Next generation sequencing (NGS) methods are revolutionizing the ability to
- diagnose current and emerging diseases
- control outbreaks
- understand transmission patterns of pathogens
- identify and prevent health risks associated with food consumption and environment.
NGS offers the potential to complement many currently used complex, multi-step procedures, and even to replace them in the future with a single, more efficient and fast workflow. The challenge, however, lies in quickly extracting and correctly interpreting the necessary information from this large amount of NGS data. This PhD project aims at the development of appropriate pipelines (tools), which can later be used in the routine, for the analysis of NGS data in the field of outbreak investigation and of the detection and identification of authorized and unauthorized GMOs. It is a first step in integrating genomic information into a proactive public health policy.
The typing and detection/identification methods routinely used at Sciensano are efficient. However, without preparing for the new challenges for the future, and thus focusing on more high-throughput, multiplex, and open approach-enabling technologies with a higher resolution, these methods can quickly become obsolete and inadequate. Recent advances and innovations in DNA-sequencing technologies (i.e. next generation sequencing, NGS) are revolutionizing the ability to diagnose current and emerging diseases, to control outbreaks, understand transmission patterns of pathogens as well as to identify and prevent health risks associated with food consumption and environment.
Indeed, NGS has emerged as a cost-effective and convenient approach for addressing many biological questions and therefore, it holds the potential to complement, and in the future even replace, many complex multifaceted procedures that are currently used to support a proactive public health policy, with a single, more efficient and fast workflow. While in the past NGS was exclusively applied as a research tool, with the continuous evolution of this technology, it will soon become sufficiently fast, accurate and cheap to be used in routine practice. However, the challenge will lay in the ability to rapidly analyze the massive amount of NGS data obtained in order to extract and interpret the required information correctly. This will strongly determine the success of the use of NGS technologies.
This PhD project aims at the development of adapted pipelines (tools), which can later be used in the routine, for the analysis of NGS data. Two case studies from ongoing activities at Sciensano were selected for which the NGS data analysis poses a challenge:
- The first data analysis problem is related to the ‘assembly’ of sequence reads and the use of this data for outbreak investigation. It will involve the development and critical evaluation of a cross-platform data assembly tool and SNP detection tool for ‘whole genome sequence’ data from pathogens (case studies Salmonella, Neisseria and E. coli).
- The second is related to the time required for a full NGS data analysis and the size of certain genomes, such as when using NGS for the detection and identification of authorized and unauthorized GMOs. This will involve the development of a searchable database with GM elements, linked to an NGS data analysis tool for GMOs. It is a first step in integrating genomic information into a proactive public health policy.