Computational multiplexing to optimise next-generation sequencing

Descripción del proyecto

Democratización de la secuenciación de nueva generación con un nuevo marco computacional

La secuenciación de nueva generación (SNG) ha hecho por la medicina y la biología lo que los transistores por los circuitos electrónicos y la potencia de cálculo. El número de muestras secuenciadas de ácidos nucleicos se duplica cada dos años. Sin embargo, aunque el rendimiento ha aumentado espectacularmente, apenas se ha abordado la preparación de bibliotecas de secuencias y los métodos limitados para hacerlo son caros y requieren mucho tiempo. El equipo del proyecto MultiSeq, financiado por el Consejo Europeo de Investigación, pretende avanzar en su solución bioinformática a estos problemas para democratizar la SNG. La solución consiste en una estrategia de multiplexación para reducir el número de bibliotecas, seguida de una demultiplexación computacional. Los algoritmos se integrarán en un marco informático y se pondrán a prueba en un entorno industrial. Además, se investigarán los requisitos de comercialización.

Objetivo

Recent advances in Next-Generation Sequencing (NGS) of nucleic acids (i.e. DNA or RNA) have transformed biology and medicine. Today NGS is one of the main pillars of research in various biological disciplines, and it has already pervaded numerous fields of applications ranging from clinics to the biotechnological industry. Given its versatility and high demand, the global market for NGS is rapidly expanding, with the number of sequenced samples doubling every two years. However, while major advances in NGS were mainly related to a rapid increase in sequencing throughput per machine, the preparation of sequencing libraries - the other
integral step of NGS, has largely remained unchanged. Currently, this step is the major financial and operational bottleneck for sequencing projects, limiting the widespread adoption and utility of NGS. Current state-of-the-art solutions for overcoming these problems either require high upfront costs and/or are laborious. We are developing a bioinformatics solution to these problems, which minimizes the cost and time of library preparation. Our approach, called MultiSeq, allows designing a multiplexing strategy to reduce the number of libraries followed by computational demultiplexing. We plan to extend the experimental proof of concept of our method by applying it to broadly sequenced species. In addition, we will integrate our algorithms into a versatile computing framework and develop a pilot project in an industrially relevant context. In parallel, we will perform market analysis and evaluate the most suitable IP protection and commercialization strategies of our technology. If successful, MultiSeq will be a game-changing approach that will impact sequencing technology and related industries by further democratizing the field of NGS and benefiting both the scientific community and society.