Statistika višestrukog poravnanja

master's thesis

• Author: Mateja Vragović
• Mentor: Pavle Goldstein (mentor)
• Committee member: Pavle Goldstein (predsjednik povjerenstva)
• Committee member: Nikola Sarapa (član povjerenstva)
• Committee member: Ivica Nakić (član povjerenstva)
• Committee member: Zrinka Franušić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Mathematics); Zagreb
• Defense date and country: 2015-07-13, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Mathematics

Abstract

U ovom diplomskom radu bavimo se opisom profila familije proteina. Opisati profil familije znači procijeniti parametre pridruženog skrivenog Markovljevog modela. Profil familije omogućuje da se novi članovi familije mogu poravnati s već poravnatom familijom. Procjena parametara modela nije jednostavan proces, neke parametre procijenimo, druge fiksiramo. Promijenimo li samo jednu vrijednost u procjeni, promijenit će se poravnanje, dakle možemo reći da procjena nije robusna. Iz poravnate familije proteina procijenimo inicijalne parametre, na temelju kojih iterativno želimo dobiti parametre pomoću kojih će poravnanje familije biti najsličnije originalnom. Proceduru za procjenu parametara ponovili smo dva puta i sa svakom procjenom dobili bolje poravnanje. Iako smo napravili samo dvije procjene, ta procedura može biti iterirana. S druge strane ali moramo imati na umu da ne moramo dobiti potpuno isto poravnanje.

Abstract (english)

In this thesis, we want to describe a protein family profile. To describe the family profile means to estimate the parameters of the Hidden Markov Model associated to an alignment. Family profile enables aligning of newly discovered family members with an already existing alignment. Estimation of the model parameters is not a simple procedure, some parameters are estimated, others are given. If we change a single value in the estimate, the resulting alignment might change, therefore, our estimate is not robust. From a given protein family we estimate the initial parameters, and we would like to get parameters so that the alignment of the family will be the most similar to original alignment. We repeated the estimation procedure twice and that yielded a better alignment. Although we have made only two model estimations, this procedure can, clearly, be iterated. However, we do not expect to obtain an alignment identical to the one we started with.

• Keywords: opis profila familije proteina; skriveni Markovljev model; procjena parametara modela
• Keywords (english): describing a protein family profile; Hidden Markov Model; estimation of the model parameters
• Language: croatian
• URN:NBN: urn:nbn:hr:217:042613
• 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: 2019-02-19 15:37:30