Why Perform Microbiome Big-data Mining?
Data mining is an essential process for unlocking the full potential of microbiome data, given the vast and complex nature of microbial datasets.The microbiome refers to the collection of all microorganisms, including bacteria, fungi, viruses, and archaea, along with their genetic material, in a particular environment. This includes environments such as the human body, soil, water, and plants. Microbiome data mining focuses on integrating and analyzing large-scale microbiome data to reveal complex relationships between microbial communities, their environments, and hosts. Our data mining service applies advanced computational methodologies to help researchers unravel microbial interactions within ecosystems and their influence on health and disease, offering deeper insights into microbial dynamics and their broader ecological and biomedical impacts.
What Can We Offer ?
Our team offers comprehensive microbiome big-data mining services that streamline the complex process of dataset identification and analysis. By curating data from various public sources and automatically retrieving relevant metadata, we ensure that clients receive well-organized and pertinent datasets. Our dedicated bioinformaticians enhance query accuracy and relevance, while our expertise in harmonizing metadata allows for a unified dataset presentation. With the ability to perform both standard and bespoke analyses, we provide tailored insights that empower clients to navigate the vast landscape of public microbiome data effectively, transforming a daunting task into a streamlined, efficient experience.
Workflow for Microbiome Big-Data Mining
Microbiome Big-Data Download
Currently, there is a wide variety of microbiome databases, with the most common being the National Center for Biotechnology Information (NCBI) and the European Nucleotide Archive (ENA). We have also summarized some microbiome-specific databases, known as Metagenomic Databases. Each database has its unique data inclusion standards, allowing users to filter data of interest based on the provided metadata, such as the species of microorganisms, hosts, and sampling environments. Most raw data is stored in SRA or FASTQ file formats, and NCBI and ENA typically provide processed data as well. Users can quickly access NCBI data via WWW or FTP, while ENA data can be conveniently downloaded through the Sequence Retrieval System (SRS). Our data mining service offers cross-database data retrieval, download, and integration, helping researchers conduct related studies more efficiently.
Applications of Microbiome Big-Data Mining Service
We can provide the following services, but not limited to these.
Microbial Diversity Analysis
Our microbial diversity analysis assesses and characterizes the variety of microbial species within a given environment or sample. It encompasses both alpha diversity, which measures diversity within specific areas, and beta diversity, which compares diversity between different ecosystems. we also analyze microbial communities to evaluate their richness and evenness. This analysis has broad applications in ecology, health research, and environmental monitoring, helping to understand the role of microbial diversity in ecosystem functions and human health.
Our metagenomic analysis focuses on the genetic composition of microbial populations in specific habitats. We investigate genetic diversity, population structures, and evolutionary relationships, revealing functional characteristics and ecological roles. This work uncovers the biological significance of microbial communities, contributing to nutrient cycling, pathogenicity, and biotechnological advancements in agriculture and environmental remediation.
Metatranscriptomic Analysis
Metatranscriptomics provides insights into microbial gene expression across ecological settings. We analyze RNA from environmental samples to study gene transcription patterns and regulatory mechanisms. This understanding of transcriptional landscapes enhances our knowledge of microbial ecology and identifies potential biotechnological targets.
Viral Metagenomics explores the viral components of microbiomes, shedding light on viral diversity and ecology. Our service identifies novel viruses, enhancing understanding of viral evolution and interactions within microbial communities. This research has significant public health implications, particularly in monitoring and controlling viral outbreaks.
Bacterial genomic analysis is key to understanding microbial genetics. We utilize advanced sequencing technologies to investigate bacterial genomes, focusing on diversity, adaptation, and functional traits. Our findings address critical issues such as antibiotic resistance and pathogenicity, contributing to advancements in healthcare and environmental sustainability.
Our fungal genomics service examines the diversity and ecological roles of fungi, including model organisms and pathogens. By studying unsequenced species, we reveal genomic structures and metabolic pathways, enhancing our understanding of fungal biology and its applications in biotechnology, agriculture, and medicine.
