Title |
Primer3_masker: integrating masking of template sequence with primer design software.
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Published in |
Bioinformatics, January 2018
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DOI | 10.1093/bioinformatics/bty036 |
Pubmed ID | |
Authors |
Triinu Kõressaar, Maarja Lepamets, Lauris Kaplinski, Kairi Raime, Reidar Andreson, Maido Remm |
Abstract |
Designing PCR primers for amplifying regions of eukaryotic genomes is a complicated task because the genomes contain a large number of repeat sequences and other regions unsuitable for amplification by PCR. We have developed a novel k-mer based masking method that uses a statistical model to detect and mask failure-prone regions on the DNA template prior to primer design. We implemented the software as a standalone software primer3_masker and integrated it into the primer design program Primer3. The standalone version of primer3_masker is implemented in C. The source code is freely available at https://github.com/bioinfo-ut/primer3_masker/ (standalone version for Linux and macOS) and at https://github.com/primer3-org/primer3/(integrated version). Primer3 web application that allows masking sequences of 196 animal and plant genomes is available at http://primer3.ut.ee/. [email protected]. Supplementary data are available at Bioinformatics online. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Italy | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 325 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 57 | 18% |
Student > Master | 49 | 15% |
Student > Bachelor | 44 | 14% |
Researcher | 35 | 11% |
Other | 13 | 4% |
Other | 29 | 9% |
Unknown | 98 | 30% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 92 | 28% |
Agricultural and Biological Sciences | 65 | 20% |
Medicine and Dentistry | 20 | 6% |
Immunology and Microbiology | 11 | 3% |
Computer Science | 6 | 2% |
Other | 23 | 7% |
Unknown | 108 | 33% |