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Cited by 2,623Gene expression and cancer classificationMolecular Biology Techniques and ApplicationsGenomics and Phylogenetic Studies

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The RIN: an RNA integrity number for assigning integrity values to RNA measurements

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Schroeder, Andreas, Mueller, Odilo, Stocker, Susanne, Salowsky, Ruediger, Leiber, Michael, Gassmann, Marcus, Lightfoot, Samar, Menzel, Wolfram, Granzow, Martin, Ragg, Thomas·2006·BMC Molecular Biology
DOI 10.1186/1471-2199-7-3PMC PMC1413964
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Abstract

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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Integrity signals

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Crossref · 10.1186/1471-2199-7-3

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Figures

Figure 1 Application environment. (1) Role of RNA in gene expression and protein production, (2) extracted RNA molecules, measurement of RNA sizes applying Agilents 2100 bioanalyzer and (3) assignment of integrity categories to RNA samples. In the sample, RNA molecules of different sizes occur, which is measured by the 2100 bioanalyzer. The distinction with regard to integrity is based on this size distribution in each sample.
Figure 1 Application environment. (1) Role of RNA in gene expression and protein production, (2) extracted RNA molecules, measurement of RNA sizes applying Agilents 2100 bioanalyzer and (3) assignment of integrity categories to RNA samples. In the sample, RNA molecules of different sizes occur, which is measured by the 2100 bioanalyzer. The distinction with regard to integrity is based on this size distribution in each sample.CC BY · PubMed Central
Figure 2 RNA integrity categories. The figure shows typical representatives of the ten integrity categories. RIN values range from 10 (intact) to 1 (totally degraded). The gradual degradation of rRNA is reflected by a continuous shift towards shorter fragment sizes.
Figure 2 RNA integrity categories. The figure shows typical representatives of the ten integrity categories. RIN values range from 10 (intact) to 1 (totally degraded). The gradual degradation of rRNA is reflected by a continuous shift towards shorter fragment sizes.CC BY · PubMed Central
Figure 3 Evidence-based model selection. Dependency of model evidence on a logarithmic scale from the number of features used and from the degree of non-linearity in the hidden layer. The values are average values over a 10-fold cross-validation procedure. The highest evidence is reached for models with 5 to 7 input features and 2 to 5 hidden neurons respectively. All these models have a low generalization error below 0.25.
Figure 3 Evidence-based model selection. Dependency of model evidence on a logarithmic scale from the number of features used and from the degree of non-linearity in the hidden layer. The values are average values over a 10-fold cross-validation procedure. The highest evidence is reached for models with 5 to 7 input features and 2 to 5 hidden neurons respectively. All these models have a low generalization error below 0.25.CC BY · PubMed Central
Figure 4 Generalization errors. Dependency of the generalization error of the model from the number of features used and from the degree of non-linearity in the hidden layer. The values are average values over a 10-fold cross-validation procedure. Models with highest evidence (5 to 7 input features and 2 to 5 hidden neurons respectively) have a low generalization error below 0.25.
Figure 4 Generalization errors. Dependency of the generalization error of the model from the number of features used and from the degree of non-linearity in the hidden layer. The values are average values over a 10-fold cross-validation procedure. Models with highest evidence (5 to 7 input features and 2 to 5 hidden neurons respectively) have a low generalization error below 0.25.CC BY · PubMed Central
Figure 5 Receiver operating characteristics of categorical misclassifications. The figure shows the Receiver Operating Characteristics for distinguishing electropherograms of category 10 against the set union of other categories. The area under the curve (AUC) gives a measure of classification performance. Random assignment is equal to an area of 0.5, whereas perfect assignment is equal to an area of 1.0. Only few experiments are exchanged over more than one categorical border.
Figure 5 Receiver operating characteristics of categorical misclassifications. The figure shows the Receiver Operating Characteristics for distinguishing electropherograms of category 10 against the set union of other categories. The area under the curve (AUC) gives a measure of classification performance. Random assignment is equal to an area of 0.5, whereas perfect assignment is equal to an area of 1.0. Only few experiments are exchanged over more than one categorical border.CC BY · PubMed Central
