| 1. |
- Alexeyenko, Andrey, et al.
(författare)
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Comparative interactomics with Funcoup 2.0
- 2012
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 40:D1, s. D821-D828
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Tidskriftsartikel (refereegranskat)abstract
- FunCoup (http://FunCoup.sbc.su.se) is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup achieves high coverage by orthology-based integration of data sources from different model organisms and from different platforms. We here present release 2.0 in which the data sources have been updated and the methodology has been refined. It contains a new data type Genetic Interaction, and three new species: chicken, dog and zebra fish. As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks. The number of high-confidence network links has increased dramatically. For instance, the human network has more than eight times as many links above confidence 0.5 as the previous release. FunCoup provides facilities for analysing the conservation of subnetworks in multiple species. We here explain how to do comparative interactomics on the FunCoup website.
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| 2. |
- Morgan, Daniel, 1988-, et al.
(författare)
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A generalized framework for controlling FDR in gene regulatory network inference
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 35:6, s. 1026-1032
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Inference of gene regulatory networks (GRNs) from perturbation data can give detailed mechanistic insights of a biological system. Many inference methods exist, but the resulting GRN is generally sensitive to the choice of method-specific parameters. Even though the inferred GRN is optimal given the parameters, many links may be wrong or missing if the data is not informative. To make GRN inference reliable, a method is needed to estimate the support of each predicted link as the method parameters are varied.Results: To achieve this we have developed a method called nested bootstrapping, which applies a bootstrapping protocol to GRN inference, and by repeated bootstrap runs assesses the stability of the estimated support values. To translate bootstrap support values to false discovery rates we run the same pipeline with shuffled data as input. This provides a general method to control the false discovery rate of GRN inference that can be applied to any setting of inference parameters, noise level, or data properties. We evaluated nested bootstrapping on a simulated dataset spanning a range of such properties, using the LASSO, Least Squares, RNI, GENIE3 and CLR inference methods. An improved inference accuracy was observed in almost all situations. Nested bootstrapping was incorporated into the GeneSPIDER package, which was also used for generating the simulated networks and data, as well as running and analyzing the inferences.
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| 3. |
- Morgan, Daniel, et al.
(författare)
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Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.
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| 4. |
- Seçilmiş, Deniz, 1991-, et al.
(författare)
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Knowledge of the perturbation design is essential for accurate gene regulatory network inference
- 2022
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- The gene regulatory network (GRN) of a cell executes genetic programs in response to environmental and internal cues. Two distinct classes of methods are used to infer regulatory interactions from gene expression: those that only use observed changes in gene expression, and those that use both the observed changes and the perturbation design, i.e. the targets used to cause the changes in gene expression. Considering that the GRN by definition converts input cues to changes in gene expression, it may be conjectured that the latter methods would yield more accurate inferences but this has not previously been investigated. To address this question, we evaluated a number of popular GRN inference methods that either use the perturbation design or not. For the evaluation we used targeted perturbation knockdown gene expression datasets with varying noise levels generated by two different packages, GeneNetWeaver and GeneSpider. The accuracy was evaluated on each dataset using a variety of measures. The results show that on all datasets, methods using the perturbation design matrix consistently and significantly outperform methods not using it. This was also found to be the case on a smaller experimental dataset from E. coli. Targeted gene perturbations combined with inference methods that use the perturbation design are indispensable for accurate GRN inference.
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| 5. |
- Seçilmiş, Deniz, et al.
(författare)
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Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data
- 2020
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Ingår i: npj Systems Biology and Applications. - : Springer Science and Business Media LLC. - 2056-7189. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where similar to 1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.
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| 6. |
- Studham, Matthew E., et al.
(författare)
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Functional association networks as priors for gene regulatory network inference
- 2014
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 30:12, s. 130-138
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences. Results: This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data.
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| 7. |
- Tjärnberg, Andreas, et al.
(författare)
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Avoiding pitfalls in L-1-regularised inference of gene networks
- 2015
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Ingår i: Molecular Biosystems. - : Royal Society of Chemistry (RSC). - 1742-206X .- 1742-2051. ; 11:1, s. 287-296
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Tidskriftsartikel (refereegranskat)abstract
- Statistical regularisation methods such as LASSO and related L-1 regularised regression methods are commonly used to construct models of gene regulatory networks. Although they can theoretically infer the correct network structure, they have been shown in practice to make errors, i.e. leave out existing links and include non-existing links. We show that L-1 regularisation methods typically produce a poor network model when the analysed data are ill-conditioned, i.e. the gene expression data matrix has a high condition number, even if it contains enough information for correct network inference. However, the correct structure of network models can be obtained for informative data, data with such a signal to noise ratio that existing links can be proven to exist, when these methods fail, by using least-squares regression and setting small parameters to zero, or by using robust network inference, a recent method taking the intersection of all non-rejectable models. Since available experimental data sets are generally ill-conditioned, we recommend to check the condition number of the data matrix to avoid this pitfall of L-1 regularised inference, and to also consider alternative methods.
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| 8. |
- Tjärnberg, Andreas, et al.
(författare)
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GeneSPIDER - gene regulatory network inference benchmarking with controlled network and data properties
- 2017
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Ingår i: Molecular Biosystems. - : Royal Society of Chemistry (RSC). - 1742-206X .- 1742-2051. ; 13:7, s. 1304-1312
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Tidskriftsartikel (refereegranskat)abstract
- A key question in network inference, that has not been properly answered, is what accuracy can be expected for a given biological dataset and inference method. We present GeneSPIDER - a Matlab package for tuning, running, and evaluating inference algorithms that allows independent control of network and data properties to enable data-driven benchmarking. GeneSPIDER is uniquely suited to address this question by first extracting salient properties from the experimental data and then generating simulated networks and data that closely match these properties. It enables data-driven algorithm selection, estimation of inference accuracy from biological data, and a more multifaceted benchmarking. Included are generic pipelines for the design of perturbation experiments, bootstrapping, analysis of linear dependence, sample selection, scaling of SNR, and performance evaluation. With GeneSPIDER we aim to move the goal of network inference benchmarks from simple performance measurement to a deeper understanding of how the accuracy of an algorithm is determined by different combinations of network and data properties.
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| 9. |
- 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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| 10. |
- Tjärnberg, Andreas, et al.
(författare)
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Optimal Sparsity Criteria for Network Inference
- 2013
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Ingår i: Journal of Computational Biology. - : Mary Ann Liebert Inc. - 1066-5277 .- 1557-8666. ; 20:5, s. 398-408
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Tidskriftsartikel (refereegranskat)abstract
- Gene regulatory network inference (that is, determination of the regulatory interactions between a set of genes) provides mechanistic insights of central importance to research in systems biology. Most contemporary network inference methods rely on a sparsity/regularization coefficient, which we call zeta (zeta), to determine the degree of sparsity of the network estimates, that is, the total number of links between the nodes. However, they offer little or no advice on how to select this sparsity coefficient, in particular, for biological data with few samples. We show that an empty network is more accurate than estimates obtained for a poor choice of zeta. In order to avoid such poor choices, we propose a method for optimization of zeta, which maximizes the accuracy of the inferred network for any sparsity-dependent inference method and data set. Our procedure is based on leave-one-out cross-optimization and selection of the zeta value that minimizes the prediction error. We also illustrate the adverse effects of noise, few samples, and uninformative experiments on network inference as well as our method for optimization of zeta. We demonstrate that our zeta optimization method for two widely used inference algorithms-Glmnet and NIR-gives accurate and informative estimates of the network structure, given that the data is informative enough.
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