A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units

Andrea Manconi, Alessandro Orro, Emanuele Manca, Giuliano Armano, Luciano Milanesi
National Research Council, Institute for Biomedical Technologies, Segrate (MI), 20090, Italy
BMC Bioinformatics, 15(Suppl 1):S10, 2014


   title={A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units},

   author={Manconi, Andrea and Orro, Alessandro and Manca, Emanuele and Armano, Giuliano and Milanesi, Luciano},

   journal={BMC Bioinformatics},

   number={Suppl 1},



   publisher={BioMed Central Ltd}


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BACKGROUND: Single Nucleotide Polymorphism (SNP) genotyping analysis is very susceptible to SNPs chromosomal position errors. As it is known, SNPs mapping data are provided along the SNP arrays without any necessary information to assess in advance their accuracy. Moreover, these mapping data are related to a given build of a genome and need to be updated when a new build is available. As a consequence, researchers often plan to remap SNPs with the aim to obtain more up-to-date SNPs chromosomal positions. In this work, we present G-SNPM a GPU (Graphics Processing Unit) based tool to map SNPs on a genome. METHODS: G-SNPM is a tool that maps a short sequence representative of a SNP against a reference DNA sequence in order to find the physical position of the SNP in that sequence. In G-SNPM each SNP is mapped on its related chromosome by means of an automatic three-stage pipeline. In the first stage, G-SNPM uses the GPU-based short-read mapping tool SOAP3-dp to parallel align on a reference chromosome its related sequences representative of a SNP. In the second stage G-SNPM uses another short-read mapping tool to remap the sequences unaligned or ambiguously aligned by SOAP3-dp (in this stage SHRiMP2 is used, which exploits specialized vector computing hardware to speed-up the dynamic programming algorithm of Smith-Waterman). In the last stage, G-SNPM analyzes the alignments obtained by SOAP3-dp and SHRiMP2 to identify the absolute position of each SNP. RESULTS and CONCLUSIONS: To assess G-SNPM, we used it to remap the SNPs of some commercial chips. Experimental results shown that G-SNPM has been able to remap without ambiguity almost all SNPs. Based on modern GPUs, G-SNPM provides fast mappings without worsening the accuracy of the results. G-SNPM can be used to deal with specialized Genome Wide Association Studies (GWAS), as well as in annotation tasks that require to update the SNP mapping probes.
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