The prokaryotes are mostly single-celled organisms that, by definition, lack membrane-bound nuclei and other organelles, which means the processes of transcription, translation, and mRNA degradation can all occur simultaneously. Prokaryotic transcription often covers more than one gene and produces polycistronic mRNAs that specify more than one protein, which is a main difference compared with eukaryotic transcripts.
Prokaryotic RNA sequencing hyphenate next generation sequencing (NGS) to reveal the presence and quantity of RNA at a given moment, by analyzing the changing cellular transcriptome. Novogene’s prokaryotic RNA sequencing, adopted stranded RNA library to enable a more accurate estimate of transcript expression, especially for both antisense RNA and other overlapping genes, compared with non-stranded RNA-seq. It specifically aims at prokaryotes with reference genomes, providing clients with cost-effective and considerate solutions for transcriptome profiling, gene structure analysis, and more.
If you work on prokaryotes without reference genomes, we can also provide customized services to meet your research objectives. For details, please contact us for details.
Functional genomics research
- TSS analysis
- Promoter region analysis
- 5′ UTR analysis
- Operator analysis
RNA regulation mechanism research
- sRNA identification
- Antisense transcript identification
Comparative Transcriptome Research
- Extensive experience with thousands of projects successfully completed and multiple articles published in journals of high Impact Factors.
- Unsurpassed data quality with a guaranteed Q30 score ≥ 80% that exceeds Illumina’s official benchmarks.
- We use industry standard software and mature in-house pipeline to detect differential expressions, to discover novel transcripts and to make functional annotations.
- Easily visualize data analysis results with Novogene’s user-friendly in-house software.
|Library Type||Sample Type||Amount||RNA Integrity Number
|Prokaryotic RNA Library||Total RNA||≥ 3 μg||≥ 6.0, smooth base line||OD260/280 = 1.8-2.2;
OD260/230 ≥ 1.8;
Note: For detailed information, please contact us.
Sequencing Parameter and Analysis
|Platform Type||Illumina Novaseq 6000|
|Read Length||Pair-end 150|
|Recommended Sequencing Depth||≥ 2GB raw data / sample for the species with reference genome|
|Standard Data Analysis||
Note: Sequencing depths and bioinformatic analysis requests can be customized based on the project needs. Please contact us for more information.
Competitive control of endoglucanase gene engXCA expression in the plant pathogen Xanthomonas campestris by the global transcriptional regulators HpaR1 and Clp
Transcriptional regulators are key players in pathways that allow bacteria to alter gene expression in response to environmental conditions. However, work to understand how such transcriptional regulatory networks interact in bacterial plant pathogens is limited.
Xanthomonas campestris pv. campestris (Xcc), a model bacterium used to study the plant–pathogen interactions.
• Total RNA of single bacterial colonies
• Sequencing Strategy
• Library preparation: strand-specific RNA library
• Sequencing: HiSeq 2000 platform
Table 1 HpaR1 is a global regulatory protein that affects the expression of a number of genes overlapping with the Clp protein.
|ORF number in strain 8004(AT33913)||Gene name||Predicted product||Fold change
|Putative HpaR1/Clp co-binding sites|
|Cell envelope and cell structure||XC_1459
|phuR||Outer membrane haemin recepton||2.11|
Energy and carbon metabolism
|2,5-Diketo-d-gluconate reductase B||-2.63|
|atpE||F0F1 ATP synthase subuni C||-2.56|
The data generated here describe how two global transcriptional regulators, HpaR1 and Clp, co-regulate a subset of virulence genes in Xcc. The RNA-seq helps to revel the influence of HpaR1 on the global transcriptome of Xcc.
Production of primary metabolites in Microcystis aeruginosa in regulation of nitrogen limitation
Sustainable biofuels have attracted much attention, and microalgae are considered as
the promising alternative feedstocks for the biofuel production. Although many studies focused on the accumulation of carbohydrates and lipids in different microalgae, limited reports uncovered the regulating mechanism of N deficiency. To promote the development and utilization of Microcystis aeruginosa, a potential feedstock for biofuel production, This paper investigated the growth, photosynthetic abilities, and the content of carbohydrates, lipids as well as proteins in the cells under different N levels, and analyzed the transcriptome to uncover the response mechanism to N deficiency.
• Microcystis aeruginosa cells
• Sequencing Strategy
• Library preparation: mRNA library
• Sequencing: Illumina platform
Table 2 * Varied expression of genes relating to photosynthesis and metabolism of carbohydrates, N and lipids in response to N deficiency.
|Gene ID||Description||Normal-N readcount||Non-N readcount||Fold change Non-N vs. Normal-N|
|MAE_02680||Precorrin-6y C5, methyltransferase||121.3±10.7||66.3±3.2||-45.30%|
|MAE_25690||Precorrin-4 C11, methyltransferase||34.7±10.3||16.7±2.9||-51.90%|
|MAE_16230||Light-independent protochlorophyllide reductase subunit L||785.3±305.2||296.7±85.6||-62.20%|
Only part of varied expression of genes are listed in this table. Please refer to the literature for more information.
N deficiency reduced M. aeruginosa photosynthetic abilities by triggering the down-regulation of genes involving in Chl synthesis, antenna proteins, photosynthetic electron transfer chain, and carbon fixation, which affected the cell growth. The accumulated carbohydrates under N deficiency can be used to produce bioethanol, while the remainder lipids after carbohydrate extraction can also be extracted to produce biodiesel for sufficient usage.
Error Rate Distribution
The x-axis shows the base position along each sequencing read and the y-axis shows the base error rate.
GC Content Distribution
Horizontal axis for reads position, vertical axis for single base percentage. Different color for different base type.
Composition of raw data
Overview of Mapping Status
Distributions of gene expression levels
Volcano plot for differentially expressed genes
Significantly Enriched GO Terms in DEGs