Bioinformatic pipelines for whole transcriptome sequencing data exploitation in leukemia patients with complex...

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Chromothripsis causes extensive genome rearrangements, leading to complex structural variants (cSVs) and fusion genes in cancer.
• Whole transcriptome sequencing (total RNA-Seq) is crucial for studying the functional impact of cSVs at the RNA level.
• Analyzing transcriptomic data, especially with cSVs, presents significant bioinformatic challenges requiring specialized tools.

Purpose of the Study:

• To develop and validate a comprehensive bioinformatic workflow for analyzing total RNA-Seq data.
• To accurately identify differential gene expression and detect de novo fusion genes in the context of cSVs.
• To provide a robust tool for cancer research and clinical applications.

Main Methods:

• A two-pipeline bioinformatic workflow was designed for total RNA-Seq data analysis.
• Pipeline 1: Statistical analysis for differential gene expression, integrating transcriptomic array data for precision.
• Pipeline 2: Identification of de novo fusion genes using consensus fusion calling to minimize false positives, validated against genomic array data.

Main Results:

• A novel workflow was established for differential gene expression analysis and de novo fusion gene detection from total RNA-Seq.
• Differential gene expression results showed concordance with transcriptomic arrays, validating the workflow's analytical capabilities.
• Consensus fusion gene detection effectively identified true positives, with fusion gene junction coordinates aligning with genomic breakpoints.

Conclusions:

• The developed workflow provides accurate and consistent results for total RNA-Seq data analysis, validated against other genomic techniques.
• The findings offer insights into the biological consequences of cSVs, impacting cancer patient outcomes and management.
• The bioinformatic workflow is broadly applicable to diverse research questions utilizing transcriptomic data.