Metagenomic Sequencing
Technical Information
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meta_genomicsMetagenomics is the study of genomes contained within an entire microbial community. This technology has opened up a new era in the study of microbial diversity with direct access to the genomes of numerous non-cultivatable microorganisms in their natural habitat. Metagenomic sequencing analyzes microbial community diversity, gene composition and function, as well as metabolic pathways associated with the specific environment. This approach has been applied to environmental studies as well as biomarker research.


  1. Comprehensive: Enable investigation of all microbes of a certain environment in a single experiment, as well as analysis of microbial community diversity and gene function
  2. Short Turnaround Time: Rapid acquisition of microbial information compared to traditional methods that investigate individual strains

Richness of Human Gut Microbiome Correlates With Metabolic Markers. Nature. 500:41-46 (2013).

We are facing a global metabolic health crisis provoked by an obesity epidemic. Here we reported the human gut microbial composition in a population sample of 123 non-obese and 169 obese Danish individuals. This study showed for the first time that only a few bacterial species are sufficient to distinguish between individuals with high and low bacterial richness, and even between lean and obese participants. Our classifications based on variation in the gut microbiome identified subsets of individuals in the general white adult population who may be at increased risk of progressing to adiposity-associated co-morbidities.

A Metagenome-wide Association Study of Gut Microbiota in Type 2 Diabetes. Nature. 490:55-60 (2012).

Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. Our work here laid an important foundation for comprehensively understanding the genetic characteristics of gut microbiota and their relationship to T2D risk, as well as providing a new way of classifying microbes detected by DNA sequence. The work here also opened the way for transferring the potential value of a gut-microbiota-based approach into a means for clinical assessment and diagnosis of patients at risk of this disease. High-throughput sequencing technologies and Metagenomics serve as robust tools for researchers to comprehensively explore the gut microbiota related with diseases, and shed new light into disease prevention and treatment.

A Human Gut Microbial Gene Catalogue Established by Metagenomic Sequencing. Nature. 464:59-65 (2010).

In 2010, BGI established the first human microbial gene catalogue by Metagenomics sequencing. In order to understand the impact of gut microbes on human health and well-being, 124 faecal samples of European individuals were sequenced. This study confirmed that bacterial species abundance and bacterial genes differentiated between healthy individuals and disease affected patients. These findings offer an important theoretical basis for further exploring the relationship between human gut microbes, obesity, enteritis, and other diseases.

Bioinformatics Analysis:

    1. Data processing
      1. Remove adapter pollution
      2. Remove low-quality reads
      3. Remove host contamination (if any)
      4. Data statistics
    2. Metagenome assembly
      1. k-mer analysis to evaluate the sequencing depth for each sample before assembly
      2. GC-depth analysis with mapped reads after assembly
    3. Analysis of species composition and abundance
      1. Statistics of clean reads alignment to the known bacterial, fungal, and archaea genome databases
    4. Genome components analysis
      1. Gene prediction (towards those contigs of length ≥ 500 bp)
      2. Prophage detection
      3. Transposable elements (TEs) detection
    5. Generate non-redundant gene catalog
    6. Gene functional annotations
      1. Gene functional annotation based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) database
      2. Gene functional annotation based on the CAZy (Carbohydrate-Active Enzymes Database) database
      3. Gene functional annotation based on eggNOG (Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups) database
      4. Antibiotics resistance factors annotation based on ARDB (Antibiotic Resistance Genes Database)
    7. Comparative analysis among samples (Sample groups ≥ 2, samples in each group ≥ 10)
      1. Screening factors (species or function) significantly correlated with sample grouping
      2. Principal component analysis (PCA) based on significant factors
      3. Clustering based on significant screened factors
    8. Customized analysis (case by case)
Specific custom analysis will be determined based upon customer requirements.

Sample Requirements:

  1. Sample type: genomic DNA
  2. Sample quantity: ≥ 2µg
  3. Sample concentration: ≥30 ng/µL

Turnaround Time:

The standard turnaround time for the whole workflow (including library construction, sequencing and bioinformatics analysis) is 40-70 business days.

Completion Criteria:

Generate 1G clean data at minimum