WellGreen-i Co., Ltd.

― High-precision and rapid gene identification using high-quality gene expression data generated and utilized through WGI’s AI and DX technologies ―

◆ 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.

WGI’s RNA-seq analysis service enables highly accurate and rapid gene discovery, which is difficult to achieve with conventional methods.

WGI provides contract analysis services for gene identification and other applications using RNA-seq data.
This service actively utilizes WGI’s proprietary AI and DX technologies as well as high-quality omics data, in addition to conventional methods.
Conventional methods are not our primary focus because they are insufficient for achieving highly accurate gene discovery.
WGI’s proprietary analysis platform employs advanced AI, DX, and bioinformatics approaches and high-quality big data to overcome the limitations of conventional methods.
Below, we outline the main issues with conventional methods.

Issues with Using Databases Providing RNA Experimental Data

◆Inconsistent Terminology in RNA-seq Experimental Condition Descriptions

Even for identical or similar experimental conditions, different terms (e.g., cold treatment, low temperature) are used by different registrants.
As a result, unless you identify and search using all relevant terms, it is impossible to comprehensively search the database.

◆Issues with Difficulty in Grasping RNA-seq Experimental Conditions

The experimental condition items at the time of RNA extraction are diverse, including material (strain/variety), growth stage, organ/tissue, time of day, and growth environment.
To fully understand the experimental conditions, it is necessary to consult the individual web page for each experiment, which requires significant time and effort.
Furthermore, not all experiments provide detailed descriptions of all experimental condition items.

Issues with Conventional Gene Discovery Methods Based on Gene Expression Matrices

◆Challenges in Narrowing Down Differentially Expressed Gene (DEG) Candidates to a Feasible Number for Wet-Lab and Field Validation

In almost all cases, DEG groups obtained from dry analysis of gene expression matrices contain both trait-related genes and unrelated genes.

◆Issues with Threshold Criteria for DEG Selection

When multiple genes directly control a trait (phenotypic change), the magnitude (absolute value or threshold) of transcriptional changes (gene expression levels) required to alter the phenotype is not necessarily uniform among genes.

◆Issues with Conventional Co-Expression Analysis Based on Correlation Coefficients

Conventional methods use the correlation coefficient between the expression profiles of two genes (gene pairs), requiring calculations for all possible pairs.
Gene pairs that show significantly high correlation coefficients are identified as co-expressed gene pairs.
The most critical issue is that even gene pairs with high correlation coefficients may include outlier samples with expression values deviating significantly from the regression line (see example below).
Gene pairs with such outliers may show expression changes depending on experimental conditions, raising doubts about their validity as candidate genes involved in the same biological function.

An Example of a Gene Pair that Shows a High Correlation Coefficient Despite the Presence of Outliers

At WGI, we have developed a proprietary digital filtering analysis technology for gene expression matrices. In addition, we accelerate the accurate identification of trait-associated genes through multifaceted analyses using WGI’s proprietary high-quality omics data, including gene functions, regulatory mechanisms, and gene families.

Issues with Conventional Gene Discovery Methods Using Information Beyond Expression Matrices

◆Issues with the Inability to Utilize High-Quality Biological Function Information of Genes

Biological function information of genes is widely used to identify trait-associated genes from candidate DEG groups.
However, conventional methods for predicting gene function have the following limitations:

As a result, the predicted functional information of genes is often low in quality and reliability, making it difficult to determine which candidate genes should be prioritized for wet-lab validation.

◆Issues with Enrichment Analysis Using Functional Annotations

Enrichment analysis of functional annotations is commonly used to infer the global functions of DEG groups.
This approach statistically compares the frequency of functional annotations between candidate genes and a background group.
However, enrichment analysis has limitations both in the prediction method for functional annotations and in statistical approaches.

Therefore, although frequency distributions of Gene Ontology or KEGG pathways in DEG groups may serve as reference, performing enrichment analysis and deriving biological interpretations is generally inappropriate.

◆Issues with Background Gene Sets in Enrichment Analysis

In enrichment analysis, the frequency distribution of functional annotations is compared between a candidate gene group (DEGs) and a reference gene group (background).
Here, there are issues with the definition and use of this background group.

Although concerns have been raised about the background definition in conventional enrichment analysis, this issue remains unresolved.

At WGI, we utilize our proprietary advanced analysis platforms and high-quality big data to circumvent the limitations of conventional RNA-seq analysis methods, enabling highly accurate and rapid identification of trait-associated genes.

High-precision gene classification based on WGI's proprietary analysis of expression profile similarity and reciprocity
Comprehensive knowledge on gene functions, regulatory mechanisms, and metabolism obtained via large-scale literature mining using WGI’s LA2K technology
Knowledge on gene functions, expression regulation, and metabolism for orthologs (gene families) across species
Information on transcription factors and cis-elements predicted using WGI’s proprietary AI technology
Integration of the above knowledge via gene-compound networks