Data Integration and Multi-Omics Analysis
Integrating multi-omics data is essential for a comprehensive understanding of biological systems. Our service combines microbiome data with genomics, metabolomics, and transcriptomics to elucidate complex biological interactions. This integrated approach enhances knowledge of health and disease mechanisms and reveals potential therapeutic targets.
Correlation Analysis
Our service provides microbiome correlation analysis to uncover relationships between microbial species and their environments or host systems. Utilizing methods such as Pearson, Spearman, SparCC, SPIEC-EASI, and CoNet, we detect patterns of microbial co-occurrence and interaction. Additionally, we emphasize proper data preprocessing, statistical adjustments, and visualization to ensure meaningful and reliable results from these studies.
Bioinformatics Analysis Content
| Data preprocessing | FastQC |
| Trimmomatic | |
| Fastp | |
| Genome assembly | Trinity |
| Megahit | |
| PIMGAVir | |
| Taxonomic Annotation | Bowtie2 |
| blastx | |
| Diversity Analysis | Alpha diversity analysis(Shannon、Simpson、Chao1 index) |
| Beta diversity analysis( PCA、PCoA、NMDS) | |
| Pathway Enrichment Analysis | Pathway Enrichment (e.g., KEGG, Reactome) |
| Gene Set Enrichment Analysis (GSEA) | |
| Differential Abundance Analysis | LEfSe |
| ANCOM | |
| Functional Annotation | Enrichment Analysis (e.g., GO, KEGG) |
| Microbial Interaction Analysis | |
| Phylogenetic Analysis | Identify Homologous Regions (e.g ,MAFFT) |
| Phylogenetic Tree Construction(e.g ,UPGMA,IQ-TREE,TrimAL,PhyML) |
What Are the Advantages of Our Service?
Comprehensive Data Analysis
Our microbiome data mining service offers in-depth analysis across various aspects of microbiome research, including microbial diversity, community structure, and functional profiling. This holistic approach ensures that clients receive a well-rounded understanding of their data.
Advanced Bioinformatics Tools
We utilize advanced bioinformatics tools and algorithms to process and analyze microbiome data. This ensures high accuracy in data interpretation and helps uncover significant biological insights that can drive research forward.
Customizable Solutions
We understand that each research project has unique requirements. Our service offers customizable data mining solutions tailored to specific research questions, allowing clients to focus on their particular areas of interest.
Expert Support and Consultation
Our team of biological specialists provides expert guidance throughout the data analysis process. We assist clients in interpreting results and translating them into actionable insights, enhancing their research outcomes.
Integration of Multi-Omics Data
Our service seamlessly integrates microbiome data with other omics data (e.g., genomics, transcriptomics, and metabolomics), offering a comprehensive view of biological systems. This integration helps to uncover complex interactions within microbial communities and their hosts.
High-Quality Data Processing
We adhere to rigorous quality control standards throughout the data processing pipeline, ensuring that the results are reliable and reproducible. This high-quality data processing is crucial for making informed decisions based on the analysis.
Innovative Research Applications
Our microbiome data mining service supports a wide range of applications, from understanding disease mechanisms to improving agricultural practices. This versatility allows clients to explore innovative solutions in various fields, including healthcare, environmental science, and biotechnology.
Scalable Solutions
Whether working with small pilot studies or large-scale projects, our service can scale to meet the demands of any research endeavor. We have the infrastructure and expertise to handle diverse datasets effectively.
Rapid Turnaround Times
We recognize the importance of timely results in research. Our efficient data processing workflows ensure that clients receive their analysis quickly, enabling them to make prompt decisions based on the findings.
Commitment to Ethical Practices
We are dedicated to conducting research with integrity and ethical considerations. Our service adheres to the highest standards of data privacy and confidentiality, ensuring that client data is handled securely.
By leveraging these advantages, CD Genomics' Microbiome Data Mining Service empowers researchers to unlock the potential of microbiome data, fostering advancements in understanding microbial communities and their roles in health and disease.
What Does Microbiome Big-Data Mining Reveal?
