Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe.

Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations.

Insights into the potential for mutualistic and harmful host-microbe interactions affecting brown alga freshwater acclimation.

Microbes can modify their hosts' stress tolerance, thus potentially enhancing their ecological range. An example of such interactions is Ectocarpus subulatus, one of the few freshwater-tolerant brown algae. This tolerance is partially due to its (un)cultivated microbiome.

Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines.

Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation.

Comparative Transcriptome Analysis Provides Insights into Response of Ulva compressa to Fluctuating Salinity Conditions.

Ulva compressa, a green tide-forming species, can adapt to hypo-salinity conditions, such as estuaries and brackish lakes. To understand the underlying molecular mechanisms of hypo-salinity stress tolerance, transcriptome-wide gene expression profiles in U. compressa were created using digital gene expression profiles.

Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species.

To capture the functional diversity of microbiota, one must identify metabolic functions and species of interest within hundreds or thousands of microorganisms. We present Metage2Metabo (M2M) a resource that meets the need for de novo functional screening of genome-scale metabolic networks (GSMNs) at the scale of a metagenome, and the identification of critical species with respect to metabolic cooperation. M2M comprises a flexible pipeline for the characterisation of individual metabolisms and collective metabolic complementarity.