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- 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. |
- 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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| 3. |
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| 4. |
- 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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