Data Statistics of Microbiome Big-data Quality Control
The quality control of Microbiome was performed by assessing key metrics such as the number of detected reads, the Q30 score, the number of absolute sequence variants(ASV). The table below summarizes these quality control results, highlighting the quality of each sample based on these metrics.
Table 1. Quality Control Metrics for Microbiome Big-data
| Sample ID | Total Reads | Quality Score (Q30) | ASV Identified |
|---|---|---|---|
| Sample 1 | 5,000,000 | 99.90% | 1,150 |
| Sample 2 | 4,800,000 | 99.50% | 1,120 |
| Sample 3 | 6,200,000 | 99.80% | 1,300 |
Taxonomy Distribution Histogram of All Samples
The heatmap takes the family as an example, as shown in Figure 1.
Figure 1. Abundance heatmap of family level.The ordinate represents the taxonomic classification of microorganisms, and the data has been normalized for comparative analysis.
Beta Diversity Analysis Show the Diversity of Samples
Beta diversity is a measure of the diversity between different ecological communities or ecosystems, reflecting the changes in species composition among them. It quantifies how distinct or similar the species are between different habitats or environments.
Figure 2. PCoA analysis based on bray Curtis .Each point represents a sample, plotted by a principal component on the X- axis and another principal component on the Y- axis, which was colored by group. The percentage on each axis indicates the contribution value to discrepancy among samples.
KEGG Classification of Mined Microbial Data
Figure 3. KEGG_classification. The height of the bars or data points on the x-axis indicates the number or proportion of genes or sequences that belong to each KEGG category. The y-axis labels represent different KEGG functional categories.
UPGMA Analysis
It construct a phylogenetic or classification tree using a similarity- or distance-based clustering method, to represent the evolutionary relationships or similarities between samples.
Figure 4. UPGMA clustering tree based on unweighted unifrac. The different colors represent different grouping.
Beta Diversity Analysis in Tomato Rhizosphere
Bray-Curtis Dissimilarity Analysis was used to compare the composition of tomato rhizosphere microbial communities. ANOSIM and PERMANOVA analyses showed significant differences in the rhizosphere microbial communities at different geographical locations. Specifically, the microbial communities of field-grown (NF) tomatoes in Shandong and Heilongjiang differed significantly, while the microbial communities of greenhouse-grown (GH) tomatoes from both locations were more similar
Figure 5.Bray-Curtis dissimilarity analysis(principal coordinates PCo1 and PCo2)(A) The NF rhizosphere microbiota from both Shandong and Heilongjiang Provinces show clear separation from their respective GH microbiota along both axes (P < 0.001, PERMANOVA using Adonis). (Zhou, 2022)
Composition of Microbial Co-occurrence Networks in Greenhouse Tomatoes and Natural-field Tomatoes
By comparing the structural differences in the co-occurrence network of greenhouse tomatoes and natural field tomatoes, the research team found that the microbial co-occurrence networks of greenhouse and natural field tomatoes are significantly different. Natural field tomatoes have a higher clustering coefficient and higher modules than greenhouse tomatoes. performance, and network stability.
Figure 6. co-occurrence analysis of of bacterial and fungal. (a,b): Bacterial co-occurrence networks for GH (a) and NF (b) plants, with nodes colored by bacterial modules.(c,d): Fungal co-occurrence networks for GH (c) and NF (d) plants, with nodes colored by fungal modules.(e,f): Identification of keystone for bacterial(e) and fungal(f) taxa in NF and GH microbiomes, with network metrics comparison. (Zhou, 2022)
Title:Metagenomic insights into the microbe-mediated B and K2 vitamin biosynthesis in the gastrointestinal microbiome of ruminants
Publication: Microbiome
Main Methods: Microbial genomes data mining
Abstract:This study investigated the biosynthesis of B and K2 vitamins by the gastrointestinal microbiome of ruminants, using both previously collected metagenomic datafrom various gastrointestinal tract (GIT) regions of seven ruminant species, along with 17,425 microbial genomes obtained from the public NCBI database. The analysis identified over a million genes and 167 enzymes involved in vitamin biosynthesis, revealing that this process was most active in the stomach microbiome, while certain vitamins were more abundant in the large intestine. Among the genomes analyzed, 2,366 were capable of producing at least one vitamin, though few could synthesize multiple vitamins, and only a small proportion could complete the synthesis of cobalamin. A high-grain diet generally enhanced vitamin biosynthesis, except for cobalamin, which was reduced. The study concludes by emphasizing the regional and dietary influences on vitamin production, offering new insights into the microbiome's role in optimizing vitamin synthesis in ruminants.
