| 1. |
- Andersson, Claes R., et al.
(author)
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Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors
- 2007
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In: BMC Systems Biology. - : Springer Science and Business Media LLC. - 1752-0509. ; 1, s. 45-
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Journal article (peer-reviewed)abstract
- Background: We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process. Results: We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach. Conclusion: The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.
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| 2. |
- Wabnik, Krzysztof, et al.
(author)
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Gene expression trends and protein features effectively complement each other in gene function prediction
- 2009
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 25:3, s. 322-330
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Journal article (peer-reviewed)abstract
- MOTIVATION: Genome-scale 'omics' data constitute a potentially rich source of information about biological systems and their function. There is a plethora of tools and methods available to mine omics data. However, the diversity and complexity of different omics data types is a stumbling block for multi-data integration, hence there is a dire need for additional methods to exploit potential synergy from integrated orthogonal data. Rough Sets provide an efficient means to use complex information in classification approaches. Here, we set out to explore the possibilities of Rough Sets to incorporate diverse information sources in a functional classification of unknown genes. RESULTS: We explored the use of Rough Sets for a novel data integration strategy where gene expression data, protein features and Gene Ontology (GO) annotations were combined to describe general and biologically relevant patterns represented by If-Then rules. The descriptive rules were used to predict the function of unknown genes in Arabidopsis thaliana and Schizosaccharomyces pombe. The If-Then rule models showed success rates of up to 0.89 (discriminative and predictive power for both modeled organisms); whereas, models built solely of one data type (protein features or gene expression data) yielded success rates varying from 0.68 to 0.78. Our models were applied to generate classifications for many unknown genes, of which a sizeable number were confirmed either by PubMed literature reports or electronically interfered annotations. Finally, we studied cell cycle protein-protein interactions derived from both tandem affinity purification experiments and in silico experiments in the BioGRID interactome database and found strong experimental evidence for the predictions generated by our models. The results show that our approach can be used to build very robust models that create synergy from integrating gene expression data and protein features. AVAILABILITY: The Rough Set-based method is implemented in the Rosetta toolkit kernel version 1.0.1 available at: http://rosetta.lcb.uu.se/
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| 3. |
- Andersson, Robin, et al.
(author)
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A Segmental Maximum A Posteriori Approach to Genome-wide Copy Number Profiling
- 2008
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 24:6, s. 751-758
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Journal article (other academic/artistic)abstract
- MOTIVATION: Copy number profiling methods aim at assigning DNA copy numbers to chromosomal regions using measurements from microarray-based comparative genomic hybridizations. Among the proposed methods to this end, Hidden Markov Model (HMM)-based approaches seem promising since DNA copy number transitions are naturally captured in the model. Current discrete-index HMM-based approaches do not, however, take into account heterogeneous information regarding the genomic overlap between clones. Moreover, the majority of existing methods are restricted to chromosome-wise analysis. RESULTS: We introduce a novel Segmental Maximum A Posteriori approach, SMAP, for DNA copy number profiling. Our method is based on discrete-index Hidden Markov Modeling and incorporates genomic distance and overlap between clones. We exploit a priori information through user-controllable parameterization that enables the identification of copy number deviations of various lengths and amplitudes. The model parameters may be inferred at a genome-wide scale to avoid overfitting of model parameters often resulting from chromosome-wise model inference. We report superior performances of SMAP on synthetic data when compared with two recent methods. When applied on our new experimental data, SMAP readily recognizes already known genetic aberrations including both large-scale regions with aberrant DNA copy number and changes affecting only single features on the array. We highlight the differences between the prediction of SMAP and the compared methods and show that SMAP accurately determines copy number changes and benefits from overlap consideration.
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| 4. |
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| 5. |
- Bergström, Ulrika, et al.
