Metatranscriptomic Sequencing

With decades of experience in the fields of genomics sequencing, CD Genomics is devoted to providing unprecedented amounts of microbial metatranscriptomic data. Our strong expertise in the informative and unbiased metatranscriptomic sequencing service is guaranteed by state-of-the-art high throughput sequencers, flexible sequencing strategies, and professional bioinformatics pipelines.

The Introduction of Metatranscriptomic Sequencing

Metatranscriptomics is the culture-independent profiling (including protein-coding and non-coding DNA) of microbial community-wide gene expression, which is capable of monitoring RNA-based regulation and expressed biological signatures of complex bacterial communities in a given sample at a given moment and under specific conditions. It elucidates three aspects of a microbial community, including gene activity diversity, gene expression abundance, and differential gene expression analysis. The gene expression analysis can tell which genes exhibit the highest change in expression levels in different conditions potentially to identify biomarkers and expression signatures.

While metagenomics shows us the microbial species present in the community and their genomic potentials, metatranscriptomics presents the function and activity of the complete set of transcripts, as well as community structure. Metatranscriptomics offers a more informative perspective compared with metagenomics in revealing active biochemical functions, which has become a focus for applications in the environmental, medical, and energy fields as well as the field of drug discovery.

Advantages of Metatranscriptomic Sequencing

  • A culture-free method to reveal the true extent of microbial diversity
  • Permitting function-based activity screens
  • More targeted than shotgun random sequencing
  • Cost-efficient and time-effective
  • A wide range of applications, including basic research, ecological applications, clinical applications, industrial applications, and so on

Metatranscriptomic Sequencing Workflow

Our highly experienced expert team executes quality management following every procedure to ensure confident and unbiased results. The general workflow for metatranscriptomic sequencing is outlined below. After RNA isolation from qualified samples, rRNA depletion, library construction, high-throughput sequencing, and bioinformatics analysis are in turn performed. We can help you make decisions on the library construction method, the depth of coverage, and the data analysis strategies based on your research aims.

Workflow Diagram of Metatranscriptomic Sequencing.

Service Specifications

Sample Requirements
  • Samples sources including environmental and clinical samples, RNA and cDNA samples
  • Total RNA≥ 4 μg, Minimum Quantity: 3 μg, Concentration≥50 ng/μL
  • Cells≥ 5×106
  • Environmental Samples ≥ 1.5g
  • The general workflow consists of sample quality control, RNA isolation, purification and qualification; library construction (including fragmentation, end repair, 5' adaptor ligation, random primer tags and RT-PCR), and library qualification control.
Note: Sample amounts are listed for reference only. For detailed information, please contact us with your customized requests.

Sequencing Strategy
  • HiSeq platforms, PE125/150, MGI DNBSEQ-T7/DNBSEQ-G400
  • 5Gb raw data per sample (10G if eukaryotic data are included)
  • More than 80% of bases with a ≥Q30 quality score
  • PacBio's SMRT technology is also available for long fragment sequencing, which provides more accurate contiguous sequences.
Bioinformatics Analysis
We provide multiple customized bioinformatics analyses:
  • Two assembly strategies: de novo assembly or mapping reads to reference genomes
  • Raw data quality control, unigene clusterings assembly and analysis
  • The unigene clusterings analysis consists of functional annotations (such as GO and KEGG annotations), gene expression levels (including unigene abundance and coverage analysis, differential expression analysis, and unigene quality evaluation), and structure identification (such as coding SNPs, SSRs, and ORFs)
  • More data mining upon your request
Note: Recommended data outputs and analysis contents displayed are for reference only. For detailed information, please contact us with your customized requests.

Analysis Pipeline

The Data Analysis Pipeline of Metatranscriptomic Sequencing.


  • The original sequencing data
  • Experimental results
  • Data analysis report
  • Details in Metatranscriptomic Sequencing for your writing (customization)

CD Genomics provides full metatranscriptomic sequencing service package including sample standardization, library construction, Hiseq sequencing, raw data alignment, down-stream bioinformatics processing and statistical analysis. We can tailor this pipeline to your research interest. If you have additional requirements or questions, please feel free to contact us, our specialists are more than happy to assist you.


  1. Warnecke F, Hess M. A perspective: metatranscriptomics as a tool for the discovery of novel biocatalysts. Journal of Biotechnology, 2009, 142(1): 91-95.

The Metatranscriptomic Sequencing Results Display Figure-1.

The Metatranscriptomic Sequencing Results Display Figure-2.

1. What are the noteworthy issues of RNA samples?

The contamination should be rigorously excluded when sampling. In detail, sampling-related instruments and consumables should be sterilized and RNase-free. The freshly obtained samples should be immediately frozen by putting into liquid nitrogen, or directly submitting original environmental or clinical samples to us. The recommended total RNA amount for submission is 6 µg or more with a concentration of greater than 50 ng/µl.

