Gemi: PCR Primers Prediction from Multiple Alignments
Designing primers and probes for polymerase chain reaction (PCR) is a preliminary and critical step that requires the identification of highly conserved regions in a given set of sequences. This task can be challenging if the targeted sequences display a high level of diversity, as frequently encountered in microbiologic studies. We developed Gemi, an automated, fast, and easy-to-use bioinformatics tool with a user-friendly interface to design primers and probes based on multiple aligned sequences. This tool can be used for the purpose of real-time and conventional PCR and can deal efficiently with large sets of sequences of a large size.
Polymerase chain reaction (PCR) has been increasingly used over the last two decades to detect, quantify, and/or sequence nucleic acids from various sources [1, 2]. The number of publications in PubMed referencing “PCR” has increased from 2846 in 1990 to 20,426 in 2000 and 44,231 in 2010. This method has a wide range of applications, particularly in the field of microbiology [3–5] where primers and probes are often designed with the aim to hybridize to the greatest number of genome sequences for given groups of viruses, bacteria, or parasites [5–7]. However, designs can be challenging if primers and probes are meant to hybridize to sequences with considerable nucleotide diversity; the task becomes more complicated as the nucleotide diversity increases. Thus, identifying conserved regions in the targeted nucleotide sequences is a critical step in PCR primer design [5–9].
Several tools are available to design primers [6–14]. However, these tools often present limitations in their capabilities to parse numerous and/or large sequences, which are frequently encountered situations, or to deal with degenerate positions, and some of them are not easily usable without skill in bioinformatics.
Here, we present Gemi, which means “to find” in ancient Egyptian, a simple, automated, fast, and versatile tool to find universal primers and probes within a set of multiple, variable, and long sequences. The main criterion used to identify primers and probes in Gemi is nucleotide conservation, but our tool provides the dissociation temperature (), length, and GC percentage in the final output file for each of the chosen primers or probes. The application executes directly on a PC computer and provides a simple and user-friendly interface that allows designing primers easily and quickly. In addition, Gemi can parse several hundred long (>1 kilobase) sequences within seconds. We believe that our tool can be particularly useful in the field of microbiology.
2. Algorithm and Method
The input file for Gemi is a multiple aligned FASTA file. Once it is uploaded to the program, a consensus sequence will be constructed. Gemi also accepts a single sequence (that can be manually curated) and uses it as a consensus. Unlike other programs, degenerate nucleotides are included in the consensus sequence and follow the IUPAC-IUB nomenclature system  (see Section 1 in the supplementary file, SI-1 of the Supplementary Material available online at http://dx.doi.org/10.1155/2012/783138). Gemi then searches for primers and probes by sliding a window of a chosen size, which corresponds to the required size of the PCR product, along the full-length consensus sequence. The step value by which the window slides is the sliding value (Sections 2 and 3 in the supplementary file, SI-1).
Several parameters can be modified by the user from the main window, although default values are provided for each parameter (Figure 1). These parameters include the size of the sliding window, the sliding value, the number of degenerate positions, and the size and of the oligos (Figure 2). The default sliding value is 20. Another default value proposed for the identification of appropriate oligos is that the number of variable sites is zero at the 3′ end positions of the primers and probes. Another criterion is that the appropriate oligos must not contain more than three variable/degenerate nucleotide positions. However, more relaxed parameters can be chosen.
Two options have been implemented in Gemi to identify potential oligos, which can be chosen by ticking boxes (Figure 1). The first option consists of delineating a size for the nucleotide fragment that will contain hybridization sites for the primers and probes. One possibility is to choose a short window size to design real-time PCR primer/probe sets, classically, <150 nucleotides. This possibility requires ticking the “search for probe” box. Another possibility is to choose a larger window size to design primers for Sanger sequencing, classically, >200 nucleotides. A second option consists of using Gemi without delineating a size for the nucleotide fragment that will contain hybridization sites for the primers and probes. This option will result in the generation of a list of all possible oligos along the consensus sequence, whatever their respective location, with the start and end positions of the oligos reported. This latter option is particularly convenient for identifying primers in highly variable sequences, when first options failed, and it allows the user to manually select the best combination of proposed oligos (Figure 2 and Section 3 in the supplementary information, SI-1).
Regardless of the chosen option, the final report presents the sequence, length, GC content, , and position on the consensus sequence for each oligo. The of small oligos is estimated using the Wallace rule for the dissociation temperature [16, 17]. For longer oligos, the nearest-neighbor method is used to calculate the melting temperature () . Here, the is calculated using the equation in : °C * (#A + #T) + 4°C * (#C + #G), where “#” refers to the number of As, Cs, Gs, or Ts in the oligo.
