Event Title
Assessment of Algal Diversity in Lake Sinclair: A Molecular Approach
Faculty Mentor
Yen Kang France
Keywords
Yen Kang France
Abstract
Algae are heterogeneous, photoautotrophic organisms ubiquitously found in aquatic habitats. Traditional algal identification relies on morphology based on microscopy, but the microscopy alone limits accurate species level identification due to cryptic species and resolution limitation. In this study, we present molecular based assessment of algal diversity from Lake Sinclair using rbcL-3P and the LSU D2/D3 genes. The rbcL-3P sequences were amplified via PCR from total genomic DNA extracted from the lake sample, and the cloned sequences were analyzed via BLAST for the highest percent identity. Out of the 48 samples sequenced, we identified 24 sequences to be diatom with a sequence identity with a sequence identity greater than 96.6%. Six sequences were unidentifiable due to the fact that there were no reference sequences in the database. The next step in our project would be to combine our results with the morphological analysis to validate our methods.
Session Name:
Poster Presentation Session #1 - Poster #19
Start Date
4-4-2014 11:30 AM
End Date
4-4-2014 12:15 PM
Location
HSB 3rd Floor Student Commons
Assessment of Algal Diversity in Lake Sinclair: A Molecular Approach
HSB 3rd Floor Student Commons
Algae are heterogeneous, photoautotrophic organisms ubiquitously found in aquatic habitats. Traditional algal identification relies on morphology based on microscopy, but the microscopy alone limits accurate species level identification due to cryptic species and resolution limitation. In this study, we present molecular based assessment of algal diversity from Lake Sinclair using rbcL-3P and the LSU D2/D3 genes. The rbcL-3P sequences were amplified via PCR from total genomic DNA extracted from the lake sample, and the cloned sequences were analyzed via BLAST for the highest percent identity. Out of the 48 samples sequenced, we identified 24 sequences to be diatom with a sequence identity with a sequence identity greater than 96.6%. Six sequences were unidentifiable due to the fact that there were no reference sequences in the database. The next step in our project would be to combine our results with the morphological analysis to validate our methods.