| 1. |
- Castresana Aguirre, Miguel, 1991-
(author)
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From networks to pathway analysis
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Biological mechanisms stem from complex intracellular interactions spanning across different levels of regulation. Mapping these interactions is fundamental for the understanding of all types of biological conditions, including complex diseases. Each experimental approach carries its own bias and noise. Combining heterogeneous data sources reduces noise and gives a broader sense of the interactions between genes known as functional association, where both direct and indirect interactions are captured.FunCoup is one of the most comprehensive functional association databases, providing networks for 22 organisms in all domains of life. FunCoup uses a naïve Bayesian integration approach to combine 11 different data types and increases the coverage by transferring associations between species via orthologs. Additional insights into the mechanisms of a gene network are provided through tissue specificity filtering and directed regulatory links.Even though FunCoup provides a comprehensive map of the intracellular machinery, gaining insights into conditions such as diseases requires a functional level analysis rather than a gene level analysis. Thus, studying genes that are involved in a condition from a functional perspective requires the usage of pathway enrichment analysis. Several approaches exist, from basic gene overlap to more elaborate analyses that use functional association networks. ANUBIX is a novel network-based analysis (NBA) method that overcomes the high false positive rate issue that previous state-of-the-art NBA approaches have. Additionally, even with accurate methods, a commonly ignored problem is that gene sets derived from experiments are often noisy or contain multiple mechanisms, mixing different pathways which weakens their association to the condition under study. To increase the sensitivity of pathway analysis, we developed a pipeline to cluster gene sets into more homogeneous parts with the aim of unraveling all the mechanisms activated in the studied condition. To facilitate the usage of these tools, we built a web server called PathBIX, a user-friendly platform that allows interactive analysis of all species in FunCoup against multiple pathway databases.
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| 2. |
- Hillerton, Thomas, 1992-
(author)
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In silico modelling for refining gene regulatory network inference
- 2023
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Doctoral thesis (other academic/artistic)abstract
- Gene regulation is at the centre of all cellular functions, regulating the cell's healthy and pathological responses. The interconnected system of regulatory interactions is known as the gene regulatory network (GRN), where genes influence each other to maintain strict and robust control. Today a large number of methods exist for inferring GRNs, which necessitates benchmarking to determine which method is most suitable for a specific goal. Paper I presents such a benchmark focusing on the effect of using known perturbations to infer GRNs. A further challenge when studying GRNs is that experimental data contains high levels of noise and that artefacts may be introduced by the experiment itself. The LSCON method was developed in paper II to reduce the effect of one such artefact that can occur if the expression of a gene shows no or minimal change across most or all experiments. With few fully determined biological GRNs available, it is problematic to use these to evaluate an inference method's correctness. Instead, the GRN field relies on simulated data, using a known GRN and generating the corresponding data. When simulating GRNs, capturing the topological properties of the biological GRN is vital. The FFLatt algorithm was developed in paper III to create scale-free, feed-forward loop motif-enriched GRNs, capturing two of the most prominent topological features in biological GRNs. Once a high-quality GRN is obtained, the next step is to simulate gene expression data corresponding to the GRN. In paper IV, building on the FFLatt method, an open-source Python simulation tool called GeneSNAKE was developed to generate expression data for benchmarking purposes. GeneSNAKE allows the user to control a wide range of network and data properties and improves on previous tools by featuring a variety of perturbation schemes along with the ability to control noise and modify the perturbation strength.
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| 3. |
- Persson, Emma, 1991-
(author)
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Big data networks and orthology analysis
- 2023
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Doctoral thesis (other academic/artistic)abstract
- Understanding biological systems in complex organisms is important in life science in order to comprehend the interplay of genes, proteins, and compounds causing complex diseases. As biological systems are intricate, bioinformatics tools, models, and algorithms are of the utmost importance to understand the bigger picture and decipher biological meaning from the vast amounts of information available from biological experiments and predictions. Bioinformatics programs and algorithms do not only depend on information from experiments, but also on information generated from other tools in order to draw accurate conclusions and make predictions. Prediction of orthologs, genes having a common ancestry, separated by a speciation event, are important building blocks for a wide variety of tools and analysis pipelines, as they can be used to transfer gene function between species. Orthologs can for example be used to map genes of model organisms to genes in humans in studies of drug targets. They are extensively used in functional association networks in order to transfer information between species. Functional association networks are models of associations between genes or proteins, where associations can be derived from experimental evidence of different types, from the species itself, or transferred from other species using orthologs. The networks can be used to explore the context and neighbors of a gene, but also for a variety of higher-level analyses, e.g. network-based pathway enrichment analysis. In pathway enrichment analysis the networks can be utilized to contextualize experimental gene sets and annotate them with biological functions. As these tools depend on each other, it is of great importance that the networks used in pathway enrichment analysis are comprehensive and accurate, and that the orthologs used in the networks are relevant and significant. In this thesis, the development and improvement of five bioinformatics tools within three areas of bioinformatics are presented. Despite the tools residing within slightly different areas, they all rely on each other, and can all on different levels improve our understanding of biological functions and biological meaning, from the level of orthology analysis to functional association networks to pathway enrichment analysis.
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| 4. |
- Seçilmiş, Deniz, 1991-
(author)
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Improving the accuracy of gene regulatory network inference from noisy data
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Gene regulatory networks (GRNs) control physiological and pathological processes in a living organism, and their accurate inference from measured gene expression can identify therapeutic mechanisms for complex diseases such as cancers. The biggest obstacle in achieving the accurate reconstruction of GRNs is called ‘noise’, which considerably alters the measured gene expression because the noise generally dominates the biological signal. This situation needs to be addressed carefully so that GRN inference methods do not estimate a fit to the noise instead of the underlying biological signal. Potential noise compensation approaches are a must if the goal is to reconstruct the true system. To this end, within the scope of this doctoral thesis, I developed two methods that, in different ways, overcome the obstacles introduced by noise in gene expression data. Method 1 allows the collection of more informative subsets of genes whose expression is not as highly affected as those which cause the system to be overall uninformative. Method 2 infers a perturbation design that is better suited to the gene expression data than the originally intended design, and therefore produces more accurate GRNs at high noise levels. Furthermore, a benchmark study was carried out which compares the methodological backgrounds of GRN inference methods in terms of whether they utilize knowledge of the perturbation design or not, which clearly shows that utilization of the perturbation design is essential for accurate inference of GRNs. Finally a method is presented to improve GRN inference accuracy by selecting the GRN with the optimal sparsity based on information theoretical criteria. The three new methods (PAPERS I, II and IV) can also be used together, which is shown in this thesis to improve the GRN inference accuracy considerably more than the methods separately. As inference of accurate GRNs is a major challenge in gene regulation, the methods presented in this thesis represent an important contribution to move the field forward.
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| 5. |
- Friedrich, Stefanie, 1973-
(author)
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Computational Analysis of Tumour Heterogeneity
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of non-responding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity.The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity.
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