ALPACA · ALgorithms for PAngenome Computational Analysis

ALgorithms for PAngenome Computational Analysis

Graph-based, instead of sequence-based data structures have decisive benefits with respect to storage, primary analysis, comparison and knowledge extraction when dealing with large, biologically coherent collections of genomes ("pan-genomes"). As a few prominent examples, consider the systematic exploration of the genetic foundations of microbial resistance, the identification of rare diseases, or the complexity of cancer, both on the individual level and on the level of cancer types and subtypes. With genome data rapidly amassing, the urgent need for a shift in computational paradigms, from ordinary sequence-based to graph-based representations of genome collections is no longer deniable: beyond the general acknowledgment of the movement, from which "computational pan-genomics", as a computer science centric area of genomics research emerged, high-impact journals are publishing special genome graph collections thereby recognizing the importance of computer science. The main objective of the project is leading the paradigm shift from sequence- to graph-based representations of genomes. We will provide new graph-based representations of evolutionarily related collections of genomes, together with the computational operations that implement their practical benefits, which is instrumental for leveraging the potential of the big genome data (and preventing serious congestion of resources). We will obtain decisive improvements in terms of 1) Redundancy reduction and data compression, 2) The convenient highlighting of commonalities and differences, 3) Visualization, and 4) Comprehensive annotation. To amplify the benefits of those improvements we will provide software implementations of quality competitive with sequence-based software packages in terms of computational complexity. The schematic Figure 1 highlights a large set of operations for which efficient and reliable computational frameworks and algorithms are necessary. In summary, the ITN will raise a new class of researchers who master the complexity of the era of computational pan-genomics, and thus required to bring along an innovative, unique set of skills: being both highly interdisciplinary and multi-specialized, able to address problems ranging from fundamental algorithms and data structures to software development, big data management and analysis, statistics and machine learning, all closely entangled with genomics and genetics, and bioinformatics in general, and able to bring together the academic and industrial sectors engaged in related business. This explains why interdisciplinary and intersectoral training of ESRs is a compelling necessity for the future development of computational pan-genomics.

Data: CORDIS, © European Union