Dataset Information

Cross-species cluster co-conservation: a new method for generating protein interaction networks.

ABSTRACT: Co-conservation (phylogenetic profiles) is a well-established method for predicting functional relationships between proteins. Several publicly available databases use this method and additional clustering strategies to develop networks of protein interactions (cluster co-conservation (CCC)). CCC has previously been limited to interactions within a single target species. We have extended CCC to develop protein interaction networks based on co-conservation between protein pairs across multiple species, cross-species cluster co-conservation.

SUBMITTER: Karimpour-Fard A

PROVIDER: S-EPMC2375023 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cross-species cluster co-conservation: a new method for generating protein interaction networks.

Karimpour-Fard Anis A Detweiler Corrella S CS Erickson Kimberly D KD Hunter Lawrence L Gill Ryan T RT

Genome biology 20070101 9

Co-conservation (phylogenetic profiles) is a well-established method for predicting functional relationships between proteins. Several publicly available databases use this method and additional clustering strategies to develop networks of protein interactions (cluster co-conservation (CCC)). CCC has previously been limited to interactions within a single target species. We have extended CCC to develop protein interaction networks based on co-conservation between protein pairs across multiple sp ...[more]

PMID: 17803817

Similar Datasets

Project description:Copper mining tailings are characterized by high concentrations of heavy metals and an acidic pH, conditions that require an extreme adaptation for any organism. Currently, several bacterial species have been isolated and characterized from mining environments; however, very little is known about the structure of microbial communities and how their members interact with each other under the extreme conditions where they live. This work generates a co-occurrence network, representing the bacterial soil community from the Cauquenes copper tailing, which is the largest copper waste deposit worldwide. A representative sampling of six zones from the Cauquenes tailing was carried out to determine pH, heavy metal concentration, total DNA extraction, and subsequent assignment of Operational Taxonomic Units (OTUs). According to the elemental concentrations and pH, the six zones could be grouped into two sectors: (1) the "new tailing," characterized by neutral pH and low concentration of elements, and (2) the "old tailing," having extremely low pH (~3.5) and a high concentration of heavy metals (mainly copper). Even though the abundance and diversity of species were low in both sectors, the Pseudomonadaceae and Flavobacteriaceae families were over-represented. Additionally, the OTU identifications allowed us to identify a series of bacterial species with diverse biotechnological potentials, such as copper bioleaching and drought stress alleviation in plants. Using the OTU information as a template, we generated co-occurrence networks for the old and new tailings. The resulting models revealed a rearrangement between the interactions of members living in the old and new tailings, and highlighted conserved bacterial drivers as key nodes, with positive interactions in the network of the old tailings, compared to the new tailings. These results provide insights into the structure of the soil bacterial communities growing under extreme environmental conditions in mines.

Project description:BackgroundOne of the biggest challenges in conservation is to manage multiple habitats for the effective conservation of multiple species, especially when the focal species are mobile and use multiple resources across heterogeneous protected areas. The application of ecological network tools and the analysis of the resulting species-habitat networks can help to describe such complex spatial associations and improve the conservation of species at the landscape scale.MethodsTo exemplify the application of species-habitat networks, we present a case study on butterflies inhabiting multiple grassland types across a heterogeneous protected area in North-East Italy. We sampled adult butterflies in 44 sites, each belonging to one of the five major habitat types in the protected area, that is, disturbed grasslands, continuous grasslands, evolved grasslands, hay meadows and wet meadows. First, we applied traditional diversity analyses to explore butterfly species richness and evenness. Second, we built and analyzed both the unipartite network, linking habitat patches via shared species, and the bipartite network, linking species to individual habitat patches.Aims(i) To describe the emerging properties (connectance, modularity, nestedness, and robustness) of the species-habitat network at the scale of the whole protected area, and (ii) to identify the key habitats patches for butterfly conservation across the protected area, that is, those supporting the highest number of species and those with unique species assemblages (e.g., hosting specialist species).ResultsThe species-habitat network appeared to have a weak modular structure, meaning that the main habitat types tended to host different species assemblages. However, the habitats also shared a large proportion of species that were able to visit multiple habitats and use resources across the whole study area. Even butterfly species typically considered as habitat specialists were actually observed across multiple habitat patches, suggesting that protecting them only within their focal habitat might be ineffective. Our species-habitat network approach helped identifying both central habitat patches that were able to support the highest number of species, and habitat patches that supported rare specialist species.

Dataset Information

Cross-species cluster co-conservation: a new method for generating protein interaction networks.

Publications

Cross-species cluster co-conservation: a new method for generating protein interaction networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets