Project description:Microbial communities in the rhizosphere make significant contributions to crop health and nutrient cycling. However, their ability to perform important biogeochemical processes remains uncharacterized. Important functional genes, which characterize the rhizosphere microbial community, were identified to understand metabolic capabilities in the maize rhizosphere using GeoChip 3.0-based functional gene array method.
Project description:Microbial communities in the rhizosphere make significant contributions to crop health and nutrient cycling. However, their ability to perform important biogeochemical processes remains uncharacterized. Important functional genes, which characterize the rhizosphere microbial community, were identified to understand metabolic capabilities in the maize rhizosphere using GeoChip 3.0-based functional gene array method. Triplicate samples were taken for both rhizosphere and bulk soil, in which each individual sample was a pool of four plants or soil cores. To determine the abundance of functional genes in the rhizosphere and bulk soils, GeoChip 3.0 was used.
Project description:Microbial communities in the rhizosphere make significant contributions to crop health and nutrient cycling. However, their ability to perform important biogeochemical processes remains uncharacterized. Important functional genes, which characterize the rhizosphere microbial community, were identified to understand metabolic capabilities in the maize rhizosphere using GeoChip 3.0-based functional gene array method. Triplicate samples were taken for both rhizosphere and bulk soil, in which each individual sample was a pool of four plants or soil cores. To determine the abundance of functional genes in the rhizosphere and bulk soils, GeoChip 3.0 was used.
Project description:Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of target identifications from decoy identifications by database searching and filtering algorithms in metaproteomics. Sipros Ensemble was developed here for metaproteomics using an ensemble approach to addressing this challenge.