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Prokaryotic RNA Sequencing


The prokaryotes are mostly single-celled organisms that, by definition, lack membrane-bound nuclei and other organelles, which leads to 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 uses 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.

Service Specifications Demo Reports


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 over thousands of projects being successfully completed and multiple articles being published on journals of high Impact Factors.
  • Unsurpassed data quality with a guaranteed Q30 score ≥80% that exceeds Illumina’s official benchmarks.
  • Comprehensive data analysis using widely accepted mainstream software and mature in-house pipeline to detect differential expressions, to discover novel transcripts and to make functional annotations.
  • Free, powerful Novogene in-house software enabling clients to visualize data analysis results flexibly with a user-friendly interface.

Sample Requirements


Library Type Sample Type Amount RNA Integrity Number
(Agilent 2100)
Prokaryotic RNA Library Total RNA ≥ 3 μg ≥ 6.0, smooth base line OD260/280 = 1.8-2.2;
OD260/230 ≥ 1.8;

Sequencing Parameter and Analysis


Platform Type Illumina Novaseq 6000
Read Length Pair-end 150
Recommended Sequencing Depth ≥20 million read pair per sample for the species with reference genome
Standard Data Analysis
Data Quality Control
Novel Transcript Prediction
Gene expression quantification & Differential expressed genes profiling & Functional analysis
Operon Analysis
SNP and InDel
UTR Analysis
Antisense Transcript Prediction
sRNA Analysis

Note: For detailed information, please refer to the Service Specifications & Demo Reports and contact us for customized requests.

Project Workflow

Sample Quality Control

Library Quality Control

Data Quality Control

Total RNA

Total RNA


Bioinformatics Analysis

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



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

Table 1 HpaR1 is a global regulatory protein that affects the expression of a number of genes overlapping with the Clp protein.


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

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%
MAE_61890 Biliverdin reductase 114.3±12.3 60.3±3.8 47.20%

Table 2 * Varied expression of genes relating to photosynthesis and metabolism of carbohydrates, N and lipids in response to N deficiency.


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

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