We are analyzing https://link.springer.com/article/10.1186/1471-2199-7-3.

Title:
The RIN: an RNA integrity number for assigning integrity values to RNA measurements | BMC Molecular Biology
Description:
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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Keywords {🔍}

rna, features, integrity, model, data, samples, categories, rin, feature, values, information, ratio, figure, degradation, area, number, error, experiments, set, models, table, based, results, electropherogram, size, article, ribosomal, electropherograms, shows, evidence, approach, quality, software, training, target, google, scholar, algorithm, additional, sregion, file, authors, agilent, categorical, peak, full, expression, process, methods, compared,

Topics {✒️}

open access article article download pdf bio-analytical device based 10-fold cross-validation procedure real-time pcr data real-time-pcr experiment voltage-induced size separation automated bio-analytical device real-time pcr experiments iterative forward search laser-induced fluorescence detection privacy choices/manage cookies automated high-throughput approach real-time pcr full size image bio-analytical device rna quality control nucleic acids research thomas ragg authors’ original file rna string material constant background signal artificial neural networks construct feature spaces high gene expression scope andreas schroeder diplomarbeit universität karlsruhe evolutionary parameter optimization full size table rna quality assessment laser-induced fluorescence precise analytical instrumentation parametric kernel estimators collaboration partners ambion acknowledge christine miller electrophoretic traces reveals examining electrophoretic traces assessing rna quality low gene expression rna integrity assessment author information authors experienced expert users rna molecule plays evaluating rna status categories form clusters measuring rna integrity judge rna integrity full access trained neural networks receiver operating characteristics

Schema {🗺️}

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         description: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. 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. 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). 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.
         datePublished:2006-01-31T00:00:00Z
         dateModified:2006-01-31T00:00:00Z
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            Model Evidence
            Fast Region
            Ribosomal Band
            Marker Height
            Biochemistry
            general
            Nucleic Acid Chemistry
            Cell Biology
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      headline:The RIN: an RNA integrity number for assigning integrity values to RNA measurements
      description: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. 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. 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). 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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         Fast Region
         Ribosomal Band
         Marker Height
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         Cell Biology
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      name:Agilent Technologies, Waldbronn, Germany
      name:Agilent Technologies, Santa Clara, USA
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