| 1. |
- Bresell, Anders, et al.
(författare)
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Ontology annotation treebrowser: an interactive tool where the complementarity of medical subject headings and gene ontology improves the interpretation of gene lists
- 2006
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Ingår i: Applied Bioinformatics. - 1175-5636. ; 5:4, s. 225-236
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Tidskriftsartikel (refereegranskat)abstract
- Gene expression and proteomics analysis allow the investigation of thousands of biomolecules in parallel. This results in a long list of interesting genes or proteins and a list of annotation terms in the order of thousands. It is not a trivial task to understand such a gene list and it would require extensive efforts to bring together the overwhelming amounts of associated information from the literature and databases. Thus, it is evident that we need ways of condensing and filtering this information. An excellent way to represent knowledge is to use ontologies, where it is possible to group genes or terms with overlapping context, rather than studying one-dimensional lists of keywords. Therefore, we have built the ontology annotation treebrowser (OAT) to represent, condense, filter and summarise the knowledge associated with a list of genes or proteins. The OAT system consists of two disjointed parts; a MySQL® database named OATdb, and a treebrowser engine that is implemented as a web interface. The OAT system is implemented using Perl scripts on an Apache web server and the gene, ontology and annotation data is stored in a relational MySQL® database. In OAT, we have harmonized the two ontologies of medical subject headings (MeSH) and gene ontology (GO), to enable us to use knowledge both from the literature and the annotation projects in the same tool. OAT includes multiple gene identifier sets, which are merged internally in the OAT database. We have also generated novel MeSH annotations by mapping accession numbers to MEDLINE entries. The ontology browser OAT was created to facilitate the analysis of gene lists. It can be browsed dynamically, so that a scientist can interact with the data and govern the outcome. Test statistics show which branches are enriched. We also show that the two ontologies complement each other, with surprisingly low overlap, by mapping annotations to the Unified Medical Language System®. We have developed a novel interactive annotation browser that is the first to incorporate both MeSH and GO for improved interpretation of gene lists. With OAT, we illustrate the benefits of combining MeSH and GO for understanding gene lists. OAT is available as a public web service at: http://www.ifm.liu.se/bioinfo/oat
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| 2. |
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| 3. |
- Freyhult, Eva, et al.
(författare)
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Predicting RNA structure using mutual information.
- 2005
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Ingår i: Applied Bioinformatics. - 1175-5636. ; 4:1, s. 53-59
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: With the ever-increasing number of sequenced RNAs and the establishment of new RNA databases, such as the Comparative RNA Web Site and Rfam, there is a growing need for accurately and automatically predicting RNA structures from multiple alignments. Since RNA secondary structure is often conserved in evolution, the well known, but underused, mutual information measure for identifying covarying sites in an alignment can be useful for identifying structural elements. This article presents MIfold, a MATLAB((R)) toolbox that employs mutual information, or a related covariation measure, to display and predict conserved RNA secondary structure (including pseudoknots) from an alignment. RESULTS: We show that MIfold can be used to predict simple pseudoknots, and that the performance can be adjusted to make it either more sensitive or more selective. We also demonstrate that the overall performance of MIfold improves with the number of aligned sequences for certain types of RNA sequences. In addition, we show that, for these sequences, MIfold is more sensitive but less selective than the related RNAalifold structure prediction program and is comparable with the COVE structure prediction package. CONCLUSION: MIfold provides a useful supplementary tool to programs such as RNA Structure Logo, RNAalifold and COVE, and should be useful for automatically generating structural predictions for databases such as Rfam. AVAILABILITY: MIfold is freely available from http://www.lcb.uu.se/~evaf/MIfold/
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| 4. |
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| 5. |
- Lindlöf, Angelica
(författare)
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Gene identification through large-scale EST sequence processing
- 2003
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Ingår i: Applied Bioinformatics. - : Adis International. - 1175-5636. ; 2:3, s. 123-129
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Forskningsöversikt (refereegranskat)abstract
- The technology of sequencing expressed sequence tags (ESTs) offers a relatively cheap alternative to whole genome sequencing and has become a valuable resource for gene discovery. The inherent characteristics of ESTs, such as transcript redundancy, low sequence quality and high error rates, require processing of the sequences before any gene prediction can be made. The process includes EST pre-processing, analysis and similarity searches, and the data are generally stored in a database to organise the results and thereby assist the search for interesting genes.
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| 6. |
- Narayanan, Ajit, et al.
(författare)
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Artificial intelligence techniques for bioinformatics
- 2002
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Ingår i: Applied Bioinformatics. - 1175-5636. ; 1:4, s. 191-222
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Forskningsöversikt (refereegranskat)abstract
- This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.
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| 7. |
- Oresic, Matej, 1967-, et al.
(författare)
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Phenotype characterisation using integrated gene transcript, protein and metabolite profiling
- 2004
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Ingår i: Applied bioinformatics. - : Adis International Ltd.. - 1175-5636. ; 3:4, s. 205-217
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Tidskriftsartikel (refereegranskat)abstract
- Multifactorial diseases present a significant challenge for functional genomics. Owing to their multiple compartmental effects and complex biomolecular activities, such diseases cannot be adequately characterised by changes in single components, nor can pathophysiological changes be understood by observing gene transcripts alone. Instead, a pattern of subtle changes is observed in multifactorial diseases across multiple tissues and organs with complex associations between corresponding gene, protein and metabolite levels. This article presents methods for exploratory and integrative analysis of pathophysiological changes at the biomolecular level. In particular, novel approaches are introduced for the following challenges: (i) data processing and analysis methods for proteomic and metabolomic data obtained by electrospray ionisation (ESI) liquid chromatography-tandem mass spectrometry (LC/MS); (ii) association analysis of integrated gene, protein and metabolite patterns that are most descriptive of pathophysiological changes; and (iii) interpretation of results obtained from association analyses in the context of known biological processes. These novel approaches are illustrated with the apolipoprotein E3-Leiden transgenic mouse model, a commonly used model of atherosclerosis. We seek to gain insight into the early responses of disease onset and progression by determining and identifying--well in advance of pathogenic manifestations of disease--the sets of gene transcripts, proteins and metabolites, along with their putative relationships in the transgenic model and associated wild-type cohort. Our results corroborate previous findings and extend predictions for three processes in atherosclerosis: aberrant lipid metabolism, inflammation, and tissue development and maintenance.
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