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The RIN: an RNA integrity number for assigning integrity values to RNA measurements
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Background: The integrity of RNA molecules is of paramount importance for experiments that try to reflect the snapshot of gene expression at the moment of RNA extraction. Until recently, there has been no reliable standard for estimating the integrity of RNA samples and the ratio of 28S:18S ribosomal RNA, the common measure for this purpose, has been shown to be inconsistent. The advent of microcapillary electrophoretic RNA separation provides the basis for an automated high-throughput approach, in order to estimate the integrity of RNA samples in an unambiguous way. Methods: A method is introduced that automatically selects features from signal measurements and constructs regression models based on a Bayesian learning technique. Feature spaces of different dimensionality are compared in the Bayesian framework, which allows selecting a final feature combination corresponding to models with high posterior probability. Results: This approach is applied to a large collection of electrophoretic RNA measurements recorded with an Agilent 2100 bioanalyzer to extract an algorithm that describes RNA integrity. The resulting algorithm is a user-independent, automated and reliable procedure for standardization of RNA quality control that allows the calculation of an RNA integrity number (RIN). Conclusion: Our results show the importance of taking characteristics of several regions of the recorded electropherogram into account in order to get a robust and reliable prediction of RNA integrity, especially if compared to traditional methods.
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“In this study, we also excluded 9 cortex and 15 cerebellum samples that had an RNA Integrity Number (RIN)63 less than 7.”
From Pathogenic tau-induced piRNA depletion promotes neuronal death through transposable element dysregulation in neurodegenerative tauopathies (2018) · cites this paper as [63]
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Metadata
- Authors
- Schroeder, A., Mueller, O., Stocker, S., Salowsky, R., Leiber, M., Gassmann, M., Lightfoot, S., Menzel, W., Granzow, M., & Ragg, T.
- Affiliations
- Agilent Technologies (United States) (US)
- Agilent Technologies (Germany) (DE)
- Karlsruhe Institute of Technology (DE)
- Corresponding author
- Mueller, Odilo
- Ragg, Thomas
- Venue
- BMC Molecular Biology
- Published
- 2006-01-31
- Volume
- 7
- Issue
- 1
- Pages
- 3-3
- ISSN
- 1471-2199 (ISSN-L)
- PMC
- PMC1413964
- OpenAlex
- W1696199194
- Cited by
- 2,623
- References
- 18
- Citations by year
- 2026: 97 · 2025: 106 · 2024: 135 · 2023: 111 · 2022: 132 · 2021: 172 · 2020: 129 · 2019: 108 · 2018: 169 · 2017: 138 · 2016: 166 · 2015: 149 · 2014: 162 · 2013: 180 · 2012: 145
- FWCI
- 33.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- BMC Molecular Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Johns Hopkins University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- RNA integrity number, RNA quality control, Agilent 2100 Bioanalyzer, Bayesian feature selection, regression models, gene expression
- MeSH terms
- Algorithms, Animals, Bayes Theorem, Humans, Quality Control, RNA, Software, Linear Models, Polymerase Chain Reaction, Neural Networks, Computer, Gene Expression Profiling, Electrophoresis, Microchip, Mice, Rats
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