g.nome, A Transparent Bioinformatics Pipeline that Enables Differential Expression and Alternative Splicing Analysis...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Reproducibility and accessibility are crucial in bioinformatics.
• Complex bioinformatics pipelines can be a bottleneck for biologists without computational expertise.
• High throughput RNA sequencing (RNAseq) generates vast transcriptomic data requiring efficient analysis.

Purpose of the Study:

• Introduce g.nome, a bioinformatics platform designed for independent data analysis.
• Simplify the analysis of transcriptomic datasets for non-bioinformaticians.
• Facilitate the identification and prioritization of critical findings from RNAseq data.

Main Methods:

• g.nome integrates contemporary bioinformatics tools.
• A user-friendly graphical interface simplifies job execution and data analysis.
• The platform was used to analyze RNA sequencing data from colorectal cancer cells (HCT116).

Main Results:

• g.nome enabled straightforward identification of splicing changes.
• The platform facilitated the prioritization of splicing alterations for further experimental validation.
• Analysis focused on the consequences of localizing argonaute (AGO) to the nuclei of HCT116 cells.

Conclusions:

• g.nome enhances the accessibility of transcriptomic data analysis for biologists.
• The platform supports independent research by simplifying complex bioinformatics workflows.
• g.nome aids in uncovering and validating significant biological insights, such as splicing changes in cancer.