Shotgun Metagenomics Sequencing Data Analysis

Research Advances in Shotgun Metagenomics Data Analysis

Microbial communities consisting of bacteria, fungus, viruses, and single-celled eukaryotes play a critical role in the environment and human health. However, the complexity and diversity of these communities make culturing and classifying their members challenging using traditional laboratory techniques. To overcome these limitations, shotgun metagenomics sequencing has emerged as a relatively new and powerful environmental sequencing method that can provide insight into the biodiversity and function of communities. However, analysis of shotgun metagenomics sequencing is complicated by the complex structure of the data. Fortunately, new tools and data resources have been developed to circumvent these complexities and allow researchers to determine which microbes are present in the community and how they function.

Fig. 1. Shotgun metagenomic sequencing data analysis pipeline. (Gao B, et al, 2021)

Application of Shotgun Metagenomics Data Analysis

Our shotgun metagenomics data analysis can be used for but not limited to the following research:

Environmental research: our shotgun metagenomics data analysis allows researchers to explore the diversity and functional potential of microbes in different environmental ecological niches. By studying microbial communities in soil, water, and other ecosystems, our researchers can gain insight into nutrient cycling, bioremediation processes, and the effects of environmental change. CD Genomics' shotgun metagenomics service facilitates environmental monitoring, identification of new microbial species, and discovery of potential biotechnology applications.

Human microbiome research: Our shotgun metagenomics allows characterization of the human microbiome, helping researchers understand its composition, dynamics and functional contributions. CD Genomics' expertise in the analysis of shotgun metagenomics data allows us to help you uncover the intricate interactions between the human microbiome and host health.

Biotechnology innovation: our shotgun metagenomics data analysis can help harness the power of microbes. By identifying novel genes, enzymes, and metabolic pathways from complex microbial communities, we can help you can discover new therapeutic targets, develop innovative drug discovery strategies, and design microbial factories to sustainably produce valuable compounds.

CD Genomics Data Analysis Pipeline:

CD Genomics offers a comprehensive service for shotgun metagenomics data analysis, allowing the elucidation of taxonomic composition, functional potential, and even the recovery of entire genomic sequences from microbial communities. Our services cover the entire workflow of shotgun metagenomics data analysis from pre-processing to data analysis.

(1) Data pre-processing and quality control

CD Genomics performs stringent quality control checks to remove low-quality reads, trim adapter sequences, and filter out contaminants. This step ensures that only high-quality, reliable data is used for downstream analysis.

(2) Taxonomic analysis of the microbiome

CD Genomics assigns taxonomy to sequencing reads using a computational pipeline and reference database. By comparing the mapped reads to a comprehensive set of microbial genomes, the relative abundance and diversity of different taxa in a sample can be determined. This analysis provides insights into the taxonomic composition and structure of the microbial community.

(3) Functional analysis of the microbiome

To reveal the functional potential of microbial communities, CD Genomics performs functional annotation of metagenomics data. This involves predicting and annotating genes, functional pathways and biological processes encoded in sequenced reads. Various bioinformatics tools and databases are used to annotate genes based on their homology to known functional elements.

(4) Sequence analysis of genomic features of the microbiome

CD Genomics uses advanced alignment algorithms or ab initio assembly methods to match sequencing reads to known genomes or to reconstruct the genome of individual microbial species within a community. This step allows for taxonomic analysis and identification of functional elements in metagenomics data.

(5) Post-processing statistical and biological analysis

CD Genomics uses advanced statistical analysis methods, data visualization techniques and comparative genomics approaches to identify significant patterns, potential interactions and functional features in microbial communities. Results are presented in a comprehensive report or visualization format to facilitate data interpretation and further exploration.

(6) Validation

CD Genomics provides comprehensive follow-up support for researchers, including in-depth consultation, interpretation of analysis results, and assistance with further data exploration or downstream analysis.

Sample Submission Guidelines of Sequencing

Shotgun Metagenomics Data Analysis Content:

What Are the Advantages of Our Services?

Comprehensive Data Analysis Pipeline

We offer a comprehensive data analysis pipeline for shotgun metagenomics, ensuring accurate, reproducible results and minimizing researchers' technical burden, focusing on biological interpretation

Expertise in Multi-Omics Integration

CD Genomics excels in multi-omics integration, combining metagenomics with other omics to reveal dynamic microbial interactions and their environmental or host impacts.

Customized Solutions and Consultation

We provide tailored solutions and personalized consultations, guiding clients through experimental design, sample collection, and data interpretation for actionable metagenomics insights.