3. Results and Discussion
We developed Gemi to supply the critical needs for the design of PCR primers and probes with an easy-to-use, fast and efficient. Several other tools for the design of PCR systems have been previously described . Nonetheless, some limitations can be pointed out for these tools [19–33]. The first limitation is that some tools, such as Primer3 that is an online and powerful tool to design primers based on a single, short and conserved sequence, cannot parse sequences with degenerate bases, what can be accomplished by Gemi . Other software as BatchPrimer3 or Primaclade accepts only one sequence [21, 22]. Other tools exist that can overcome this shortcoming [29–33], such as PrimerIdent, which accepts only eight sequences, one of them being used as template . GeneFisher can parse multiple sequences but fails to deal with sequences with degenerate bases . The web-based tool Greene SCPrimer designs degenerate primers from multiple sequence alignments by constructing phylogenetic tree, which is a slow process . The easyPAC tool can design degenerate primers and also performs mapping to reference files for real-time PCR, but it performs slower than Gemi for the primer and probe design . We previously described SVARAP for the analysis of sequence variability and primer design , which can analyze a maximum of 100 sequences with a maximal length of 4,000 nucleotides. Other tools have specific applications, such as PhiSiGns that identifies gene signatures in phage genomes . Besides, some tools for the design of PCR systems require bioinformatics skills, such as “Prosig”  or the PriMux package that is based on python scripts to search for primers and probes on nonaligned multiple sequences .
Existing tools often search for oligos by taking into account parameters such as the GC-content, the , or the formation of secondary structures. However, the most critical issue for several PCR-based assays is the identification of conserved regions where primers and probes can hybridize, in addition to the length and number of query sequences. These issues are particularly important in the field of microbiology. Moreover, the lack of user-friendly interface and cross-platform tool are challenging issues for biologist without prior knowledge of the programming tools.
Gemi has several advantages compared to other tools (Table 1). It is able to automatically and rapidly predict PCR primers for numerous long and variable sequences. Additionally, Gemi can be used to design PCR systems for both real-time PCR and sequencing. Moreover, no training in bioinformatics is required to use Gemi, which has a user-friendly interface.
Using Gemi on a PC with 512 MB RAM, it succeeded to construct consensus and to identify primers and probes among 61 aligned full-length hepatitis C virus genomes with a length of about 10000 nucleotides within few seconds (Table 1 and Section 4 in supplementary file, SI-1), while easyPAC failed to identify any primer and Greene SCPrimer hardly runs to design primers even with shorter sequences.
Some advanced options such as identification of secondary structures, and prediction of hairpins and primer-dimer formation are not presented in this version; these issues may be addressed in future versions of Gemi. Alternatively, prediction of the hairpin or dimer formation could be determined by other tools as OligoCalc tool .
4. Availability and Implementation
Gemi is a cross-platform application which is distributed under GNU-GPL license and is free to use for academic and research purposes. The portable desktop version of this tool facilitates its free distribution and usage. The software and documentation are freely available for research use at https://sourceforge.net/projects/gemi/. A script version of Gemi will be available upon request.
The tool runs on Windows 7 without any preliminary installations. For older versions, the software requires Microsoft.NET (Dot Net) Framework version 2.0, which is freely available from Microsoft website. For the Linux, Ubuntu, and Mac OS X users, please download Mono tool to run the software from http://www.mono-project.com/ or http://monodevelop.com/ (see the program’s user guide).
The input file is a standard FASTA format file that contains a single sequence or a multiple sequence alignment, which can be created using any available alignment tool. The output file is generated as a tabulated text file that is easily read using any text processing program and contains the sequence of PCR product (if chosen), sequence of primers and probes (if chosen), positions of the oligos on the consensus, , number of degenerate nucleotide, and GC content (see supplementary file and program’s user guide).
We presented a simple, robust and fast tool, GEMI, which fulfils the regular requirements for biologists to design primers and probes. We believe that this tool can be helpful for research or diagnosis for a wide range of applications that includes detection, quantification, and genotyping in microbiology.
The authors are grateful to Prof. Didier Raoult for his advice and to Prof. Heebal Kim, Dr. Fadi Bittar, and Audrey Ferretti for their helpful comments. H. Sobhy is receiving grant from Infectiopole Sud foundation (2011–2014).
Supplemental data on methods and performance.
