Publication Date
Spring 2019
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Philip Heller
Second Advisor
Katerina Potika
Third Advisor
Thomas Austin
Keywords
COI gene, Classification, Profile Hidden Markov Models
Abstract
Traditional classification systems for living organisms like the Linnaean taxonomy involved classification based on morphological features of species. This traditional system is being replaced by molecular approaches which involve using gene sequences. The COI gene, also known as the ”DNA barcode” since it is unique in every species, can be used to uniquely identify organisms and thus, classify them. Classifying using gene sequences has many advantages, including correct identification of cryptic species(individuals which appear similar but belong to different species) and species which are extremely small in size. In this project, I worked on classifying COI sequences of unknown species to a genus, using Profile Hidden Markov Models.
(Taxonomy Ranks: Kingdom → Phylum → Class → Order →Family → Genus → Species)
Recommended Citation
Poojary, Sphoorti, "Species Classification using DNA Barcoding and Profile Hidden Markov Models" (2019). Master's Projects. 668.
DOI: https://doi.org/10.31979/etd.cev7-sh4v
https://scholarworks.sjsu.edu/etd_projects/668
