How is Bioinformatics Transforming Microbiome Research?

The term "microbiome" refers to the broad ecosystem of microorganisms that live inside the human body.

In recent years, the field of bioinformatics has emerged as a transformative tool for studying the microbiome; by integrating biology and data science, this cutting-edge discipline offers insights into microbial communities that influence health, ecology, and beyond.

​​​​​​​Image Credit: bluesroad/Shutterstock.comImage Credit: bluesroad/Shutterstock.com

Introduction

The term microbiome refers to the collection of microorganisms, including bacteria, fungi, and viruses, that inhabit a particular environment.1 This encompasses a wide range of habitats, from the soils to the human gut.1

In agriculture, the soil microbiome plays a crucial role in plant growth, soil fertility, and as a biocontrol agent, significantly contributing to sustainable farming practices.1 Similarly, in human health, probiotic microbes are increasingly recognized for their benefits and are being incorporated into functional foods.1

An understanding of microbial communities is essential for the restoration of degraded ecosystems. This understanding can be applied in innovative practices such as fecal microbiota transplantation (FMT) and bioaugmentation.2

In this context, bioinformatics has played a pivotal role by providing analytical and interpretive tools for the genetic material of microorganisms. Metagenomic research, which involves the analysis of genetic material from environmental samples, is particularly promising for understanding microbial communities.3

However, these studies face challenges due to the high diversity of microorganisms and the complexity of their interactions. Bioinformatics tools, such as the Bruijn graph assembly, are being developed to address these challenges, enabling the assembly of metagenomes and the exploration of microbial functions and taxonomies.3

Is Machine Learning the Future of Bioinformatics?

Bioinformatics in Microbiome Research: Foundations and Techniques

The foundation of bioinformatics in microbiome research is data acquisition. Researchers obtain raw sequencing data from platforms such as Illumina4 or Ion Torrent5, or they access shared data from public databases such as the Human Microbiome Project (HMP)6, the Earth Microbiome Project (EMP)7, or the Global Ocean Sampling (GOS)8.

Once acquired, data management is crucial. The process involves transferring data to repositories for storage and future access and quality control steps, filtering, trimming, assembly, annotation, and alignment.9 Data structuring, cleaning, and enrichment are also involved.9

Once the data has been processed, the next step is the data analysis. This involves chimera detection, OTU picking, and taxonomy assignment, as well as community analysis, which computes and analyzes community composition and diversity.10

To perform microbiome analyses, it is necessary to employ metagenomic sequencing. enabling the capture of all genetic material from samples of interest, thus simultaneously facilitating the comprehensive analysis of thousands of organisms.11 

The process of metagenomic sequencing involves several key steps, including DNA extraction, library preparation, sequencing, and a series of computational procedures, such as assembly, annotation, and statistical analysis.11

The process of microbial genome assembly involves compiling sequence reads into contiguous sequences, known as contigs.12 Two primary algorithms facilitate this process: Overlap Layout Consensus (OLC) and De Bruijn Graph (DBG). DBG is favored in metagenomic studies due to its efficiency with large data sets.12

The next step is to perform a functional annotation analysis of the metagenomic data. This is made by classifying predicted metagenomics proteins into families and assigning them functions based on databases such as SEED, KEGG, MetaCyc, and EggNOG, employing advanced methods such as hidden Markov models (HMM).11

Recently, deep learning techniques such as DeepFRI have been employed to enhance traditional methods, resulting in robust predictions of protein functions across a diverse range of sequences.13

Advancements in Microbiome Analysis

Metagenomics is a field that is heavily reliant on bioinformatics. The integration of computational techniques with biological data has enabled the handling of the vast information generated by high-throughput sequencing technologies.14

Furthermore, with the advent of artificial intelligence (AI) and machine learning (ML), executing more sophisticated analysis and predictive modeling is possible.13

These advancements are crucial in academic research and have significant industrial applications, including food production, probiotics, cosmetics, enzyme discovery, and more.14

Several studies have demonstrated the value of bioinformatics approaches in facilitating more comprehensive insights into microbiome research.15 For instance, a trait-based approach was developed to explore the functional and interaction potential of marine bacteria, leading to the identification of genome functional clusters (GFCs) that group bacterial taxa with common ecology.15

This approach revealed that specific GFCs, particularly among Alpha- and Gammaproteobacteria, are enriched in interaction traits, such as siderophore, vitamin B, and phytohormone production.15

These traits may predict the potential for synergistic or antagonistic interactions with other bacteria and phytoplankton.15 This method enables the comprehension of the fundamental principles that regulate community dynamics and assembly.15

Challenges in Microbial Data Analysis

Microbiome research generates complex, large datasets that are difficult to analyze due to their compositional and high-dimensional nature. To manage this, big data technologies and computational tools like ATLAS are recommended, alongside machine learning and deep learning for feature classification and selection.16

Limitations in existing databases and methods also affect accurate taxonomic classification; improved accuracy requires interdisciplinary cooperation and sophisticated computational techniques.16

Additionally, identifying functional gene networks requires scalable bioinformatics software like METABOLIC17 and the integration of multimodal data. Recent computational advances like the NetMoss18 algorithm help identify disease biomarkers, with ongoing research needed to improve data integration and model interpretability.

