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The RIN: an RNA integrity number for assigning integrity values to RNA measurements
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 ribosoma...
Autores principales: | , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2006
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1413964/ https://www.ncbi.nlm.nih.gov/pubmed/16448564 http://dx.doi.org/10.1186/1471-2199-7-3 |
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author | Schroeder, Andreas Mueller, Odilo Stocker, Susanne Salowsky, Ruediger Leiber, Michael Gassmann, Marcus Lightfoot, Samar Menzel, Wolfram Granzow, Martin Ragg, Thomas |
author_facet | Schroeder, Andreas Mueller, Odilo Stocker, Susanne Salowsky, Ruediger Leiber, Michael Gassmann, Marcus Lightfoot, Samar Menzel, Wolfram Granzow, Martin Ragg, Thomas |
author_sort | Schroeder, Andreas |
collection | PubMed |
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. |
format | Text |
id | pubmed-1413964 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-14139642006-03-30 The RIN: an RNA integrity number for assigning integrity values to RNA measurements Schroeder, Andreas Mueller, Odilo Stocker, Susanne Salowsky, Ruediger Leiber, Michael Gassmann, Marcus Lightfoot, Samar Menzel, Wolfram Granzow, Martin Ragg, Thomas BMC Mol Biol Methodology Article 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. BioMed Central 2006-01-31 /pmc/articles/PMC1413964/ /pubmed/16448564 http://dx.doi.org/10.1186/1471-2199-7-3 Text en Copyright © 2006 Schroeder et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Schroeder, Andreas Mueller, Odilo Stocker, Susanne Salowsky, Ruediger Leiber, Michael Gassmann, Marcus Lightfoot, Samar Menzel, Wolfram Granzow, Martin Ragg, Thomas The RIN: an RNA integrity number for assigning integrity values to RNA measurements |
title | The RIN: an RNA integrity number for assigning integrity values to RNA measurements |
title_full | The RIN: an RNA integrity number for assigning integrity values to RNA measurements |
title_fullStr | The RIN: an RNA integrity number for assigning integrity values to RNA measurements |
title_full_unstemmed | The RIN: an RNA integrity number for assigning integrity values to RNA measurements |
title_short | The RIN: an RNA integrity number for assigning integrity values to RNA measurements |
title_sort | rin: an rna integrity number for assigning integrity values to rna measurements |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1413964/ https://www.ncbi.nlm.nih.gov/pubmed/16448564 http://dx.doi.org/10.1186/1471-2199-7-3 |
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