Multiplex Manager 1.0 is a user-friendly cross-platform program that designs efficient combinations of existing genetic marker loci into multiplex polymerase chain reactions and optimizes using prior marker information. The program has the flexibility to solve two design problems: combining all markers into the smallest number of reactions, or alternatively, selecting a subset from many available markers to design an efficient and robust multiplex. Our program minimizes the number of reactions, the genetic linkage, and the difference in annealing temperature. At the same time it maximizes the spacing between markers, the heterozygosity, and the number of alleles. The final output provides easily interpreted and informative graphical representations of reactions, as well as the option of manually editing final reactions. Multiplex Manager 1.0 is freely available at www.multiplexmanager.com.

Introduction

Multiplex PCR, where multiple genetic loci are are amplified in a single reaction, is a technique increasingly employed to dramatically improve the time and cost efficiency of genetic studies. However, designing a successful multiplex reaction is often a time-consuming and difficult process, requiring the simultaneous optimization of multiple factors, such as allele size range, annealing temperatures, complementarity of primers, heterozygosity, number of alleles, and genetic linkage. In addition, there are often extrinsic factors to consider, such as known artifacts associated with a particular method of fragment analysis. Most programs currently available for planning multiplex PCR are concerned with de novo primer design from flanking sequence (1,2,3), or only address a single variable involved with planning a multiplex, such as primer complementarity in the program AutoDimer (4). Multiplex Manager solves a different problem: evaluating a variety of factors in order to create novel multiplex reactions using previously published primer sequences. There are several benefits in designing multiplexes for new combinations of previously published markers: researchers do not have to invest time in marker development; they can utilize previously existing information (e.g., heterozygosity) to ensure suitability of the selected markers; and they can streamline their existing molecular techniques while still maintaining continuity of information. We have developed Multiplex Manager 1.0 to meet the growing demand for a program that facilitates effective and efficient multiplex planning for previously published genetic markers as well as utilizing the available prior information.

Materials and methods

In Multiplex Manager, users enter information about their markers into the Marker Data window depicted in Figure 1A. The minimum data requirements to execute Multiplex Manager are entries in the marker name and allele size range fields. However, to obtain the most successful multiplex, we recommend providing as much information as possible in the Marker Data window. For example, minimizing the difference in primer annealing temperatures simplifies the task of achieving uniform amplification across loci within a multiplex, and thus makes a more reliable and robust PCR reaction (5). Users can supply empirical information about the primer annealing temperatures. Alternatively, if users do not have an empirical estimate of annealing temperature for each marker, Multiplex Manager can estimate annealing temperature based on the thermodynamic parameters of neighboring bases in the primer sequences (6). After entering all available information about the genetic markers into the Marker Data window, users move to the Dyes and Options window to further define the nature of the optimization required.

Figure 1. Worked example of how to use Multiplex Manager 1.0. (Click to enlarge)

One of the most crucial elements of efficient multiplex PCR is the ability to differentiate markers with the same allele size range, usually using fluorescent primer labeling. Users specify which fluorescent dyes are to be used in the Dyes and Options window depicted in Figure 1B. Users can edit and prioritize the four default dyes (6-FAM, VIC, NED, and PET) or add new dyes with an appropriate color in the first column. Users can enter known artifacts that may be associated with certain dyes in the second column. The program will not assign a marker to a particular dye if the allele size range overlaps with the specified artifact. The third column allows the enforcement of a marker to be labeled with a particular dye.

At the bottom of the Dyes and Options window are the Control Parameters, which the program uses to generate solutions. Researchers must specify which of the following two design scenarios is appropriate for their data. Scenario one is when a small number of markers must be combined into the smallest number of reactions. Scenario two is when there is a large database of available markers from which a smaller subset must be chosen. Scenario two is common for organisms that have a large number of genetic markers available, such as humans (7), Atlantic salmon (Salmo salar) (8), the fruit fly (Drosophila melanogaster) (9), rice (Oryza sativa) (10), or cotton (Gossypium hirsutum) (11).

Users can select which criteria they wish to apply when generating a solution by clicking the Edit option of the Control Parameters. The default marker selection criteria are: minimize the number of reactions, maximize the space between markers in the same dye, and minimize the difference in annealing temperatures of markers in the same reaction. When selecting the best N markers from a larger list (design scenario two), there are three additional marker selection criteria: maximizing the average heterozygosity, the number of alleles, and the standard deviation genetic location (minimizing genetic linkage) (Table 1). Multiplex Manager calculates a raw ranking score (R_i) for the enabled marker selection criteria (Table 1). The raw ranking values are later used to calculate a weighted suitability score (SS) (Table 1). Users must also define the primer complementarity threshold in the Control Parameters section. For a pair of primers, the complementarity score is calculated as the number of base pair matches (A-T; C-G) minus the number of mismatches (4). The default complementarity threshold score is set to 7 because empirical work has demonstrated that reactions with a score ≤7 can be successfully multiplexed (5). This threshold may be relaxed at the user's discretion.