LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies

Abstract

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org.

Detecting Causality from Nonlinear Dynamics with Short-term Time Series

“…Due to the limit of experiment technique and other constraints, usually only very short-term and often noisy timeresolved measurements can be available in gene expressions. Though various methods based on Bayesian inference, regression analysis, econometrics models and standard similarity measures have been used to analyze such short time series data171819, inferring genetic networks from short data is still regarded as an ‘ill-posed' inverse problem and a challenging task2021.…”

“…The existing methods for GRN inference can usually reach an AUC around 0.7 for synthetical data but only around 0.5 for real experimental data21. The CCM method, which relies on finding nearest neighbors and thus requires long-term data for the convergence, has AUC around 0.5 in Fig.…”

“…Though various methods based on Bayesian inference, regression analysis, econometrics models and standard similarity measures have been used to analyze such short time series data171819, inferring genetic networks from short data is still regarded as an ‘ill-posed' inverse problem and a challenging task2021. On the other hand, in some occasions even though long-term data can be measured, only short (recent) pieces can correctly reflect the causal relation between subsystems due to the nonstationary and fast switching property of the concerned systems.…”

The G-box transcriptional regulatory code inArabidopsis

“…The network provided by Ara-BOX-cis was constructed using time-course RNA-seq samples that are relevant to biological processes related to G-box functions; it is considered the best practice for plant gene regulatory network reconstruction to use gene expression samples that are most likely to make large perturbations in the sub-network under study (52). It also uses network inference approaches that have been found to be most effective in side-by-side comparisons of network inference algorithms (40) and that has been previously been used to successfully reconstruct the root gene expression network in Arabidopsis and legumes (53, 54). Although it has fewer genes than other network approaches such as AraNET (55) and GeneMANIA (56), Ara-BOX-cis has the benefit of identifying possible links between TFs for which there is prior information suggesting a regulatory link, which is similar to the ‘weak prior’ approach suggested in Krouk et al, 2013 (57).…”

A blueprint of seed desiccation sensitivity in the genome ofCastanospermum australe

“…Changes in soluble sugar content and composition have been described as a characteristic of late maturation in DT seeds and correlate with the acquisition of longevity and preparation for the dry state (Wang et al , 2013; Leprince et al , 2017). Whereas in all DT legume seeds, raffinose family oligosaccharides (RFO) are the predominant sugars that increase during late maturation, C. australe seeds were composed of 7-10% of soluble sugars, with sucrose being the most abundant sugar detected and only minute amounts (0.7% of total soluble sugars at the brown pod stage) of RFOs accumulating.…”