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Title: Validating next generation sequencing for meiofaunal community analysis and interaction prediction
Author: Nichols, Ben
ISNI:       0000 0004 5368 9657
Awarding Body: University of Glasgow
Current Institution: University of Glasgow
Date of Award: 2015
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Advances in DNA sequencing technologies, particularly the advent of next generation sequencing (NGS) platforms, have revolutionised the field of metagenomics and allowed great progress to be made in the way that microbial communities are analysed. However, the wealth of data now available thanks to these advancements has made the possibilities far more numerous than just the obvious applications, with a wide variety of novel and diverse studies conceivable. The technologies themselves have also created further areas for research as better methods of handling the, often overwhelming in quantity and misleading in content, data are sought. The analysis carried out in this thesis demonstrates the wide range of study possible stemming from two experiments involving the sequencing of meiofauna DNA. The first of these involves community analysis of marine benthic meiofauna with particular emphasis on diversity and distribution. The second experiment involves the sequencing of pooled nematode samples in order to investigate the effects of sample richness and species relatedness on the generation of chimeric reads in sequencing data. It is shown that the data generated from these two experiments can be used to help formulate an algorithm to simulate PCR and therefore assist the generation of realistic noisy NGS data. These data can, in turn, be used to generate a simulated in silico microbial community for analysis, the results of which reveal insights into the accuracy of chimera detection software and the reliability of metagenetic community analyses. Worryingly, these results suggest that findings from similar in vitro studies are not as reliable as originally perceived. The same experimental data may also be used to investigate interactions between meiofauna species based on the incidental presence of prey species highlighted from the sequencing of individual meiofauna organisms. It is shown that these data can be used to accurately predict a nematode’s feeding type without having to examine the organism directly. It is also shown that there is no correlation between this method of inferring interactions between species and other methods which have been used in the past. This suggests that the earlier methods are inadequate when used for the detection of feeding interactions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA Mathematics ; QA75 Electronic computers. Computer science ; QH301 Biology ; QH426 Genetics ; QR Microbiology