Cutting-Edge Bioinformatics Support

We provide cutting-edge bioinformatics support, utilizing the latest algorithms and software for efficient large dataset analysis, ensuring clients benefit from the latest advancements.

High-Quality Reporting and Visualization

We provide high-quality reports with detailed methods, comprehensive data summaries, insightful interpretations, and clear visualizations to facilitate understanding and presentation of metagenomics research findings.

With extensive experience in multi-omics joint analysis

we offer a wide range of advanced sequencing services. Our expertise includes, but is not limited to, integrated Genomics and Metabolomics Analysis, Genomics and Transcriptomics Analysis, Microbiomics and Metabolomics Analysis, Proteomics and Transcriptomics Analysis, and Transcriptomics and Metabolomics Analysis. We are committed to leveraging our professional knowledge to help you overcome your research challenges.

Example Data Analysis Report

To showcase the quality and detail of a CD Genomics report for shotgun metagenomic sequencing data analysis, we offer a sample report upon request. You can contact us to obtain this report. Additionally, refer to a client-published article for more insights. " Gene functions of the Ambystoma altamirani skin microbiome vary across space and time but potential antifungal genes are widespread and prevalent." which includes some of the data we provided.

How It Works

CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.

CD Genomics is a leading provider of shotgun metagenomics data analysis services with its cutting-edge technology, expert team, customized solutions, and commitment to data security. Through our comprehensive analysis process, researchers can unravel the complexity of microbial ecosystems, paving the way for groundbreaking discoveries and advances in a variety of scientific fields. If you are interested in our services, please contact us for more detailed information.

Reference

1. Gao B, Chi L, Zhu Y, et al. An introduction to next generation sequencing bioinformatic analysis in gut microbiome studies[J]. Biomolecules, 2021, 11(4): 530.

What Does Analysis of Shotgun Metagenomic Sequencing Show?

Data Quality Control

Fig 1. Per base sequence content

Fig 2. Per sequence GC content for HPC1

Metaphlan Species Annotation

Fig 3. Merged_abundance. Different circles represent different taxonomic levels.

Taxonomy Distribution Histogram of All Samples

Fig 4. Abundance heatmap of family level

Functional Database Annotation

Fig 5. Statistical of specific function database common and unique annotation in HPC1.

KEGG

Fig 6. KEGG_classification.

CARD Annotation

CAZy Annotation

Fig 7. CAZy function classification for HPC1.

PHI Annotation

Fig 8. PHI phenotype classification for HPC1.

VFDB Annotation

TCDB Annotation

Fig 9. TCDB classification statistics for HPC1.

Title: Shotgun metagenomics and systemic targeted metabolomics highlight indole-3-propionic acid as a protective gut microbial metabolite against influenza infection

Publication: Gut Microbes

Main Methods: shotgun metagenomics, metabolomics

Abstract:

This study explores the gut-to-lung axis in influenza A virus infection using metagenomics and metabolomics. Significant changes in the gut microbiota were noted, including reduced Lactobacillaceae and Bifidobacteriaceae and increased Akkermansia muciniphila. Metabolic pathway alterations, especially in short-chain fatty acids and tryptophan metabolites, were observed. A decrease in indole-3-propionic acid (IPA) correlated with higher viral load and inflammation. IPA supplementation reduced viral load and inflammation. Antibiotic treatment targeting IPA-producing bacteria worsened outcomes, which were reversed by IPA supplementation. IPA emerged as a potential influenza severity biomarker.

Research Results:

Influenza infection leads to a change in the microbiota's composition, as assessed by shotgun metagenomic: Mice infected with a sublethal dose of IAV experienced peak body weight loss (17-18%) between days 8-10, with partial recovery by day 14. Lung viral load and systemic inflammatory cytokines were elevated at day 7. Shotgun sequencing of cecal contents showed reduced α-diversity and bacterial richness on days 7 and 14, with β-diversity analysis revealing significant differences in gut microbiota composition over time. These findings highlight IAV's impact on gut microbiota, particularly at day 7 post-infection.

Fig 10. Altered composition of the gut microbiota during IAV infection. NMDS and Bray Curtis analyses show gut microbiota changes during IAV infection. Significant phylum and species-level differences were found using Kruskal-Wallis test.

Differential shotgun sequencing analyses revealed significant changes in gut microbiota during IAV infection. Notably, Bacteroidota and Bacillota abundances fluctuated, with Bacillota decreasing on D7 and rebounding on D14. Verrucomicrobiota increased on D7. Species-level changes included significant fluctuations in Lactobacillus and Akkermansia muciniphila. LEfSe analysis highlighted species discriminant at D7 and D14.

