CD Genomics uses bioinformatics to provide prokaryotic transcriptome analysis service and help you analyze prokaryotic mRNA information accurately and quickly. Our unique data analysis skills and high-quality data analysis platform will exceed our clients' expectations on personalized data analysis.
Introduction of Prokaryotic 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. Transcriptome sequencing generally refers to the sequencing of all mRNAs.
Transcriptomics is a powerful tool for understanding gene structures and RNA-based regulation in any organism. Since prokaryotic mRNA does not have a polyA tail structure, it is necessary to use the method of removing rRNA to construct a library to obtain sequencing data. Transcriptome sequencing (RNA-seq) of prokaryotic can quantitatively measure the change in the expression level of each transcript in a specific tissue or cell during growth or under different conditions. The analysis of sequencing data by bioinformatics not only quantitatively analyzes the expression of mRNA, differentially expressed genes, and their corresponding functions, but also analyzes the molecular regulatory mechanisms and functions of Non-coding RNA (sRNAs) to reveal the formation of different phenotypes of microorganisms.
Fig 1. Prokaryotic cell RNA enrichment pipeline. (Sorek R , Cossart P.2009)
Application of Prokaryotic Transcriptome Analysis
Prokaryotic transcriptome sequencing data analysis can be used for but not limited to the following research:
Gene expression level
Gene structure level
Advantages of CD Genomics
Standardized analysis process.
Strict data quality control.
Reliable analysis results.
Fast analysis cycle.
An experienced analytical team.
CD Genomics Data Analysis Pipeline
Bioinformatics Analysis Content
CD Genomics prokaryotic transcriptome bioinformatics analysis project includes not only the analysis of gene expression and differential gene function, but also the analysis of gene structure level.
Gene structure level analysis
Quality evaluation of sequencing data
Raw data filtering
Map to reference genome
New transcript prediction
Antisense transcript prediction
SNP detection and analysis
Gene expression level analysis
Gene expression level analysis
Differential gene expression level analysis
GO and KEGG annotation of differential genes.
GO/KEGG enrichment analysis of differential genes.
Analysis of protein interaction networks
Visualization result display
In addition, CD Genomics provides personalized analysis services for prokaryotic transcriptome data analysis, which can be customized according to customer needs. If you have any questions, 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.
CD Genomics has over a decade of experience in prokaryotic transcriptome data analysis. If you have any questions about how we can help you, please get in touch. We look forward to working with you!
Demo Results of "Prokaryotic Transcriptome Analysis”
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.
Table 1. Statistics of rRNA data quality
|Sample ID||Clean Reads||rRNA||%rRNA||no_rRNA||%no_rRNA|
The x-axis shows the log10 (FPKM) and the y-axis shows gene density. Different colors represent different samples.
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.
Q1. What are some differences between prokaryotic transcriptomes and eukaryotic transcriptomes?
A1. One of the main differences between the two is the presence of introns in eukaryotes, which must be accounted for in transcriptome analysis. Prokaryotic genomes, on the other hand, typically have fewer regulatory structures and simpler gene expression patterns.
Q2. What are some common abbreviations used in prokaryotic transcriptome sequencing analysis?
A2. Some common abbreviations in prokaryotic transcriptome sequencing analysis include RNA-seq (RNA sequencing), TPM (transcripts per million), RPKM (reads per kilobase per million), DEG (differentially expressed genes), and GO (gene ontology) analysis.
Q3. What are some of the challenges in prokaryotic transcriptome sequencing analysis?
A3. One of the main challenges is the lack of annotated genomes for many prokaryotic species, which can make it difficult to identify genes and pathways. Additionally, the presence of overlapping transcripts and variable expression levels can complicate analysis.
Q4. How do you assess the quality of prokaryotic transcriptome sequencing samples?
A4. Sample quality can be assessed using techniques such as gel electrophoresis, spectrophotometry, or RNA integrity number (RIN) values. These measurements can provide information on RNA purity, concentration, and integrity.
Q5. What are some considerations when choosing the appropriate sequencing depth for prokaryotic transcriptome sequencing?
A5. The appropriate sequencing depth depends on the complexity of the transcriptome and the research question. In general, higher sequencing depth can increase sensitivity for detecting low-abundance transcripts, but may not be necessary if the goal is to identify differentially expressed genes.
Q6. What are some commonly used tools for prokaryotic transcriptome data analysis?
A6. There are many software tools available for read mapping (e.g., STAR, HISAT), differential expression analysis (e.g., DESeq2, edgeR), functional annotation (e.g., Blast2GO, KEGG), and visualization (e.g., Heatmap or PCA) of prokaryotic transcriptome data.
Q7. How do you evaluate the accuracy of prokaryotic transcriptome sequencing analysis results?
A7. Accuracy of analysis results can be evaluated by comparing differentially expressed genes with known functions, and validating results using quantitative PCR (qPCR) or alternative high-throughput sequencing methods like nanostring.
Q8. What are some potential applications of prokaryotic transcriptome sequencing analysis?
A8. Prokaryotic transcriptome sequencing analysis can provide insights into gene expression patterns, transcript regulation, and metabolic pathways. This information can be used to design genetic engineering strategies, understand host-pathogen interactions, or identify biomarkers for disease diagnosis.
RNA-seq Sample Preparation Kits Strongly Affect Transcriptome Profiles of a Gas-Fermenting Bacterium
Abstract: The use of RNA sequencing (RNA-seq) has become a standard practice across various biological fields of study. This technique allows researchers to capture the transcriptome of interest as closely to its native state as possible without introducing technical bias. However, the selection of appropriate commercial products for library preparation is crucial to achieve accurate results. Researchers often lack the resources and time to test various RNAseq kits for their samples. In this study, the performance of three commercial RNA-seq library preparation kits from NuGEN Technologies, Qiagen, and Zymo Research was evaluated through a side-by-side comparison of RNA-seq data from Clostridium autoethanogenum. While all three vendors advertise their products as suitable for prokaryotes, significant differences were found in their rRNA removal, strand specificity, and most importantly, transcript abundance distribution profiles. The RNA-seq data obtained with Qiagen products delivered the best results in terms of library strandedness and transcript abundance distribution range, and were most similar to published data. These findings highlight the importance of finding organism-specific workflows and library preparation products for RNA-seq studies to ensure accurate and reliable results.
Material and Methods: In this study, a derivative of Clostridium autoethanogenum DSM 10061 strain, DSM 23693, was used for all experiments. The cells were grown autotrophically in bioreactor chemostat continuous cultures under strict anaerobic conditions. Four independent experiments were conducted with cultures grown on both CO and syngas feed gas mixes. Total RNA extracts were prepared and RNA-seq libraries were constructed using NuGEN, Qiagen, and Zymo kits for rRNA removal and strand-specific RNA-seq library construction. RNA sequencing of the 12 mRNA libraries was performed and the data were analyzed using R scripts. The RNA-seq data have been deposited in the NCBI Gene Expression Omnibus repository under accession number GSE200959.
Results: The study compared the performance of three commercial RNA sequencing kits in terms of removal of ribosomal RNA and strand specificity for C. autoethanogenum. The transcript abundance distribution profiles varied significantly among the three tested kits. Qiagen's products delivered the best results in terms of library strandedness and transcript abundance distribution range, and were the most similar to published data.
Conclusion: The study highlights the importance of selecting the appropriate RNA sequencing library preparation kits to capture the transcriptome of interest accurately. Researchers dealing with similar challenges can use this study as guidance to select the suitable organism-specific workflows and library preparation products for RNA-sequencing studies in bacteria.