Transcriptator: An Automated Computational Pipeline to Annotate Assembled Reads and Identify Non Coding RNA.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA sequencing (RNA-seq) offers high-accuracy transcript counts for transcriptome exploration.
• Interpreting large RNA-seq datasets into biological insights presents challenges due to a lack of user-friendly tools.
• Biologists require accessible computational solutions for analyzing complex transcriptomic data.

Purpose of the Study:

• To develop Transcriptator, a user-friendly web application for analyzing RNA-seq and microarray data.
• To provide tools for functional and Gene Ontology (GO) enrichment analysis of transcriptomic data.
• To facilitate the interpretation of large datasets, particularly for non-referenced organisms.

Main Methods:

• Development of a Python-based computational pipeline integrated with a Java user interface.
• Utilization of web services from BLAST, QuickGO, and DAVID for annotation and analysis.
• Integration of PORTRAIT for non-coding RNA identification and GO-Slim analysis via QuickGO web services.

Main Results:

• Transcriptator generates reports on statistical analysis of functional and GO annotation enrichment.
• The application clusters transcripts based on functional annotations and provides tabular reports.
• It identifies enriched biological themes, pathways, domains, and protein-protein interaction information.

Conclusions:

• Transcriptator is a versatile tool for both next-generation sequencing (NGS) and microarray data analysis.
• It aids in characterizing de-novo assembled reads and performing functional enrichment analysis.
• The software provides easy-to-read tables and interactive charts for enhanced data comprehension.