Nucleic Acids Research Advance Access originally published online on March 5, 2009
Nucleic Acids Research 2009 37(6):e48; doi:10.1093/nar/gkp139
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Nucleic Acids Research, 2009, Vol. 37, No. 6 e48
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Methods Online |
Identification of RNA molecules by specific enzyme digestion and mass spectrometry: software for and implementation of RNA mass mapping
1Population Genetics—Instituto de Patologia e Imunologia Molecular da Universidad do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal 2Bioinformatics Unit—CIC bioGUNE, Parque Tecnológico de Bizkaia Edificio 801 A, 48160 Derio, Spain and 3Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
*To whom correspondence should be addressed. Tel: +351 225570700; Fax: +351 225570700; Email: rmatthiesen{at}ipatimup.pt Correspondence may also be addressed to Finn Kirpekar. Tel: +45 65502414; Fax: +45 65932781; Email: f.kir{at}bmb.sdu.dk
Received June 19, 2008. Revised January 20, 2009. Accepted February 11, 2009.
The idea of identifying or characterizing an RNA molecule based on a mass spectrum of specifically generated RNA fragments has been used in various forms for well over a decade. We have developed software—named RRM for ‘RNA mass mapping’—which can search whole prokaryotic genomes or RNA FASTA sequence databases to identify the origin of a given RNA based on a mass spectrum of RNA fragments. As input, the program uses the masses of specific RNase cleavage of the RNA under investigation. RNase T1 digestion is used here as a demonstration of the usability of the method for RNA identification. The concept for identification is that the masses of the digestion products constitute a specific fingerprint, which characterize the given RNA. The search algorithm is based on the same principles as those used in peptide mass fingerprinting, but has here been extended to work for both RNA sequence databases and for genome searches. A simple and powerful probability model for ranking RNA matches is proposed. We demonstrate viability of the entire setup by identifying the DNA template of a series of RNAs of biological and of in vitro transcriptional origin in complete microbial genomes and by identifying authentic 16S ribosomal RNAs in a ‘small ribosomal subunit RNA’ database. Thus, we present a new tool for a rapid identification of unknown RNAs using only a few picomoles of starting material.