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Semantic methods for the cross-species metabolic pathways comparison : application to human, mice and chicken lipid metabolism
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Cross-species comparison of metabolic pathways is an important task in biology. It is a major stake for both human health and agronomy. Currently, knowledge is acquired from some experiments on a relatively low number of species referred to as "models". A better understanding of a species determines whether to validate or not an inference made from these experimental data. It also determines whether or to what extent results obtained on model species can be transposed to another species. This thesis proposes a cross-species metabolic pathways comparison method. Our method compares each step of a metabolic pathway using the associated Gene Ontology annotations. This work (i) validates the interest of the semantic similarity measures for interpreting these annotations, (ii) proposes to use jointly a semantic particularity measure and (iii) proposes a method based on similarity and particularity patterns to interpret each metabolic pathway step. We applied the different steps of this approach to the comparative study of lipid metabolism for human, mice and chicken. Several gene products are involved throughout a metabolic pathway. They are asso- ciated to some annotations in order to describe their biological roles. Based on a shared ontology, these annotations allow to compare data from different species and to take into account several level of abstraction. Several semantic measures quantifying the similarity between gene products from their annotations have been developed previously. We have identified and used a semantic similarity measure appropriate for cross-species compari- sons. Because they focus on the common part of the compared gene products, the semantic similarity measures ignore their specific characteristics. Therefore, cross-species meta- bolic pathways comparison has to quantify not only the similarity of the gene products involved, but also their particularity. We have developed a semantic particularity measure addressing this issue. For each pathway step, we proposed to create a profile combining its semantic similarity and its two semantic particularity values. Concerning the results interpretation, it is not possible to establish formally that two gene products are similar or that one of them have some significant particularities without having a similarity threshold and a particularity threshold. So far, these interpretations were based on an implicit or an arbitrary threshold. To address this gap, we developed a threshold definition method for the semantic similarity and particularity measures. We last applied a cross-species similarity measure and our particularity measure to compare the lipid metabolism between human, mice and chicken. We then interpreted the results using the previously defined thresholds. In all three species, we observed some particularities, including on similar genes. They concerned notably some biological pro- cesses and cellular components. The molecular functions present a strong similarity and few particularities. These results are biologically relevant.
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Ontologies and taxonomies are among the most important computational resources for molecular biology and bioinformatics. A series of recent papers has shown that the Gene Ontology (GO), the most prominent taxonomic resource in these fields, is marked by flaws of certain characteristic types, which flow from a failure to address basic ontological principles. As yet, no methods have been proposed which would allow ontology curators to pinpoint flawed terms or definitions in ontologies in a systematic way.
We present computational methods that automatically identify terms and definitions which are defined in a circular or unintelligible way. We further demonstrate the potential of these methods by applying them to isolate a subset of 6001 problematic GO terms. By automatically aligning GO with other ontologies and taxonomies we were able to propose alternative synonyms and definitions for some of these problematic terms. This allows us to demonstrate that these other resources do not contain definitions superior to those supplied by GO.
Our methods provide reliable indications of the quality of terms and definitions in ontologies and taxonomies. Further, they are well suited to assist ontology curators in drawing their attention to those terms that are ill-defined. We have further shown the limitations of ontology mapping and alignment in assisting ontology curators in rectifying problems, thus pointing to the need for manual curation.
The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.