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― Accelerating Gene Discovery Using Gene Family Information Based on Functional Domains and Gene Networks ―

◆ In the following descriptions, the letter following each “Task” (e.g., Task A) corresponds to the codes (A–Q) listed under “Outsourcing task(s)” in the Contact Us form.

High-Precision Cross-Species Gene Family Information to Accelerate Gene Discovery (Task F, G, H)

At WGI, we classify both intra- and inter-species homologs (gene families) based not only on sequence patterns but also on protein functional domains.
Genes belonging to the same group are presumed to share similar biological functions and to be derived from common orthologs.
By combining gene expression profile data and AI text-mining (LA2K) technology to obtain knowledge-based information on gene functions and expression regulation mechanisms, we can accelerate gene discovery and the elucidation of molecular evolutionary processes.
WGI's analytical methods are based on approaches distinct from conventional methods, enabling high-precision and rapid gene discovery without being constrained by the limitations of traditional techniques.

◆ Integration of Homologous Gene Information via Intraspecies Gene Networks

In addition to conventional gene family classification based on DNA sequence similarity, WGI performs homolog classification (gene family construction) using shared protein functional domain information to verify the biological functional equivalence of genes.
The relationships among genes within the genome can be visualized as gene networks (see figure below), allowing the integration of various information types to accelerate gene discovery.
Biological function and trait information, as well as knowledge on biological processes associated with homologous gene groups, can be identified using AI text mining (LA2K) technology and integrated into the network.
When biological function knowledge is available for a single gene in a gene family, other homologous genes in the same family are suggested to have similar functions.
Gene expression profile information, especially the similarity or reciprocal expression patterns, can also suggest shared biological functions.
Information on transcriptional regulation extracted via WGI’s proprietary AI text mining (LA2K) technology (Task B, C) and AI-driven sequence analysis platform (Task I) can also be integrated.
With the addition of metabolic information, compound data on metabolic products acted upon by enzyme genes can also be incorporated.

An Example of the Gene Network within a Species

◆ Integration of Homologous Gene Information via Cross-Species Gene Networks

At WGI, we construct cross-species gene families by classifying intra- and inter-species homologs based on shared protein functional domain information.
Similar to single-species analyses, gene relationships across genomes can be visualized as networks (see figure below), enabling the integration of diverse types of information.
Biological functions, traits, and biological process knowledge associated with homologous gene groups can be identified using AI text mining (LA2K) technology and integrated into the network.
Information such as biological function knowledge, similarity/reciprocity in gene expression profiles, transcriptional regulators (transcription factors), associated traits, and biological processes (e.g., metabolic products) can be comprehensively integrated.
Similarity and reciprocity in gene expression profiles also suggest functional equivalence among genes.
By referencing homologous gene information from well-studied model species, it is possible to maximize functional inference for genes in non-model species with limited available information.

At WGI, we classify intra- and inter-species homologs (cross-species gene family construction) based on the shared information of protein functional domains.
By cross-referencing regulatory mechanisms, gene expression profiles, and biological function knowledge of genes within the same family, we accelerate gene function prediction, gene discovery, and elucidation of molecular evolution.
To achieve this, network analysis that enables efficient handling of massive information is an extremely effective approach.
WGI constructs integrated networks that combine all types of omics and knowledge-based information.
The network data are provided in formats compatible with widely-used free software, allowing easy operation and editing.

An Example of the Gene Network among Multiple Species

By utilizing gene networks that integrate cross-species gene information, gene discovery and molecular evolution analysis can be accelerated even in species with limited literature reports or experimental data.