Two combinatorial optimization problems for SNP discovery using base-specific cleavage and mass spectrometryReport as inadecuate




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BMC Systems Biology

, 6:S5

First Online: 12 December 2012

Abstract

BackgroundThe discovery of single-nucleotide polymorphisms SNPs has important implications in a variety of genetic studies on human diseases and biological functions. One valuable approach proposed for SNP discovery is based on base-specific cleavage and mass spectrometry. However, it is still very challenging to achieve the full potential of this SNP discovery approach.

ResultsIn this study, we formulate two new combinatorial optimization problems. While both problems are aimed at reconstructing the sample sequence that would attain the minimum number of SNPs, they search over different candidate sequence spaces. The first problem, denoted as SNP -  M S P Open image in new window, limits its search to sequences whose in silico predicted mass spectra have all their signals contained in the measured mass spectra. In contrast, the second problem, denoted as SNP - M S Q Open image in new window, limits its search to sequences whose in silico predicted mass spectra instead contain all the signals of the measured mass spectra. We present an exact dynamic programming algorithm for solving the SNP -  M S P Open image in new window problem and also show that the SNP - M S Q Open image in new window problem is NP-hard by a reduction from a restricted variation of the 3-partition problem.

ConclusionsWe believe that an efficient solution to either problem above could offer a seamless integration of information in four complementary base-specific cleavage reactions, thereby improving the capability of the underlying biotechnology for sensitive and accurate SNP discovery.

Electronic supplementary materialThe online version of this article doi:10.1186-1752-0509-6-S2-S5 contains supplementary material, which is available to authorized users.

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Author: Xin Chen - Qiong Wu - Ruimin Sun - Louxin Zhang

Source: https://link.springer.com/



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