An online notebook resource for reproducible inference, analysis and publication of gene regulatory networks

Guebila, Marouen Ben; Weighill, Deborah; Lopes‐Ramos, Camila M.; Burkholz, Rebekka; Pop, Romana T; Palepu, Kalyan; Shapoval, Mia; Fagny, Maud; Schlauch, Daniel; Glass, Kimberly; Altenbuchinger, Michael; Kuijjer, Marieke L.; Platig, John; Quackenbush, John

doi:10.1038/s41592-022-01479-2

Cited by 12 publications

(11 citation statements)

References 9 publications

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“…This tool facilitates contributions from the community using a forkbranch model; new contributed features are tested through ZooKeeper before being added to the core codebase. A second component called Netbooks allows access to a set of cloudbased Jupyter notebook use cases and tutorials [34]. Finally, GRAND database can store genome-scale networks and visualize them on the browser [35].…”

Section: Creating a Community Ecosystem For Collaborative Software De...mentioning

confidence: 99%

“…To facilitate their use and integration into analytical pipelines, we gathered these into the Network Zoo (netZoo; netzoo.github.io), a platform that harmonizes the codebase for these methods, in line with recent similar efforts [32,33], and provides implementations in R, Python, MATLAB, and C. In building netZoo, we also created the ZooKeeper, an online server that helps ensure consistency of the codebase as it is continuously updated in response to user feedback. The netZoo codebase has helped develop an ecosystem of online resources for GRN inference and analysis to both scientists and method developers that includes tools to integrate contributions from the community, to share use cases [34], and to host and visualize networks online [35].…”

Section: Introductionmentioning

confidence: 99%

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The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

et al. 2023

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Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

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Section: Creating a Community Ecosystem For Collaborative Software De...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

et al. 2023

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“…COBRA is available in R and Python through the netZoo packages [Ben Guebila et al, 2023] netZooR v1.5 and netZooPy v0.9.16 (https://netzoo.github.io). We also provide tutorials in R and Python through Netbooks (http://netbooks.networkmedicine.org/) [Ben Guebila et al, 2022b]. The code and data for the analysis are available at the GitHub repository (https://github.com/QuackenbushLab/cobra-experiments).…”

Section: Code and Data Availabilitymentioning

confidence: 99%

Higher-order correction of persistent batch effects in correlation networks

Micheletti,

Schlauch,

Quackenbush

et al. 2023

Preprint

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Systems biology methods often rely on correlations in gene expression profiles to infer co-expression networks, commonly used as input for gene regulatory network inference or to identify functional modules of co-expressed or co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene. These adjustments have been shown to improve the fidelity of DE analysis. However, these methods do not address the potential for spurious differential co-expression (DC) between groups. Consequently, uncorrected, artifactual DC can skew the correlation structure, leading network inference methods that use gene co-expression to identify false, nonbiological associations, even when the input data is corrected using standard batch correction.In this work, we demonstrate the persistence of confounders in covariance after standard batch correction using synthetic and real-world gene expression data examples. Subsequently, we introduce Co-expression Batch Reduction Adjustment (COBRA), a method for computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters expressing the co-expression matrix as a function of the sample covariates, allowing control for continuous and categorical covariates. COBRA is computationally efficient, leveraging the inherently modular structure of genomic data to estimate accurate gene regulatory associations and facilitate functional analysis for high-dimensional genomic data.

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“…In addition, contributions from the community follow a fork-branch model and are tested through ZooKeeper before being added to the core codebase. Finally, users can access a set of cloud-based Jupyter notebook use cases and tutorials hosted through Netbooks [28] and visualize a set of networks hosted in the GRAND database [29].…”

Section: Creating a Community Ecosystem For Collaborative Software De...mentioning

confidence: 99%

“…To facilitate their use and integration into analytical pipelines, we gathered these into the Network Zoo (netZoo; netzoo.github.io), a platform that harmonizes the codebase for these tools and provides implementations in R, Python, MATLAB, and C. In building netZoo, we also created the Zookeeper, a tool that helps ensure consistency of the codebase as it is continuously updated in response to user feedback. The netZoo serves as a centralized resource for biological networks including GRN inference and analysis by providing an ecosystem of tools available to both scientists and method developers and includes resources to integrate contributions, to share use cases [28], and to host and visualize networks [29].…”

Section: Introductionmentioning

confidence: 99%

The Network Zoo: a multilingual package for the inference and analysis of biological networks

Guebila

Wang

Lopes‐Ramos

et al. 2022

Preprint

Self Cite

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Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand 'the zoo' to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

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An online notebook resource for reproducible inference, analysis and publication of gene regulatory networks

Cited by 12 publications

References 9 publications

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

Higher-order correction of persistent batch effects in correlation networks

The Network Zoo: a multilingual package for the inference and analysis of biological networks

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