As one of the providers of eukaryotic transcriptome without reference genome data analysis, CD Genomics uses bioinformatics to help you quickly and accurately explore the gene expression of species without reference genomes. Our unique data analysis skills can also meet customers' personalized data analysis needs and provide you with a high-quality data analysis platform, a fast analysis cycle and a high-quality report.
Introduction of Eukaryotic Transcriptome Analysis
Transcriptome refers to the collection of all transcripts produced by a particular cell or tissue in a particular 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 (RNA-seq) usually refers to the sequencing of all mRNA.
Transcriptome sequencing of eukaryotes without 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 the method of bioinformatics, the longest transcripts were spliced into unigene as the reference sequence for follow-up analysis. After the transcripts were assembled and annotated by splicing software, the gene expression level and structure information were analyzed in an all-round way to maximize the research content of non-ginseng species. It provides a powerful technical means for studying the changes of transcriptional level, molecular mechanism and regulatory network of species without reference genomes. At present, it has been widely used in a variety of animal and plant research without reference genome, clinical diagnosis, drug research and development, molecular breeding and other fields.
Fig 1. Heatmap and K-means clustering graph.
Application Field
Eukaryotic Transcriptome without Reference Genome sequencing data analysis can be used for, but not limited to, the following research:
Sample full transcript information acquisition
Gene annotation and screening.
Gene differential expression research.
Advantages of CD Genomics
Experienced analytical team
Standardized analysis process
Strict data quality control
Reliable analysis results
CD Genomics Data Analysis Pipeline
CD Genomics provides eukaryotic transcriptome (without reference genome) sequencing data analysis service. We ensure data reliability through strict data quality control.
Bioinformatics Analysis Content
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Transcript assembly |
Quality control of sequencing data |
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Transcript assembly |
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Assembly quality analysis |
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Unigene Expression Statistics |
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CDS prediction |
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SNP analysis |
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SSR analysis |
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Transcription factor annotation |
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Unigene function annotation |
Annotation of Nr protein database |
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SwissProt protein database annotation |
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COG/KOG annotation and classification |
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GO function annotation and classification |
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Pathway metabolic pathway notes |
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Differentially expressed gene analysis |
Quantification of gene expression level |
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Correlation analysis between samples |
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Gene differential expression analysis |
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Functional enrichment analysis of differentially expressed genes |
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Pathway metabolic pathway annotation of differentially expressed genes |
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Protein interaction network analysis of differentially expressed genes |
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Visualization of analysis results |
If you need any eukaryotic transcriptome (without reference genome) sequencing data analysis, such as SMART protein domain database 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, strict data quality control and professional analysis process, CD Genomics has successfully conducted eukaryotic transcriptome (without reference genome) sequencing data analysis on a variety of species. We ensure each project is carried out accurately and quickly. Please contact us for more information and a detailed quote.
Demo Results of "Eukaryotic Transcriptome without Reference Genome”
Quality Control
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.
Transcriptome Reconstruction
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.
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 recommended sample quality for De novo RNA-seq analysis?
The recommended sample quality for De novo RNA-seq analysis is high-quality RNA with an RIN (RNA Integrity Number) value of at least 7.0. Low-quality RNA can result in poor data quality and inaccurate results.
2 How do I choose the appropriate sequencing depth for my De novo RNA-seq experiment?
The appropriate sequencing depth for your De novo RNA-seq experiment depends on several factors, such as the complexity of the transcriptome, the biological question being addressed, and the available budget. Generally, a sequencing depth of 20-30 million reads per sample is recommended.
3 What are the key steps involved in data quality control for De novo RNA-seq analysis?
The key steps involved in data quality control for De novo RNA-seq analysis include trimming low-quality reads, removing adapter sequences, filtering out contaminating sequences, and assessing sequencing quality metrics such as mapping rate, expression correlation, and gene saturation.
4 What is the best tool for de novo assembly of my non-model organism transcriptome?
There are several tools available for de novo assembly of non-model organism transcriptomes, including Trinity, Oases, and Trans-ABySS. The choice of tool depends on the specific requirements of your experiment, such as sensitivity, specificity, and the ability to handle alternative splicing events.
5 How do I quantify gene expression levels in my De novo RNA-seq data?
Gene expression quantification in De novo RNA-seq data can be performed using tools such as RSEM, Kallisto, and Salmon. These tools use a probabilistic model to estimate gene expression levels based on the number of reads mapped to each transcript.
6 What are the challenges in performing differential gene expression analysis for De novo RNA-seq data?
The main challenge in performing differential gene expression analysis for De novo RNA-seq data is the lack of a reference genome or annotation. This requires the use of de novo transcriptome assembly, which can introduce errors and biases that may affect downstream analysis.
7 What are some commonly used tools for pathway analysis of my De novo RNA-seq data?
Some commonly used tools for pathway analysis of De novo RNA-seq data include GOseq, KEGG, and GSEA. These tools can help identify enriched biological pathways and functional categories based on differentially expressed genes.
8 How can I perform personalized gene set enrichment analysis (GSEA) for my De novo RNA-seq data?
Personalized GSEA for De novo RNA-seq data can be performed using tools such as Piano and PLAGE. These tools allow the creation of custom gene sets based on prior knowledge or experimental results, which can improve the sensitivity and specificity of pathway analysis.
Transcriptome Analysis of Goat Mammary Gland Tissue Reveals the Adaptive Strategies and Molecular Mechanisms of Lactation and Involution
Abstract:
Discusses the generation of RNA-seq data to assemble the transcriptome of the noble crayfish. Highlights the potential of the transcriptome data in understanding the crayfish plague infection and its impact on the noble crayfish's innate immune system;
Material and Methods:
RNA isolation from one individual female noble crayfish;
Tissue samples taken from hepatopancreas, ovaries, green glands, and abdominal musculature;
De novo assembly using Trinity software;
Transcriptome analysis using Transrate software and BUSCO analysis;
GO term analysis;
Results: A total of 194 million read pairs were generated and assembled into 45,415 transcripts with open reading frames.The assembly showed 91% of the total reads were realigned and BUSCO analysis indicated that the assembly is 64% complete.13,770 transcripts were assigned at least one GO term.Despite limited targeted tissue preparation for immune response, the assembly identified some transcripts with candidate genes involved in the noble crayfish immune response system, which can be used for future research
Conclusion: The first de novo transcriptome assembly for the noble crayfish is an important foundation for future genomic research Adds to the general knowledge and further characterization of transcriptomes of non-model organisms Provides a key approach to understanding how the crayfish plague infection impacts noble crayfish's innate immune systems.
Future Prospects: The article provides a foundation for future genomic research on the noble crayfish and adds to the general knowledge and further characterization of transcriptomes of non-model organisms. Specifically, the transcriptome data can provide insight into how the crayfish plague infection impacts the innate immune system of noble crayfish and can be used as a model for other species of interest. The identified candidate genes involved in the noble crayfish's immune response system can be used for future research as well. Overall, the article has broad implications for future research on crayfish and other non-model organisms.
