfully conducted eukaryotic transcriptome (with reference genome) sequencing data analysis on a variety of species. We ensure your project will be carried out accurately and quickly. Please contact us for more information and a detailed quote.
- Matthew K; Jason R. Using Dendritic Heat Maps to Simultaneously Display Genotype Divergence with Phenotype Divergence.PLoS One,2016; 11(8): e0161292
Demo Results of "Eukaryotic Transcriptome with Reference Genome”
It is used to identify the separation situation of AT and GC by checking the distribution of GC content. According to the principle of complementary bases, the content of AT and GC should be equal at each sequencing cycle and be constant and stable in the whole sequencing procedure.
The dispersion degree of insert length can directly reflect the effect of magnetic beads purification during the library preparation. The length of the insert is calculated through the distance between the start and end of reads at both ends of the inserts on the reference genome.
Gene Expression Distribution
The x-axis shows the sample names and the y-axis shows the log10(FPKM). Each box has five statistical magnitudes (max value, upper quartile, median, lower quartile and min value).
Correlation between Samples.
The scatter diagrams demonstrate the correlation coefficient between samples.
Differential Expression Transcripts
The expression of differential transcripts or genes is visualized by volcano plot. The Volcano plot provides a way to perform a quick visual identification of the RNA transcripts displaying large-magnitude changes which are also statistically significant. The plot is constructed by plotting the FDR (-log10) on the y-axis, and the expression fold change (log2) between the two experimental groups on the x-axis. There are two regions of interest in the plot: those points that are found towards the top of the plot (high statistical significance) and at the extreme left or right (strongly down and up-regulated respectively)
Gene ontology (GO - Gene Ontology Consortium, 2000) enrichment analysis is a set of the internationally standardized classification system of gene function description that attempts to identify GO terms that are significantly associated with differentially expressed protein coding genes. GO molecules are divided into three main categories:
1) Cellular Component: used to describe the subcellular structure, location and macromolecular complexes;
2) Molecular Function: used to describe the gene, gene products, individual functions;
3) Biological Process: used to describe the products encoded by genes involved in biological processes.
KEGG is called Kyoto Encyclopedia of Genes and Genomes, it is the main public database of the pathways. A systematic analysis of the metabolic pathways of gene products and compounds in cells and the database of the function of these gene products . (KEGG PATHWAY), drug (KEGG DRUG), disease (KEGG DISEASE), functional model (KEGG MODULE), gene sequence (KEGG GENES) and the genome of the genome (KEGG GENOME) and so on. The KO (KEGG ORTHOLOG) system links the various KEGG annotation systems, and KEGG has developed a complete KO annotation system to annotate genomic or transcriptome functionalities of newly sequenced species.
The scatter plot is a graphical representation of the KEGG enrichment analysis results. In this figure, the degree of KEGG enrichment is measured by rich factor, qvalue, and the number of genes enriched in this pathway. Wherein the rich factor refers to the ratio of the number of differentially expressed genes located in the pathway to the total number of annotated genes located in the pathway. The larger the rich factor, the greater the degree of enrichment. Qvalue is the corrected pvalue after the multiple hypothesis test. The value range of qvalue is [0,1], the closer to zero, the more significant the enrichment is.
1 What is the difference between mRNA-seq and total RNA-seq?
mRNA-seq selectively targets and sequences the coding RNA (mRNA) in a sample, while total RNA-seq sequences all RNA present in the sample, including both coding and non-coding RNA. Total RNA-seq can provide a more comprehensive view of the transcriptome, as it captures not only mRNA but also other functional RNA species such as rRNA, tRNA, and miRNA. However, mRNA-seq is often preferred when studying gene expression changes, as it is more specific and can provide higher coverage of expressed genes.
2 How can I evaluate the quality of my RNA sample before sequencing?
There are several methods for assessing RNA sample quality, including electrophoresis, spectrophotometry, and fluorometry. The RNA integrity number (RIN) is a commonly used metric for assessing RNA quality, and it can be determined using an automated electrophoresis system such as the Agilent Bioanalyzer or TapeStation. A RIN value of 8 or higher is generally considered to be of high quality, while a value below 7 may indicate degraded RNA. Spectrophotometry can be used to measure RNA concentration and purity, while fluorometry can be used to assess RNA integrity and quantify the amount of RNA that is suitable for sequencing.
