Gene network Protocols | Interactome | Systems Biology

Identifying Causal Genes and Building Regulatory Networks in Crops Using the CisTrans-ECAS Method

YY Yutong Yan

LM Luchang Ming

WX Weibo Xie

348 Views

Feb 5, 2026

Pinpointing causal genes for complex traits from genome-wide association studies (GWAS) remains a central challenge in crop genetics, particularly in species with extensive linkage disequilibrium (LD) such as rice. Here, we present CisTrans-ECAS, a computational protocol that overcomes this limitation by integrating population genomics and transcriptomics. The method’s core principle is the decomposition of gene expression into two distinct components: a cis-expression component (cis-EC), regulated by local genetic variants, and a trans-expression component (trans-EC), influenced by distal genetic factors. By testing the association of both components with a phenotype, CisTrans-ECAS establishes a dual-evidence framework that substantially improves the reliability of causal inference. This protocol details the complete workflow, demonstrating its power not only to identify causal genes at loci with weak GWAS signals but also to systematically reconstruct gene regulatory networks. It provides a robust and powerful tool for advancing crop functional genomics and molecular breeding.

Computational Cellular Mathematical Model Aids Understanding the cGAS-STING in NSCLC Pathogenicity

SK Shweta Khandibharad

PG Pooja Gulhane

SS Shailza Singh

0 Q&A

3072 Views

Mar 5, 2025

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. According to 2020 reports, globally, 2.2 million cases are reported every year, with the mortality number being as high as 1.8 million patients. To study NSCLC, systems biology offers mathematical modeling as a tool to understand complex pathways and provide insights into the identification of biomarkers and potential therapeutic targets, which aids precision therapy. Mathematical modeling, specifically ordinary differential equations (ODEs), is used to better understand the dynamics of cancer growth and immunological interactions in the tumor microenvironment. This study highlighted the dual role of the cyclic GMP-AMP synthase–stimulator of interferon genes (cGAS/STING) pathway's classical involvement in regulating type 1 interferon (IFN I) and pro-inflammatory responses to promote tumor regression through senescence and apoptosis. Alternative signaling was induced by nuclear factor kappa B (NF-κB), mutated tumor protein p53 (p53), and programmed death-ligand1 (PD-L1), which lead to tumor growth. We identified key regulators in cancer progression by simulating the model and validating it with the following model estimation parameters: local sensitivity analysis, principal component analysis, rate of flow of metabolites, and model reduction. Integration of multiple signaling axes revealed that cGAS-STING, phosphoinositide 3-kinases (PI3K), and Ak strain transforming (AKT) may be potential targets that can be validated for cancer therapy.

Synthetic Genetic Interaction (CRISPR-SGI) Profiling in Caenorhabditis elegans

JC John A. Calarco

AN Adam D. Norris

0 Q&A

8822 Views

Mar 5, 2018

Genetic interaction screens are a powerful methodology to establish novel roles for genes and elucidate functional connections between genes. Such studies have been performed to great effect in single-cell organisms such as yeast and E. coli (Schuldiner et al., 2005; Butland et al., 2008; Costanzo et al., 2010), but similar large-scale interaction studies using targeted reverse-genetic deletions in multi-cellular organisms have not been feasible. We developed a CRISPR/Cas9-based method for deleting genes in C. elegans and replacing them with a heterologous fluorescent reporter (Norris et al., 2015). Recently we took advantage of that system to perform a large-scale, reverse genetic screen using null alleles in animals for the first time, focusing on RNA binding protein genes (Norris et al., 2017). This type of approach should be similarly applicable to many other gene classes in C. elegans. Here we detail the protocols involved in generating a library of double mutants and performing medium-throughput competitive fitness assays to test for genetic interactions resulting in fitness changes.

Computational Identification of MicroRNA-targeted Nucleotide-binding Site-leucine-rich Repeat Genes in Plants

Zhu-Qing Shao

Yan-Mei Zhang

Bin Wang

Jian-Qun Chen

0 Q&A

11608 Views

Nov 5, 2015

Plant genomes harbor dozens to hundreds of nucleotide-binding site-leucine-rich repeat (NBS-LRR, NBS for short) type disease resistance genes (Shao et al., 2014; Zhang et al., 2015). Proper regulation of these genes is important for normal growth of plants by reducing unnecessary fitness costs in the absence of pathogen infection. Recent studies have revealed that microRNAs are involved in regulation of NBS genes in plants (Zhai et al., 2011; Shivaprasad et al., 2012). This protocol describes computational methods for the genome-wide identification of plant NBS genes potentially regulated by microRNAs.

A Conceptual Outline for Omics Experiments Using Bioinformatics Analogies

Prashanth Suravajhala

JB Jeffrey W. Bizzaro

0 Q&A

13563 Views

Feb 5, 2015

Hypothetical proteins (HP) are those that are not characterized in the laboratory and so remain “orphaned” in genomic databases. In recent times there has been a lot of progress in characterizing HPs in the laboratory. Various methods, such as sequence capture and Next Generation Sequencing (NGS), have been used to rapidly identify HP functions and their encoded genes. Applications and methods, such as the isolation of single genes, are greatly facilitated by pull-down assays to characterize proteins. Furthermore, there are methods to extract proteins from either the whole cell or a subcellular fraction. But the weakness is that some assays are fairly expensive and laborious, and characterizing HP function is always imperfect. In the recent past, statistical interpretations of the in silico selection strategies have improved the identification of the most promising candidates, including those from various annotation methods, such as protein interaction networks (PIN). Given the improvements in technology that have permitted a substantial increase in computational annotation, we ask if the prediction of HP function in silico (validation of models through algorithms and data subsets) could likewise be improved. In this work, we apply a bioinformatics analogy to each step of a wet lab experiment performed to predict aspects confirming protein function. Although it may be a less bona fide approach, assigning a putative function from conservation observed in homologous protein sequences might be worthwhile to consider prior to a wet lab experiment.

SGR-based Reporter to Assay Plant Transcription Factor-promoter Interactions

Shisong Ma

Savithramma Dinesh-Kumar

0 Q&A

11044 Views

Aug 20, 2014

We developed an in vivo method to assay plant transcription factor (TF)–promoter interactions using the transient expression system in Nicotiana benthamiana (N. benthamiana) plants. The system uses the Arabidopsis stay green (SGR) gene as a reporter. Induction of SGR expression in N. benthamiana causes chlorophyll degradation and causes leaves to turn yellow.

Gene Networks Based on the Graphical Gaussian Model

Shisong Ma

1 Q&A

14980 Views

Feb 20, 2012

This protocol describes how to build a gene network based on the graphical Gaussian model (GGM) from large scale microarray data. GGM uses partial correlation coefficient (pcor) to infer co-expression relationship between genes. Compared to the traditional Pearson’ correlation coefficient, partial correlation is a better measurement of direct dependency between genes. However, to calculate pcor requires a large number of observations (microarray slides) greatly exceeding the number of variables (genes). This protocol uses a regularized method to circumvent this obstacle, and is capable of building a network for ~20,000 genes from ~2,000 microarray slides. For more details, see Ma et al. (2007). For help regarding the script, please contact the author.