Algorithms for Structural Variation Analysis in Challenging Genomic Regions

Project Information

ASVA-CGR

Grant agreement ID: 101180581

DOI

10.3030/101180581

Project closed

EC signature date 25 June 2024

Start date 1 September 2024

End date 31 August 2026

Funded under

• Widening participation and spreading excellence

Total cost

No data

EU contribution

€ 149 219,52

Coordinated by

UNIVERZITA KOMENSKEHO V BRATISLAVE
Slovakia

Periodic Reporting for period 1 - ASVA-CGR (Algorithms for Structural Variation Analysis in Challenging Genomic Regions)

Reporting period: 2024-09-01 to 2026-08-31

Summary of the context and overall objectives of the project

The project aimed to design algorithms and develop software tools to facilitate and improve genomic analyses of structural variations from sequencing datasets. Accurate SV characterization plays a key role in unveiling their effects on human health, including their links with genetic diseases and tumors. Accordingly, our work pursued two complementary objectives: (i) the development of a new software tool for the discovery of cancer-related structural variations and (ii) an investigation of current best practices, along with their limitations, for benchmarking SV callers. The project aimed to address questions such as: How can we more effectively characterize somatic structural variations? And how can we improve the structural variations evaluation and benchmarking practices?

In parallel, we investigated how pangenomics can help us achieve our goal of characterizing structural variations. Relying on a single genome reference can fail to capture human genetic diversity, introducing biases that may hinder the discovery of rare variants and reduce the accuracy of downstream analyses. For these reasons, bioinformatics as a field is transitioning from the use of a single linear reference genome to pangenome references, which capture genetic diversity across multiple individuals. In this project, we focused on designing data structures and algorithms that enable the practical use of pangenome graphs within existing genomic analysis pipelines and on developing software tools that leverage this framework to improve the analysis of sequencing data. With these developments, pangenome graphs can improve our understanding of structural variations.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

As of August 31, 2026, the project has produced five peer-reviewed publications and two preprints. Three additional manuscripts are currently in preparation. A key outcome of the project is the development of open-source software tools that are now available for use by the broader scientific community. As a key component of the project, we investigated current best practices used to evaluate software tools for structural variations discovery. Our analysis highlighted several pitfalls that can hinder the benchmarking of these tools. To overcome these limitations and to streamline SV callers evaluation, we introduced a modular and extensible framework for SV benchmarking. We also developed a new somatic structural variations caller and collaborated in the creation of a new benchmark dataset for small and structural variants. Keeping the context of pangenomics in mind, we also developed (i) a tool for quantifying alternative splicing events from spliced pangenome graphs, (ii) data structures for indexing pangenome graphs, (iii) a tool for augmenting an existing pangenome graph with novel variants supported by a newly sequenced individual not already present in the pangenome, and (iv) several algorithms for pangenome graph personalization (selecting parts of a pangenome graph mostly likely to match a studied individual).

Over the past two years, we also collaborated with other researchers on projects related to detecting differences between sequencing datasets, clustering third-generation transcriptomic datasets, and indexing massive bacterial databases. The project has contributed significantly to strengthening international collaborations between the host institution (Comenius University in Bratislava) and the University of California-Davis and the University of Milano-Bicocca. These partnerships were central to the project’s success. Through close collaboration with both experienced researchers and early-stage researchers, we were able to meet the project objectives and improve the quality of the resulting work.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

In the context of this project, our main contribution consists of a systematic study of open challenges in the evaluation of structural variations callers. Through this work, we identified several often overlooked pitfalls that can bias SV benchmarking and we proposed field-standard best practices that can aid researchers in evaluating their software tools. In addition, the new benchmarking dataset we helped create is expected to become a cornerstone for assessing SV callers' performance. This will help the bioinformatics community to develop next-generation tools capable of accurately characterizing structural variations, including those that remain insufficiently captured today. Finally, the somatic structural variation caller we are developing achieves very high accuracy, outperforming the top state-of-the-art competitor. Improving the characterization of somatic structural variations in both easy and complex regions of the human genome will shed more light on how these rearrangements contribute to cancer development.

Our contributions in the area of pangenomics were designed specifically to address gaps in the state-of-the-art. Our goal was to provide the scientific community with new methods, data structures, and tools that enable analyses that were previously not possible. First, we developed the first pangenome-based approach for the detection and differential quantification of alternative splicing events. This work represents an initial step toward the adoption of spliced pangenomes, enabling more direct investigations of how genetic variation influences splicing patterns. Second, we introduced the first data structure for indexing pangenome graphs that can scale to large graphs without pruning complex regions and support queries of arbitrary length. Third, we proposed an approach to augment a pangenome graph directly from raw sequencing reads. Unlike state-of-the-art methods that rely on high-quality de novo assemblies, our approach avoids the costly and computationally intensive assembly step and the need for high-coverage data coming from multiple sequencing technologies. Finally, our work on pangenome graph personalization provides a cleaner combinatorial problem formulation, making it easier to analyze theoretically and opening new avenues for further theoretical investigation.