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MRPrimerW2: an enhanced tool for rapid design of valid high-quality primers with multiple search modes for qPCR experiments

Title
MRPrimerW2: an enhanced tool for rapid design of valid high-quality primers with multiple search modes for qPCR experiments
Authors
Jeon, HajinBae, JeongminHwang, Sang HyunHwang, Kyu-YoungLee, Hyun-SeobKim, HyerinKim, Min-Soo
DGIST Authors
Kim, Min-Soo
Issue Date
2019-07
Citation
Nucleic Acids Research, 47(W1), W614-W622
Type
Article
Article Type
Article
Keywords
QUANTITATIVE GENE-EXPRESSIONPCR TECHNOLOGYQUANTIFICATIONDATABASE
ISSN
0305-1048
Abstract
For the best results in quantitative polymerase chain reaction (qPCR) experiments, it is essential to design high-quality primers considering a multitude of constraints and the purpose of experiments. The constraints include many filtering constraints, homology test on a huge number of off-target sequences, the same constraints for batch design of primers, exon spanning, and avoiding single nucleotide polymorphism (SNP) sites. The target sequences are either in database or given as FASTA sequences, and the experiment is for amplifying either each target sequence with each corresponding primer pairs designed under the same constraints or all target sequences with a single pair of primers. Many websites have been proposed, but none of them including our previous MRPrimerW fulfilled all the above features. Here, we describe the MRPrimerW2, the update version of MRPrimerW, which fulfils all the features by maintaining the advantages of MRPrimerW in terms of the kinds and sizes of databases for valid primers and the number of search modes. To achieve it, we exploited GPU computation and a disk-based key-value store using PCIe SSD. The complete set of 3 509 244 680 valid primers of MRPrimerW2 covers 99% of nine important organisms in an exhaustive manner. Free access: http://MRPrimerW2.com. © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.
URI
http://hdl.handle.net/20.500.11750/10398
DOI
10.1093/nar/gkz323
Publisher
Oxford University Press
Related Researcher
  • Author Kim, Min-Soo InfoLab
  • Research Interests Big Data Systems; Big Data Mining & Machine Learning; Big Data Bioinformatics; 데이터 마이닝 및 빅데이터 분석; 바이오인포메틱스 및 뉴로인포메틱스; 뇌-기계 인터페이스(BMI)
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Collection:
Department of Information and Communication EngineeringInfoLab1. Journal Articles


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