Morphogens in the evolution of size, shape and patterning.

Much of the striking diversity of life on Earth has arisen from variations in the way that the same molecules and networks operate during development to shape and pattern tissues and organs into different morphologies. However, we still understand very little about the potential for diversification exhibited by different, highly conserved mechanisms during evolution, or, conversely, the constraints that they place on evolution. With the aim of steering the field in new directions, we focus on morphogen-mediated patterning and growth as a case study to demonstrate how conserved developmental mechanisms can adapt during evolution to drive morphological diversification and optimise functionality, and to illustrate how evolution algorithms and computational tools can be used alongside experiments to provide insights into how these conserved mechanisms can evolve. We first introduce key conserved properties of morphogen-driven patterning mechanisms, before summarising comparative studies that exemplify how changes in the spatiotemporal expression and signalling levels of morphogens impact the diversification of organ size, shape and patterning in nature. Finally, we detail how theoretical frameworks can be used in conjunction with experiments to probe the role of morphogen-driven patterning mechanisms in evolution. We conclude that morphogen-mediated patterning is an excellent model system and offers a generally applicable framework to investigate the evolution of developmental mechanisms.

Revealing Cellular Heterogeneity and Key Regulatory Factors of Triple-Negative Breast Cancer through Single-Cell RNA Sequencing.

BACKGROUND: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC). TNBC has a poor prognosis due to high intratumoral heterogeneity and metastasis, pointing to the need to explore distinct molecular subtypes and gene regulatory networks. METHODS: The scRNA-seq data of five primary BC samples were downloaded from the Gene Expression Omnibus (GEO) database. Clustering was performed based on filtered and normalized data using the Seurat R package to identify marker genes, which were subsequently annotated to each subset using the CellMarker database. AUCell R package was applied to calculate the hallmark score for each epithelial cell. Marker genes of each subset were screened with FindAllMarkers and their biological functions were analyzed using the Database for Annotation Visualization and Integrated Discovery (DAVID) database. Next, cell-cell communication was performed with the CellChat R package. To identify the key regulatory genes, single-cell regulatory network inference and clustering (SCENIC) analysis was conducted. Finally, the expression and potential biological functions of the key regulatory factors were verified through cellular experiments. RESULTS: A total of 29,101 cells were classified into nine cell subsets, namely, Fibroblasts, Fibroepithelial cells, Epithelial cells 1, Epithelial cells 2, Epithelial cells 3, Endothelial cells, T cells, Plasma B cells and Macrophages. Particularly, the epithelial cells had a higher proportion and higher transforming growth factor-ß (TGF-ß) activity in the TNBC pathotype as compared to the non-TNBC pathotype. Furthermore, four epithelial cell subsets (marked as Stearoyl-CoA Desaturase (SCD1), marker of proliferation Ki67 (MKI67), Annexin A3 (ANXA3) and aquaporin 5 (AQP5)) were identified as having the greatest impact on the TNBC pathotype. Cell-cell interaction analysis revealed that ANXA3-epithelial cell subset suppressed the T cell function through different mechanisms. C-fos gene (FOS) and X-box binding protein 1 (XBP1) were considered critical regulons involved in TNBC progression. Notably, cellular experiments demonstrated that silencing XBP1 and overexpressing FOS inhibited cancer cell invasion. CONCLUSION: The four epithelial cell subsets and two critical regulons identified based on the scRNA-seq data could help explore the underlying intratumoral heterogeneity molecular mechanism and develop effective therapies for TNBC.

A Low Expression of NRF2 Enhances Oxidative Stress and Autophagy in Myofibroblasts, Promoting Progression of Chronic Obstructive Pulmonary Disease.

