Confidence: a web app for cross-platform differential gene expression analysis, gene scoring, and enrichment analysis

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• RNA sequencing (RNA-seq) is crucial for quantifying transcript abundance and identifying differential gene expression.
• Current RNA-seq analysis faces challenges including variability in results from different packages, complex bioinformatics requirements, and lack of intuitive gene prioritization methods.

Purpose of the Study:

• To develop a user-friendly, web-based application called Confidence for simultaneous statistical analysis of RNA-seq count data.
• To address the need for intuitive gene prioritization and overcome limitations of existing RNA-seq analytical packages.

Main Methods:

• Developed Confidence, a web application for RNA-seq data analysis.
• Incorporated a Confidence Score (CS) from 1 to 4 for gene prioritization, with 4 indicating high confidence.
• Integrated pathway analysis for biological context of prioritized genes.

Main Results:

• Confidence provides a rapid and intuitive 'wide-net' approach to RNA-seq analysis.
• The Confidence Score (CS) enables unbiased gene selection and identification of novel genes associated with disease/treatment models across species.
• The application facilitates the generation of publication-quality figures.

Conclusions:

• Confidence offers a novel strategy for target gene prioritization in RNA-seq analysis.
• The tool simplifies complex bioinformatics tasks, making advanced RNA-seq analysis more accessible.
• Confidence aids in identifying significant genes and placing them within relevant biological pathways for further investigation.