(author)
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Differential gene expression in the olfactory bulb following exposure to the olfactory toxicant 2,6-dichlorophenyl methylsulphone and its 2,5-dichlorinated isomer in mice
- 2007
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In: Neurotoxicology. - : Elsevier BV. - 0161-813X .- 1872-9711. ; 28:6, s. 1120-1128
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Journal article (peer-reviewed)abstract
- 2,6-Dichlorophenyl methylsulphone and a number of structurally related chemicals are CYP-activated toxicants in the olfactory mucosa in mice and rats. This toxicity involves both the olfactory neuroepithelium and its subepithelial nerves. In addition, 2,6-dichlorophenyl methylsulphone, induces glial acidic fibrillary protein expression (Gfap, a biomarker for gliosis) in the olfactory bulb, as well as long-lasting learning deficits and changes in spontaneous behavior in mice and rats. So far the 2,5-dichlorinated isomer has not been reported to cause toxicity in the olfactory system, although it gives rise to transient changes in spontaneous behavior. In the present study we used 15k cDNA gene arrays and real-time RTPCR to determine 2,6-dichlorophenyl methylsulphone-induced effects on gene expression in the olfactory bulb in mice. Seven days following a single ip dose of 2,6-dichlorophenyl methylsulphone, 56 genes were found to be differentially expressed in the olfactory bulb. Forty-one of these genes clustered into specific processes regulating, for instance, cell differentiation, cell migration and apoptosis. The genes selected for real-time RT-PCR were chosen to cover the range of B-values in the cDNA array analysis. Altered expression of Gfap, mt-Rnr2, Ncor1 and Olfml3 was confirmed. The expression of these genes was measured also in mice dosed with 2,5-dichlorophenyl methylsulphone, and mt-Rnr2 and Olfml3 were found to be altered also by this isomer. Combined with previous data, the results support the possibility that the persistent neurotoxicity induced by 2,6-dichlorophenyl methylsulphone in mice represents both an indirect and a direct effect on the brain. The 2,5-dichlorinated isomer, negative with regard to CYP-catalyzed toxicity in the olfactory mucosa, may prove useful to resolve this issue.
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| 6. |
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| 7. |
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| 8. |
- Hvidsten, Torgeir R, et al.
(author)
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A comprehensive analysis of the structure-function relationship in proteins based on local structure similarity
- 2009
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In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 4:7, s. e6266-
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Journal article (peer-reviewed)abstract
- BACKGROUND: Sequence similarity to characterized proteins provides testable functional hypotheses for less than 50% of the proteins identified by genome sequencing projects. With structural genomics it is believed that structural similarities may give functional hypotheses for many of the remaining proteins. METHODOLOGY/PRINCIPAL FINDINGS: We provide a systematic analysis of the structure-function relationship in proteins using the novel concept of local descriptors of protein structure. A local descriptor is a small substructure of a protein which includes both short- and long-range interactions. We employ a library of commonly reoccurring local descriptors general enough to assemble most existing protein structures. We then model the relationship between these local shapes and Gene Ontology using rule-based learning. Our IF-THEN rule model offers legible, high resolution descriptions that combine local substructures and is able to discriminate functions even for functionally versatile folds such as the frequently occurring TIM barrel and Rossmann fold. By evaluating the predictive performance of the model, we provide a comprehensive quantification of the structure-function relationship based only on local structure similarity. Our findings are, among others, that conserved structure is a stronger prerequisite for enzymatic activity than for binding specificity, and that structure-based predictions complement sequence-based predictions. The model is capable of generating correct hypotheses, as confirmed by a literature study, even when no significant sequence similarity to characterized proteins exists. CONCLUSIONS/SIGNIFICANCE: Our approach offers a new and complete description and quantification of the structure-function relationship in proteins. By demonstrating how our predictions offer higher sensitivity than using global structure, and complement the use of sequence, we show that the presented ideas could advance the development of meta-servers in function prediction.
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| 9. |
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| 10. |
- Hvidsten, Torgeir R., et al.
(author)
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Local descriptors of protein structure : A systematic analysis of the sequence-structure relationship in proteins using short- and long-range interactions
- 2009
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In: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 75:4, s. 870-884
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Journal article (peer-reviewed)abstract
- Local protein structure representations that incorporate long-range contacts between residues are often considered in protein structure comparison but have found relatively little use in structure prediction where assembly from single backbone fragments dominates. Here, we introduce the concept of local descriptors of protein structure to characterize local neighborhoods of amino acids including short- and long-range interactions. We build a library of recurring local descriptors and show that this library is general enough to allow assembly of unseen protein structures. The library could on average re-assemble 83% of 119 unseen structures, and showed little or no performance decrease between homologous targets and targets with folds not represented among domains used to build it. We then systematically evaluate the descriptor library to establish the level of the sequence signal in sets of protein fragments of similar geometrical conformation. In particular, we test whether that signal is strong enough to facilitate correct assignment and alignment of these local geometries to new sequences. We use the signal to assign descriptors to a test set of 479 sequences with less than 40% sequence identity to any domain used to build the library, and show that on average more than 50% of the backbone fragments constituting descriptors can be correctly aligned. We also use the assigned descriptors to infer SCOP folds, and show that correct predictions can be made in many of the 151 cases where PSI-BLAST was unable to detect significant sequence similarity to proteins in the library. Although the combinatorial problem of simultaneously aligning several fragments to sequence is a major bottleneck compared with single is that correct alignments imply correct long range distance constraints. The lack of these constraints is most likely the major reason why structure prediction methods fail to consistently produce adequate models when good templates are unavailable or undetectable. Thus, we believe that the current study offers new and valuable insight into the prediction of sequence-structure relationships in proteins.
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| 11. |
- Hvidsten, Torgeir R., et al.
