umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
GOLDsurfer: Three dimensional display of linkage disequilibrium
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umbio AB, Umeå, Sweden.
Wellcome Trust Centre for Human Genetics, University of Oxford.
2004 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 20, no 17, p. 3241-3243Article in journal (Refereed) Published
Abstract [en]

GOLDsurfer is a java-based analysis and graphics program for three-dimensional plotting of linkage disequilibrium (LD). Simultaneous presentation of LD measures, including recombination rate estimates and disease association statistics, helps to clarify LD patterns and facilitates interpretations based on multiple indices of local genetic data.

Place, publisher, year, edition, pages
Oxford: Oxford University Press , 2004. Vol. 20, no 17, p. 3241-3243
Identifiers
URN: urn:nbn:se:umu:diva-20653DOI: 10.1093/bioinformatics/bth341PubMedID: 15201180OAI: oai:DiVA.org:umu-20653DiVA, id: diva2:209255
Available from: 2009-03-24 Created: 2009-03-24 Last updated: 2017-12-13Bibliographically approved
In thesis
1. A multivariate approach to computational molecular biology
Open this publication in new window or tab >>A multivariate approach to computational molecular biology
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis describes the application of multivariate methods in analyses of genomic DNA sequences, gene expression and protein synthesis, which represent each of the steps in the central dogma of biology. The recent finalisation of large sequencing projects has given us a definable core of genetic data and large-scale methods for the dynamic quantification of gene expression and protein synthesis. However, in order to gain meaningful knowledge from such data, appropriate data analysis methods must be applied.

The multivariate projection methods, principal component analysis (PCA) and partial least squares projection to latent structures (PLS), were used for clustering and multivariate calibration of data. By combining results from these and other statistical methods with interactive visualisation, valuable information was extracted and further interpreted.

We analysed genomic sequences by combining multivariate statistics with cytological observations and full genome annotations. All oligomers of di- (16), tri- (64), tetra- (256), penta- (1024) and hexa-mers (4096) of DNA were separately counted and normalised and their distributions in the chromosomes of three Drosophila genomes were studied by using PCA. Using this strategy sequence signatures responsible for the differentiation of chromosomal elements were identified and related to previously defined biological features. We also developed a tool, which has been made publicly available, to interactively analyse single nucleotide polymorphism data and to visualise annotations and linkage disequilibrium.

PLS was used to investigate the relationships between weather factors and gene expression in field-grown aspen leaves. By interpreting PLS models it was possible to predict if genes were mainly environmentally or developmentally regulated. Based on a PCA model calculated from seasonal gene expression profiles, different phases of the growing season were identified as different clusters. In addition, a publicly available dataset with gene expression values for 7070 genes was analysed by PLS to classify tumour types. All samples in a training set and an external test set were correctly classified. For the interpretation of these results a method was applied to obtain a cut-off value for deciding which genes could be of interest for further studies.

Potential biomarkers for the efficacy of radiation treatment of brain tumours were identified by combining quantification of protein profiles by SELDI-MS-TOF with multivariate analysis using PCA and PLS. We were also able to differentiate brain tumours from normal brain tissue based on protein profiles, and observed that radiation treatment slows down the development of tumours at a molecular level.

By applying a multivariate approach for the analysis of biological data information was extracted that would be impossible or very difficult to acquire with traditional methods. The next step in a systems biology approach will be to perform a combined analysis in order to elucidate how the different levels of information are linked together to form a regulatory network.

Place, publisher, year, edition, pages
Umeå: Kemi, 2005. p. 149
Keywords
PLS, PCA, biomarker, Drosophila, SNP, linkage, disequilibrium, bioinformatics, computational, molecular, biology, genomics, microarray, SELDI, proteomics
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:umu:diva-609 (URN)91-7305-965-X (ISBN)
Public defence
2005-11-04, 10:00
Supervisors
Available from: 2005-10-12 Created: 2005-10-12 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMed
By organisation
Department of Chemistry
In the same journal
Bioinformatics

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 47 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf