Project description
Graph-based representation of the genome sequence data
Modern sequencing technology produces genome sequence data on a gigantic scale reaching into exabytes. The emerging urgent question is how these volumes of data could be arranged and analysed in a computationally efficient and biomedically meaningful manner. This EU-funded project is going to explore graph-based representation of large genome datasets and determine their advantages over traditional sequence-based presentation of pan-genomic data. Genomes that are evolutionarily close vary only a little and graph-based pan-genomic representation allows to remove redundancies while highlighting important differences. The research is going to demonstrate the advantage of the shift to the new data representation approach using comparative analysis, compression, integration and exploitation of genome data as the fundamental points.
Objective
Genomes are strings over the letters A,C,G,T, which represent nucleotides, the building blocks of DNA. In view of ultra-large amounts of genome sequence data emerging from ever more and technologically rapidly advancing genome sequencing devices—in the meantime, amounts of sequencing data accrued are reaching into the exabyte scale—the driving, urgent question is: how can we arrange and analyze these data masses in a formally rigorous, computationally efficient and biomedically rewarding manner?
Graph based data structures have been pointed out to have disruptive benefits over traditional sequence based structures when representing pan-genomes, sufficiently large, evolutionarily coherent collections of genomes. This idea has its immediate justification in the laws of genetics: evolutionarily closely related genomes vary only in relatively little amounts of letters, while sharing the majority of their sequence content. Graph-based pan-genome representations that allow to remove redundancies without having to discard individual differences, make utmost sense. In this project, we will put this shift of paradigms—from sequence to graph based representations of genomes—into full effect. As a result, we can expect a wealth of practically relevant advantages, among which arrangement, analysis, compression, integration and exploitation of genome data are the most fundamental points. In addition, we will also open up a significant source of inspiration for computer science itself.
Fields of science
Keywords
Programme(s)
Coordinator
20126 MILANO
Italy