(author)
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Rough sets in bioinformatics
- 2007
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In: Transactions on Rough Sets VII: Lecture Notes in Computer Science 4400. ; , s. 225-243
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Research review (pop. science, debate, etc.)
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| 12. |
- Strömbergsson, Helena, 1975-
(author)
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Chemogenomics: Models of Protein-Ligand Interaction Space
- 2009
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Doctoral thesis (other academic/artistic)abstract
- The large majority of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand recognition is thus central to drug discovery and design. Improved experimental techniques have resulted in an immense growth of drug target information. This has stimulated the development of chemogenomics and proteochemometrics (PCM) that take target information as well as ligand information into account to study the genomic effect of potential drugs. This thesis is concerned with modeling protein-ligand recognition, and the aim is to develop models that generalize to the entire protein-ligand space. To this end, protein-ligand interaction data has been extracted and manually curated from public databases, protein and ligand descriptors have been computed, and predictive models have been induced with machine-learning methods. An introduction to chemogenomics, machine learning, and PCM modeling is given in the thesis summary, which is followed by five research papers. Paper I shows that it is possible to induce interpretable models with a non-linear rule-based method, and paper II demonstrates that local descriptors of protein structure may be used to induce PCM models that cover proteins differing in sequence and fold. In paper III, such local descriptors are used to induce a PCM model on a large dataset that includes all major enzyme classes. This demonstrates that the local descriptors may be used to induce generalized models that span the entire known structural enzyme-ligand space. Paper IV describes a step towards proteome-wide PCM models, and shows that it is possible to predict high- and low-affinity complexes using a set of protein and ligand descriptors that do not require knowledge of 3D structure. Finally, paper V presents a method to visualize and compare protein-ligand chemogenomic subspaces, which may be used to predict unwanted cross-interactions of drugs with other proteins in the proteome.
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| 13. |
- Strömbergsson, Helena, et al.
(author)
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Generalized modeling of enzyme-ligand interactions using proteochemometrics and local protein substructures
- 2006
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In: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 65:3, s. 568-579
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Journal article (peer-reviewed)abstract
- Modeling and understanding protein-ligand interactions is one of the most important goals in computational drug discovery. To this end, proteochemometrics uses structural and chemical descriptors from several proteins and several ligands to induce interaction-models. Here, we present a new and generalized approach in which proteins varying greatly in terms of sequence and structure are represented by a library of local substructures. Using linear regression and rule-based learning, we combine such local substructures with chemical descriptors from the ligands to model binding affinity for a training set of hydrolase and lyase enzymes. We evaluate the predictive performance of these models using cross validation and sets of unseen ligand with unknown three-dimensional structure. The models are shown to generalize by outperforming models using descriptors from only proteins or only ligands, or models using global structure similarities rather than local similarities. Thus, we demonstrate that this approach is capable of describing dependencies between local structural properties and ligands in otherwise dissimilar protein structures. These dependencies are often, but not always, associated with local substructures that are in contact with the ligands. Finally, we show that strongly bound enzyme-ligand complexes require the presence of particular local substructures, while weakly bound complexes may be described by the absence of certain properties. The results demonstrate that the alignment-independent approach using local substructures is capable of describing protein-ligand interaction for largely different proteins and hence opens up for proteochemometrics-analysis of the interaction-space of entire proteomes. Current approaches are limited to families of closely related proteins. families of closely related proteins.
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| 14. |
- Wilczynski, Bartek, et al.
(author)
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Using local gene expression similarities to discover regulatory binding site modules
- 2006
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In: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 7, s. 505-
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Journal article (peer-reviewed)abstract
- Background: We present an approach designed to identify gene regulation patterns using sequence and expression data collected for Saccharomyces cerevisae. Our main goal is to relate the combinations of transcription factor binding sites (also referred to as binding site modules) identified in gene promoters to the expression of these genes. The novel aspects include local expression similarity clustering and an exact IF-THEN rule inference algorithm. We also provide a method of rule generalization to include genes with unknown expression profiles. Results: We have implemented the proposed framework and tested it on publicly available datasets from yeast S. cerevisae. The testing procedure consists of thorough statistical analyses of the groups of genes matching the rules we infer from expression data against known sets of coregulated genes. For this purpose we have used published ChIP-Chip data and Gene Ontology annotations. In order to make these tests more objective we compare our results with recently published similar studies. Conclusion: Results we obtain show that local expression similarity clustering greatly enhances overall quality of the derived rules, both in terms of enrichment of Gene Ontology functional annotation and coherence with ChIP-Chip binding data. Our approach thus provides reliable hypotheses on co-regulation that can be experimentally verified. An important feature of the method is its reliance only on widely accessible sequence and expression data. The same procedure can be easily applied to other microbial organisms.
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