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4.3.8 Ranking the Design Results

A critical part of primer design is the ranking of the candidate designs using some sort of 459 scoring equation. Unfortunately, there is no agreed upon “currency” for goodness of primer 460 performance. Instead there are many metrics that have vastly different units, such as free-energy 461 differences for folding and hybridization, amount bound, amplicon folding, target conservation, 462 off-target hybridization, primer dimerization, primer-amplicon cross-hybridization, and a long 463 list of non-thermodynamic rules (G-quartets, sequence complexity (or information entropy), 464 amplicon length, etc.). It is still something of an art to combine all of these disparate scoring 465 terms into one big equation and produce a result meaningful to a user (such as a final score that 466 ranges from 1 to 100). In addition, there is no agreement in the community as to what the relative 467 weighting of different scoring terms should be. For this reason, it is recommended to use 468 software that exposes the scoring equation and the weights used for each scoring term (e.g., 469 PanelPlex provides a detailed description of the scoring). Transparency by software vendors 470 regarding their scoring methods will give users the ability to change the scoring weights and also 471 to be more informed about what the modeling is and is not accounting for. In the future when 472 training and validation datasets become available as described in Metrology (section 5.0), the 473 scoring terms and weights can be optimized by solving for the optimal weighting terms. These 474 datasets will also support the ability to evaluate the predictive quality of software from different 475 commercial and non-commercial sources. 476 477 4.3.9 Combining All of the Recommendations into a Coherent Design Pipeline 478 In Figure 7, we provide an example of a design pipeline that includes the aspects of the modern 479 approach described above. Foremost in this modern approach is the integrated use of sequence 480

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