M2P 2023

Genogra: Next-generation genome analysis platform

  • Di Donato, Guido Walter (GenoGra )
  • Zeni, Alberto (DEIB)
  • Coggi, Mirko (DEIB)
  • Bruno, Guglielmo (DEIB)
  • Santambrogio, Marco Domenico (DEIB)

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Computational genomics is a branch of bioinformatics with multiple applications in the medical, pharmaceutical, zootechnical, agrotechnical, and biochemical fields. Despite the variety of possible applications, all disciplines that exploit the process of genomic analysis rely on workflows that include three steps: primary, secondary, and tertiary analysis. The incredible increase in genomic data throughput enabled by Next Generation Sequencing technologies has made secondary analysis the main bottleneck of the analysis workflow, dramatically increasing its cost impact. This is because current string-based secondary analysis tools have several limitations in terms of standardization, integration, analysis time, and quality of results. Although the companies in the field have proposed solutions that attempt to solve some of these problems, string-based secondary analysis process is still fragmented into multiple steps and, consequently, too time-consuming, difficult, and errorprone: this prevents the application of genome analysis on a large scale. In response to these problems, GenoGra is developing the first genome analysis platform based on genome graphs, to enable a much simpler, efficient, and scalable analysis flow. Genome graphs [1] are networks of interconnected genomic sequences, which enable a compact and accurate representation of the inter-individual and intra-individual variability of the genome. However, despite the advantages in terms of quality and scalability of the analysis, graph-based tools are not widespread yet. This is due to the lack of user-friendly optimized solutions that can provide high performance of analysis while guaranteeing a good ease of use. In this context, GenoGra's platform offers end-to-end solutions for graph-based genome analysis, both for diagnostic and research purposes, providing access to simple yet highly efficient analysis tools that completely mask the complexity of underlying hardware acceleration to end users. This is possible thanks to GenoGra’s core technology: a proprietary “search engine” [2] for sequences of interest within the genome graphs, which leverages hardware acceleration on Graphics Processing Units (GPUs). This talk provides an overview of GenoGra’s technology and its fields of application, with a focus on the path that made a research work become the technological innovation leading to the creation of a startup.