Development of a bioinformatics platform for analysis of quantitative transcriptomics and proteomics data: the...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics
• Proteomics

Background:

• RNA sequencing and mass spectrometry are common methods for identifying differentially expressed biomarkers.
• Data from these methods require computational skills for interpretation, posing challenges for non-programmers.
• Existing bioinformatics tools often lack flexibility for comprehensive visualization and functional interpretation.

Purpose of the Study:

• To develop an accessible web application for analyzing and visualizing differential expression data.
• To provide a user-friendly platform for researchers with limited computational expertise.
• To facilitate the interpretation of transcriptomics and proteomics data into biological meaning.

Main Methods:

• Developed OMnalysis, a Shiny-based web application using R.
• Accepts tabular data from common analysis software (e.g., edgeR, DESeq2, MaxQuant Perseus).
• Features include customizable visualizations, multiple hypothesis testing corrections, network-based pathway analysis, and support for multiple biological databases (KEGG, Reactome, STRINGdb, etc.).

Main Results:

• OMnalysis provides a single platform for analyzing and visualizing differential expression data.
• It integrates multiple databases for extensive pathway enrichment analysis.
• Literature information from PubMed is fetched to support identified biomarkers.

Conclusions:

• OMnalysis offers a well-organized user interface for quick interpretation of differential transcriptomics and proteomics data.
• The application is freely available and supported by peer-reviewed R packages with updated databases.
• It empowers researchers to derive biological meaning from complex omics data without extensive programming knowledge.