| 1. |
- Hillerton, Thomas, et al.
(författare)
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GeneSNAKE: a Python package for benchmarking and simulation of gene regulatory networks and expression data.
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Understanding how genes interact with and regulate each other is a key challenge in systems biology. One of the primary methods to study this is through gene regulatory networks (GRNs). The field of GRN inference however faces many challenges, such as the complexity of gene regulation and high noise levels, which necessitates effective tools for evaluating inference methods. For this purpose, data that corresponds to a known GRN, from various conditions and experimental setups is necessary, which is only possible to attain via simulation. Existing tools for simulating data for GRN inference have limitations either in the way networks are constructed or data is produced, and are often not flexible for adjusting the algorithm or parameters. To overcome these issues we present GeneSNAKE, a Python package designed to allow users to generate biologically realistic GRNs, and from a GRN simulate expression data for benchmarking purposes. GeneSNAKE allows the user to control a wide range of network and data properties. GeneSNAKE improves on previous work in the field by adding a perturbation model that allows for a greater range of perturbation schemes along with the ability to control noise and modify the perturbation strength. For benchmarking, GeneSNAKE offers a number of functions both for comparing a true GRN to an inferred GRN, and to study properties in data and GRN models. These functions can in addition be used to study properties of biological data to produce simulated data with more realistic properties. GeneSNAKE is an open-source, comprehensive simulation and benchmarking package with powerful capabilities that are not combined in any other single package, and thanks to the Python implementation it is simple to extend and modify by a user.
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| 2. |
- Dessimoz, Christophe, et al.
(författare)
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Toward community standards in the quest for orthologs
- 2012
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 28:6, s. 900-904
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The identification of orthologs-genes pairs descended from a common ancestor through speciation, rather than duplication-has emerged as an essential component of many bioinformatics applications, ranging from the annotation of new genomes to experimental target prioritization. Yet, the development and application of orthology inference methods is hampered by the lack of consensus on source proteomes, file formats and benchmarks. The second 'Quest for Orthologs' meeting brought together stakeholders from various communities to address these challenges. We report on achievements and outcomes of this meeting, focusing on topics of particular relevance to the research community at large. The Quest for Orthologs consortium is an open community that welcomes contributions from all researchers interested in orthology research and applications.
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| 3. |
- Guala, Dimitri, et al.
(författare)
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Experimental validation of predicted cancer genes using FRET
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Huge amounts of data are generated in genome wide experiments, designed to investigatediseases with complex genetic causes. Follow up of all potential leads produced by suchexperiments is currently cost prohibitive and time consuming. Gene prioritization toolsalleviate these constraints by directing further experimental efforts towards the mostpromising candidate targets. Recently a gene prioritization tool called MaxLink was shown tooutperform other widely used state-of-the-art prioritization tools in a large scale in silicobenchmark. An experimental validation of predictions made by MaxLink has however beenlacking. In this study we used Fluorescent Resonance Energy Transfer, an establishedexperimental technique for detection of protein-protein interactions, to validate potentialcancer genes predicted by MaxLink. Our results provide confidence in the use of MaxLink forselection of new targets in the battle with polygenic diseases.
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| 4. |
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| 5. |
- 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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| 6. |
- 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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| 7. |
- Hong, Junmei, et al.
(författare)
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Focusing on RISC assembly in mammalian cells.
- 2008
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Ingår i: Biochem Biophys Res Commun. - : Elsevier BV. - 1090-2104 .- 0006-291X. ; 368:3, s. 703-8
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- RISC (RNA-induced silencing complex) is a central protein complex in RNAi, into which a siRNA strand is assembled to become effective in gene silencing. By using an in vitro RNAi reaction based on Drosophila embryo extract, an asymmetric model was recently proposed for RISC assembly of siRNA strands, suggesting that the strand that is more loosely paired at its 5' end is selectively assembled into RISC and results in target gene silencing. However, in the present study, we were unable to establish such a correlation in cell-based RNAi assays, as well as in large-scale RNAi data analyses. This suggests that the thermodynamic stability of siRNA is not a major determinant of gene silencing in mammalian cells. Further studies on fork siRNAs showed that mismatch at the 5' end of the siRNA sense strand decreased RISC assembly of the antisense strand, but surprisingly did not increase RISC assembly of the sense strand. More interestingly, measurements of melting temperature showed that the terminal stability of fork siRNAs correlated with the positions of the mismatches, but not gene silencing efficacy. In summary, our data demonstrate that there is no definite correlation between siRNA stability and gene silencing in mammalian cells, which suggests that instead of thermodynamic stability, other features of the siRNA duplex contribute to RISC assembly in RNAi.
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| 8. |
- 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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| 9. |
- 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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| 10. |
- Tjärnberg, Andreas, et al.
(författare)
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GeneSPIDER - Generation and Simulation Package for Informative Data ExploRation
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- A range of tools are available to model, simulate and analyze gene regulatory networks (GRNs). However, these tools provide limited ability to control network topology, system dynamics, design of experiments, data properties, or noise characteristics. Independent control of these properties is the key to drawing conclusions on which inference method to use and what result to expect from it, as well as obtaining desired approximations of real biological systems. To draw conclusions on the relation between a network or data property and the performance of an inference method in simulations, system approximations with varying properties are needed. We present a Matlab package \gs for generation and analysis of networks and data in a dynamical systems framework with focus on the ability to vary properties. It supplies not only essential components that have been missing, but also wrappers to existing tools in common use. In particular, it contains tools for controlling and analyzing network topology (random, small-world, scale-free), stability of linear time-invariant systems, signal to noise ratio (SNR), and Interampatteness. It also contains methods for design of perturbation experiments, bootstrapping, analysis of linear dependence, sample selection, scaling of the SNR, and performance evaluation. GeneSPIDER offers control of network and data properties in simulations, together with tools to analyze these properties and draw conclusions on the quality of inferred GRNs. It can be fetched freely from the online =git= repository https://bitbucket.org/sonnhammergrni/genespider.
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