BACKGROUND: The inflammation phenotypes are often closely related to oxidative stress and autophagy pathway activation, which could be developed as a treatment target. AIMS: The aim of this study was to explore the underlying mechanism of inflammation in chronic obstructive pulmonary disease (COPD). METHODS: The lung tissue single-cell RNA-seq (scRNA-seq) dataset of GSE171541 was downloaded from the Gene Expression Omnibus (GEO) database. The marker genes were obtained from the CellMarker database. "Seurat" and "harmony" R packages were used for single-cell profiling analysis. Then, the "AUCell" R package was employed to calculate the reactive oxygen species (ROS) and autophagy pathway scores. Gene regulation network analysis was performed by applying the "SCENIC" package, followed by conducting correlation analysis with Spearman's rank correlation method. The cigarettes were used to develop a traumatic model in mice, and the expression of relevant genes was measured by qRT-PCR. RESULTS: The scRNA-seq analysis classified 12 cell subgroups in which the contractility of myofibroblasts was closely associated with the progression of COPD. Further analysis showed that ROS and autophagy pathways were significantly activated in myofibroblasts and that the nuclear factor erythroid 2-related factor 2 (NRF2) and its mediated oxidative stress pathway were inhibited in myofibroblasts. In addition, the downregulated NRF2 gene was negatively correlated with the expression of autophagy and ROS activation. In the traumatic mice model, NRF2 was downregulated in COPD mice but further elevated in the COPD+NRF2 mice group. Interestingly, the mRNA levels of Kelchlike ECH-associated protein 1 (Keap1), NADPH oxidase (NOX), and Cathepsin B (CTSB) were upregulated in COPD group in comparison to the control group but they were downregulated by NRF2. These results suggested that low-expressed NFR2 promoted autophagy and ROS pathway activation in myofibroblasts for COPD progression. CONCLUSION: We identified a cell myofibroblast cluster closely associated with COPD progression using the scRNA-seq analysis. The downregulated NFR2, as a key risk factor, mediated myofibroblast death by activating the oxidative stress and autophagy pathway for COPD progression.

Identification of estrogen response-associated STRA6+ granulosa cells within high-grade serous ovarian carcinoma by single-cell sequencing.

Background: High-grade serous ovarian carcinoma (HGSOC) is a pathologic subtype of ovarian cancer (OC) with a more lethal prognosis. Extensive heterogeneity results in HGSOC being more susceptible to treatment resistance and adverse treatment effects. Revealing the heterogeneity involved is crucial. Methods: We downloaded the single-cell RNA-seq (scRNA) data from GEO database and performed a scRNA analysis for cell landscape of HGSOC by using the Seurat package. The highly expressed genes were uploaded into the DAVID and KEGG database for enrichment analysis, and the AUCell package was used to calculate cancer-associated hallmark score. The SCENIC analysis was used for key regulons, the estrogen response enrichment scores in TCGA-OV RNA-seq dataset were calculated by using the GSVA package. Besides, the expression of STRA6 and IRF1 and the cell invasion and migration in si-STRA6 OC cells were detected by using the quantitative reverse transcription (qRT)-PCR method and Transwell assay respectively. Results: We successfully constructed a single-cell atlas of HGSOC and delineated the heterogeneity of epithelial cells therein. There were five epithelial cell subpopulations, GLDC + Epithelial cells, PEG3+ leydig cells, STRA6+ granulosa cells, POLE2+ Epithelial cells, and AURKA + Epithelial cells. STRA6+ granulosa cells have the potential to promote tumor growth as well as the highest estrogen response early activity through the biological pathways analysis of highly expressed genes and estrogen response score of ssGSEA. We found that IRF1 and STRA6 expression was remarkably upregulated in the OC cancer cell line HEY. Silencing of STRA6 markedly decreased the invasion and migration ability of the OC cancer cell line HEY. Conclusion: There is extreme heterogeneity of epithelial cells in HGSOC, and STRA6+ granulosa cells may be able to promote cancer progression. Our findings are benefit to the heterogeneity identification of HGSOC and develop targeted therapy strategy for HGSOC patients.

Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis.

