Project description
Genomic computing: a foundational approach to bioinformatics
Harnessing high-throughput next-generation sequencing technologies and bioinformatics, scientists have answered targeted questions about how certain DNA sequences control aspects of biology. However, much as the Human Genome Project was a fundamental research project not targeting a specific disease or population, computational genomics must move toward a fundamental bottoms-up analysis of the information hidden in the voluminous repositories of well-curated sequence data. The European Research Council-funded GeCo project will expedite this process. It will provide data abstractions and technological solutions, improving cooperation between research and clinical networks. GeCo will also enable open access to new processed data repositories and create a replicable method for genomic data management with user-friendly technology that supports clinicians and biologists.
Objective
Next-generation sequencing technology has dramatically reduced the cost and time of reading the DNA. Huge investments are targeted to sequencing the DNA of large populations, and repositories of well-curated sequence data are being collected. Answers to fundamental biomedical problems are hidden in these data, e.g. how cancer arises, how driving mutations occur, how much cancer is dependent on environment. But genomic computing has not comparatively evolved. Bioinformatics has been driven by specific needs and distracted from a foundational approach; hundreds of methods solve individual problems, but miss the broad perspective.
The objective of GeCo is to rethink genomic computing through the lens of basic data management. We will first design the data model, using few general abstractions that guarantee interoperability between existing data formats. Next, we will design a new-generation query language inspired by classic relational algebra and extended with orthogonal, domain-specific abstractions for genomics. Query processing will trace metadata and computation steps, opening doors to the seamless integration of descriptive statistics and high-level data analysis (e.g. DNA region clustering and extraction of regulatory networks).
Genomic computing is a “big data” problem, therefore we will also achieve computational efficiency by using parallel computing on both clusters and public clouds; the choice of a suitable data model and of computational abstractions will boost performance in a principled way. The resulting technology will be applicable to individual and federated repositories, and will be exploited for providing integrated access to curated data, made available by large consortia, through user-friendly search services. Our most far-fetching vision is to move towards an Internet of Genomes exploiting data indexing and crawling. The PI’s background in distributed data, data modelling, query processing and search will drive a radical paradigm shift.
Fields of science
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Funding Scheme
ERC-ADG - Advanced GrantHost institution
20133 Milano
Italy