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Information Extraction (IE) systems have been proposed in recent years, to extract genic interactions from bibliographical re-sources. But they are limited to single in-teraction relations, and have to face a trade-off between recall and precision, by focus-ing either on specific interactions (for pre-cision), or general and unspecified inter-actio...
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... a protein results from the expression of a gene ("product of"), and a protein complex results from the assembly of sev- eral proteins ("complex with"). Figure 2 shows, on an example sentence, the result of the IE sys- tem provided as instances of the ontology. Note that, as a normalized representation of the text, not all the meaning is kept: for instance, we do not stress anymore about the "DNA binding" nature of the "GerE" protein; the fact that the transcrip- tion happens from "several" promoters is lost. ...
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Citations
... where Protein, Interaction_Action and Gene are ontology concepts, and obj and subject are syntactic dependencies. Many complex gene interaction cases are handled with the same method including those involving regulon membership and promoter binding (detailed method in [29]). Relation extraction rules are learned by the supervised Inductive Logic Programming method, LP-Propal. ...
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The entire complement of proteins expressed by a genome forms the proteome. The proteome is
organized in structured networks of protein interactions: the interactome. In these networks, most of
the proteins have few interactions whereas a few proteins have many connections: these proteins are
called centres of interactions or hubs.
This thesis focused on an important biological question: understanding the biological function of a
cluster of hubs (CoH), discovered in Bacillus subtilis, and which is located at the interface of several
essential cellular processes: DNA replication, cell division, chromosome segregation, stress response
and biogenesis of the bacterial cell wall.
The partners of the protein of the cluster of hubs were first identified by the technique of two-hybrid in
yeast, which helped us to define it rigorously in a network composed of 287 proteins connected by 787
interactions. This network shows many proteins in a new context, thereby facilitate functional analysis
of individual proteins and links between the major cellular processes.
After conducting a study of the genomic context of genes of the CoH, an integrative biology approach
has been initiated by analyzing heterogeneous transcriptome data available in public databases.
Statistical analysis of these data identified groups of genes co-regulated with the genes of the cluster of
hubs. At first, the analysis of correlations between the expression of genes across various conditions
has been performed on the basis of classical statistics such as the unsupervised classification. This first
analysis allowed us to associate genes in the CoH to functional groups, to validate and to identify
regulons. It also enabled us to highlight the limitations of this approach and the need to resort to
methods allowing identification of the conditions in which genes are co-regulated.
To this end, we have (i) generated transcriptome data to promote the differential expression of genes
coding for proteins CoH and (ii) used bi-clustering methods, to identify groups of genes co -expressed
in a wide range of conditions. This led us to identify associations of expression in specific conditions
among the genes of the CoH.
Therefore, it has been possible to combine two approaches: the study of the transcriptome and the
interactome, both of them were conducted in a systematic manner in the whole genome. The
integration of these two kinds of data allowed us to clarify the functional context of genes of interest
and to make assumptions about the nature of interactions between proteins cluster hub. It appears
finally composed of a few groups of co-expressed proteins (party hubs) which can interact together
and other proteins expressed in an uncorrelated manner (date hubs). The CoH could form a large group
of date hubs whose function could be to ensure the connection between basic cellular processes,
whatever the environmental conditions B. subtilis could be exposed.
Generation and processing of such a data set is a major scientific challenge, it require the mobilization
of skills, knowledge, and tools to access to a better understanding of living organisms. The constituted
data set may be used to implement other statistical methods. All of this will provide methods to
ultimately extract information from large data sets which are currently produced. This is the major
issue of integrative biology.