The plant genome produces an extremely large collection of long noncoding RNAs (lncRNAs) that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes. Here, we mapped the transcriptional heterogeneity of lncRNAs and their associated gene regulatory networks at single-cell resolution. We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing (scRNA-seq) datasets from juvenile Arabidopsis seedlings. We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers. We further demonstrated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity. In addition, we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs, and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs. The analysis results are available at the single-cell-based plant lncRNA atlas database (scPLAD; https://biobigdata.nju.edu.cn/scPLAD/). Overall, this work demonstrates the power of integrative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.

Inferring Gene Regulatory Networks and Predicting the Effect of Gene Perturbations via IQCELL.

IQCELL is a platform that infers Boolean gene regulatory networks from single-cell RNA sequencing data. Boolean networks can be simulated under normal and perturbed conditions. In this chapter, we provide a detailed guideline for implementing IQCELL from a raw dataset. The steps include processing data, inferring informative genes, inferring gene regulatory network, and simulating the resulted network under normal and perturbed conditions.

Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks.

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise and dropout events inherent in scRNA-seq data present challenges for GRN inference. In recent years, the dramatic increase in data on experimentally validated transcription factors binding to DNA has made it possible to infer GRNs by supervised methods. In this study, we address the problem of GRN inference by framing it as a graph link prediction task. In this paper, we propose a novel framework called GNNLink, which leverages known GRNs to deduce the potential regulatory interdependencies between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce a graph convolutional network-based interaction graph encoder to effectively refine gene features by capturing interdependencies between nodes in the network. Finally, the inference of GRN is obtained by performing matrix completion operation on node features. The features obtained from model training can be applied to downstream tasks such as measuring similarity and inferring causality between gene pairs. To evaluate the performance of GNNLink, we compare it with six existing GRN reconstruction methods using seven scRNA-seq datasets. These datasets encompass diverse ground truth networks, including functional interaction networks, Loss of Function/Gain of Function data, non-specific ChIP-seq data and cell-type-specific ChIP-seq data. Our experimental results demonstrate that GNNLink achieves comparable or superior performance across these datasets, showcasing its robustness and accuracy. Furthermore, we observe consistent performance across datasets of varying scales. For reproducibility, we provide the data and source code of GNNLink on our GitHub repository: https://github.com/sdesignates/GNNLink.

Building High-Confidence Gene Regulatory Networks by Integrating Validated TF-Target Gene Interactions Using ConnecTF.

Many methods are now available to identify or predict the target genes of transcription factors (TFs) in plants. These include experimental approaches such as in vivo or in vitro TF-target gene-binding assays and various methods for identifying regulated targets in mutants, transgenics, or isolated plant cells. In addition, computational approaches are used to infer TF-target gene interactions from the regulatory elements or gene expression changes across treatments. While each of these approaches has now been applied to a large number of TFs from many species, each method has its own limitations which necessitates that multiple data types are integrated to build the most accurate representation of the gene regulatory networks operating in plants. To make the analyses of TF-target interaction datasets available to the broader research community, we have developed the ConnecTF web platform ( https://connectf.org/ ). In this chapter, we describe how ConnecTF can be used to integrate validated and predicted TF-target gene interactions in order to dissect the regulatory role of TFs in developmental and stress response pathways. Using as our examples KN1 and RA1, two well-characterized maize TFs involved in developing floral tissue, we demonstrate how ConnecTF can be used to (1) compare the target genes between TFs, (2) identify direct vs. indirect targets by combining TF-binding and TF-regulation datasets, (3) chart and visualize network paths between TFs and their downstream targets, and (4) prune inferred user networks for high-confidence predicted interactions using validated TF-target gene data. Finally, we provide instructions for setting up a private version of ConnecTF that enables research groups to store and analyze their own TF-target gene interaction datasets.

Prediction of Transcription Factor Regulators and Gene Regulatory Networks in Tomato Using Binding Site Information.

