RNA-seq: impact of RNA degradation on transcript quantification.

Irene Gallego Romero,Jenny Tung,Athma A Pai,Yoav Gilad

doi:10.1186/1741-7007-12-42

Irene Gallego Romero, Jenny Tung + Show 2 more

Open Access

https://doi.org/10.1186/1741-7007-12-42

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Abstract

BackgroundThe use of low quality RNA samples in whole-genome gene expression profiling remains controversial. It is unclear if transcript degradation in low quality RNA samples occurs uniformly, in which case the effects of degradation can be corrected via data normalization, or whether different transcripts are degraded at different rates, potentially biasing measurements of expression levels. This concern has rendered the use of low quality RNA samples in whole-genome expression profiling problematic. Yet, low quality samples (for example, samples collected in the course of fieldwork) are at times the sole means of addressing specific questions.ResultsWe sought to quantify the impact of variation in RNA quality on estimates of gene expression levels based on RNA-seq data. To do so, we collected expression data from tissue samples that were allowed to decay for varying amounts of time prior to RNA extraction. The RNA samples we collected spanned the entire range of RNA Integrity Number (RIN) values (a metric commonly used to assess RNA quality). We observed widespread effects of RNA quality on measurements of gene expression levels, as well as a slight but significant loss of library complexity in more degraded samples.ConclusionsWhile standard normalizations failed to account for the effects of degradation, we found that by explicitly controlling for the effects of RIN using a linear model framework we can correct for the majority of these effects. We conclude that in instances in which RIN and the effect of interest are not associated, this approach can help recover biologically meaningful signals in data from degraded RNA samples.

Highlights

The use of low quality RNA samples in whole-genome gene expression profiling remains controversial
Based on the RNA Integrity Number (RIN) values we chose to focus on 20 samples from five time points (0 hours, 12 hours, 24 hours, 48 hours and 84 hours) that spanned the entire scale of RNA quality
We added a spike-in of non-human control RNA to each sample, which allowed us to confirm the effects of RNA degradation on the RNA sequencing results

Summary

Introduction

The use of low quality RNA samples in whole-genome gene expression profiling remains controversial It is unclear if transcript degradation in low quality RNA samples occurs uniformly, in which case the effects of degradation can be corrected via data normalization, or whether different transcripts are degraded at different rates, potentially biasing measurements of expression levels. It remains largely unclear whether most transcript types decay at similar rates under such conditions or whether rates of RNA decay in dying tissues are associated with transcriptspecific properties These questions are of great importance for studies that rely on sample collection in the field or in clinical settings (both from human populations as well as from other species), in which tissue samples often cannot immediately be stored in conditions that prevent RNA degradation. The degree to which this confounder affects estimates of gene expression levels is not well understood

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