Microbiology Discovery

Microbiology Discovery

ISSN 2052-6180
Methodology

Determining RNA quality for NextGen sequencing: some exceptions to the gold standard rule of 23S to 16S rRNA ratio§

Arvind A Bhagwat1*, Z. Irene Ying1, Jeff Karns1 and Allen Smith2

*Correspondence: Arvind A Bhagwat Arvind.bhagwat@ars.usda.gov

§Mention of brand or firm name does not constitute an endorsement by the U.S. Department of 13 Agriculture above others of a similar nature not mentioned.

1. Environmental Microbial & Food Safety Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, 10300 Baltimore Avenue, BARC-E, Beltsville, MD 20705-2350, USA.

Author Affiliations

2. Diet Genomics and Immunology Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, 10300 Baltimore Avenue, BARC-E, Beltsville, MD 20705-2350, USA.

Abstract

Background: Using next-generation-sequencing technology to assess entire transcriptomes requires high quality starting RNA. Traditionally, the ratios of 23S to 16S ribosomal RNA bands from agarose gel electrophoresis have been used to judge integrity of RNA. Currently, RNA quality is routinely judged using automated microfluidic gel electrophoresis platforms and associated algorithms.

Findings: Here we report that the two most popular automated platforms (i.e., BioAnalyzer and Experion systems of Agilent Technology and Bio-Rad Laboratories respectively) and their associate algorithms are based on limited data sets of model organisms. The systems perform data interpretation with presumption that prokaryotic rRNA molecules are eluted in two unique peaks corresponding to 23S and 16S molecules. However, certain microorganisms carry intervening sequences in their rRNA structural genes that are subsequently excised during ribosome formation. In such instances, the 23S and 16S rRNA components are eluted in multiple peaks. As a result, current algorithms used by microfluidic platforms read such samples as 'degraded' and assign them poor RNA quality scores. We observed RNA isolated from several Citrobacter and Salmonella isolates generated false quality scores and low 23S to 16S ribosomal RNA ratios.

Conclusions: For RNA-sequencing projects involving non-model organisms, relying solely on automated algorithms for 'quality control' of RNA could be misleading. Multiple peaks corresponding to 23S or 16S RNA could be due to occurrence of multiple intervening sequences in rRNA genes.

Keywords: RNA-Seq, RNA quality, salmonella transcriptome, agarose gel electrophoresis

ISSN 2052-6180
Volume 1
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