Gene regulatory networks (GRNs) represent the regulatory links between transcription factors (TF) and their target genes. In plants, they are essential to understand transcriptional programs that control important agricultural traits such as yield or (a)biotic stress response. Although several high- and low-throughput experimental methods have been developed to map GRNs in plants, these are sometimes expensive, come with laborious protocols, and are not always optimized for tomato, one of the most important horticultural crops worldwide. In this chapter, we present a computational method that covers two protocols: one protocol to map gene identifiers between two different tomato genome assemblies, and another protocol to predict putative regulators and delineate GRNs given a set of functionally related or coregulated genes by exploiting publicly available TF-binding information. As an example, we applied the motif enrichment protocol on tomato using upregulated genes in response to jasmonate, as well as upregulated and downregulated genes in plants with genotypes OENAM1 and nam1, respectively. We found that our protocol accurately infers the expected TFs as top enriched regulators and identifies GRNs functionally enriched in biological processes related with the experimental context under study.

Identification and characterization of enhancer elements controlling cell type-specific and signalling dependent chromatin programming during hematopoietic development.

The development of multi-cellular organisms from a single fertilized egg requires to differentially execute the information encoded in our DNA. This complex process is regulated by the interplay of transcription factors with a chromatin environment, both of which provide the epigenetic information maintaining cell-type specific gene expression patterns. Moreover, transcription factors and their target genes form vast interacting gene regulatory networks which can be exquisitely stable. However, all developmental processes originate from pluripotent precursor cell types. The production of terminally differentiated cells from such cells, therefore, requires successive changes of cell fates, meaning that genes relevant for the next stage of differentiation must be switched on and genes not relevant anymore must be switched off. The stimulus for the change of cell fate originates from extrinsic signals which set a cascade of intracellular processes in motion that eventually terminate at the genome leading to changes in gene expression and the development of alternate gene regulatory networks. How developmental trajectories are encoded in the genome and how the interplay between intrinsic and extrinsic processes regulates development is one of the major questions in developmental biology. The development of the hematopoietic system has long served as model to understand how changes in gene regulatory networks drive the differentiation of the various blood cell types. In this review, we highlight the main signals and transcription factors and how they are integrated at the level of chromatin programming and gene expression control. We also highlight recent studies identifying the cis-regulatory elements such as enhancers at the global level and explain how their developmental activity is regulated by the cooperation of cell-type specific and ubiquitous transcription factors with extrinsic signals.

A translatome-transcriptome multi-omics gene regulatory network reveals the complicated functional landscape of maize.

BACKGROUND: Maize (Zea mays L.) is one of the most important crops worldwide. Although sophisticated maize gene regulatory networks (GRNs) have been constructed for functional genomics and phenotypic dissection, a multi-omics GRN connecting the translatome and transcriptome is lacking, hampering our understanding and exploration of the maize regulatome. RESULTS: We collect spatio-temporal translatome and transcriptome data and systematically explore the landscape of gene transcription and translation across 33 tissues or developmental stages of maize. Using this comprehensive transcriptome and translatome atlas, we construct a multi-omics GRN integrating mRNAs and translated mRNAs, demonstrating that translatome-related GRNs outperform GRNs solely using transcriptomic data and inter-omics GRNs outperform intra-omics GRNs in most cases. With the aid of the multi-omics GRN, we reconcile some known regulatory networks. We identify a novel transcription factor, ZmGRF6, which is associated with growth. Furthermore, we characterize a function related to drought response for the classic transcription factor ZmMYB31. CONCLUSIONS: Our findings provide insights into spatio-temporal changes across maize development at both the transcriptome and translatome levels. Multi-omics GRNs represent a useful resource for dissection of the regulatory mechanisms underlying phenotypic variation.

Pan-cancer analyses of classical protein tyrosine phosphatases and phosphatase-targeted therapy in cancer.

