title: The RIN: an RNA integrity number for assigning integrity values to RNA measurements.
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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Agilent Technologies, Hewlett-Packard-Strasse 8, 76337 Waldbronn, Germany. a.schroder@abp.nl
authors.forename: Andreas
authors.initials: A
authors.lastname: Schroeder
authors.forename: Odilo
authors.initials: O
authors.lastname: Mueller
authors.forename: Susanne
authors.initials: S
authors.lastname: Stocker
authors.forename: Ruediger
authors.initials: R
authors.lastname: Salowsky
authors.forename: Michael
authors.initials: M
authors.lastname: Leiber
authors.forename: Marcus
authors.initials: M
authors.lastname: Gassmann
authors.forename: Samar
authors.initials: S
authors.lastname: Lightfoot
authors.forename: Wolfram
authors.initials: W
authors.lastname: Menzel
authors.forename: Martin
authors.initials: M
authors.lastname: Granzow
authors.forename: Thomas
authors.initials: T
authors.lastname: Ragg
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D046209:Electrophoresis, Microchip / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D016014:Linear Models; D051379:Mice; D016571:Neural Networks, Computer; D016133:Polymerase Chain Reaction; D011786:Quality Control; D012313:RNA / Q000032:analysis / Q000302:isolation & purification / Q000592:standards*; D051381:Rats; D012984:Software
chemical_list: D012313:RNA