Today’s post starts with a linocut of an Illumina MiSeq Next Generation Sequencer (NGS), as part of an ongoing campaign to demystify these state-of-the-art £80,000 pound instruments. It’s just a box stuffed with clever electronics. The problem is that tech-leaning biologists go misty-eyed at the very mention of NGS, and start to make outrageous claims for what it can do. But how much are they actually going to change the way that we assess the state of the environment? I approach this topic as an open-minded sceptic (see “Replaced by a robot?” and “Glass half full or glass half empty?” and other posts) but I have friends who know what buttons to press, and in what order. Thanks to them, enough of my samples have been converted into reams of NGS data for me now to be in a position to offer an opinion on their usefulness.
So here are three situations where I think that that NGS may offer advantages over “traditional” biology:
- reducing error / uncertainty when assessing variables with highly-contagious distributions.
Many of the techniques under consideration measure “environmental DNA” (“eDNA”) in water samples. eDNA is DNA released into water from skin, faeces, mucus, urine and a host of other ways. In theory, we no longer need to hunt for Great Crested Newts in ponds (a process with a high risk of “type 2 errors” – “false negatives”) but can take water samples and detect the presence of newts in the pond directly from these. The same logic applies to lake fish, many of which move around the lake in shoals, which may be missed by sampler’s nets altogether or give false estimates of true abundance. In both of these cases, the uncertainties in traditional methods can be reduced by increasing effort, but this comes at a cost, so methods based on eDNA show real potential (the Great Crested Newt method is already in use).
- Ensuring consistency when dealing with cryptic / semi-cryptic species
I’ve written many posts about the problems associated with identifying diatoms. We have ample evidence, now, that there are far more species than we thought 30 years ago. This, in turn, is challenging the ability to create consistent datasets when analysts spread around several different laboratories are trying to make fine distinctions between species based on a very diffuse literature. Those of us who study diatoms now work at the very edge of what can be discriminated with the light microscope and the limited data we do now have from molecular studies suggests that there are sometimes genetic differences even when it is almost impossible to detect variation in morphology. NGS has the potential for reducing the analytical error that results from these difficulties although, it is important to point out, many other factors (spatial and temporal) contribute to the overall variation between sites and, therefore, to our understanding of the effect of human pressures on diatom assemblages.
- Reducing costs
This is one of the big benefits of NGS in the short term. The reduction in cost is partly a result of the expenses associated with tackling the first two points by conventional means. You can usually reduce uncertainty by increasing effort but, as resources are usually limited, this increase in effort means channelling funds that could be used more profitably elsewhere. However, there will also be a straightforward time saving, because of the economies of scale that accompanies high-throughput NGS. A single run of an Illumina MiSeq can process 96 samples in a few hours, whereas each would have required one to two hours for analysis by light microscope. Even when the costs of buying and maintaining the NGS machines are factored in, NGS still offers a potential cost saving over conventional methods.
It is worth asking whether these three scenarios – statistical, taxonomic and financial – really amount to better science, or whether NGS is just a more efficient means of applying the same principles (“name and count”) that underpins most ecological assessment at present. From a manager’s perspective, less uncertainty and lower cost is a beguiling prospect. NGS may, as a result, give greater confidence in decision making, according to the current rules. That may make for better regulation, but it does not really represent a paradigm shift in the underlying science.
The potential, nonetheless, is there. A better understanding of genetic diversity, for example, may make it easier to build emerging concepts such as ecological resilience into ecological assessment (see “Baffled by the benthos (2)” and “Making what is important measurable”). Once we have established NGS as a working method, maybe we can assess functional genes as well as just taxonomic composition? The possibilities are endless. The Biomonitoring 2.0 group is quick to make these claims. But it is important to remember that, at this stage, they are no more than possibilities So far, we are still learning to walk …
Baird, D.J. & Hajibabaei, M. (2012). Biomonitoring 2.0: a new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Molecular Ecology 21: 2039-2044.