Protein tyrosine phosphatases function in dephosphorylating target proteins to regulate signaling pathways that control a broad spectrum of fundamental physiological and pathological processes. Detailed knowledge concerning the roles of classical PTPs in human cancer merits in-depth investigation. We comprehensively analyzed the regulatory mechanisms and clinical relevance of classical PTPs in more than 9000 tumor patients across 33 types of cancer. The independent datasets and functional experiments were employed to validate our findings. We exhibited the extensive dysregulation of classical PTPs and constructed the gene regulatory network in human cancer. Moreover, we characterized the correlation of classical PTPs with both drug-resistant and drug-sensitive responses to anti-cancer drugs. To evaluate the PTP activity in cancer prognosis, we generated a PTPscore based on the expression and hazard ratio of classical PTPs. Our study highlights the notable role of classical PTPs in cancer biology and provides novel intelligence to improve potential therapeutic strategies based on pTyr regulation.

Computational solutions for spatial transcriptomics.

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

Exploring long non-coding RNA networks from single cell omics data.

Single-cell omics technologies provide an unprecedented opportunity to decipher molecular mechanisms underlying various biological processes in a cellular heterogeneity manner. The emergence of such techniques promotes the exploration of lncRNAs, which are known to be tissue- and cell-specific noncoding transcripts involving the regulation of multiple important cellular processes. In this review, we introduce the advancement of lncRNA studies which benefit from single-cell omics data analysis. We discuss the expression heterogeneity of lncRNAs, their cell-type specificity and associated gene regulatory networks (GRNs) from a single-cell perspective. We also summarized the state-of-the-art single-cell omics resources and tools for the construction of single-cell GRNs (scGRNs) that could be potentially used for lncRNA functional study. Finally, we highlight the challenges and prospective for scGRN exploration in lncRNA biology.

The Contribution of Autophagy and LncRNAs to MYC-Driven Gene Regulatory Networks in Cancers.

MYC is a target of the Wnt signalling pathway and governs numerous cellular and developmental programmes hijacked in cancers. The amplification of MYC is a frequently occurring genetic alteration in cancer genomes, and this transcription factor is implicated in metabolic reprogramming, cell death, and angiogenesis in cancers. In this review, we analyse MYC gene networks in solid cancers. We investigate the interaction of MYC with long non-coding RNAs (lncRNAs). Furthermore, we investigate the role of MYC regulatory networks in inducing changes to cellular processes, including autophagy and mitophagy. Finally, we review the interaction and mutual regulation between MYC and lncRNAs, and autophagic processes and analyse these networks as unexplored areas of targeting and manipulation for therapeutic gain in MYC-driven malignancies.

Lysosome dysfunction as a cause of neurodegenerative diseases: Lessons from frontotemporal dementia and amyotrophic lateral sclerosis.

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are fatal neurodegenerative disorders that are thought to exist on a clinical and pathological spectrum. FTD and ALS are linked by shared genetic causes (e.g. C9orf72 hexanucleotide repeat expansions) and neuropathology, such as inclusions of ubiquitinated, misfolded proteins (e.g. TAR DNA-binding protein 43; TDP-43) in the CNS. Furthermore, some genes that cause FTD or ALS when mutated encode proteins that localize to the lysosome or modulate endosome-lysosome function, including lysosomal fusion, cargo trafficking, lysosomal acidification, autophagy, or TFEB activity. In this review, we summarize evidence that lysosomal dysfunction, caused by genetic mutations (e.g. C9orf72, GRN, MAPT, TMEM106B) or toxic-gain of function (e.g. aggregation of TDP-43 or tau), is an important pathogenic disease mechanism in FTD and ALS. Further studies into the normal function of many of these proteins are required and will help uncover the mechanisms that cause lysosomal dysfunction in FTD and ALS. Mutations or polymorphisms in genes that encode proteins important for endosome-lysosome function also occur in other age-dependent neurodegenerative diseases, including Alzheimer's (e.g. APOE, PSEN1, APP) and Parkinson's (e.g. GBA, LRRK2, ATP13A2) disease. A more complete understanding of the common and unique features of lysosome dysfunction across the spectrum of neurodegeneration will help guide the development of therapies for these devastating diseases.

