Using Reverse Metagenomics to characterise nematode communities and soil health

(YU_UBIO19ARIES)

Using Reverse Metagenomics to characterise nematode communities and soil health

(YU_UBIO19ARIES)

Project Description

Supervisors

Prof Douglas W Yu (UEA Biological Sciences)

Prof Matt Hutchings (UEA)

Dr Matt Clark (Natural History Museum)

Scientific background

Free-living nematodes are present in huge numbers in the soil. A few species are estimated to cause tens of billions ($USD) of damage to crops globally each year. Other soil nematode species are predators of pests, with some used as biocontrol agents. Currently identification is reliant on morphology, which is slow, costly, and fails to identify to species. Thus farmers use preventative spraying with toxic and indiscriminate chemicals to protect crops. If it were possible to classify nematode communities, farmers could match crop to suitable fields. This would reduce costs and pesticide use, improve soil health, and potentially protect biodiversity.

The supervisors have developed a method called Reverse Metagenomics to identify eukaryotic species in mixed samples. Briefly, each species of interest is ‘genome skimmed’:  low-coverage sequencing. These constitute the reference database. Then an environmental ‘query’ sample is sequenced with long-read technology. Each of the long reads is classified to a reference skim. We have used this method to characterise pollen collected by bees.

Research methodology

We will extend Reverse Metagenomics to nematodes by skimming individual nematodes, followed by kmer-matching (Ondov et al. 2016) to cluster species. We will use a low-cost library-prep method developed by co-supervisor Matt Clark (Mascher et al. 2017). From each skim, we can also assemble high-copy-number genes for assignment against databases. We expect many nematodes not to have names, but higher level assignment can be achieved with phylogenetic placement. This way, we can take mixed samples of nematodes and generate both skims and quantify their compositions. The student will develop this pipeline and work with a commercial company to apply the method to UK agricultural soils.

Training

The student will be trained in a full suite of genomics methods: Illumina (short-) and long-read (Min/GridION/PromethION) sequencing, kmer analysis, and statistical analysis. The student will also learn bioinformatic scripting and project management. We plan an internship at Prof Yu’s lab at the Kunming Institute of Zoology.

Person specification

Any science background is suitable, although some previous laboratory experience will be helpful for you to know whether you like that side of things. Programming will be a large part of the Ph.D.

References

  • Tang, M., Hardman, C.J., Ji, Y.Q., Meng, G.L., Liu, S.L., Tan, M.H., Yang, S.Z., Moss, E.D., Yang, C.X., Bruce, C., Nevard, T., Potts, S.G., Zhou, X., and D.W. Yu. (2015) High-throughput monitoring of wild bee diversity and abundance via mitogenomics. Meth Ecol & Evol. 6:1034–1043.
  • Leggett, R.M., Clark, M.D. 2017. A world of opportunities with nanopore sequencing. Journal of Experimental Botany. 68:20
  • Mascher, M., H. Gundlach, A. Himmelbach, S. Beier, S. O. Twardziok, T. Wicker, V. Radchuk, …, Clark, M.D., et al. 2017. A chromosome conformation capture ordered sequence of the barley genome. Nature 544:427.
  • Ondov, B. D., T. J. Treangen, P. Melsted, A. B. Mallonee, N. H. Bergman, S. Koren, and A. M. Phillippy. 2016. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biology 17:132.
  • Stirling, G.R. 2000. Nematode Monitoring Strategies for vegetable crops. A report for the Rural Industries Research and Development Corporation. RIRDC Publication No 00/25.

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