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described in references 8, 9, and 11). Such k-mer algorithms are computationally efficient for 369 large databases and long sequences and can be applied to databases of pathogenic viruses and 370 bacteria. An optimal design region from a pathogen would show high conservation among the 371 variants of the desired target (e.g., clinical isolates of a pathogen) and show a lack of 372 conservation to near-neighbor organisms or to contaminating organisms that could cause false 373 positives. Thus, we recommend the use of k-mer algorithms to analyze inclusivity and 374 exclusivity genome databases to determine optimal locations of signature design regions. One 375 such algorithm is described in the literature by Yuriy Fofanov’s group (8) and applied to the 376 development of an assay for the 2001 pandemic H1N1 influenza A. Such a k-mer algorithm is 377 also available in the commercial PanelPlex-Consensus program (DNA Software, Inc., Ann 378 Arbor, MI). Other alternative approaches include: Uniquemer (11); BioVelocity (9); or Core/pan 379 genome analyses to identify unique genes that can be assay targets (12, 13). In all these 380 approaches, the key first step is to create the inclusivity, exclusivity and environmental 381 background panels. 385 programs (such as those available from many commercial oligonucleotide synthesis vendors) 386 utilize nearest-neighbor thermodynamic rules to compute the 2-state  G  T ,  H  ,  S  (these are 387 the standard state change in Gibbs free energy at temperature T, standard state enthalpy change, 388 and standard state entropy change, respectively), and melting temperature, Tm (15). In 389 performing such hybridization predictions, most programs rely on the 2-state melting 390 temperature to determine hybridization quality. The Tm is intuitively useful because it is the 391 382 383 4.3.5 Physical Chemistry Modeling 384 Predicting the strength of primer hybridization is critical for assay design (14). Most design

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