Dataset Information

Tree of life based on genome context networks.

ABSTRACT: Efforts in phylogenomics have greatly improved our understanding of the backbone tree of life. However, due to the systematic error in sequence data, a sequence-based phylogenomic approach leads to well-resolved but statistically significant incongruence. Thus, independent test of current phylogenetic knowledge is required. Here, we have devised a distance-based strategy to reconstruct a highly resolved backbone tree of life, on the basis of the genome context networks of 195 fully sequenced representative species. Along with strongly supporting the monophylies of three superkingdoms and most taxonomic sub-divisions, the derived tree also suggests some intriguing results, such as high G+C gram positive origin of Bacteria, classification of Symbiobacterium thermophilum and Alcanivorax borkumensis in Firmicutes. Furthermore, simulation analyses indicate that addition of more gene relationships with high accuracy can greatly improve the resolution of the phylogenetic tree. Our results demonstrate the feasibility of the reconstruction of highly resolved phylogenetic tree with extensible gene networks across all three domains of life. This strategy also implies that the relationships between the genes (gene network) can define what kind of species it is.

SUBMITTER: Ding G

PROVIDER: S-EPMC2566592 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Tree of life based on genome context networks.

Ding Guohui G Yu Zhonghao Z Zhao Jing J Wang Zhen Z Li Yun Y Xing Xiaobin X Wang Chuan C Liu Lei L Li Yixue Y

PloS one 20081009 10

Efforts in phylogenomics have greatly improved our understanding of the backbone tree of life. However, due to the systematic error in sequence data, a sequence-based phylogenomic approach leads to well-resolved but statistically significant incongruence. Thus, independent test of current phylogenetic knowledge is required. Here, we have devised a distance-based strategy to reconstruct a highly resolved backbone tree of life, on the basis of the genome context networks of 195 fully sequenced rep ...[more]

PMID: 18852873

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Project description:The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom.

Dataset Information

Tree of life based on genome context networks.

Publications

Tree of life based on genome context networks.

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