SeesawPred: A Web Application for Predicting Cell-fate Determinants in Cell Differentiation.

Area of Science:

• Computational biology
• Molecular biology
• Genomics

Background:

• Cellular differentiation involves a complex process of cell specialization.
• Identifying cell-fate determinants, such as transcription factors (TFs), is critical but challenging, especially for closely related cell types.
• Existing methods often rely on pre-compiled datasets, limiting their application to novel systems.

Purpose of the Study:

• To develop a novel computational tool, SeesawPred, for predicting cell-fate determinants from transcriptomics data.
• To provide a user-friendly web application that accepts uploaded gene expression data for analyzing novel differentiation systems.
• To improve the accuracy and applicability of cell-fate determinant prediction compared to existing methods.

Main Methods:

• Development of SeesawPred, a web application utilizing a gene regulatory network (GRN) model.
• Integration of user-uploaded transcriptomics data for analysis.
• Computational prediction of transcription factors (TFs) that act as cell-fate determinants.

Main Results:

• SeesawPred successfully predicted known cell-fate determinants in various mouse and human cell differentiation examples.
• The tool demonstrated superior performance compared to state-of-the-art prediction methods.
• The application's flexibility in accepting user data enables its use in novel differentiation systems.

Conclusions:

• SeesawPred offers a powerful and flexible computational approach for identifying cell-fate determinants.
• The web application enhances the study of cell differentiation by enabling analysis of novel systems.
• This tool advances the field of computational biology in predicting key regulatory factors in cell fate decisions.