Research Results:
Distribution of B and K2 Vitamin Biosynthesis Activity Across the GIT
This study reanalyzed metagenomic data from 370 GIT samples of seven ruminant species, previously published by the team, and identified 1,135,807 genes and 167 KEGG orthologs(KO). These genes and orthologs are involved in the biosynthesis of eight B vitamins and one K2 vitamin. Comparing the gene abundance involved in B and K2 vitamin biosynthesis across different GIT regions revealed that the pathway abundance for pantothenate biosynthesis was the highest, while menaquinone had the lowest pathway abundance. Through a taxonomic analysis of these vitamin biosynthetic genes, it was found that the B and K2 vitamin biosynthetic genes were phylogenetically assigned to the phyla Bacteroidetes (44.24%), followed by Firmicutes (33.24%) and Proteobacteria (6.15%).
Figure 7.The comparative abundance and taxonomic profiling of de novo B and K2 vitamin biosynthesis. (a) Total abundance of microbial genes for B and K2 vitamin biosynthesis across 10 GIT regions, with median and interquartile range indicators.(b) Phylogenetic distribution of B and K2 vitamin biosynthetic genes at the phylum level across 10 GIT regions. The B and K2 vitamins include thiamine (THI), riboflavin (RIB), niacin (NIA), pantothenate (PAN), pyridoxine (PYR), biotin (BIO), folate (FOL), cobalamin (COB), and menaquinone (MEN). (Jiang, 2022).
Compilation of 2,366 Genomes Involved in de novo Vitamin Biosynthesis
This study recruited 17,425 non-redundant genomes, including 10,373 metagenome-assembled genomes (MAG) from prior research and 7,052 from public ruminant microbial genome collections. After filtering, 5,318 high-quality genomes were retained, with 2,366 predicted to de novo synthesize at least one B or K2 vitamin. These genomes ranged from 1.19 to 7.74 megabases in size and had GC content from 24.21% to 74.64%. The rumen had the highest genome density.By comparing vitamin-producing genomes (VPGs) and non-producing genomes (NPGs), it was found that VPGs included 1,024 genomes in Bacteroidetes (43.3%) and 905 in Firmicutes (38.3%). Evaluation of vitamin synthesis capacity revealed that 1,135 genomes could synthesize one vitamin, 1,167 could synthesize two to four, and 32 could synthesize seven. Folate was mainly produced by Proteobacteria-associated genes, while cobalamin was primarily produced by Firmicutes-associated genes.
Figure 8. 2366 genomes identified to synthesize B and K2 vitamins. (a)The workflow for identifying genomes capable of synthesizing B and K2 vitamins.(b) Genomic statistics for 2366 VPGs. (c) The quantity of genomes obtained from the GIT regions.(d) Comparison of taxonomy and core KOs between vitamin-producing genomes VPGs and NPGs.(e)The maximum-likelihood tree of 2366 VPGs.(f)Correlation network of vitamins and genomes, with genomes colored according to their taxonomic classifications. (Jiang, 2022).
