prikaz prve stranice dokumenta coRdon: an R package for codon usage analysis and prediction of gene expressivity
  Download
PDF 2.4 MB
master's thesis
coRdon: an R package for codon usage analysis and prediction of gene expressivity
Zagreb: University of Zagreb, Faculty of Science, 2018. urn:nbn:hr:217:686131
Elek, Anamaria
University of Zagreb
Faculty of Science
Department of Biology

Institutional repository: Repository of the Faculty of Science

Cite this document

Elek, A. (2018). coRdon: an R package for codon usage analysis and prediction of gene expressivity (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:686131

Elek, Anamaria. "coRdon: an R package for codon usage analysis and prediction of gene expressivity." Master's thesis, University of Zagreb, Faculty of Science, 2018. https://urn.nsk.hr/urn:nbn:hr:217:686131

Elek, Anamaria. "coRdon: an R package for codon usage analysis and prediction of gene expressivity." Master's thesis, University of Zagreb, Faculty of Science, 2018. https://urn.nsk.hr/urn:nbn:hr:217:686131

Elek, A. (2018). 'coRdon: an R package for codon usage analysis and prediction of gene expressivity', Master's thesis, University of Zagreb, Faculty of Science, accessed 22 November 2024, https://urn.nsk.hr/urn:nbn:hr:217:686131

Elek A. coRdon: an R package for codon usage analysis and prediction of gene expressivity [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2018 [cited 2024 November 22] Available at: https://urn.nsk.hr/urn:nbn:hr:217:686131

A. Elek, "coRdon: an R package for codon usage analysis and prediction of gene expressivity", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:217:686131

Metadata
TitlecoRdon: an R package for codon usage analysis and prediction of gene expressivity
Title (croatian)Razvoj paketa coRdon u programskom jeziku R za analizu korištenja sinonimnih kodona i predviđanje ekspresije gena
AuthorAnamaria Elek
MentorKristian Vlahoviček (mentor)
Committee memberKristian Vlahoviček (predsjednik povjerenstva)
Committee memberDamjan Franjević (član povjerenstva)
Committee memberSven Jelaska (član povjerenstva)
Committee memberRosa Karlić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Biology)
Zagreb
Defense date and country2018-09-13, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Biology
Abstract
Not all synonymous codons are used with equal frequency in prokaryotic genomes - this selective preference is termed codon usage (CU) bias and is an independent determinant of gene expression regulation at the level of translation. The synonymous codons corresponding to the most abundant cognate tRNA are considered optimal for translation. Codon usage bias can therefore be used to predict the relative expression levels of genes, by comparing CU bias of a specific gene to the CU bias of a set of genes known to be highly expressed. This approach can be efficiently used to predict highly expressed genes within a single genome but has also been demonstrated relevant at the level of entire microbial communities – metagenomes. This is possible because CU bias is shared among the microbial species in the same environment. By analysing CU bias of a metagenome, one can identify genes with high predicted expression across the given ecological niche, and identify enriched functions, i.e. the functional fingerprint of the analysed microbial community. Software tools to efficiently analyse and visualize metagenomics data in the context of translational optimisation and CU bias are scarce and this limits the ability to generate useful knowledge. As the part of this thesis, I have developed a Bioconductor-based R package for comprehensive analysis of CU and CU-based gene expressivity. The package also includes several methods for visualization of CU, and the CUbased functional analysis of annotated DNA sequences. The package is fast and flexible enough to handle even the largest metagenomics datasets.
Abstract (english)
U genomima različitih prokariota svi sinonimni kodoni ne koriste se jednakom učestalošću - njihovo selektivno korištenje zaseban je mehanizam regulacije ekspresije gena na razini translacije. Sinonimni kodoni koje prepoznaju najbrojnije tRNA molekule u stanici smatraju se optimiziranima za translaciju. Stoga se selektivno korištenje sinonimnih kodona može koristiti za predviđanje relativne ekspresivnosti gena, uspoređujući obrazac korištenja kodona u određenom genu s kodonima koji se učestalo pojavljuju u skupu visoko eksprimiranih gena. Na ovaj način moguće je identificirati potencijalno jako eksprimirane gene na razini genoma, ali još važnije, i na razini čitavih metagenoma, jer je pokazano da je uzorak selektivnog korištenja kodona zajednički organizmima unutar iste mikrobne zajednice, bez obzira na njihovu filogenetsku udaljenost. Stoga je analizirajući korištenje kodona na razini metagenoma moguće predvidjeti koji geni su relativno visoko eksprimirani u određenoj mikrobnoj zajednici, kao i koje funkcije su zastupljene među tim genima - tzv. funkcijski otisak mikrobne zajednice. U sklopu ovog rada razvila sam paket u programskom jeziku R, baziran na sintaksi i smjernicama projekta Bioconductor, za kompletnu analizu korištenja sinonimnih kodona i ekspresivnosti gena. Uz to što omogućava računanje više različitih statistika koje kvantificiraju korištenje sinonimnih kodona, paket uključuje i nekoliko metoda za vizualizaciju korištenja sinonimnih kodona, omogućava funkcionalnu analizu anotiranih slijedova DNA te je dovoljno brz i fleksibilan za analizu velikih količina metagenomskih podataka.
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:217:686131
Study programmeTitle: Molecular Biology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra molekularne biologije (magistar/magistra molekularne biologije)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2018-09-28 09:48:18