Techniques for Small Non-Coding RNA Analysis in Seeds of Forest Tree Species.

In recent years, the scientific community has become aware that epigenetic mechanisms play a more important role in gene regulatory networks (GRNs) than was hitherto thought, as accumulating evidence has shown that changes in epigenetics without genetic variation can affect complex traits over multiple generations. Within the epigenetic machinery, small non-coding RNAs (sRNAs, 18-24 nucleotides in length) are evolutionarily conserved RNA molecules that target mRNAs for deregulation or translational repression. They commonly have high-level regulatory functions in GRNs by mediating DNA and/or histone methylation and gene silencing essential for plant developmental programs and adaptability. Local adaptation enables plants to acquire a high fitness by, for example, properly timing developmental transitions to match plant growth stages with organism's favorable seasons. In particular, the seed represents a key evolutionary adaptation of seed plants that facilitates dispersal and reinitiates the development coupled in time with suitable environmental conditions. With the advent of high-throughput sequencing for sRNAs and computational approaches for sRNA detection and categorization, it is now feasible to unravel how sRNAs contribute to the fitness of tree species that can survive hundreds of years (e.g., conifers). Of particular interest is to disentangle the roles of sRNAs from complex genomic information in tree species with intimidating genomic sizes (commonly 20-30 Gb in conifers) and abundant nongenic components (e.g., >60% transposable elements). In this chapter, we use seeds of the conifer Picea glauca as a study system to describe the methods and protocols we used or have recently updated, from high-quality RNA isolation to sRNA identification, sequence conservation, abundance comparison, and functional analysis.

Molecular sensor of nicotine in taste of Drosophila melanogaster.

Nicotine is an alkaloid and potent parasympathomimetic stimulant found in the leaves of many plants including Nicotiana tabacum, which functions as an anti-herbivore chemical and an insecticide. Chemoreceptors embedded in the gustatory receptor neurons (GRNs) enable animals to judge the quality of bitter compounds and respond to them. Various taste receptors such as gustatory receptors (GRs), ionotropic receptors (IRs), transient receptor potential channels (TRPs), and pickpocket channels (PPKs) have been shown to have important roles in taste sensation. However, the mechanism underlying nicotine taste sensation has not been resolved in the insect model. Here we identify molecular receptors to detect the taste of nicotine and provide electrophysiological and behavioral evidence that gustatory receptors are required for avoiding nicotine-laced foods. Our results demonstrate that gustatory receptors are reasonable targets to develop new pesticides that maximize the insecticidal effects of nicotine.

RACIPE: a computational tool for modeling gene regulatory circuits using randomization.

BACKGROUND: One of the major challenges in traditional mathematical modeling of gene regulatory circuits is the insufficient knowledge of kinetic parameters. These parameters are often inferred from existing experimental data and/or educated guesses, which can be time-consuming and error-prone, especially for large networks. RESULTS: We present a user-friendly computational tool for the community to use our newly developed method named random circuit perturbation (RACIPE), to explore the robust dynamical features of gene regulatory circuits without the requirement of detailed kinetic parameters. Taking the network topology as the only input, RACIPE generates an ensemble of circuit models with distinct randomized parameters and uniquely identifies robust dynamical properties by statistical analysis. Here, we discuss the implementation of the software and the statistical analysis methods of RACIPE-generated data to identify robust gene expression patterns and the functions of genes and regulatory links. Finally, we apply the tool on coupled toggle-switch circuits and a published circuit of B-lymphopoiesis. CONCLUSIONS: We expect our new computational tool to contribute to a more comprehensive and unbiased understanding of mechanisms underlying gene regulatory networks. RACIPE is a free open source software distributed under (Apache 2.0) license and can be downloaded from GitHub ( https://github.com/simonhb1990/RACIPE-1.0 ).