Integration of quantitated expression estimates from polyA-selected and rRNA-depleted RNA-seq libraries.

Area of Science:

• Bioinformatics
• Genomics
• Transcriptomics

Background:

• Fast alignment-free algorithms simplify RNA sequencing (RNA-seq) processing, especially for novel genomes.
• Integrating existing RNA-seq data with new libraries is challenging due to variations in sequencing depth and RNA extraction methods.
• PolyA-selection and rRNA-depletion methods yield different transcriptome fractions, complicating data comparison.

Purpose of the Study:

• To develop a systematic method for comparing RNA-seq data generated using polyA-selection and rRNA-depletion library types.
• To enable the integration of previously processed RNA-seq datasets with newly sequenced libraries.

Main Methods:

• Developed a two-part process using the Kallisto tool for transcript quantification.
• Defined a standardized RNA space using a reference transcript set for consistent expression estimation.
• Applied a ratio-based correction to rRNA-depleted library estimates to align with polyA-selected data.

Main Results:

• Achieved near-perfect correlation between gene expression estimates from both library types.
• Demonstrated that the method is independent of library type and effective across all expression levels.
• Validated the approach using two RNA-seq datasets from ovine macrophages.

Conclusions:

• A combination of reference transcriptome filtering and ratio-based correction harmonizes expression profiles from polyA-selected and rRNA-depleted libraries.
• This method facilitates meta-analysis and the integration of RNA-seq data into transcriptional atlas projects.
• Enables more robust and comprehensive comparative transcriptomic studies.