A new method for designing degenerate primers and its use in the identification of sequences in Brachiaria showing similarity to apomixis-associated genes

MRPrimerW: a tool for rapid design of valid high-quality primers for multiple target qPCR experiments

Design of high-quality primers for multiple target sequences is essential for qPCR experiments, but is challenging due to the need to consider both homology tests on off-target sequences and the same stringent filtering constraints on the primers. Existing web servers for primer design have major drawbacks, including requiring the use of BLAST-like tools for homology tests, lack of support for ranking of primers, TaqMan probes and simultaneous design of primers against multiple targets. Due to the large-scale computational overhead, the few web servers supporting homology tests use heuristic approaches or perform homology tests within a limited scope. Here, we describe the MRPrimerW, which performs complete homology testing, supports batch design of primers for multi-target qPCR experiments, supports design of TaqMan probes and ranks the resulting primers to return the top-1 best primers to the user. To ensure high accuracy, we adopted the core algorithm of a previously reported MapReduce-based method, MRPrimer, but completely redesigned it to allow users to receive query results quickly in a web interface, without requiring a MapReduce cluster or a long computation. MRPrimerW provides primer design services and a complete set of 341 963 135 in silico validated primers covering 99% of human and mouse genes. Free access: http://MRPrimerW.com.

MRPrimer: a MapReduce-based method for the thorough design of valid and ranked primers for PCR

Primer design is a fundamental technique that is widely used for polymerase chain reaction (PCR). Although many methods have been proposed for primer design, they require a great deal of manual effort to generate feasible and valid primers, including homology tests on off-target sequences using BLAST-like tools. That approach is inconvenient for many target sequences of quantitative PCR (qPCR) due to considering the same stringent and allele-invariant constraints. To address this issue, we propose an entirely new method called MRPrimer that can design all feasible and valid primer pairs existing in a DNA database at once, while simultaneously checking a multitude of filtering constraints and validating primer specificity. Furthermore, MRPrimer suggests the best primer pair for each target sequence, based on a ranking method. Through qPCR analysis using 343 primer pairs and the corresponding sequencing and comparative analyses, we showed that the primer pairs designed by MRPrimer are very stable and effective for qPCR. In addition, MRPrimer is computationally efficient and scalable and therefore useful for quickly constructing an entire collection of feasible and valid primers for frequently updated databases like RefSeq. Furthermore, we suggest that MRPrimer can be utilized conveniently for experiments requiring primer design, especially real-time qPCR.

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.

Flexgrepps--flexible greedy peptide pool search: computation of near-optimal sets of degenerate polypeptides for antigenic screening

Although synthesizing and utilizing individual peptides and DNA primers has become relatively inexpensive, massively parallel probing and next-generation sequencing approaches have dramatically increased the number of molecules that can be subjected to screening; this, in turn, requires vast numbers of peptides and therefore results in significant expenses. To alleviate this issue, pools of related molecules are often used to downselect prior to testing individual sequences. A computational selection process to create pools of related sequences at large scale has not been reported for peptides. In the case of PCR primers, there have been successful attempts to address this problem by designing degenerate primers that can be produced at the same cost as conventional, unique primers and then be used to amplify several different genomic regions. We present an algorithm, "FlexGrePPS" (Flexible Greedy Peptide Pool Search), that can create a near-optimal set of peptide pools. This approach is also applicable to nucleotide sequences and outperforms most DNA primer selection programs. For the proteomic compression with FlexGrePPS, the main body of our work presented here, we demonstrate the feasibility of the computation of an exhaustive cover of pathogenic proteomes with degenerate peptides that lend themselves to antigenic screening. Furthermore, we present preliminary data that demonstrate the experimental utility of highly degenerate peptides for antigenic screening. FlexGrePPS provides a near-optimal solution for proteomic compression and there are no programs available for comparison. We also demonstrate computational performance of our GreedyPrime implementation, which is a modified version of FlexGrePPS applicable to the design of degenerate primers and is comparable to existing programs for the design of degenerate primers. Specifically, we focus on the comparisons with PAMPS and DPS-DIP, software tools that have recently been shown to be superior to other methods. FlexGrePPS forms the foundation of a novel antigenic screening methodology that is based on the representation of an entire proteome by near-optimal degenerate peptide pools. Our preliminary wet lab data indicate that the approach will likely prove successful in comprehensive wet lab studies, and hence will dramatically reduce the expenses for antigenic screening and make whole proteome screening feasible. Although FlexGrePPS was designed for computational performance in order to handle vast data sets, there is the very surprising finding that even for small data sets the primer design version of FlexGrePPS, GreedyPrime, offers similar or even superior results for MP-DPD and most MDPD instances when compared to existing methods; despite the much longer run times, other approaches did not fare significantly better in reducing the original data sets to degenerate primers. The FlexGrePPS and GreedyPrime programs are available at no charge under the GNU LGPL license at http://sourceforge.net/projects/flexgrepps/.

easyPAC: A Tool for Fast Prediction, Testing and Reference Mapping of Degenerate PCR Primers from Alignments or Consensus Sequences

The PCR-amplification of unknown homologous or paralogous genes generally relies on PCR primers predicted from multi sequence alignments. But increasing sequence divergence can induce the need to use degenerate primers which entails the problem of testing the characteristics, unwanted interactions and potential mispriming of degenerate primers. Here I introduce easyPAC, a new software for the prediction of degenerate primers from multi sequence alignments or single consensus sequences. As a major innovation, easyPAC allows to apply all customary primer test procedures to degenerate primer sequences including fast mapping to reference files. Thus, easyPAC simplifies and expedites the designing of specific degenerate primers enormously. Degenerate primers suggested by easyPAC were used in PCR amplification with subsequent de novo sequencing of TDRD1 exon 11 homologs from several representatives of the haplorrhine primate phylogeny. The results demonstrate the efficient performance of the suggested primers and therefore show that easyPAC can advance upcoming comparative genetic studies.