Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.
Elife
; 132024 Jun 04.
Article
en En
| MEDLINE
| ID: mdl-38832759
ABSTRACT
Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Biología Computacional
/
Genómica
/
Metabolómica
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Metagenómica
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Microbiota
Límite:
Humans
Idioma:
En
Revista:
Elife
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos