Bioinformatics Vol. 19 Suppl. 1 2003
Pages i44-i53
© 2003 Oxford University Press
SNP and mutation discovery using base-specific cleavage and MALDI-TOF mass spectrometry
SEQUENOM Inc., 3595 John Hopkins Court, San Diego, CA 92121, USA
Received on January 6, 2003
; accepted on February 20, 2003
Motivation: Single Nucleotide Polymorphisms (SNPs) are believed to contribute strongly to the genetic variability in living beings, in particular their disease or drug side effect predispositions. Mutation-induced sequence variations are playing an important role in the development of cancer, among others. From this, it is clear that SNP and mutation discovery is of great interest in today’s Life Sciences. Currently, such discovery is often performed utilizing electrophoresis-based Sanger Sequencing. Discovery of SNPs can also be performed by multiple sequence alignment of publicly available sequence data, but recent studies indicate that only a small percentage of SNPs can be discovered using this approach and, in particular, that SNPs with low frequency are often missed. Other SNP discovery methods only indicate the presence of a SNP in a sample region, but fail to resolve its characterization and localization.
Results: We present a method to discover mutations and SNPs using base-specific cleavage and mass spectrometry. An amplicon of known reference sequence with length usually between 100 and 1000 nt is amplified, transcribed, and cleaved using base-specific endonucleases such as RNAse A or T1. The resulting cleavage products (or fragments) are analyzed by MALDI-TOF mass spectrometry and, comparing the measured spectra with those predicted in-silico, the goal is to discover and pinpoint sequence variations of the sample sequence compared to the reference sequence. A time-efficient algorithm for discovering sequence variations is presented that enables fast analysis of such variations even if the sample sequence differs significantly from the reference sequence.
Contact: boecker{at}CeBiTec.uni-bielefeld.de
* Currently at AG Genome Informatics, Technische Fakultät, Universität Bielefeld, Germany.
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