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Napovedovanje fenotipa iz podatkov o genotipu posameznikov in celotnih generacij : diplomsko delo
ID Svetelšek, Miha (Author), ID Curk, Tomaž (Mentor) More about this mentor... This link opens in a new window

URLURL - Presentation file, Visit http://eprints.fri.uni-lj.si/2615/ This link opens in a new window

Abstract
V diplomski nalogi smo modelirali povezavo med genotipom in fenotipom tridesetih vzorcev kvasovke S. cerevisiae. Na podlagi podatkov in predznanja smo določili mutacije posameznih nukleotidov in z njimi povezane gene, s katerimi je možno zgraditi dober model za napovedovanje fenotipa. Poleg določanja pomembnih mest v genomu (SNV-jev) nam zgrajeni model omogoča tudi določevanje pomembnih genotipov oziroma starševskega izvora, ki je povezan z opazovanim fenotipom. Vrednotenje modelov pokaže, da lahko z linearno regresijo zanesljivo napovedujemo fenotip. Fenotip relativno dobro napoveduje tudi model, ki je zgrajen le na podlagi podatkov o dveh izvornih starših in začetne populacije. Empirično smo določili povezavo med številom vzorcev, ki jih uporabimo za izgradnjo napovednih modelov, in napovedno napako modelov.

Language:Slovenian
Keywords:bioinformatika, genotip, fenotip, posameznik, populacija, linearna regresija, logistična regresija, računalništvo, računalništvo in informatika, računalništvo in matematika, univerzitetni študij, diplomske naloge
Work type:Undergraduate thesis
Typology:2.11 - Undergraduate Thesis
Organization:FRI - Faculty of Computer and Information Science
Publisher:[M. Svetelšek]
Year:2014
Number of pages:78 str.
PID:20.500.12556/RUL-68690 This link opens in a new window
UDC:004.9:57(043.2)
COBISS.SI-ID:10718036 This link opens in a new window
Publication date in RUL:10.07.2015
Views:1495
Downloads:254
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Secondary language

Language:English
Title:Predicting the phenotype from genotype data on individual and pooled segregants
Abstract:
We have modeled the relationship between genotype and phenotype using data on thirty yeast S. cerevisiae samples. Using prior knowledge, we have determined mutations of individual nucleotides and related genes with which it is possible to build a good prediction model for the phenotype. The constructed models allow us to determine the location of important mutations in the genome (SNVs), to rank samples based on phenotype, and to determine signi_cant genotypes or parental origin, which is connected to the observed phenotype. Evaluation of these models shows that the phenotype can be predicted very reliably with linear regression. The phenotype can be predicted relatively well from data on two starting parents and the _rst pool of segregants. We also show the relation between the number of samples used to build a predictive model and its predictive error.

Keywords:bioinformatics, genotype, phenotype, individual segregant, pool of segregants, linear regression, logistic regression, computer science, computer and information science, computer science and mathematics, diploma

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