Pipeline for Analyzing Activity of Metabolic Pathways in Planktonic Communities Using Metatranscriptomic Data.
J Comput Biol
; 28(8): 842-855, 2021 08.
Article
en En
| MEDLINE
| ID: mdl-34264744
ABSTRACT
In this article, we present our novel pipeline for analysis of metabolic activity using a microbial community's metatranscriptome sequence data set for validation. Our method is based on expectation-maximization (EM) algorithm and provides enzyme expression and pathway activity levels. Further expanding our analysis, we consider individual enzymatic activity and compute enzyme participation coefficients to approximate the metabolic pathway activity more accurately. We apply our EM pathways pipeline to a metatranscriptomic data set of a plankton community from surface waters of the Northern Gulf of Mexico. The data set consists of RNA-seq data and respective environmental parameters, which were sampled at two depths, six times a day over multiple 24-hour cycles. Furthermore, we discuss microbial dependence on day-night cycle within our findings based on a three-way correlation of the enzyme expression during antipodal times-midnight and noon. We show that the enzyme participation levels strongly affect the metabolic activity estimates that is, marginal and multiple linear regression of enzymatic and metabolic pathway activity correlated significantly with the recorded environmental parameters. Our analysis statistically validates that EM-based methods produce meaningful results, as our method confirms statistically significant dependence of metabolic pathway activity on the environmental parameters, such as salinity, temperature, brightness, and a few others.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Plancton
/
Bacterias
/
Perfilación de la Expresión Génica
/
Redes y Vías Metabólicas
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
J Comput Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
/
INFORMATICA MEDICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos