The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases.

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Large scale gene regulatory network inference with a multi-level strategy

Molecular BioSystems ◽

10.1039/c5mb00560d ◽

2016 ◽

Vol 12 (2) ◽

pp. 588-597 ◽

Cited By ~ 14

Author(s):

Jun Wu ◽

Xiaodong Zhao ◽

Zongli Lin ◽

Zhifeng Shao

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Large Scale ◽

Network Inference ◽

Biological Processes ◽

Molecular Processes ◽

Gene Regulatory Network Inference ◽

Cell Functions ◽

Multi Level ◽

Gene Regulatory

Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology.

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Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

10.1101/735514 ◽

2019 ◽

Author(s):

Daniel Morgan ◽

Matthew Studham ◽

Andreas Tjärnberg ◽

Holger Weishaupt ◽

Fredrik J. Swartling ◽

...

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Network Inference ◽

Gene Regulatory Network Inference ◽

Validation Data ◽

Regulatory Interactions ◽

Link Type ◽

Gene Regulatory ◽

Novel Interactions

AbstractThe 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. Reliable inference of GRNs is however still a major challenge in systems biology.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 crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.Data available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

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Inferring Gene Regulatory Networks from Genetical Genomics Data

Handbook of Research on Computational Methodologies in Gene Regulatory Networks ◽

10.4018/978-1-60566-685-3.ch004 ◽

2010 ◽

pp. 79-107 ◽

Cited By ~ 2

Author(s):

Bing Liu ◽

Ina Hoeschele ◽

Alberto de la Fuente

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Network Inference ◽

Dna Marker ◽

Search Space ◽

Genetical Genomics ◽

Gene Regulatory Network Inference ◽

Gene Regulatory

In this chapter, we review the current state of Gene Regulatory Network inference based on ‘Genetical Genomics’ experiments (Brem & Kruglyak, 2005; Brem, Yvert, Clinton & Kruglyak, 2002; Jansen, 2003; Jansen & Nap, 2001; Schadt et al., 2003) as a special case of causal network inference in ‘Systems Genetics’ (Threadgill, 2006). In a Genetical Genomics experiment, a segregating or genetically randomized population is DNA marker genotyped and gene-expression profiled on a genomewide scale. The genotypes are regarded as natural, multifactorial perturbations resulting in different gene-expression ‘phenotypes’, and causal relationships can therefore be established between the measured genotypes and the gene-expression phenotypes. In this chapter, we review different computational approaches to Gene Regulatory Network inference based on the joint analysis of DNA marker and expression data and additionally of DNA sequence information if available. This includes different methods for expression QTL mapping, selection of regulator-target pairs, construction of an encompassing network, which strongly constrains the network search space, and pairwise and multivariate methods for Gene Regulatory Network inference, such as Bayesian Networks and Structural Equation Modeling.

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gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

10.1101/2021.07.13.452214 ◽

2021 ◽

Author(s):

Marouen Ben Guebila ◽

Daniel C Morgan ◽

Kimberly Glass ◽

Marieke Lydia Kuijjer ◽

Dawn L DeMeo ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Network ◽

Regulatory Networks ◽

Large Scale ◽

Network Inference ◽

Cost Effective ◽

Gene Regulatory Network Inference ◽

Genomic Studies ◽

On Line ◽

Gene Regulatory

Gene regulatory network inference allows for the study of transcriptional control to identify the alteration of cellular processes in human diseases. Our group has developed several tools to model a variety of regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, and gene regulation in individual samples (LIONESS). These methods work by performing repeated operations on data matrices in order to integrate information across multiple lines of biological evidence. This limits their use for large-scale genomic studies due to the associated high computational burden. To address this limitation, we developed gpuZoo, which includes GPU-accelerated implementations of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than the multi-core CPU implementation of the same methods. gpuZoo takes advantage of the modern multi-GPU device architecture to build a population of sample-specific gene regulatory networks with similar runtime and cost improvements by combining GPU acceleration with an efficient on-line derivation. Taken together, gpuZoo allows parallel and on-line gene regulatory network inference in large-scale genomic studies with cost-effective performance. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.

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Context likelihood of relatedness with maximal information coefficient for Gene Regulatory Network inference

2015 18th International Conference on Computer and Information Technology (ICCIT) ◽

10.1109/iccitechn.2015.7488088 ◽

2015 ◽

Author(s):

M. A. H. Akhand ◽

R. N. Nandi ◽

S. M. Amran ◽

K. Murase

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Network Inference ◽

Gene Regulatory Network Inference ◽

Information Coefficient ◽

Gene Regulatory ◽

Maximal Information Coefficient

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Stages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role

Evolutionary Algorithms ◽

10.5772/15182 ◽

2011 ◽

Cited By ~ 4

Author(s):

Alina Sirbu ◽

Heather J. ◽

Martin Crane

Keyword(s):

Evolutionary Algorithm ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Network Inference ◽

Gene Regulatory Network Inference ◽

Gene Regulatory

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A posterior probability approach for gene regulatory network inference in genetic perturbation data

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2016041 ◽

2016 ◽

Vol 13 (6) ◽

pp. 1241-1251 ◽

Cited By ~ 9

Author(s):

William Chad Young ◽

Adrian E. Raftery ◽

Ka Yee Yeung

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Posterior Probability ◽

Network Inference ◽

Gene Regulatory Network Inference ◽

Probability Approach ◽

Perturbation Data ◽

Gene Regulatory

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BRANE Cut: Biologically-Related A priori Network Enhancement with Graph cuts for Gene Regulatory Network Inference

10.1101/032383 ◽

2015 ◽

Author(s):

Aurélie Pirayre ◽

Camille Couprie ◽

Frédérique Bidard ◽

Laurent Duval ◽

Jean-Christophe Pesquet

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Gene Networks ◽

Network Inference ◽

State Of The Art ◽

A Priori ◽

Graph Cuts ◽

Gene Regulatory Network Inference ◽

Gene Regulatory ◽

Inference Methods

Background: Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires additional constraints that incorporate a priori information on gene interactions. Methods: Weighting all possible pairwise gene relationships by a probability of edge presence, we formulate the regulatory network inference as a discrete variational problem on graphs. We enforce biologically plausible coupling between groups and types of genes by minimizing an edge labeling functional coding for a priori structures. The optimization is carried out with Graph cuts, an approach popular in image processing and computer vision. We compare the inferred regulatory networks to results achieved by the mutual-information-based Context Likelihood of Relatedness (CLR) method and by the state-of-the-art GENIE3, winner of the DREAM4 multifactorial challenge. Results: Our BRANE Cut approach infers more accurately the five DREAM4 in silico networks (with improvements from 6% to 11%). On a real Escherichia coli compendium, an improvement of 11.8% compared to CLR and 3% compared to GENIE3 is obtained in terms of Area Under Precision-Recall curve. Up to 48 additional verified interactions are obtained over GENIE3 for a given precision. On this dataset involving 4345 genes, our method achieves a performance similar to that of GENIE3, while being more than seven times faster. The BRANE Cut code is available at: http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-cut.html Conclusions: BRANE Cut is a weighted graph thresholding method. Using biologically sound penalties and data-driven parameters, it improves three state-of-the-art GRN inference methods. It is applicable as a generic network inference post-processing, due its computational efficiency.

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