Our shotgun metagenomic sequencing platform stands as an invaluable resource, enabling customers to acquire intricate microbial genetic data from environmental samples. We are dedicated to delivering high-quality data, complemented by tailored bioinformatics analyses, to fulfill the specific requirements of our clients.
Shotgun metagenomic sequencing represents a sophisticated, untargeted methodology that capitalizes on next-generation sequencing (NGS) technologies to elucidate the complete genetic landscape of microbial communities within diverse environments. In contrast to traditional sequencing techniques, which predominantly target specific genes or organisms, shotgun metagenomics facilitates a comprehensive examination by capturing and analyzing the entire genomic complement present in a sample. This methodological approach is particularly advantageous for investigating intricate ecosystems such as terrestrial soils, aquatic habitats, and the human microbiome, where a multitude of microorganisms coexist and interact.
Moreover, shotgun metagenomic sequencing can also integrate third-generation sequencing platforms, which provide extended read lengths and heightened resolution. This technological refinement significantly enhances our capacity to resolve complex genomic architectures and identify previously uncharacterized microbial taxa. Consequently, it broadens our understanding of microbial diversity and functionality within their respective ecosystems, offering profound insights into microbial ecology and evolution.
In the realm of microbial research, two cornerstone methodologies, amplicon-based NGS and shotgun metagenomic sequencing, are engineered for distinct investigative frameworks. Amplicon sequencing zeros in on specific genetic loci, exemplified by the 16S rRNA gene in bacteria and archaea, allowing for a precise elucidation of the taxonomic architecture within microbial communities. This targeted modus operandi is lauded for its cost-efficiency and the robustness of established protocols for library preparation and data analysis.
In contrast, shotgun metagenomic sequencing presents a broader perspective by undertaking an untargeted exploration of the entire genomic landscape within a sample. This approach reveals a wider array of microorganisms, including those that are unculturable and lack well-characterized genetic markers. Furthermore, it enables a comprehensive analysis of functional genes, providing deep insights into the ecological roles and interactions that characterize complex microbial ecosystems.
The selection of either amplicon-based or shotgun metagenomic sequencing hinges significantly on the research objectives at hand. When the aim is to delineate the structural delineations of specific microbial taxa, amplicon sequencing suffices admirably. Yet, in quests to decode intricate microbial interplay, unearth novel species, or elucidate the comprehensive functional dynamics within microbial consortia, shotgun metagenomic sequencing ascends as the more illuminating and informative strategy.
The integration of shotgun metagenomic sequencing into infectious disease research has revolutionized pathogen identification, transmission tracking, and the monitoring of antibiotic resistance. Unlike traditional diagnostic methods, this cutting-edge technology uncovers previously undetected pathogens, offering a more comprehensive view of infectious agents and their behaviors.
In recent investigations, metagenomic next-generation sequencing (mNGS) has been pivotal in diagnosing community-acquired pneumonia (CAP). The superior detection capabilities of mNGS are particularly valuable in cases of mixed infections, where the presence of multiple pathogens complicates both diagnosis and therapeutic strategies.
Moreover, shotgun metagenomic sequencing has played a crucial role in the surveillance of infectious disease outbreaks. Its application in monitoring the mpox virus (MPXV), for instance, has yielded vital data on transmission routes and genomic mutations, thereby informing and optimizing public health responses.
The applications of antibiotic resistance genes screening include, but are not limited to, the following areas:
Metagenomic shotgun sequencing can profoundly expedite research across various domains:
(i) unraveling the genetic diversity of host-associated microbes;
(ii) elucidating the functional heterogeneity within microbial consortia;
(iii) advancing gene prediction and annotation techniques;
(iv) deciphering the intricate host-microbe interactions;
(v) uncovering microbiota-based disease mechanisms.
Our platform is equipped to assist a diverse clientele, encompassing fields such as scientific inquiry, medical research, agricultural studies, bioengineering, the pharmaceutical industry, and environmental remediation.
We offer excellent selection of customized bioinformatics analysis, including standard analysis such as metagenome assembly, taxonomic assignment, functional annotation, alpha and beta diversity analysis, gene prediction (KEGG, GO, COG), etc.
ANALYSIS CONTENT | DETAILS |
---|---|
Data QC | Removal of low-quality sequences and adapter sequences |
Taxonomic assignment | Sequence alignment and taxonomic assignment |
Microbial diversity analysis | α and β diversity analysis, meta-analysis, LEfSe, VENN, PCA. |
Function annotations | KEGG, eggNOG, GO, etc. |
CAZy | Prediction of genes coding for carbohydrate-active enzyme and correlation analysis |
CARD | Prediction of resistance genes and correlation analysis |
CAG (co-abundance genes)/MLG (linkage groups) analysis | Study the association between disease and microbial strains CAG, co-abundance genes group MLG, metagenomic linkage groups |
CNV | Correlation analysis between microbial copy number variation (CNV) and disease |
Pathogen prediction | VirSorter, VirFinder, MARVEL |
Note: The above content includes only a portion of the bioinformatics analysis. For more information or to customize the analysis, please contact us directly.
Note:
1. Ensure the DNA is purified, not degraded.
2. Transport nucleic acid samples with sufficient ice packs or dry ice.
3. If you wish to obtain more accurate and detailed information regarding sample requirements, please feel free to contact us directly.
Partial results of our Shotgun Metagenomic Sequencing service are shown below:
Abundance and phylogenetic distribution of eight key enzymes of the phosphorus biogeochemical cycle in grassland soils
Journal: Environmental Microbiology Reports
Impact factor: 4.3
Published: 10 May 2023
Background
Grasslands are vital biomes characterized by diverse plant communities and significant ecosystem services, particularly in nutrient cycling, especially phosphorus (P). This study investigates the abundance and diversity of prokaryotic P-enzymes in grasslands using metagenomic data, aiming to correlate these profiles with environmental factors and identify key drivers of their distribution.
Materials & Methods
Sample preparation:
Method:
Data Analysis:
Results
The analysis of phosphorus (P)-enzyme coding genes in soil metagenomes revealed that alkaline phosphatase genes, particularly phoD, were significantly more abundant than acid phosphatase and phytase genes, regardless of soil properties. Co-variation studies showed strong positive correlations among most P-enzyme genes, while edge-PCA analysis indicated that the distribution of these genes varied with soil type, pH, and organic carbon content. Specifically, certain gene variants were more prevalent in soils with low pH and high organic carbon, whereas others were associated with soils of higher pH and lower organic carbon levels.
Fig 1. Phylogenetic placements of the predicted proteins of each metagenome with respect to the reference bases of each enzyme: (A) PhoD, (B) PhoX, (C) Nsap-A and (D) BPP.
Fig 2. Correlation matrix of KR-CAP axes.
Conclusions
This study examined bacterial P-enzyme coding genes in grassland soils, finding that alkaline phosphatase genes, particularly phoD, were the most abundant and affected by soil pH and organic carbon. The results highlight the need for integrated approaches to better understand microbial community dynamics and phosphorus cycling in soils.
Reference
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