Lokalno poravnanje i prepoznavanje motiva

master's thesis

• Author: Antonija Medved
• Mentor: Pavle Goldstein (mentor)
• Committee member: Pavle Goldstein (predsjednik povjerenstva)
• Committee member: Ilja Gogić (član povjerenstva)
• Committee member: Boris Muha (član povjerenstva)
• Committee member: Goran Muić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Mathematics); Zagreb
• Defense date and country: 2016-09-27, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Mathematics

Abstract

Cilj ovog rada bio je ubrzati proces iterativnog traženja mogućih varijanti motiva nekog proteina u danom proteomu. U tu svrhu je opisana funkcija sličnosti koju koristimo za uspoređivanje dvaju nizova te je dana distribucija najsličnijih podnizova po svakom retku proteoma. Procedura je poboljšana indeksiranjem proteoma i uvođenjem vektora koji bilježi broj mjesta poklapanja simbola na odgovarajućim pozicijama između dvaju nizova. Na kraju, vidjeli smo da metoda daje vrlo dobre rezultate testirajući je na stvarnom proteomu biljke Arabidopsis thaliana.

Abstract (english)

In this paper, we are concerned with optimization of iterative motif scanning. To be specific, we want to reduce processing time of finding the closest match to a given motif in each sequence of given proteome. First, we describe the scoring model and comment distribution of best score in each sequence. Then we improve the model by indexing the proteome and generating a vector that represents the number of positions at which the corresponding symbols are equal between two sequences. Finally, we have seen that this model gives very good results when applied on real-life plant proteome.

• Keywords: varijante motiva nekog proteina u proteomu; proteom; indeksiranje proteoma; Arabidopsis thaliana
• Keywords (english): variants of motif in proteome; proteome; indexing the proteome; Arabidopsis thaliana
• Language: croatian
• URN:NBN: urn:nbn:hr:217:655940
• Study programme: Title: Mathematical Statistics; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2019-01-23 11:50:21