Year: 2020
Type: article
Abstract: Poor quality of biological samples will result in an inaccurate analysis of next‐generation sequencing (NGS). Therefore, methods to accurately evaluate sample integrity are needed. Among methods for evaluating RNA quality, the RNA integrity number equivalent (RINe) is widely used, whereas the DV200, which evaluates the percentage of fragments of >200 nucleotides, is also used as a quality assessment standard. In this study, we compared the RINe and DV200 RNA quality indexes to determine the most suitable RNA index for the NGS analysis. Seventy‐one RNA samples were extracted from formalin‐fixed paraffin‐embedded tissue samples ( n = 30), fresh‐frozen samples ( n = 25), or cell lines ( n = 16). After assessing RNA quality using the RINe and DV200, we prepared two kinds of stranded mRNA sequencing libraries. Finally, we calculated the correlation between each RNA quality index and the amount of library product (1 st PCR product per input RNA). The DV200 measure showed stronger correlation with the amount of library product than the RINe ( R 2 = 0.8208 for the DV200 versus 0.6927 for the RINe). Receiver operating characteristic curve analyses revealed that the DV200 was the better marker for predicting efficient library production than the RINe using a threshold of >10 ng/ng for the amount of the 1 st PCR product per input RNA (cutoff value for the RINe and DV200, 2.3 and 66.1%; area under the curve, 0.99 and 0.91; sensitivity, 82% and 92%; and specificity, 93% and 100%, respectively). Our results indicate that NGS libraries prepared using RNA samples with the DV200 value > 66.1 % exhibit greater sensitivity and specificity than those prepared with the RINe values > 2.3. These findings suggest that the DV200 is superior to the RINe, especially for low‐quality RNA, because it is a more consistent assessment of the amount of the 1 st NGS library product per input.
Source: BioMed Research International
DOI: 10.1155/2020/9349132
Institutions: Okayama University Hospital, Okayama University, Kindai University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,680
Awards: G2725415978, Novel therapeutic method for HER2 active cancer focusing on HER2 novel binding molecule cytokeratin 19, G5156170010, Identification of factors related to penetrance and predictive model for ovarian cancer among BRCA1or BRCA2 mutation carriers, G586280527, Next generation lung cancer mice model for conquering cancer stem, In vivo efficacy of triplet therapy with osimertinib, cetuximab and bevacizumab for lung cancer cells harboring EGFR T790M
OpenAlex ID: W3007740300