Bioinformatics and the Future of Microbial Ecology

The application of AI and ML in bioinformatics is revolutionizing the field of microbiome research.19 These technologies enable the analysis of genomic, phenotypic, and environmental data to predict microbial interactions and their impacts, which is crucial for understanding complex microbial-host dynamics and creating innovative solutions.19

AI-driven approaches facilitate rapid drug discovery, precision diagnostics, and personalized treatment plans by predicting microbial compound efficacy and antibiotic resistance.19

Implications of Bioinformatics in Broader Scientific Research

Health, agriculture, and environmental science are just a few of the vital industries where microbiome research has found new and innovative applications thanks to bioinformatics.1

Because of its ability to handle and evaluate intricate biological datasets, it is vital to the advancement of disease prevention and therapy in the medical field through improved diagnostics and therapeutic development.1

Bioinformatics helps agriculture adopt more sustainable methods by clarifying the mutually beneficial relationships between plants and soil microbes, which reduces the need for chemical inputs.1 According to environmental science, It supports the maintenance and restoration of natural ecosystems, as well as ecosystem monitoring and bioremediation.1

References

  1. Suman J, et al. (2022). Microbiome as a Key Player in Sustainable Agriculture and Human Health. Frontiers in Soil Science, 2. https://doi.org/10.3389/fsoil.2022.821589
  2. Song, L. (2023). Toward Understanding Microbial Ecology to Restore a Degraded Ecosystem. International Journal of Environmental Research and Public Health, 20(5), 4647. https://doi.org/10.3390/ijerph20054647
  3. Howe, A., & Chain, P. S. G. (2015). Challenges and opportunities in understanding microbial communities with metagenome assembly (accompanied by IPython Notebook tutorial). Frontiers in Microbiology, 6. https://doi.org/10.3389/fmicb.2015.00678
  4. Illumina | Sequencing and array solutions to fuel genomic discoveries. (n.d.). [Online] https://www.illumina.com/
  5. Ion Torrent | Thermo Fisher Scientific - IE. (n.d.).[Online] https://www.thermofisher.com/ve/en/home/brands/ion-torrent.html
  6. NIH Human Microbiome Project - Home. (n.d.). [Online] https://hmpdacc.org/
  7. earthmicrobiome. (n.d.). [Online] https://earthmicrobiome.org/
  8. Global Ocean Sampling Expedition (GOS). (n.d.). [Online] J. Craig Venter Institute. https://www.jcvi.org/research/gos
  9. Bioinformatics Basics: Data Acquisition and Data Wrangling - CD Genomics. (n.d.). [Online] https://bioinfo.cd-genomics.com/bioinformatics-basics-data-acquisition-and-data-wrangling.html#:~:text=Finally%2C%20data%20management%20is%20where%20data%20is%20transferred%20in%20the%20Sequence%20Read%20Archive%20of%20the%20National%20Center%20for%20Biotechnology%20Information%20%28NCBI%2DSRA%29
  10. Gao B, et al. (2021). An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies. Biomolecules, 11(4), 530. https://doi.org/10.3390/biom11040530
  11. Metagenomics Sequencing Guide. (n.d.). [Online] https://genohub.com/shotgun-metagenomics-sequencing/#:~:text=extraction%2C%20library%20preparation%2C%20sequencing%2C%20assembly%2C%20annotation%20and%20statistical%20analysis
  12. Limasset A, et al.(2016). Read mapping on de Bruijn graphs. BMC Bioinformatics, 17(1). https://doi.org/10.1186/s12859-016-1103-9
  13. Maranga M, et al. (2023). Comprehensive Functional Annotation of Metagenomes and Microbial Genomes Using a Deep Learning-Based Method. MSystems, 8(2). https://doi.org/10.1128/msystems.01178-22
  14. van den Bogert B, et al.(2019). On the Role of Bioinformatics and Data Science in Industrial Microbiome Applications. Frontiers in Genetics, 10. https://doi.org/10.3389/fgene.2019.00721
  15. Zoccarato L, et al. (2022). A comparative whole-genome approach identifies bacterial traits for marine microbial interactions. Communications Biology, 5(1). https://doi.org/10.1038/s42003-022-03184-4
  16. Hernández Medina R, et al. (2022). Machine learning and deep learning applications in microbiome research. ISME Communications, 2(1). https://doi.org/10.1038/s43705-022-00182-9
  17. Zhou Z, et al. (2022). METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome, 10(1). https://doi.org/10.1186/s40168-021-01213-8
  18. Xiao, L, et al.(2022). Large-scale microbiome data integration enables robust biomarker identification. Nature Computational Science, 2(5), 307–316. https://doi.org/10.1038/s43588-022-00247-8
  19. Jiang Y, et al. (2022). Machine Learning Advances in Microbiology: A Review of Methods and Applications. Frontiers in Microbiology, 13. https://doi.org/10.3389/fmicb.2022.925454

Further Reading

Last Updated: May 9, 2024

Deliana Infante

Written by

Deliana Infante

I am Deliana, a biologist from the Simón Bolívar University (Venezuela). I have been working in research laboratories since 2016. In 2019, I joined The Immunopathology Laboratory of the Venezuelan Institute for Scientific Research (IVIC) as a research-associated professional, that is, a research assistant.

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