2. What kind of QC methods do you adopt for the customer's samples?

We will perform QC on your total RNA samples prior to sequencing them. We use the Agilent Bioanalyzer to determine the RNA Integrity Number (RIN). If the RIN is lower than 8, the samples will not pass QC. The library QC will also be performed using the Agilent Bioanalyzer to determine library size and purity. Also, prior to loading the libraries on the sequencer, we perform qPCR quantification. The cost for this is included in the sequencing service. The raw data will pass our Q30 filter, which means more than 80% of bases with a greater than Q30 quality score.

3. What are the advantages of metatranscriptomics?

Metatranscriptomics is the genomic analysis of complete microbial transcriptomes, providing a particularly rich source of data on the global diversity of RNA viruses and their evolutionary history. Metatranscriptomics has several advantages over traditional methods such as cell culture, consensus PCR, and metagenomics approaches based on viral particle purification.

Metatranscriptomics has proven successful in characterizing the RNA viromes of diverse invertebrates. Specifically: (i) it uncovers the entire RNA virome, with sufficient coverage to assembly complete viral genomes, including those from co-infecting parasites; (ii) it offers a reliable quantification and assessment of both viral and host RNAs; (iii) it is comparatively simple, requiring minimal sample processing; and (iv) it provides more information than the genome sequence alone, allowing a characterization of viral diversity and ecology.


  1. Shi M, Neville P, Nicholson J, et al. High-Resolution Metatranscriptomics Reveals the Ecological Dynamics of Mosquito-Associated RNA Viruses in Western Australia. Journal of Virology, 2017, 91(17): e00680-17.
  2. Shi M, Zhang Y Z, Holmes E C. Meta-transcriptomics and The Evolutionary Biology of RNA Viruses. Virus research,

High-Resolution Metatranscriptomics Reveals the Ecological Dynamics of Mosquito-Associated RNA Viruses in Western Australia

Journal: Journal of Virology
Impact factor: 4.663
Published: 21 June 2017


Mosquitoes harbor a high diversity of RNA viruses, including many that impact human health. Although many efforts to reveal the extent and nature of the mosquito virome, little is known about how these viruses persist, spread, and interact with both their hosts and other microbes. To infer features of virome ecology among mosquito species from different geographic locations, the authors characterized the total transcriptome of 12 populations.


Sample collection

A total of 519 adult mosquitoes collected from four locations in Western Australia

Species identification

Taxonomic keys
Dissecting microscopes
cox1 gene


RNA isolation and quility control
Sequencing: HiSeq 2500, paired-end (100 bp)

Data Analysis

RNA virus discovery and genome anonotation
RNA quantification
Phylogenetic analyses


1. The mosquito virome

Blast analyses showed the complete genomes of 24 RNA viruses species, of which 19 were newly described here. For each library, the number of virus species varied from 1 to 10 (Table 1).

2. Virome ecology

The virus composition and abundance revealed substantial differences between the Culex and Aedes genera (Figure 1). Generally, the Aedes mosquitoes harbour fewer viruses. Of the 24 viral species discovered, only Wilkie qin-like virus (WQLV) and Wilkie narna-like virus 1 (WNLV1) were shared between the two genera (Figure 2).

Figure 1. A summary of the variety and prevalence of RNA viruses identified. (Shi et al., 2017)Figure 1. An overview of the diversity and abundance of the RNA viruses discovered.

Figure 2. The comparison of virome similarities across different host species (A) and geographic regions (B). (Shi et al., 2017)Figure 2. The similarity of viromes between host species (A) and geographic locations (B).

3. Evolutionary analysis

The authors discovered eight putative negative-sense RNA viruses and seven double-stranded RNA viruses. The positive-sense RNA viruses discovered fell with the Narnaviridae, Mesoniviridae, Negev-like viruses, and Luteoviridae-related viruses. The authors performed phylogenetic analyses and genomic characterizations (please refer to the original paper for more detailed phylogenetic information). interestingly, two viruses (WQLV and WPLV2) were co-appeared with a group of fungi termed "Unknown sp1, 2, and 3" and they had matching evolutionary histories (Figure 3).

Figure 3. The corresponding tree structures of the Wilkie qin-like viruses and a set of fungi (cox 1 gene) found in three mosquito pools. (Shi et al., 2017)Figure 3. The matching tree topologies of the Wilkie qin-like viruses and a group of fungi (cox 1 gene) discovered in three mosquito pools.


  1. Shi M, Neville P, Nicholson J, et al. High-Resolution Metatranscriptomics Reveals the Ecological Dynamics of Mosquito-Associated RNA Viruses in Western Australia. Journal of Virology, 2017, 91(17): e00680-17.
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