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A new approach to quantify and analyse tRNA sequence data
Umeå universitet, Samhällsvetenskapliga fakulteten, Enheten för demografi och åldrandeforskning (CEDAR).ORCID-id: 0000-0001-9188-5518
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
Abstract [en]

A novel quantitative multivariate approach for describing and analyse tRNA sequence data is presented. This approach is based on a multivariate chemical description of each nucleoside in the sequence. 30 theoretically calculated descriptors were used to characterize 63 nucleosides, and principal component analysis was used to extract the main variation from this multivariate description. The resulting four principal properties were interpreted as (PPa) size/bulk of the nucleoside, (PPb) polarity/hydrophobicity of the nucleoside, (PPc) electronic properties of the nucleoside and (PPd)polarity and size of the ribose moiety. These principal properties may be used to translate the tRNA letter sequence data into a quantitative chemical representation. We demonstrate the use of this quantitative description with a multivariate analysis of a set of tRNA sequences. This analysis gives models that are interpretable in terms of wich sequence positions, and nucleoside properties that discriminate the different isoacceptors. This approach is applicable on all kinds of RNA sequence data and gives information that is complementary to current sequence analysis techniques.

HSV kategori
Forskningsprogram
organisk kemi
Identifikatorer
URN: urn:nbn:se:umu:diva-142697OAI: oai:DiVA.org:umu-142697DiVA, id: diva2:1163785
Tilgjengelig fra: 2017-12-08 Laget: 2017-12-08 Sist oppdatert: 2018-06-09
Inngår i avhandling
1. Deciphering sequence data: A multivariate approach
Åpne denne publikasjonen i ny fane eller vindu >>Deciphering sequence data: A multivariate approach
1997 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

In this thesis, attention has been focused on the quantitative description of nucleic acids, proteins and peptides. The strategy was to use multivariate chemometrical methods for improving the understanding of the complex structural codes of these kinds of biological molecules. Tools have been developed that enable quantitative modelling of biological molecules, i.e. models based on data that quantitatively describes their properties. The advantage of such models is that they provide interpretations in terms of chemical characteristics for complex features such as similarity, dissimilarity and potency.

By a multivariate physical-chemical characterization of the building blocks of nucleic acids and proteins, i.e. nucleosides and amino acids, descriptive scales have been developed, so called principal properties. The scales give a description of the intrinsic properties of these building blocks. The multivariate characterization results in a multi-property matrix. A principal component analysis of the multi-property matrix gives a small number of latent variables which are considered as the principal properties of the characterized molecules.

The principal property scales may be used for a wide range of different purposes, such as detecting trends and groupings in large sequence data sets, and for analyzing quantitative relationships between structure and function. In statistical experimental design, the descriptors are well suited as design variables to select combinations of amino acids in such a way that they span a wide range of properties.

The use of these principal property descriptors is demonstrated in the quantitative modelling of relationships between structure and activity of various peptide series, DNA-promoters and in the quantitative modelling of transfer ribonucleic acid sequence data (tRNA).

sted, utgiver, år, opplag, sider
Umeå: Solfjädern Offset AB, 1997. s. 76
Emneord
Principal properties, amino acids, nucleotides, tRNA, DNA, multivariate data analysis, sequence analysis, QSAR, quantitative sequence activity relationships
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-142699 (URN)91-7191-337-8 (ISBN)
Disputas
1997-06-06, N320, Naturvetarhuset, 90187, Umeå, 14:00 (svensk)
Opponent
Veileder
Tilgjengelig fra: 2023-02-03 Laget: 2017-12-08 Sist oppdatert: 2023-02-03bibliografisk kontrollert

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