3 What is the recommended sequencing depth for a eukaryotic transcriptome project?
The recommended sequencing depth for a eukaryotic transcriptome project can vary depending on the research question and the complexity of the transcriptome. Generally, a minimum of 30 million reads per sample is recommended for reliable gene expression quantification. However, for more comprehensive transcriptome analysis, such as identifying novel transcripts or alternative splicing events, a higher sequencing depth (50-100 million reads per sample) may be necessary.
4 What are some common quality control measures for RNA-seq data?
Common quality control measures for RNA-seq data include assessing the distribution of reads across transcripts, checking for biases in read coverage, evaluating the proportion of reads that map to the reference genome, and identifying potential sources of contamination or technical artifacts. Software packages such as FastQC and MultiQC can be used to perform quality control checks on RNA-seq data, and various visualization tools are available for exploring the data and identifying potential issues.
5 How do I perform differential gene expression analysis from RNA-seq data?
Differential gene expression analysis involves comparing gene expression levels between two or more groups of samples and identifying genes that are differentially expressed between the groups. This can be done using statistical tests such as the t-test or the likelihood ratio test, and various software packages are available for this purpose, including edgeR, DESeq2, and limma-voom. The analysis typically involves normalization of gene expression levels, estimation of variance and fold changes, and correction for multiple testing. Some software packages also provide additionalfeatures, such as visualization tools for exploring differentially expressed genes and pathway analysis to identify enriched biological pathways.
6 How can I ensure the reproducibility of my RNA-seq data analysis?
Reproducibility is a key concern in RNA-seq data analysis, as small variations in the analysis pipeline or parameters can have a significant impact on the results. To ensure reproducibility, it is important to document the analysis pipeline and the parameters used at each step, and to use version control software to track changes to the code and data. It is also recommended to use established standards and guidelines for data analysis and to validate the results using independent datasets or experimental approaches. Additionally, it is important to make the data and code publicly available so that others can verify and reproduce the results.
Transcriptome Analysis of Goat Mammary Gland Tissue Reveals the Adaptive Strategies and Molecular Mechanisms of Lactation and Involution
Abstract: This article analyzes the transcriptome of lactating and involutionary mammary gland tissues in goats, identifying differentially expressed transcripts across six developmental stages. The study highlights the adaptive transcriptional changes made by genes related to cell growth, apoptosis, immunity, nutrient transport, synthesis, and metabolism to meet lactation needs. The study also identifies PDGFRB as a hub gene in the mammary gland developmental network that is highly expressed during the dry period and involution. These findings provide new insights into the molecular mechanisms involved in lactation and mammary gland involution.
Material and Methods: The study used mammary gland tissue samples from three breeds of dairy goats (Laoshan, Xinong Saanen, and Murciano-Granadina) at six different mammary developmental stages: late gestation, early lactation, peak lactation, late lactation, dry period, and involution. The researchers conducted transcriptome analysis to identify differentially expressed transcripts and used in vitro experiments to investigate the function of the PDGFRB gene in mammary gland tissue remodeling during involution.
Results Interpretation and Analysis: The study identified 13,083 differentially expressed transcripts across the six developmental stages. The results showed that genes related to cell growth, apoptosis, immunity, nutrient transport, synthesis, and metabolism made adaptive transcriptional changes to meet lactation needs. The study also identified PDGFRB as a hub gene highly expressed during the dry period and involution, providing insights into its function in tissue remodeling. In vitro experiments showed that overexpression of PDGFRB promoted goat mammary epithelial cell proliferation by regulating the PI3K/Akt signaling pathway, affecting genes related to apoptosis, matrix metalloproteinase family, and vascular development.
Conclusion: The study highlights the need for comprehensive and systematic transcriptome analysis of lactating and involutionary mammary gland tissues, especially for economically valuable lactating livestock dairy goats. The findings provide new insights into the molecular mechanisms involved in lactation and mammary gland involution.
Future Prospects: Further research could investigate the effects of breed and individual animal variability on transcriptome profiling results, as well as the importance of balancing experimental design and increasing sample sizes for accurate analysis. Additionally, studying the expression of transcription factors and non-coding RNAs with low expression levels and their potential regulatory effects on lactation and mammary gland involution could enhance understanding of the molecular mechanisms involved.