Journal
PLOS ONE
Volume 7, Issue 11, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0050226
Keywords
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Categories
Funding
- Government of Alberta (Ministry of Advanced Education and Technology)
- Musea Ventures
- BGI-Shenzhen
- Alberta's Informatics Circle of Research Excellence (iCORE)
- iPlant (NSF(National Science Foundation)) [DBI-0735191]
- NSERC Canada
- NSF [DEB-0919869, DEB-0830009, DEB-0829868, DEB-1110443, DEB-1146603, PGR-0638595, IOS-0922742, DEB-0919254, DEB-082762, EF-0629817, IOS-0421604, DDIG-0910258]
- National Institutes of Health (NIH) [1R01DA025197-01]
- USDA National Institute of Food and Agriculture
- University of Tennessee Agricultural Experiment Station
- Agency for Science, Technology and Research (Singapore)
- Max Planck Society
- University of Cologne
- Royal Botanic Gardens, Kew, UK
- International Rice Research Institute C4 project through the Bill & Melinda Gates Foundation
- Direct For Biological Sciences
- Division Of Environmental Biology [1146603] Funding Source: National Science Foundation
- Direct For Biological Sciences
- Division Of Environmental Biology [0830009] Funding Source: National Science Foundation
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Next-generation sequencing plays a central role in the characterization and quantification of transcriptomes. Although numerous metrics are purported to quantify the quality of RNA, there have been no large-scale empirical evaluations of the major determinants of sequencing success. We used a combination of existing and newly developed methods to isolate total RNA from 1115 samples from 695 plant species in 324 families, which represents >900 million years of phylogenetic diversity from green algae through flowering plants, including many plants of economic importance. We then sequenced 629 of these samples on Illumina GAIIx and HiSeq platforms and performed a large comparative analysis to identify predictors of RNA quality and the diversity of putative genes (scaffolds) expressed within samples. Tissue types (e. g., leaf vs. flower) varied in RNA quality, sequencing depth and the number of scaffolds. Tissue age also influenced RNA quality but not the number of scaffolds >= 1000 bp. Overall, 36% of the variation in the number of scaffolds was explained by metrics of RNA integrity (RIN score), RNA purity (OD 260/230), sequencing platform (GAIIx vs HiSeq) and the amount of total RNA used for sequencing. However, our results show that the most commonly used measures of RNA quality (e. g., RIN) are weak predictors of the number of scaffolds because Illumina sequencing is robust to variation in RNA quality. These results provide novel insight into the methods that are most important in isolating high quality RNA for sequencing and assembling plant transcriptomes. The methods and recommendations provided here could increase the efficiency and decrease the cost of RNA sequencing for individual labs and genome centers.
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