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.

There is an enormous and unmet medical need to find efficient methods of prevention, diagnosis and disease- modifying therapies for neurodegenerative disorders, including Alzheimer’s disease (AD), other tauopathies and Parkinson’s disease (PD). The common molecular denominator of tauopathies are pathological forms of tau protein, and in Parkinson’s disease these are pathological forms of -synuclein. Moreover, -synuclein has a distinct role in pathophysiology of tauopathies, mainly in tau hyperphosphorylation and aggregation, and vice versa. Tau pathology relates to conformational changes during oligomerization and assembly resulting in toxicity. Given their role in the pathogenesis, conformationally altered and assembled tau or -synuclein would be a promising molecular target for disease-modifying therapies. However, the field is still lacking a deeper understanding of associated structural changes in the course of assembly and their inducers on the pathway towards pathological forms of these proteins; therefore, the pharma development is hampered. The main aim of the InterTau project is the detailed structural and biophysical characterization of tau and -synuclein -synuclein protein and their variants in monomeric, oligomeric and fibrillar states relevant for AD, other tauopathies. The InterTAU consortium is composed and academic partners with cutting- edge methodologies suitable for functional and structural characterization of the tau assembly pathway by solution and solid-state nuclear magnetic resonance (NMR), cryo-electron microscopy and cellular assays corroborated by bioinformatics. The mutual transfer of complementary expertise envisaged in the project will facilitate academic outcome and biotechnological development. Specific expertise will be transferred from three institutions in North America and one institution from Argentina. The results of InterTAU will be directly translated into innovation in biotech through the non-academic partner. The platform for sharing knowledge will be a foundation of sustainable cooperation beyond the InterTau project.

More than 55 million people worldwide suffer from dementia. Alzheimer disease (AD) is the main cause of this fatal disorder, without any effective disease modifying therapy. Early diagnosis and lifestyle modifications can significantly reduce the costs of care and treatment. There is no conceptual plan implementing modern diagnostic methods in the clinical practice in Czechia and Slovakia. The interaction between universities and private sector developing molecular diagnostic tools is fragmented and lacking. Limited number of talented students are invested in applied AD-focused research. The aim of ADDIT-CE is to interlink two ecosystems in Brno and Bratislava region, embracing the full quadruple helix of innovation driving actors: excellent scientific teams from Masaryk University and Slovak Academy of Sciences, collaborating with top biotech companies: Geneton, BioVendor, and MultiplexDX. Societal actors will be represented by organisations such as Slovak and Czech Alzheimer Societies, Memory Center and Czech Brain Aging Study. The regional government will be involved via Ministry of Health Slovak Republic, and South Moravian Innovation Centre. The joined ecosystems will unite R&I activities focusing on new diagnostic methods and their applications and further interlink academia and business spheres by creating a pilot industrial PhD programme. ADDIT-CE will generate a joint cross-border strategy covering basic and applied research activities aiming on accelerating the development of new tools for preclinical AD diagnostics and lifestyle/pharmacological intervention monitoring. New cutting-edge technologies will be transferred into clinical practise. Results of ADDIT-CE will be used to develop the Slovak National Plan to Combat Dementia, to enrich the Czech National Plan for AD, and will be widely disseminated to end users and society. ADDIT-CE will join forces of the involved ecosystems to revolutionise diagnostic approaches in both countries.

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. For realizing our goals, our network will (i) decisively strengthen and form new ties in the emerging community of computational pan-genomics, (ii) perform research on all relevant frontiers, aiming at significant computational advances at the level of important breakthroughs, and (iii) boost relevant knowledge exchange between academia and industry. Last but not least, in doing so, we will train a new, “paradigm-shift-aware” generation of computational genomics researchers.

We propose an ambitious yet well-conceived and deliverable pan-European, pan-cancer, pan-disciplinary, and multi-omic approach to address the pressing unmet need for an accurate, non-invasive, acceptable and cost-effective method of detecting precancerous and early-stage cancers in those individuals with Lynch syndrome (LS), the most common monogenetic increased hereditary cancer risk. LS has historically been underfunded and underserved, leading to significant an inequality in access and treatment. As a result, LS carriers have suffered needless cancers and deaths as a result. Our consortium has brought together the leading European experts, biotechnology companies and patient advocates to guarantee deliver practice-changing results that can be rapidly upscaled and adopted across the European Union and globally. Using an innovative clinical trial design, we will evaluate several multiple promising, non-invasive, liquid biopsy-based technologies in the three most common LS cancer types for an early-stage cancers detection. By leveraging Artificial intelligence (AI), we will identify traces of cancer, ensuring applicability to diverse healthcare systems. A comprehensive framework will assess the broader socio-economic and ethical impacts, ensuring that the solutions align with the societal values and healthcare needs. Parterning with leading biomarker companies (GNT, MSInsight, MSICare, MSIPlus and Elypta), we aim to deliver a multi-omic solution for affordable, accessible and effective test to advance the detection of heritable cancer detections in LS. This action is part of the Cancer Mission cluster of projects on “Prevention & early detection (early detection heritable cancers).