| 1. |
- Guala, Dimitri, 1979-
(författare)
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Functional association networks for disease gene prediction
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Mapping of the human genome has been instrumental in understanding diseasescaused by changes in single genes. However, disease mechanisms involvingmultiple genes have proven to be much more elusive. Their complexityemerges from interactions of intracellular molecules and makes them immuneto the traditional reductionist approach. Only by modelling this complexinteraction pattern using networks is it possible to understand the emergentproperties that give rise to diseases.The overarching term used to describe both physical and indirect interactionsinvolved in the same functions is functional association. FunCoup is oneof the most comprehensive networks of functional association. It uses a naïveBayesian approach to integrate high-throughput experimental evidence of intracellularinteractions in humans and multiple model organisms. In the firstupdate, both the coverage and the quality of the interactions, were increasedand a feature for comparing interactions across species was added. The latestupdate involved a complete overhaul of all data sources, including a refinementof the training data and addition of new class and sources of interactionsas well as six new species.Disease-specific changes in genes can be identified using high-throughputgenome-wide studies of patients and healthy individuals. To understand theunderlying mechanisms that produce these changes, they can be mapped tocollections of genes with known functions, such as pathways. BinoX wasdeveloped to map altered genes to pathways using the topology of FunCoup.This approach combined with a new random model for comparison enables BinoXto outperform traditional gene-overlap-based methods and other networkbasedtechniques.Results from high-throughput experiments are challenged by noise and biases,resulting in many false positives. Statistical attempts to correct for thesechallenges have led to a reduction in coverage. Both limitations can be remediedusing prioritisation tools such as MaxLink, which ranks genes using guiltby association in the context of a functional association network. MaxLink’salgorithm was generalised to work with any disease phenotype and its statisticalfoundation was strengthened. MaxLink’s predictions were validatedexperimentally using FRET.The availability of prioritisation tools without an appropriate way to comparethem makes it difficult to select the correct tool for a problem domain.A benchmark to assess performance of prioritisation tools in terms of theirability to generalise to new data was developed. FunCoup was used for prioritisationwhile testing was done using cross-validation of terms derived fromGene Ontology. This resulted in a robust and unbiased benchmark for evaluationof current and future prioritisation tools. Surprisingly, previously superiortools based on global network structure were shown to be inferior to a localnetwork-based tool when performance was analysed on the most relevant partof the output, i.e. the top ranked genes.This thesis demonstrates how a network that models the intricate biologyof the cell can contribute with valuable insights for researchers that study diseaseswith complex genetic origins. The developed tools will help the researchcommunity to understand the underlying causes of such diseases and discovernew treatment targets. The robust way to benchmark such tools will help researchersto select the proper tool for their problem domain.
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| 2. |
- Hillerton, Thomas, 1992-
(författare)
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In silico modelling for refining gene regulatory network inference
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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. |
- Schmitt, Thomas, 1981-
(författare)
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Inference of functional association networks and gene orthology
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Most proteomics and genomics experiments are performed on a small set of well-studied model organisms and their results are generalized to other species. This is possible because all species are evolutionarily related. When transferring information across species, orthologs are the most likely candidates for functional equivalence. The InParanoid algorithm, which predicts orthology relations by sequence similarity based clustering, was improved by increasing its robustness for low complexity sequences and the corresponding database was updated to include more species.A plethora of different orthology inference methods exist, each featuring different formats. We have addressed the great need for standardization this creates with the development of SeqXML and OrthoXML, two formats that standardize the input and output of ortholog inference.Essentially all biological processes are the result of a complex interplay between different biomolecules. To fully understand the function of genes or gene products one needs to identify these relations. Integration of different types of high-throughput data allows the construction of genome-wide functional association networks that give a global picture of the relation landscape.FunCoup is a framework that performs this integration to create functional association networks for 11 model organisms. Orthology assignments from InParanoid are used to transfer high-throughput data between species, which contributes with more than 50% to the total functional association evidence. We have developed procedures to incorporate new evidence types, improved the procedures of existing evidence types, created networks for additional species, and added significantly more data. Furthermore, the integration procedure was improved to account for data redundancy and to increase its overall robustness. Many of these changes were possible because the computational framework was re-implemented from scratch.
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| 4. |
- Seçilmiş, Deniz, 1991-
(författare)
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Improving the accuracy of gene regulatory network inference from noisy data
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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