Paul Thomas - US grants

Thanks to continuing developments in DNA sequencing technology, we now know the exact genetic makeup ("genome") of thousands of different organisms, encoding millions of different proteins, and these numbers continue to grow rapidly. But simply knowing the chemical specification (the "sequence") of these proteins is only a first step: the ultimate goal is to discover how genes and proteins function to support the diversity of life, and also how some of them can be used for commercial and biotechnology applications. This research project will expand the capability of scientists and their students to advance their analyses from sequences to functions, by bringing together multiple different state-of-the-art approaches. Each of these approaches uses both computational (necessary to address a problem of this magnitude) and broad biological expertise.

The general approach in this project is to classify proteins into families of related proteins, and, wherever possible, describe how each family relates to function. These relations may be very complex, and scientific accuracy will require application of multiple, diverse methods. In order to accomplish this aim, the project will expand InterPro, a widely used resource that already contains (though with limited integration) three of the leading databases for protein family and functional classification: PANTHER, Pfam and TIGRFAM. A fourth classification resource, the Structure-Function Linkage Database (SFLD), will also be incorporated into InterPro. These four databases use complementary methodologies to represent and describe protein relationships, which will be integrated to address the problem of protein function classification with unprecedented accuracy, precision and ease-of-use. The products of this work will be used to improve sequence analysis tools that support the scientific community, as well as to provide enhanced educational materials, and will be broadly accessible over the web at http://ebi.ac.uk/interpro.

PROJECT SUMMARY Because of the staggering complexity of biological systems, biomedical research is becoming increasingly dependent on knowledge stored in a computable form. The Gene Ontology (GO) is by far the largest knowledgebase of how genes function, and has become a critical component of the computational infrastructure enabling the genomic revolution. It has become nearly indispensible in the interpretation of large- scale molecular measurements in biological research. Crucially, for human health research, GO is also one of a suite of complementary ontologies constructed in such as way to maximally promote interoperability and comparability of data sets. It represents the gene functions and biological processes that are perturbed in human disease, e.g. via the links from Human Phenotype Ontology (HPO) class abnormality of lipid metabolism, defined in relation to the GO class lipid metabolic process (GO_0006629), researchers or clinicians can find the set of genes that are known to be involved in this process. GO is a knowledge resource that can be statistically mined, either standalone or in combination with data from other knowledge resources, which enables experts to discover connections and form new hypotheses from the biological networks GO represents. All knowledge in GO is represented using semantic web technologies and so is amenable to computational integration and consistency checking. The proposed GO knowledge environment will enable a wider community of scientists to contribute to, and to utilize, a common, computable representation of biology. To ensure the knowledge environment meets the requirements of biomedical researchers, we will: a) deliver a comprehensive, detailed, computable knowledgebase of gene function, encoded in the Gene Ontology and annotations (computer-readable statements about the how specific genes function), focusing on human biology; b) provide a ?hub? for a broad community of scientists to collaboratively extend, correct and improve the knowledgebase; c) ensure the GO knowledge resource is of the highest quality with regards to depth, breadth and accuracy; d) facilitate the transfer of insights obtained from studies of non-human organisms, such as the mouse and zebrafish, to human biology; and e) enable the scientific community to use the knowledgebase in analyses of large-scale genetic and -omics data. Our aims reflect the essential requirements for realizing the overarching objectives for a biomedical data resource: efficiently capturing and integrating biological knowledge and adhering to the highest possible standard for accuracy and detail; constructing and providing a robust, flexible, powerful, and extensible technological infrastructure available not only for internal use but just as easily by the wider community; and lastly, leveraging state-of-the-art social media, web services and other technologies to disseminate the GO resource to the entire biomedical research community.

PROJECT SUMMARY Because of the staggering complexity of biological systems, biomedical research is becoming increasingly dependent on knowledge stored in a computable form. The Gene Ontology (GO) is by far the largest knowledgebase of how genes function, and has become a critical component of the computational infrastructure enabling the genomic revolution. It has become nearly indispensible in the interpretation of large- scale molecular measurements in biological research. Crucially, for human health research, GO is also one of a suite of complementary ontologies constructed in such as way to maximally promote interoperability and comparability of data sets. It represents the gene functions and biological processes that are perturbed in human disease, e.g. via the links from Human Phenotype Ontology (HPO) class abnormality of lipid metabolism, defined in relation to the GO class lipid metabolic process (GO_0006629), researchers or clinicians can find the set of genes that are known to be involved in this process. GO is a knowledge resource that can be statistically mined, either standalone or in combination with data from other knowledge resources, which enables experts to discover connections and form new hypotheses from the biological networks GO represents. All knowledge in GO is represented using semantic web technologies and so is amenable to computational integration and consistency checking. The proposed GO knowledge environment will enable a wider community of scientists to contribute to, and to utilize, a common, computable representation of biology. To ensure the knowledge environment meets the requirements of biomedical researchers, we will: a) deliver a comprehensive, detailed, computable knowledgebase of gene function, encoded in the Gene Ontology and annotations (computer-readable statements about the how specific genes function), focusing on human biology; b) provide a ?hub? for a broad community of scientists to collaboratively extend, correct and improve the knowledgebase; c) ensure the GO knowledge resource is of the highest quality with regards to depth, breadth and accuracy; d) facilitate the transfer of insights obtained from studies of non-human organisms, such as the mouse and zebrafish, to human biology; and e) enable the scientific community to use the knowledgebase in analyses of large-scale genetic and -omics data. Our aims reflect the essential requirements for realizing the overarching objectives for a biomedical data resource: efficiently capturing and integrating biological knowledge and adhering to the highest possible standard for accuracy and detail; constructing and providing a robust, flexible, powerful, and extensible technological infrastructure available not only for internal use but just as easily by the wider community; and lastly, leveraging state-of-the-art social media, web services and other technologies to disseminate the GO resource to the entire biomedical research community.