A novel probabilistic generator for large-scale gene association networks

Motivation Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators—such as GeneNetWeaver—are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. Results We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used “scale-free” model is insufficient for replicating these structures. Availability This generator is implemented in the R package “SeqNet” and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).

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A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy

Technology in Cancer Research & Treatment ◽

10.1177/1533033820909112 ◽

2020 ◽

Vol 19 ◽

pp. 153303382090911

Author(s):

Qi-en He ◽

Yi-fan Tong ◽

Zhou Ye ◽

Li-xia Gao ◽

Yi-zhi Zhang ◽

...

Keyword(s):

Prediction Model ◽

Regulatory Network ◽

Regulatory Networks ◽

Large Scale ◽

Network Inference ◽

Treatment Options ◽

Genomic Data ◽

Full Potential ◽

Gene Regulatory Network Inference ◽

Signature Genes

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

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Network reconstruction for trans acting genetic loci using multi-omics data and prior information

10.1101/2020.05.19.101592 ◽

2020 ◽

Cited By ~ 1

Author(s):

Johann S. Hawe ◽

Ashis Saha ◽

Melanie Waldenberger ◽

Sonja Kunze ◽

Simone Wahl ◽

...

Keyword(s):

Regulatory Networks ◽

Large Scale ◽

Prior Information ◽

Network Inference ◽

Model Systems ◽

Molecular Networks ◽

Biological Knowledge ◽

Omics Data ◽

Cohort Data ◽

Inference Methods

AbstractBackgroundMolecular multi-omics data provide an in-depth view on biological systems, and their integration is crucial to gain insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans -QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information has been proposed to alleviate network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans -QTL hotspots using human cohort data and data-driven prior information.ResultsWe devised a strategy to integrate QTL with human population scale multi-omics data and comprehensively curated prior information from large-scale biological databases. State-of-the art network inference methods applied to these data and priors were used to recover the regulatory networks underlying trans -QTL hotspots. We benchmarked inference methods and showed, that Bayesian strategies using biologically-informed priors outperform methods without prior data in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses.ConclusionWe demonstrate, that existing biological knowledge can be leveraged for the integrative analysis of networks underlying trans associations to deduce novel hypotheses on cell regulatory mechanisms.

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Inference of SNP-Gene Regulatory Networks by Integrating Gene Expressions and Genetic Perturbations

BioMed Research International ◽

10.1155/2014/629697 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Dong-Chul Kim ◽

Jiao Wang ◽

Chunyu Liu ◽

Jean Gao

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Network Inference ◽

Real Data ◽

Inference Method ◽

Gene Expressions ◽

Single Nucleotide ◽

Genetic Perturbations ◽

Gene Regulatory

In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data.

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Gaining confidence in inferred networks

10.1101/2020.09.19.304980 ◽

2020 ◽

Author(s):

Léo P.M. Diaz ◽

Michael P.H. Stumpf

Keyword(s):

Biological Networks ◽

Regulatory Networks ◽

Network Inference ◽

False Negative ◽

Simulated Data ◽

Real Data ◽

Point Interactions ◽

Inference Algorithms ◽

Starting Point ◽

Inference Methods

AbstractNetwork inference is a notoriously challenging problem. Inferred networks are associated with high uncertainty and likely riddled with false positive and false negative interactions. Especially for biological networks we do not have good ways of judging the performance of inference methods against real networks, and instead we often rely solely on the performance against simulated data. Gaining confidence in networks inferred from real data nevertheless thus requires establishing reliable validation methods. Here, we argue that the expectation of mixing patterns in biological networks such as gene regulatory networks offers a reasonable starting point: interactions are more likely to occur between nodes with similar biological functions. We can quantify this behaviour using the assortativity coefficient, and here we show that the resulting heuristic, functional assortativity, offers a reliable and informative route for comparing different inference algorithms.

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

Computation ◽

10.3390/computation9120146 ◽

2021 ◽

Vol 9 (12) ◽

pp. 146

Author(s):

Michael Banf ◽

Thomas Hartwig

Keyword(s):

Computational Biology ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Large Scale ◽

Network Inference ◽

Gene Regulatory Network Inference ◽

Vast Number ◽

Gene Regulatory ◽

Randomized Methods

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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Evaluating the reproducibility of single-cell gene regulatory network inference algorithms

10.1101/2020.11.10.375923 ◽

2020 ◽

Author(s):

Yoonjee Kang ◽

Denis Thieffry ◽

Laura Cantini

Keyword(s):

Single Cell ◽

Network Inference ◽

Simulated Data ◽

Ground Truth ◽

Real Data ◽

Gene Regulatory Network Inference ◽

Sequencing Platform ◽

Cell Network ◽

Inference Algorithms ◽

Inference Methods

AbstractNetworks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have been proposed. However, published benchmarks on single-cell network inference are mostly based on simulated data. Once applied to real data, these benchmarks take into account only a small set of genes and only compare the inferred networks with an imposed ground-truth.Here, we benchmark four single-cell network inference methods based on their reproducibility, i.e. their ability to infer similar networks when applied to two independent datasets for the same biological condition. We tested each of these methods on real data from three biological conditions: human retina, T-cells in colorectal cancer, and human hematopoiesis.GENIE3 results to be the most reproducible algorithm, independently from the single-cell sequencing platform, the cell type annotation system, the number of cells constituting the dataset, or the thresholding applied to the links of the inferred networks. In order to ensure the reproducibility and ease extensions of this benchmark study, we implemented all the analyses in scNET, a Jupyter notebook available at https://github.com/ComputationalSystemsBiology/scNET.

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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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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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