I. M. Mackay, K. E. Arden, and A. Nitsche, “Real-time PCR in virology,” Nucleic Acids Research, vol. 30, no. 6, pp. 1292–1305, 2002.View at: Google Scholar
J. M. Robertson and J. Walsh-Weller, “An introduction to PCR primer design and optimization of amplification reactions,” Methods in Molecular Biology, vol. 98, pp. 121–154, 1998.View at: Google Scholar
P. E. Fournier and D. Raoult, “Prospects for the future using genomics and proteomics in clinical microbiology,” Annual Review of Microbiology, vol. 65, pp. 169–188, 2011.View at: Google Scholar
D. Raoult, P. E. Fournier, and M. Drancourt, “What does the future hold for clinical microbiology?” Nature Reviews Microbiology, vol. 2, no. 2, pp. 151–159, 2004.View at: Publisher Site | Google Scholar
M. J. Espy, J. R. Uhl, L. M. Sloan et al., “Real-time PCR in clinical microbiology: applications for routine laboratory testing,” Clinical Microbiology Reviews, vol. 19, pp. 165–256, 2006.View at: Publisher Site | Google Scholar
P. Colson, C. Tamalet, and D. Raoult, “SVARAP and aSVARAP: simple tools for quantitative analysis of nucleotide and amino acid variability and primer selection for clinical microbiology,” BMC Microbiology, vol. 6, article 21, 2006.View at: Publisher Site | Google Scholar
D. A. Hysom, P. Naraghi-Arani, M. Elsheikh, A. C. Carrillo, P. L. Williams, and S. N. Gardner, “Skip the alignment: degenerate, multiplex primer and probe design using K-mer matching instead of alignments,” PLoS One, vol. 7, Article ID e34560, 2012.View at: Google Scholar
C. Frech, K. Breuer, B. Ronacher, T. Kern, C. Sohn, and G. Gebauer, “Hybseek: pathogen primer design tool for diagnostic multi-analyte assays,” Computer Methods and Programs in Biomedicine, vol. 94, no. 2, pp. 152–160, 2009.View at: Publisher Site | Google Scholar
S. N. Gardner, A. L. Hiddessen, P. L. Williams, C. Hara, M. C. Wagner, and B. W. Colston Jr., “Multiplex primer prediction software for divergent targets,” Nucleic Acids Research, vol. 37, no. 19, pp. 6291–6304, 2009.View at: Publisher Site | Google Scholar
K. A. Abd-Elsalam, “Bioinformatic tools and guideline for PCR primer design,” African Journal of Biotechnology, vol. 2, no. 5, pp. 91–95, 2003.View at: Google Scholar
H. Najafabadi, N. Torabi, and M. Chamankhah, “Designing multiple degenerate primers via consecutive pairwise alignments,” BMC Bioinformatics, vol. 9, article 55, 2008.View at: Publisher Site | Google Scholar
H. S. Najafabadi, A. Saberi, N. Torabi, and M. Chamankhah, “MAD-DPD: designing highly degenerate primers with maximum amplification specificity,” BioTechniques, vol. 44, no. 4, pp. 519–526, 2008.View at: Publisher Site | Google Scholar
R. Souvenir, J. Buhler, G. Stormo, and W. Zhang, “An iterative method for selecting degenerate multiplex PCR primers,” Methods in Molecular Biology, vol. 402, pp. 245–268, 2007.View at: Publisher Site | Google Scholar
E. Gorrón, F. Rodríguez, D. Bernal et al., “A new method for designing degenerate primers and its use in the identification of sequences in Brachiaria showing similarity to apomixis-associated genes,” Bioinformatics, vol. 26, no. 16, pp. 2053–2054, 2010.View at: Publisher Site | Google Scholar
A. Cornish-Bowden, “Nomenclature for incompletely specified bases in nucleic acid sequences: rcommendations 1984,” Nucleic Acids Research, vol. 13, no. 9, pp. 3021–3030, 1985.View at: Publisher Site | Google Scholar
J. Marmur and P. Doty, “Determination of the base composition of deoxyribonucleic acid from its thermal denaturation temperature,” Journal of Molecular Biology, vol. 5, pp. 109–118, 1962.View at: Google Scholar
R. B. Wallace, J. Shaffer, R. F. Murphy, J. Bonner, T. Hirose, and K. Itakura, “Hybridization of synthetic oligodeoxyribonucleotides to φX 174 DNA: the effect of single base pair mismatch,” Nucleic Acids Research, vol. 6, no. 11, pp. 3543–3558, 1979.View at: Publisher Site | Google Scholar
K. J. Breslauer, R. Frank, H. Blocker, and L. A. Marky, “Predicting DNA duplex stability from the base sequence,” Proceedings of the National Academy of Sciences of the United States of America, vol. 83, no. 11, pp. 3746–3750, 1986.View at: Google Scholar
