Free-living nematodes detection using next-generation sequencing technology

The role of nematodes as biological indicators and as key players in nutrient cycling is well recognized. Others have been shown to cause immense losses in food production. Despite their importance, identification of nematodes has been hindered by difficulties in species identification using classical morphology. Alternative molecular-based methods including AFLP, PCR-RFLP and DNA barcoding used alone or in combination with the traditional methods can be time consuming when analysing multiple specimens in a sample. Metabarcoding provides the possibility to identify an array of individuals from many samples simultaneously. The challenge of this approach has been how to identify the most suitable DNA marker(s) as well as the lack of robust analysis pipeline for the sequence data. An evaluation of the performance of four candidate DNA markers (NF1-18Sr2b, SSUFO4- SSUR22, D3Af-D3Br and JB3-JB5) on a mock community of nematodes showed NF1- 18Sr2b is most suitable in terms of coverage and availability of reference sequences. Assessment of the most common bioinformatic tools (QIIME, MOTHUR and USEARCH) showed USEARCH had the best clustering algorithm, was the fastest, had best operational taxonomic units (otus) to actual diversity ratio and ranked the best in userfriendliness. In another mock community experiment, read numbers of taxa showed no correlation with their actual abundance in the community largely due to bias in amplification and copy numbers of the marker region. Analysis of samples collected from a tillage and traffic experiment using morphological approach showed strong inhibition of herbivores by deep tillage and zero traffic. Bacterivores in general were inhibited by traffic and not affected by tillage. Appraisal of the metabarcoding approach using samples from the same experiment showed at broader classification levels (trophic groups and functional guild), abundance biases associated with the mock community experiment were minimal, the broad implication being metabarcoding data may be useful for assessing quality soil based on the structure of its nematode community