IVT-seq reveals extreme bias in RNA sequencing

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• RNA sequencing (RNA-seq) is crucial for analyzing gene expression and splicing.
• Understanding technical biases in RNA-seq is essential for accurate data interpretation.
• Existing library preparation methods introduce variability.

Purpose of the Study:

• To develop and apply in vitro transcription sequencing (IVT-seq) for large-scale assessment of RNA-seq biases.
• To identify sources of technical variability in common RNA-seq protocols.

Main Methods:

• Created a pool of over 1,000 full-length human in vitro transcribed RNAs.
• Sequenced these RNAs using polyA and total RNA-seq protocols.
• Employed experimental and computational analyses to pinpoint bias sources.

Main Results:

• Significant within-transcript coverage variation observed: 50% of transcripts showed >2-fold differences, 10% showed >10-fold.
• Over 6% of transcripts exhibited unpredictable coverage regions between samples.
• Ribosomal RNA (rRNA) depletion identified as a major contributor to coverage variability.
• Sequence composition also influences RNA representation.

Conclusions:

• IVT-seq effectively identifies and quantifies RNA-seq biases.
• rRNA depletion introduces substantial, previously unappreciated biases in RNA-seq data.
• Exon-level expression analysis may be unreliable; caution is advised when interpreting RNA-seq results.