Figure 6 Correlation between RNA integrity and rt-PCR experiments. The figure shows the correlation between RNA integrity values and the outcome of an real-time-PCR experiment, i.e. the average expression values of 4 housekeeping genes (GAPDH, KYNF, NEFL, β2M). The vertical line is a meaningful threshold value for RIN classification, while the horizontal separates acceptable from unacceptable real-time PCR results, a) The RIN shows a strong correlation (0.52) to the expression value of the house keeping genes. A straightforward separation into positives and negatives is possible. b) The ribosomal ratio shows a poor correlation (0.24) to the expression value of the house keeping genes.
Figure 6 Correlation between RNA integrity and rt-PCR experiments. The figure shows the correlation between RNA integrity values and the outcome of an real-time-PCR experiment, i.e. the average expression values of 4 housekeeping genes (GAPDH, KYNF, NEFL, β2M). The vertical line is a meaningful threshold value for RIN classification, while the horizontal separates acceptable from unacceptable real-time PCR results, a) The RIN shows a strong correlation (0.52) to the expression value of the house keeping genes. A straightforward separation into positives and negatives is possible. b) The ribosomal ratio shows a poor correlation (0.24) to the expression value of the house keeping genes.CC BY · PubMed Central
Figure 7 2D visualization of integrity categories. The figure shows a projection of the categories onto the two-dimensional space spanned by the first two features of the selected combination. These are total RNA ratio and 28S peak height. The experiments are clearly grouped along a curve from the left bottom corner up to the top and then to the right top corner. The variance in location of the experiments increases with larger categorical value. Categories 1 and 2 have almost no variance in this feature space. The grey border in the domain is given by the abnormality detectors for this two variables, i.e., for a data point outside the white area no RIN is computed.
Figure 7 2D visualization of integrity categories. The figure shows a projection of the categories onto the two-dimensional space spanned by the first two features of the selected combination. These are total RNA ratio and 28S peak height. The experiments are clearly grouped along a curve from the left bottom corner up to the top and then to the right top corner. The variance in location of the experiments increases with larger categorical value. Categories 1 and 2 have almost no variance in this feature space. The grey border in the domain is given by the abnormality detectors for this two variables, i.e., for a data point outside the white area no RIN is computed.CC BY · PubMed Central
Figure 8 Feature extraction. Segments of an electropherogram: The segment preceeding the lower marker is designated the pre-region. The marker-region coincides with the area occupied by the lower-marker peak. The 5S-region covers the small rRNA fragments (5S and 5.8S rRNA, and tRNA). The 18S-region and 28S-region cover the 18S peak and 28S peak, respectively. The fast-region lies between the 5S-region and the 18S-region. The inter-region lies between the 18S-region and the 28S-region. The precursor-region covers the pre-cursor RNA following the 28S-region. And finally the post-region lies beyond the precursor-region.
Figure 8 Feature extraction. Segments of an electropherogram: The segment preceeding the lower marker is designated the pre-region. The marker-region coincides with the area occupied by the lower-marker peak. The 5S-region covers the small rRNA fragments (5S and 5.8S rRNA, and tRNA). The 18S-region and 28S-region cover the 18S peak and 28S peak, respectively. The fast-region lies between the 5S-region and the 18S-region. The inter-region lies between the 18S-region and the 28S-region. The precursor-region covers the pre-cursor RNA following the 28S-region. And finally the post-region lies beyond the precursor-region.CC BY · PubMed Central

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Research works printed but not linked: 3

  1. Mueller O, Lightfoot S, Schröder A: RNA Integrity Number (RIN) Standardization of RNA Quality Control. Tech. Rep. 5989-1165EN, Agilent Technologies, Application Note. 2004, http://www.agilent.com/chem/labonachip
  2. Schröder A: Qualitätsbestimmung von RNA-Proben mittels adaptiver Verfahren. Diplomarbeit Universität Karlsruhe. 2003
  3. Agilent : 2100 expert software. Tech. Rep. 5989-0112EN, Agilent Technologies, Software Data Sheet. 2004, http://www.agilent.com/chem/labonachip

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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
Publisher
Springer Science and Business Media LLC
Published
2006-01-31
Volume
7
Issue
1
Pages
3-3
ISSN
1471-2199 (ISSN-L)
DOI
10.1186/1471-2199-7-3
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
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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