de novo tüm genom sekans verisi üzerinde gizli Markov modeli tabanlı gen bulma algoritmalarının uygulanması
Özet
Advances in nucleotide sequencing technology has led to faster sequencing of the genomes. Thanks to this advent, even small sized centers can now perform genome projects. Besides the data generation step, one of the key steps in genome sequencing projects is the genome annotation step, which is consisted of "gene finding" and adding attributes to the sequence parts which are most probably coding regions. The need for computational gene finding algorithms is known since 80's, and many gene finding algorithms are developed. In this thesis study, two annotation pipelines (PGAAP, RAST) and their core algorithms which are based on Markov models (GeneMarkS, Glimmer) are used to perform gene finding on de novo genomic sequence of Bacillus boroniphilus, and the results are compared. Because of the dynamic structure and the complexity of genome projects, it can be said that using various methods can be beneficial for obtaining different kinds of information at the post annotation step. It can be said that the method which uses hidden Markov model (GeneMarkS) and the pipeline uses this core algorithm (PGAAP) provided more reliable results for B. boroniphilus genome.