Utilizing Bioinformatics for Meiofaunal Community Composition Analysis
College
College of Arts and Sciences
Department
Biology
Abstract
The emergence of molecular analysis as a means of classification for meiofaunal communities based on genetic similarity has prompted the development of analytical technology to understand and map genetic variation more efficiently. While this technology has already affected meiofaunal research, the establishment of such a bioinformatics pipeline has remained a cumbersome process because of the wide variety of processing tactics. As the demand for faster taxonomic classification and phylogenetic placement continues to grow, high-throughput data processing has become a necessity to meiofaunal phylogenetic investigation. Accordingly, we assembled a functioning bioinformatics pipeline for the assessment of meiofaunal community variation based on the 18S ribosomal gene. Using the Linux software package QIIME, the established pipeline was successfully tested with IonTorrent sequences of soil fauna obtained by PCR of environmental DNA from the V9 hypervariable region of the 18S gene. These sequences, contributed by teachers participating in the INBRE-RET program, were compared and clustered into operational taxonomic units and matched to sequences with defined taxonomy. Finally, the sample was organized into a current phylogeny of the members of the sampled community. The specificity of taxonomic assignment was variable, with some organisms only assigned to a phylum, yet this pipeline significantly reduced the time needed to inventory community composition. The pipeline will continue further testing with a set of data from several meiofaunal communities contributed by a colleague. This customized technology is steadily becoming a feasible tactic for scientists to utilize in community analysis, and its rapid processing will allow researchers to more readily understand a current phylogenic composition of an ecosystem.
Honors Thesis Committee
Julian Smith III, Ph.D.; Cynthia Tant, Ph.D.; and Jason Hurlbert, Ph.D.
Grant Support?
Supported by a grant from the National Institutes of Health IDeA Networks for Biomedical Research Excellence (NIH-INBRE)
Start Date
21-4-2017 3:30 PM
Utilizing Bioinformatics for Meiofaunal Community Composition Analysis
DiGiorgio Campus Center, Room 220
The emergence of molecular analysis as a means of classification for meiofaunal communities based on genetic similarity has prompted the development of analytical technology to understand and map genetic variation more efficiently. While this technology has already affected meiofaunal research, the establishment of such a bioinformatics pipeline has remained a cumbersome process because of the wide variety of processing tactics. As the demand for faster taxonomic classification and phylogenetic placement continues to grow, high-throughput data processing has become a necessity to meiofaunal phylogenetic investigation. Accordingly, we assembled a functioning bioinformatics pipeline for the assessment of meiofaunal community variation based on the 18S ribosomal gene. Using the Linux software package QIIME, the established pipeline was successfully tested with IonTorrent sequences of soil fauna obtained by PCR of environmental DNA from the V9 hypervariable region of the 18S gene. These sequences, contributed by teachers participating in the INBRE-RET program, were compared and clustered into operational taxonomic units and matched to sequences with defined taxonomy. Finally, the sample was organized into a current phylogeny of the members of the sampled community. The specificity of taxonomic assignment was variable, with some organisms only assigned to a phylum, yet this pipeline significantly reduced the time needed to inventory community composition. The pipeline will continue further testing with a set of data from several meiofaunal communities contributed by a colleague. This customized technology is steadily becoming a feasible tactic for scientists to utilize in community analysis, and its rapid processing will allow researchers to more readily understand a current phylogenic composition of an ecosystem.
