Introduction
Genomics is a broad research discipline, covering things from COVID-19 sequencing to human health to the biological diversity which sustains the planet. To support the best possible genomics research, the research system of the future must be more nimble, encouraging rapid integration of changes that happen in this field while being mindful of the mature parts of the science that continue to underpin research capability. There are many ways we can approach this challenge. Here we will look at two key aspects of genomics research as examples: DNA sequencing and computational analyses.
DNA sequencing
In brief, DNA sequencing 'reads' the letters in DNA producing data composed of the base letters A, C, G, and T. There are many technologies that do this job. Each with its own particular best use case. Some of them produce small amounts of very accurate data. Here we are referring to Sanger Sequencing. This is one of the workhorses of molecular biology. The country has this capability now and will continue to need it in the future. There are other technologies (many different flavours) that produce lots and lots of sequence data. Some of these are big and expensive, but we have enough call for to have a reasonable number of in country for shared use. Other types of these instruments would be used so rarely, that a capital investment may not make sense. Still others, are very inexpensive and able to be deployed in the field rather than in a big institutional laboratory. Many more are yet to be invented. A one-size-fits-all policy would serve genomics science very poorly. What could serve genomics research and the communities it supports well is careful exploration of current capabilities and future possibilities within a flexible framework lead by researchers.
Computational analyses
There are many types of analyses done with genomic data. In computational infrastructure, like the different sequencing technologies, one size does not fit all. Our current menagerie of computing infrastructure is mainly based on technologies that are well past their prime. Changing this kind of infrastructure can be difficult due to the appearance of sunk costs and players who are comfortable working the current system. Alternatives to our current model already exist. It is time to empower researchers to choose the best approaches for the work that they are doing and let them push on the cutting edge. This can be done by providing the option of including computational costs in grants, rather than only including them in institutional overheads or a handful of publicly funded national institutions.
Conclusion
For genomics research to reach its potential benefits for all New Zealanders, we must avoid the narrow (and incorrect) a few-sizes-fits-many approaches to DNA sequencing and computational infrastructures. The private and NGO sectors in Aotearoa are already demonstrating how to do this, improving the science and innovating along the way. This is the case for the two example areas of genomics which we describe here and others as well.
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