BMC GenomicsBioinformaticsPeer-reviewed journal article
Multiple freeze-thaw cycles lead to a loss of consistency in poly(A)-enriched RNA sequencing
Benjamin P. KellmaniD, Hratch BaghdassarianiD, Tiziano PramparoiD, Isaac ShamieiD, Vahid GazestaniiD, Arjana BegzatiiD, Shangzhong LiiD, Srinivasa R. Nalabolu, Sarah Shaw MurrayiD, Linda LopeziD, Karen PierceiD, Eric CourchesneiD, Nathan E. LewisiD
Abstract
RNA-Seq is ubiquitous, but depending on the study, sub-optimal sample handling may be required, resulting in repeated freeze-thaw cycles. However, little is known about how each cycle impacts downstream analyses, due to a lack of study and known limitations in common RNA quality metrics, e.g., RIN, at quantifying RNA degradation following repeated freeze-thaws. Here we quantify the impact of repeated freeze-thaw on the reliability of downstream RNA-Seq analysis. To do so, we developed a method to estimate the relative noise between technical replicates independently of RIN. Using this approach we inferred the effect of both RIN and the number of freeze-thaw cycles on sample noise. We find that RIN is unable to fully account for the change in sample noise due to freeze-thaw cycles. Additionally, freeze-thaw is detrimental to sample quality and differential expression (DE) reproducibility, approaching zero after three cycles for poly(A)-enriched samples, wherein the inherent 3 bias in read coverage is more exacerbated by freeze-thaw cycles, while ribosome-depleted samples are less affected by freeze-thaws. The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/020792v2_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@c2c756org.highwire.dtl.DTLVardef@1ad1c03org.highwire.dtl.DTLVardef@a4136org.highwire.dtl.DTLVardef@13f403f_HPS_FORMAT_FIGEXP M_FIG C_FIG
- Citations
- 32
- FWCI
- 1.49
- Field percentile
- top 18%
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