S. Rozen and H. Skaletsky, “Primer3 on the WWW for general users and for biologist programmers,” Methods in Molecular Biology, vol. 132, pp. 365–386, 2000.View at: Google Scholar
A. Untergasser, H. Nijveen, X. Rao, T. Bisseling, R. Geurts, and J. A. Leunissen, “Primer3Plus, an enhanced web interface to Primer3,” Nucleic Acids Research, vol. 35, pp. W71–W74, 2007.View at: Publisher Site | Google Scholar
F. M. You, N. Huo, Y. Q. Gu et al., “BatchPrimer3: a high throughput web application for PCR and sequencing primer design,” BMC Bioinformatics, vol. 9, article 253, 2008.View at: Publisher Site | Google Scholar
M. D. Gadberry, S. T. Malcomber, A. N. Doust, and E. A. Kellogg, “Primaclade—a flexible tool to find conserved PCR primers across multiple species,” Bioinformatics, vol. 21, no. 7, pp. 1263–1264, 2005.View at: Publisher Site | Google Scholar
N. Kim and C. Lee, “QPRIMER: a quick web-based application for designing conserved PCR primers from multigenome alignments,” Bioinformatics, vol. 23, no. 17, pp. 2331–2333, 2007.View at: Publisher Site | Google Scholar
N. Batnyam, J. Lee, J. Lee, S. Bok Hong, S. Oh, and K. Han, “UniPrimer: a web-based primer design tool for comparative analyses of primate genomes,” Comparative and Functional Genomics, vol. 2012, Article ID 520732, 8 pages, 2012.View at: Publisher Site | Google Scholar
A. L. Gervais, M. Marques, and L. Gaudreau, “PCRTiler: automated design of tiled and specific PCR primer pairs,” Nucleic Acids Research, vol. 38, no. 2, pp. W308–W312, 2010.View at: Publisher Site | Google Scholar
S. Arvidsson, M. Kwasniewski, D. M. Riaño-Pachón, and B. Mueller-Roeber, “QuantPrime—a flexible tool for reliable high-throughput primer design for quantitative PCR,” BMC Bioinformatics, vol. 9, article 465, 2008.View at: Publisher Site | Google Scholar
J. Ye, G. Coulouris, I. Zaretskaya, I. Cutcutache, S. Rozen, and T. Madden, “Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction,” BMC Bioinformatics, vol. 13, article 134, 2012.View at: Google Scholar
C. Schretter and M. C. Milinkovitch, “OligoFaktory: a visual tool for interactive oligonucleotide design,” Bioinformatics, vol. 22, no. 1, pp. 115–116, 2006.View at: Publisher Site | Google Scholar
A. M. Pessoa, S. Pereira, and J. Teixeira, “PrimerIdent: a web based tool for conserved primer design,” Bioinformation, vol. 5, pp. 52–54, 2010.View at: Google Scholar
R. Giegerich, F. Meyer, and C. Schleiermacher, “GeneFisher—software support for the detection of postulated genes,” Proceedings of International Conference on Intelligent Systems for Molecular Biology, vol. 4, pp. 68–77, 1996.View at: Google Scholar
R. Kalendar, D. Lee, and A. H. Schulman, “FastPCR software for PCR primer and probe design and repeat search,” Genes, Genomes and Genomics, vol. 3, pp. 1–14, 2009.View at: Google Scholar
O. J. Jabado, G. Palacios, V. Kapoor et al., “Greene SCPrimer: a rapid comprehensive tool for designing degenerate primers from multiple sequence alignments,” Nucleic Acids Research, vol. 34, no. 22, pp. 6605–6611, 2006.View at: Publisher Site | Google Scholar
D. Rosenkranz, “EasyPAC: a tool for fast prediction, testing and reference mapping of degenerate PCR primers from Alignments or consensus sequences,” Evolutionary Bioinformatics Online, vol. 8, article 151, 2012.View at: Google Scholar
B. Dwivedi, R. Schmieder, D. B. Goldsmith, R. A. Edwards, and M. Breitbart, “PhiSiGns: an online tool to identify signature genes in phages and design PCR primers for examining phage diversity,” BMC Bioinformatics, vol. 13, article 37, 2012.View at: Google Scholar
J. D. Gans, J. Dunbar, S. A. Eichorst, L. V. Gallegos-Graves, M. Wolinsky, and C. R. Kuske, “A robust PCR primer design platform applied to the detection of Acidobacteria Group 1 in soil,” Nucleic Acids Research, vol. 40, no. 12, article e96, 2012.View at: Google Scholar
W. A. Kibbe, “OligoCalc: an online oligonucleotide properties calculator,” Nucleic Acids Research, vol. 35, pp. W43–W46, 2007.View at: Publisher Site | Google Scholar