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`CroCoNet` is a tool to quantitatively compare gene regulatory networks across species and identify conserved and diverged modules. It hinges on contrasting module variability within and across species in order to distinguish between-species divergence from confounding factors such as diversity across individuals, environmental differences and technical noise.
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CroCoNet is a framework to quantitatively compare gene regulatory networks across species and identify conserved and diverged modules. It hinges on contrasting module variability within and across species in order to distinguish truw evolutionary divergence from confounding factors such as diversity across individuals, environmental differences and technical noise.
Once `devtools` is available, you can install the development version of `CroCoNet` and all its dependencies from [GitHub](https://github.com/) with:
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Once `devtools` is available, you can install the development version of `CroCoNet` and all its dependencies from GitHub with:
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```{r, echo=T, eval=F, tidy=T}
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devtools::install_github("Hellmann-Lab/CroCoNet")
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```
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## :book: User Guide
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## :book: User guide
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For a step-by-step guide and detailed explanations, please check out the vignette on the analysis of an example scRNA-seq dataset:
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```{r, echo=T, eval=F, tidy=T}
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browseVignettes('CroCoNet')
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```
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You can access this vignette and the documentations of all functions also at the [CroCoNet website](hellmann-lab.github.io/CroCoNet/).
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You can access this vignette, along with the documentation of all functions, on the [CroCoNet website](hellmann-lab.github.io/CroCoNet/) as well.
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## :scroll: Citation
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Please use the following entry to cite `CroCoNet`:
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The CroCoNet framework and its biological applications are described in detail in our [preprint](https://www.biorxiv.org/content/10.1101/2025.11.18.689002v1) on bioRxiv. Please cite this manuscript as follows:
title = "{CroCoNet: a novel framework for cross-species network analysis reveals POU5F1 (OCT4) rewiring}",
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author = "Térmeg, Anita and Storozhuk, Vladyslav and Kliesmete, Zane and Edenhofer, Fiona C and Geuder, Johanna and Vieth, Beate and Janssen, Philipp and Hellmann, Ines",
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```{r, include = FALSE}
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```
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<br>
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<tt>CroCoNet</tt> (**Cro**ss-species **Co**mparison of **Net**works) is a computational pipeline and R package to quantitatively compare gene-regulatory networks across species. The approach hinges on contrasting network variability within and across species in order to distinguish divergence from detection uncertainty. By enabling robust network analyses, CroCoNet contributes towards finding meaningful cross-species differences in gene regulation.
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CroCoNet (**Cro**ss-species **Co**mparison of **Net**works) is a computational pipeline and R package for the quantitative comparison of gene-regulatory networks across species. The approach hinges on contrasting network variability within and across species in order to distinguish true evolutionary divergence from technical and biological confounders. By enabling robust network analyses, CroCoNet contributes towards finding meaningful cross-species differences in gene regulation. For a comprehensive description of CroCoNet and its biological applications, please refer to the accompanying [preprint](https://www.biorxiv.org/content/10.1101/2025.11.18.689002v1) on bioRxiv.
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In this vignette, we demonstrate the complete CroCoNet workflow using a single-cell RNA-seq dataset of early primate neural differentiation as an example. The data files for this analysis are available in the project's [Zenodo archive](https://zenodo.org/records/17610308), in the compressed folder “neural_differentiation_dataset.zip”. All file paths used in the examples below are relative to this master folder.
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This vignette provides a practical guide to CroCoNet using an example dataset of early primate neural differentiation. The data files for this analysis are available in the project's [Zenodo archive](https://zenodo.org/records/17610308), under “neural_differentiation_dataset.zip”. All file paths used in the examples below are relative to this master folder.
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@ARTICLE{Termeg2025-zb,
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title = "{CroCoNet: a novel framework for cross-species network analysis reveals POU5F1 (OCT4) rewiring}",
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author = "Térmeg, Anita and Storozhuk, Vladyslav and Kliesmete, Zane and Edenhofer, Fiona C and Geuder, Johanna and Vieth, Beate and Janssen, Philipp and Hellmann, Ines",
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journal = "bioRxiv",
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pages = "2025.11.18.689002",
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abstract = "BACKGROUND: Gene regulatory changes play a central role in shaping cellular phenotypes across species. To understand how these phenotypes evolve, it is essential to investigate the underlying gene regulatory networks (GRNs). However, most comparative analyses of GRNs remain qualitative and are therefore sensitive to false positives and false negatives. RESULTS: To address this limitation, we introduce CroCoNet ( Cro ss-species Co mparison of Net works), an R package for the quantitative comparison of GRNs across species. CroCoNet constructs comparable network modules centered on known transcriptional regulators and quantifies the variability in module topology within and between species. By contrasting these levels of variability, CroCoNet can distinguish true evolutionary divergence from technical and biological confounders. Applying CroCoNet to scRNA-seq data from the early neural differentiation of human, gorilla, and cynomolgus macaque, we identified 20 conserved and 24 diverged modules. Despite the conserved expression pattern of the pluripotency factor POU5F1 (OCT4), its associated module was among the most diverged. This result was independently confirmed through cross-species CRISPRi perturbations coupled with single-cell RNA-seq as a readout. Moreover, we found that great ape- and human-specific LTR7 elements are enriched near POU5F1 module genes, potentially contributing to the cross-species differences in network topology. CONCLUSIONS: These findings demonstrate that CroCoNet can resolve regulatory rewiring and provides a robust framework for studying GRN evolution across closely related species. CroCoNet is available as an open-source R package at https://hellmann-lab.github.io/CroCoNet/.",
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month = "18~" # nov,
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year = 2025,
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doi = "10.1101/2025.11.18.689002",
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language = "en"
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}
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@ARTICLE{Moerman2019-xh,
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title = "{GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks}",
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author = "Moerman, Thomas and Santos, Sara Aibar and González-Blas, Carmen Bravo and Simm, Jaak and Moreau, Yves and Aerts, Jan and Aerts, Stein",
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