ABSTRACT
In order to study complex microbial communities and their associated mobile genetic elements, such as the human gut microbiome, evolutionists could explore their genetic diversity with shared sequence networks. In particular, the detection of remarkable structures in gene networks of the gut microbiome could serve to identify important functions within the community, and would ease comparison of data sets from microbiomes of various sources (human, ape, mouse etc.) in a single analysis.
Subject(s)
Evolution, Molecular , Gastrointestinal Tract/microbiology , Genetic Variation , Metagenome , HumansABSTRACT
MOTIVATION: Computational methods are widely used to discover gene-disease relationships hidden in vast masses of available genomic and post-genomic data. In most current methods, a similarity measure is calculated between gene annotations and known disease genes or disease descriptions. However, more explicit gene-disease relationships are required for better insights into the molecular bases of diseases, especially for complex multi-gene diseases. RESULTS: Explicit relationships between genes and diseases are formulated as candidate gene definitions that may include intermediary genes, e.g. orthologous or interacting genes. These definitions guide data modelling in our database approach for gene-disease relationship discovery and are expressed as views which ultimately lead to the retrieval of documented sets of candidate genes. A system called ACGR (Approach for Candidate Gene Retrieval) has been implemented and tested with three case studies including a rare orphan gene disease.