A culture-independent approach, supervised machine learning, and the characterization of the microbial community composition of coastal areas across the Bay of Bengal and the Arabian Sea
Rattachement africain : in. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
BACKGROUND: Coastal areas are subject to various anthropogenic and natural influences. In this study, we investigated and compared the characteristics of two coastal regions, Andhra Pradesh (AP) and Goa (GA), focusing on pollution, anthropogenic activities, and recreational impacts. We explored three main factors influencing the differences between these coastlines: The Bay of Bengal's shallower depth and lower salinity; upwelling phenomena due to the thermocline in the Arabian Sea; and high tides that can cause strong currents that transport pollutants and debris. RESULTS: The microbial diversity in GA was significantly higher than that in AP, which might be attributed to differences in temperature, soil type, and vegetation cover. 16S rRNA amplicon sequencing and bioinformatics analysis indicated the presence of diverse microbial phyla, including candidate phyla radiation (CPR). Statistical analysis, random forest regression, and supervised machine learning models classification confirm the diversity of the microbiome accurately. Furthermore, we have identified 450 cultures of heterotrophic, biotechnologically important bacteria. Some strains were identified as novel taxa based on 16S rRNA gene sequencing, showing promising potential for further study. CONCLUSION: Thus, our study provides valuable insights into the microbial diversity and pollution levels of coastal areas in AP and GA. These findings contribute to a better understanding of the impact of anthropogenic activities and climate variations on biology of coastal ecosystems and biodiversity.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A culture-independent approach, supervised machine learning, and the characterization of the microbial community composition of coastal areas across the Bay of Bengal and the Arabian Sea
- Date Crossref
- 10/05/2024
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Yenepoya University pays non établi dans la noticeUniversité ou école supérieure
-
National Centre for Cell Science pays non établi dans la noticeStructure de recherche
-
Yenepoya Research Centre pays non établi dans la noticeStructure de recherche
-
Savitribai Phule Pune University NCCS-Complex pays non établi dans la noticeUniversité ou école supérieure
-
Agharkar Research Institute Bioenergy Group pays non établi dans la noticeStructure de recherche
-
Yenepoya (Deemed to be University) MicrobeAI Lab pays non établi dans la noticeUniversité ou école supérieure
-
Gut Microbiology Research Division pays non établi dans la noticeInstitution
Yenepoya University, National Centre for Cell Science et Yenepoya Research Centre, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.