Influenza infection is associated with changes in the gut microbiota's metabolic profile: Using shotgun sequencing, we identified significant variations in microbial functional profiles during IAV infection. Songbird models highlighted metabolic modules showing differential abundance between D0 vs. D7 and D7 vs. D14, indicating functional shifts like increased ATP synthesis and carbohydrate metabolism on D7, and decreased pathways such as SCFA metabolism by D14. This suggests dynamic microbial responses to infection, with functional profiles tending to revert to baseline by D14.

Fig 11. The module scores and category-level representation of KOs with varying songbird coefficients, highlighting functional shifts during IAV infection.

Influenza is associated with altered abundance of genes involved in metabolic pathways: Authors investigate altered functional modules in gut microbiota during IAV infection, highlighting metabolic pathways significantly affected on D7. Increased modules include lipid metabolism and lipopolysaccharide metabolism, while carbohydrate metabolism and amino acid pathways such as arginine synthesis and serine degradation show varied responses. Changes in amino acid biosynthesis, including branched-chain amino acids and aromatic amino acids, indicate broader metabolic shifts potentially impacting influenza outcomes.

Influenza is associated with changes in the concentrations of microbial-associated metabolites: They compared microbial-associated metabolite concentrations in non-infected and IAV-infected mice, focusing on the serum metabolome due to the gut-lung axis relevance in influenza. Significant decreases in cecal SCFAs and alterations in acylcarnitines, TMAO, and tryptophan metabolites were observed during infection stages. Polyamines showed transient elevation, while bile acids and branched-chain amino acids remained relatively stable. These findings suggest that IAV infection impacts microbial metabolism, potentially influencing disease outcomes.

Alterations in taxon abundance and microbiota-associated metabolites are correlated with markers of influenza severity: They explored correlations between gut microbiota composition, metabolite levels, and influenza severity markers in infected mice. Significant associations were identified through Spearman correlation tests and hierarchical clustering, revealing links between specific taxa (like Lactobacilli and Bacteroidales species) and metabolites such as SCFAs, acylcarnitines, polyamines, and amino acids. These correlations highlighted potential roles of microbial metabolites in influencing influenza outcomes, with central metabolites like IPA showing negative associations with viral load and inflammatory markers. Overall, our findings underscored complex interactions shaping disease severity during influenza infection.

Fig 12. Associations between taxonomic and metabolomic features from non-infected and IAV-infected animals.

Conclusions:

In this study, they explored the implications of narrow-spectrum antibiotics and supplementation with indole-3-propionic acid (IPA) in influenza virus replication and inflammation. We are currently investigating the mechanisms through which IPA exerts its effects and exploring strategies to optimize its therapeutic benefits during influenza infections. IPA mimetics show promise for enhancing therapeutic efficacy with reduced off-target effects compared to IPA itself. Manipulating gut microbiota and metabolites, including through prebiotics, probiotics, and postbiotics, could be valuable in preventing and treating acute viral respiratory infections like influenza and SARS-CoV-2. Our findings underscore IPA's role as a potential biomarker and therapeutic target for assessing and mitigating influenza severity.

1. What is shotgun sequencing in metagenomics?

Shotgun metagenomic sequencing provides a holistic approach to sample all genetic material from diverse organisms within complex samples, offering microbiologists insights into bacterial diversity and microbial abundance across different environments.

2. What is the difference between 16S and shotgun metagenomics?

16S rRNA sequencing focuses on a specific DNA region, but shotgun metagenomic sequencing analyzes all genomic DNA. shotgun metagenomic sequencing enables the simultaneous identification and profiling of bacteria, fungi, viruses, and other microorganisms in microbiome studies.

3. How is shotgun different from next generation sequencing?

Despite being quicker than other sequencing methods available at the time, shotgun sequencing still took over a decade to fully sequence the human genome. Next generation sequencing (NGS) is a much faster technique that does not require cloning DNA fragments.

4. What are the advantages of shotgun sequencing?

Shotgun sequencing offers several key advantages over previous methods: it is faster because it eliminates the mapping process, requires less DNA, and is less expensive than techniques that need a map.

5. What are the limitations of shotgun metagenomics?

This technique enables the potential detection of all DNA genomes in a sample, depending on sequencing depth. However, shotgun metagenomics is limited by high costs and the need for advanced laboratory and bioinformatic capabilities.