[Methods, challenges and opportunities for big data analyses of microbiome]

Area of Science:

• Microbiome research
• Genomics
• Bioinformatics

Background:

• The human microbiome is increasingly linked to chronic inflammatory diseases.
• Understanding microbial communities is crucial for human health.
• Novel research avenues are emerging in this field.

Purpose of the Study:

• To outline the primary analytical approaches for microbiome research.
• To highlight the future directions and challenges in microbiome data analysis.
• To emphasize the potential of microbiome studies in disease diagnosis and therapy.

Main Methods:

• Metataxonomic analysis via 16S rRNA sequencing.
• Metagenomic analysis using shotgun sequencing.
• Bioinformatic pipelines including quality control, diversity, taxonomy, gene annotation, and functional analyses.

Main Results:

• Two major technical approaches exist: 16S rRNA sequencing and shotgun metagenomics.
• Sequencing cost reduction will shift focus to big data analysis.
• Standardization, data accumulation, modeling, and disease prediction are key future tasks.

Conclusions:

• Future microbiome research will concentrate on big data challenges and functional verification.
• Understanding the human microbiome offers significant opportunities for disease diagnosis and therapeutic strategies.
• Interdisciplinary collaboration is essential for advancing microbiome science.