Towards the integration, annotation and association of historical microarray experiments with RNA-seq

Area of Science:

• Bioinformatics
• Genomics
• Cancer Biology

Background:

• Microarray analysis has advanced biomedicine, enabling multiple myeloma (MM) subtyping and risk score development.
• Researchers are transitioning to RNA-sequencing (RNA-seq) for its enhanced sensitivity and discovery capabilities.
• Integrating historical microarray data with new RNA-seq data requires advanced bioinformatic approaches.

Purpose of the Study:

• To develop a novel bioinformatic approach for integrating and associating historical microarray data with RNA-seq datasets.
• To facilitate a deeper molecular understanding of multiple myeloma (MM) by combining diverse transcriptomic data.

Main Methods:

• Developed custom software using a model-view-controller (MVC) approach.
• Implemented strategies to integrate, cross-reference, and associate Affymetrix probe set IDs and gene annotation information.
• Enabled direct integration of RNA-seq output with microarray data and custom gene sets.

Main Results:

• The software successfully integrates and associates data from various transcriptome reconstruction tools (e.g., Cufflinks) with Affymetrix probe set data.
• Annotation and cross-referencing processes are maximized.
• Custom gene sets, such as the MM 70 risk score (GEP-70), can be specified and assimilated into RNA-seq pipelines.

Conclusions:

• A novel bioinformatic approach facilitates the annotation and association of historical microarray data with RNA-seq data.
• This integration aids in the study of multiple myeloma (MM) cancer biology.
• The approach supports improved molecular understanding and clinical management of MM.