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DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures
Umeå universitet, Samhällsvetenskapliga fakulteten, Enheten för demografi och åldrandeforskning (CEDAR).ORCID-id: 0000-0001-9188-5518
1993 (Engelska)Ingår i: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 277, s. 239-253Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Biopolymer sequences (e.g., DNA, RNA, proteins and polysaccharides) and chemical processes (e.g., a batch or continuous polymer synthesis run in a chemical plant) have close similarities from the modelling point of view. When a set of sequences or processes is characterized by multivariate data, a three-way data matrix is obtained. With sequences the position and with processes the time is one direction in this matrix. The multivariate modelling of this matrix by principal component analysis (PCA) or partial least-squares (PLS) methods for the following purposes is discussed: classification of sequences; quantitative relationships between sequence and biological activity or chemical properties; optimizing a sequence with respect to selected properties; process diagnostics; and quantitative relationships between process variables and product quality variables. To obtain good models, a number of problems have to be adequately dealt with: appropriate characterization of the sequence or process; experimental design (selecting sequences or process settings); transforming the three-way into a two-way matrix; and appropriate modelling and validation (modelling interactions, periodicities, “time series” structures and “neighbour effects”). A multivariate approach to sequence and process modelling using PCA and PLS projections to latent structures is discussed and illustrated with several sets of peptide and DNA promoter data.

Ort, förlag, år, upplaga, sidor
1993. Vol. 277, s. 239-253
Nyckelord [en]
DNA, peptide sequences, multivariate, principal component analysis, partial least-squares projections to latent structures
Nationell ämneskategori
Organisk kemi
Identifikatorer
URN: urn:nbn:se:umu:diva-142536DOI: 10.1016/0003-2670(93)80437-POAI: oai:DiVA.org:umu-142536DiVA, id: diva2:1162000
Tillgänglig från: 2017-12-01 Skapad: 2017-12-01 Senast uppdaterad: 2018-06-09
Ingår i avhandling
1. Deciphering sequence data: A multivariate approach
Öppna denna publikation i ny flik eller fönster >>Deciphering sequence data: A multivariate approach
1997 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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).

Ort, förlag, år, upplaga, sidor
Umeå: Solfjädern Offset AB, 1997. s. 76
Nyckelord
Principal properties, amino acids, nucleotides, tRNA, DNA, multivariate data analysis, sequence analysis, QSAR, quantitative sequence activity relationships
Nationell ämneskategori
Organisk kemi
Identifikatorer
urn:nbn:se:umu:diva-142699 (URN)91-7191-337-8 (ISBN)
Disputation
1997-06-06, N320, Naturvetarhuset, 90187, Umeå, 14:00 (Svenska)
Opponent
Handledare
Tillgänglig från: 2023-02-03 Skapad: 2017-12-08 Senast uppdaterad: 2023-02-03Bibliografiskt granskad

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Sandberg Hiltunen, Maria

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