Ponds
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA895046 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR22101000_1.fastq.gz | Fastqsanger.gz | |||
| SRR22101000_2.fastq.gz | Fastqsanger.gz | |||
| SRR22101001_1.fastq.gz | Fastqsanger.gz | |||
| SRR22101001_2.fastq.gz | Fastqsanger.gz | |||
| SRR22101002_1.fastq.gz | Fastqsanger.gz |
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Project description:<p><strong>BACKGROUND:</strong> The human intestinal microbiome plays a central role in overall health status, especially in early life stages. 16S rRNA amplicon sequencing is used to profile its taxonomic composition; however, multiomic approaches have been proposed as the most accurate methods for study of the complexity of the gut microbiota. In this study, we propose an optimized method for bacterial diversity analysis that we validated and complemented with metabolomics by analyzing fecal samples.</p><p><strong>METHODS:</strong> Forty-eight different analytical combinations regarding (1) 16S rRNA variable region sequencing, (2) a feature selection approach, and (3) taxonomy assignment methods were tested. A total of 18 infant fecal samples grouped depending on the type of feeding were analyzed by the proposed 16S rRNA workflow and by metabolomic analysis.</p><p><strong>RESULTS:</strong> The results showed that the sole use of V4 region sequencing with ASV identification and VSEARCH for taxonomy assignment produced the most accurate results. The application of this workflow showed clear differences between fecal samples according to the type of feeding, which correlated with changes in the fecal metabolic profile.</p><p><strong>CONCLUSION:</strong> A multiomic approach using real fecal samples from 18 infants with different types of feeding demonstrated the effectiveness of the proposed 16S rRNA-amplicon sequencing workflow.</p>
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