Özet:
A new species identification method is introduced in this thesis. For this method, Zooplankton and fish species from the Turkish coast of Black Sea are used as a dataset. The method uses High Resolution Melting (HRM) curves to identify species. Zooplankton are essential for marine ecosystems. These organisms form a middle ground in the food chain, which sustain the energy flow between producers and consumers. Therefore, as a candidate, the most suited organism group for monitoring the Black Sea marine ecosystem is Zooplankton. However, identifying the Zooplankton species with conventional morphological methods needs time, expertise and effort. DNA barcoding can be considered as a good alternative for conventional morphology-based identification. On the other hand, it is time consuming, expensive and also limited to the information recorded in the universal DNA databases. The goals of this study were to identify Zooplankton species in Black Sea and develop a new identification methodology which is not only cost and time effective but also do not require expertise. Expediently, new software created which could use the HRM curves as a specific signature to identify or categorize the samples according to their species. In terms of high grouping capacity, this study represents a first comparison of melt curves with a specific database. The results indicate that HRM based identification is working and quite feasible. The created HRM software is managed to identify the samples as predicted in our hypothesis.
