• Human Microbiology

    Another wave of precision medicine

Human Microbiome – the Second Genome of the Human

The word microbiota represents an ensemble of microorganisms that resides in a previously established environment. Human beings have clusters of bacteria in different parts of the body, such as in the surface or deep layers of skin (skin microbiota), the mouth (oral microbiota), the vagina (vaginal microbiota), and so on. Our gut microbiota contains tens of trillions of microorganisms, including at least 1000 different species of known bacteria with more than 3 million genes (150 times more than human genes). Microbiota can, in total, weigh up to 2 kg. One third of our gut microbiota is common to most people, while two thirds are specific to each one of us. In other words, the microbiota in your intestine is like an individual identity card.

In recent years, with the development of sequencing technologies, more and more research has been done on the human microbiome.The microbiota is important in nutrition, immunity and  implicated in numerous diseases when the normal individual balance of microbes is disturbed.

Full-length 16S Sequencing or Metagenomics Sequencing ?

The most commonly used methods in the gut microbiota research are full-length 16S sequencing and metagenomics sequencing. Although full-length 16S sequencing can perform species classification, species abundance, and reveal the relationship between environmental factors and microbial communities, genetic prediction and functional annotation can not be performed. Metagenomics overcome these barriers,enabling comprehensive examination of microbial communities without cultures, which take advantage of the rich diversity of genes and biochemicals of millions of noncultivated and uncharacterized microorganisms.

Metagenomics Researches Based on PacBio Sequencing

Due to the influence of dual factors within and among species in a large number of microbial species in the community, metagenome assembly is more difficult than common animal and plant genomes. With today’s technology, the process of sequencing genomes part breaking the genome into smaller fragments, and then piecing them back together during analysis using de novo assembly methods or by mapping the reads to a pre-existing reference. Grandomics launched the metagenomics researches based on PacBio sequencing, breaking through the bottleneck of second-generation sequencing technology and overcoming many existing barriers.

Single Molecule, Real-Time (SMRT) Sequencing Empowers Scientists to:

Generate near-complete de novo assemblies of microbes and their accessory plasmids

Confidently resolve viral diversity

Characterize microbial populations down to the species level

Research Methods

Metagenomic Analysis Based on CCS Reads

By correcting the original error rate of PacBio, 2Kb of CCS reads can be obtained with an accuracy rate of 99% or more, covering most of the microbial genes or specific regions completely. And the composition, function annotation, and metabolic pathway analysis can be performed without assembly.

Metagenomic Analysis Based on PacBio Assembly Strategy

The PacBio assembly strategy based on large pieces of high-quality reads increases the specificity of reads from different microorganisms, completes the assembly of dominant genomes, and accurately restores microbial composition and functional gene information in the environment.

Research Workflow

Library Preparation and Sequencing

Detach the sample genomic DNA, build a 3Kb insert library, sequence it on the PacBio Sequel platform, and propose 1-2 SMRT cells per sample.

Bioinformatics Analysis

Data processing

1. Remove connector sequence   2. Generate CCS sequence   3. Data quality control   4. Data statistics

Gene Analysis

Generate de novo assemblies and then perform genetic prediction of assembly results and elimination of redundancy to construct the gut microgenome reference gene set.

Functional Annotation and Species Annotation

Annotate the gene set by comparing public databases (including nr, Swiss-Prot, COG, KEGG, GO, CAZy, eggNOG, and ARDB) in order to obtain gene function and species annotation information.

Diversity Analysis of Gene, Function and Species

Comparing reads to gene sets, calculating gene abundance and species abundance of each sample. Based on the abundance data, various analysis contents such as species diversity analysis, PCA analysis, cluster analysis, difference analysis, and function enrichment analysis can be performed.

Disease Association Analysis

Screening of disease-related phenotypic information, identification of disease-related markers, and construction of classifiers.