Plant Functional Gene Network Bioinformatics Analysis

Plant functional gene networks obtained through bioinformatic analysis can both measure functional association relationships between genes and predict direct interactions between genes. They can provide important information for the functional annotation of unknown functional genes. Functional gene networks can describe biological systems more broadly than direct physical interactions (e.g., protein-protein interaction networks).

Lifeasible is a professional provider of plant bioinformatics services for predicting and analyzing plant functional gene networks. With our extensive plant gene sequencing technology and comprehensive bioinformatics analysis tools, we can provide tailor-made plant functional gene network bioinformatics analysis services based on functional genomic data.

What We Offer

• Functional Gene Association Inference

Many plant species lack large-scale functional gene networks, and mining functional gene associations are the basis for constructing functional gene networks. We often use computational methods such as co-expression, genomic context, homology mapping, literature mining, gene fusion, and domain co-occurrence to infer functional associations between genes.

• Validation of Inferred Functional Gene Associations

We often use yeast two-hybrid techniques, AP-MS methods, and bimolecular fluorescence complementation (BiFC) to validate inferred functional associations between genes.

• Integrating Gene Associations to Build Functional Gene Networks

Functional associations obtained through different methods and data sources are often complementary, so integrating networks can improve the accuracy and coverage of gene networks. We typically use a Bayesian framework to integrate functional gene associations from multiple data sources.

• Evaluation of Functional Gene Networks

After constructing the functional gene network, we will use a variety of independent experiments to validate the functional associations. The subject characteristic curve (ROC) is often used to test the predictive power of the functional gene network.

• Analysis of the Basic Properties of Functional Gene Networks

Functional gene network analysis mainly includes analysis of the basic properties of the network and network clustering analysis. We mainly analyze the degree distribution of network nodes, network centrality, clustering coefficient, shortest path length, compactness, connectivity, spatial isolation, predictive ability, stability of clustering, etc.

• Visualization of Functional Gene Networks

Visualization of existing functional gene networks will help to visualize them and uncover information that is not readily available from the data. There is already a wide range of software and online tools for visualizing gene networks commonly used by Cytoscape, Gephi, and Pajek.

Applications

• To guide molecularly assisted breeding of plants.
• Provides a theoretical basis for the improvement of important agronomic traits in crops.
• Provides important information for functional annotation of unknown functional genes in plants.
• It can also be used to mine new candidate genes using gene information related to known phenotypic traits.

Why Choose Lifeasible

As big data in plant bioinformatics continues to grow, functional gene networks will be used in greater depth. We can use various biocomputational methods, experimental validation methods, and quality assessment methods to provide our clients with complete, accurate, and high-coverage functional gene networks.

How to Place an Order

Lifeasible has been dedicated to providing customizable bioinformatics analysis of plant functional gene networks for many years. Please feel free to contact us with your questions, needs, or collaborations.