Scientists have been routinely sequencing DNA since the 1970s, but in the past decade, this area of science has exploded. As sequencers have shrunk in size, DNA datasets have grown exponentially, fueled, in part, by journals' requirements to make data open access. Over the past half-century, these have accumulated into mega, giga, and terabytes of DNA data—the number of reads increasing with each technological update.
We can’t blame scientists for being so focused on sequencing since this technology has been mind-blowingly fantastic. For some time, the main questions were: how does DNA look in an organism and what is its sequence?
Anything can be sequenced now: we can survey the whole forest in one small tube and detect traces of organisms living there. Sometimes, albeit in some very specific places, we can even detect traces of organisms thought extinct. And, of course, we can even sequence you! We can do that right here in our laboratory at MWLR but we won’t do it just now. The more pressing need is to compare all that has been sequenced so far to make new discoveries. But comparing DNA with DNA is not enough unless you’ve got some other METADATA to help explain the similarities or differences you might find between them: that’s the GPS coordinates, collection date, developmental stage and sex of the specimen, as well as many other Xs, Ys and Zs. You’d think scientists being scientists would record all that data, but hey, not all of them do. Yes, some of that is due to the lack of funding, you are correct. Amy’s Vaughan research showed that critical metadata is often absent, halting the promising breakthrough in invasion genomics.
Comparing the prices of two houses sold at opposite ends of the same city will not tell you anything unless you consider such things as bedroom/bathroom ratios, land area, and the year the house was built. The same goes for the DNA data—you can’t compare it even within the same organism if you do not have other information about it, for example, the age of the specimen, the collection date and location, etc.
We know variation exists, not only because we, humans, all look different. But so are the fruit flies and other invasive insects. Even if they do not look very different to us, their genes are. More importantly, some of those genes make some fruit flies more invasive than others. Together with collaborators, Amy Vaughan and Manpreet Dhami have been doing some serious data mining to find out if this genetic variation can enlighten us on something useful – for example, to predict the invasiveness potential of a species. Or, taking a curious spin here – predict the success of reintroductions of endangered species to their former habitat for conservation, i.e. a “good case” of invasion.
Knowing that we have more and more invasive species that arrive in the country more able to establish as our climate continues to change the main question is:
Can DNA predict in advance which species are more invasive than others?
So with a team of collaborators, Amy and Manpreet started looking for any useful “keywords” that went hand-in-hand with higher invasiveness potential. These early studies published very recently by the team 1, 2, 3 are confident such signals exist. The complicated colourful graphs scientists like to look at suggest there is a way for the DNA to inform us if a species has the potential to become invasive, provided there is enough associated metadata available.
To make the most of this DNA data for invasion genomics, Amy, Manpreet and colleagues advocate for the minimum standards, based on the golden principles of CARE and FAIR which ensure that everyone is benefiting and no one is disadvantaged in the complex area of scientific research.
CARE - Guidelines for working with indigenous data:
- C - Collective benefit
- A - Authority of control
- R - Responsibility
- E - Ethics
FAIR - Make research data more discoverable and reusable:
- F - Findable
- A - Accessible
- I - Interoperable
- R - Reusable
You can find more information in Amy’s recent paper (4.) as well as some others, she and Manpreet collaborated on:
- McGaughran A, Dhami MK, Parvizi E, Vaughan AL, et al. Genomic Tools in Biological Invasions: Current State and Future Frontiers, Genome Biology and Evolution, Volume 16, Issue 1, January 2024, evad230, https://doi.org/10.1093/gbe/evad230
- Parvizi E, Vaughan AL, Dhami MK. et al.Genomic signals of local adaptation climatically heterogenous habitats in an invasive tropical fruit fly (Bactrocera tryoni). Heredity132, 18–29 (2024). https://doi.org/10.1038/s41437-023-00657-y
- Vaughan AL, Parvizi E, Matheson P, McGaughran A, & Dhami MK (2023). Current stewardship practices in invasion biology limit the value and secondary use of genomic data. Molecular Ecology Resources, 00, 1–13.
- Vaughan AL, Dhami MK. Can Transcriptomics Elucidate the Role of Regulation in Invasion Success? Mol Ecol. 2024 Nov 15:e17583. doi: 10.1111/mec.17583. Epub ahead of print. PMID: 39545269.
Want to learn more on how genomics can provide tools to understand invasive species dynamics? Come join us at the Invasomics Meeting 2.0 on 17th of February 2025, Wellington, New Zealand:
https://www.invasomics.com/invasomics
Banner image credit: Katja Schulz from Washington, D. C., USA, CC BY 2.0 via Wikimedia Commons