The Impact of a High-Grain Diet on Vitamin Biosynthesis in the Rumen Microbiome
This study reanalyzed genomic datasets related to vitamin biosynthesis from the rumen microbiome of dairy cows previously fed high-forage (CON) and high-grain (HG) diets. It was found that the HG diet had a significant impact on the microbiome involved in B vitamin and K2 vitamin synthesis; compared to the CON group, the HG group exhibited reduced alpha diversity and increased beta diversity. Genes associated with most vitamin synthesis were enhanced in the HG group, while only cobalamin synthesis was inhibited. Taxonomic profiling of the cobalamin biosynthetic genes was performed to identify specific species. Following the HG diet, significant shifts were detected in the taxa from four genera: Bacteroides, Fibrobacter, Ruminococcus, and Eubacterium.
Figure 9. Evaluation of B and K2 vitamin biosynthesis in the CON and HG groups. (a) Impact of the HG diet on the alpha diversity of B and K2 vitamins at the species level (Wilcoxon rank-sum test, n.s. P > 0.05, ***P < 0.001). (b) Principal coordinates analysis (PCoA) of the microbiota associated with B and K2 vitamin biosynthesis in both groups, with AMOVA revealing significant differences between the groups (P < 0.001). The Bray-Curtis distances between the groups are presented in the box plot (Wilcoxon rank-sum test, ***P < 0.001). (c) Comparison of the relative abundance of B and K2 vitamin biosynthesis between the groups (Wilcoxon rank-sum test, **P < 0.01, ***P < 0.001). (d) Relative abundance of the microbiota involved in cobalamin biosynthesis at the genus level across the two groups (Wilcoxon rank-sum test, *P < 0.05, **P < 0.01, ***P < 0.001). (Jiang, 2022).
Conclusion
This study identified 2,366 microbial genomes associated with the biosynthesis of B and K2 vitamins from public datasets. Using the constructed reference dataset, the researchers demonstrated the regional heterogeneity of the gastrointestinal microbiome in ruminants concerning B and K2 vitamin biosynthesis. Additionally, they analyzed the response of microbe-mediated vitamin biosynthesis to the disruption caused by a high-grain diet.
1.What is microbiome data mining?
Microbiome data mining refers to the process of analyzing complex microbiome datasets to extract meaningful insights. Our service employs advanced bioinformatics tools and algorithms to identify microbial profiles, understand their functional roles, and explore their interactions with hosts and environments.
2.How can microbiome data mining help in research?
The service can assist researchers in identifying microbial biomarkers linked to diseases, assessing microbial diversity, and elucidating the relationships between microbes and host health. These insights can inform preventative strategies and enhance diagnostic accuracy.
3.What technologies are used in microbiome data mining?
Our microbiome data mining service utilizes high-throughput sequencing technologies, including 16S/18S/ITS sequencing, metagenomics, and metatranscriptomics, combined with robust data analysis methods to ensure comprehensive insights from microbiome data.
4.How is data quality ensured during analysis?
Our service implements stringent quality control measures, including preprocessing of raw data and statistical assessments, to ensure the accuracy and reproducibility of results. This includes filtering out low-quality sequences and evaluating sequencing depth and diversity metrics.
5.In which fields can microbiome analysis results be applied?
Results from microbiome analysis can be applied across various fields, including medicine, agriculture, and environmental science. By understanding microbial functions and interactions, new therapeutic approaches, optimized agricultural practices, and sustainable environmental solutions can be developed.
6.How are the results of microbiome data analysis interpreted?
We provide comprehensive analysis reports that include visual representations of data and biological interpretations, assisting clients in understanding the ecological and functional implications of their microbiome data.
7.What is the typical timeline for microbiome data mining projects?
The timeline for data mining projects varies based on their complexity. Generally, the entire process, from sample collection to report generation, takes a few weeks.
8.What does the data analysis report include?
Our analysis reports typically include detailed data on microbial composition, functional predictions, statistical analysis results, and biological interpretations tailored to the specific research objectives of the client.
References
- Zhou, X., et al. Cross-kingdom synthetic microbiota supports tomato suppression of Fusarium wilt disease. Nature communications. 2022,13(1), 7890.
- ZJiang, Q., et al. Metagenomic insights into the microbe-mediated B and K2 vitamin biosynthesis in the gastrointestinal microbiome of ruminants. Microbiome.2022, 10(1), 109.
