DesignPrimers(tiles, identifier = "", start = 1, end = NULL, minLength = 17, maxLength = 26, maxPermutations = 4, minCoverage = 0.9, minGroupCoverage = 0.2, annealingTemp = 64, P = 4e-07, monovalent = 0.07, divalent = 0.003, dNTPs = 8e-04, minEfficiency = 0.8, worstScore = -Inf, numPrimerSets = 0, minProductSize = 75, maxProductSize = 1200, maxSearchSize = 1500, batchSize = 1000, maxDistance = 0.4, primerDimer = 1e-07, ragged5Prime = TRUE, taqEfficiency = TRUE, induceMismatch = FALSE, processors = 1, verbose = TRUE)
TileSeqs
.
maxLength
of tiles.
worstScore
of -5 will remove all primer sets scoring below -5, although this may eventually result in no primer sets meeting the design criteria.
CalculateEfficiencyPCR
.
TRUE
then a mismatch is induced at the 6th primer position. If an integer value is provided between 2 and 6 then a mismatch is induced in that primer position, where the 3'-end is defined as position 1.
NULL
to automatically detect and use all available processors.
data.frame
will be returned depending on number of primer sets requested. If no primer sets are required then columns contain the forward and reverse primers for every possible position scored by their potential to amplify other identified groups. If one or more primer sets are requested then columns contain information for the optimal set of forward and reverse primers that could be used in combination to give the fewest potential cross-amplifications.
Primers are designed from a set of tiles to target each identifier
while minimizing affinity for all other tiled groups. Arguments provide constraints that ensure the designed primer sets meet the specified criteria as well as being optimized for the particular experimental conditions. A search is conducted through all tiles in the same alignment position to estimate the chance of cross-amplification with a non-target group.
If numPrimers
is greater than or equal to one then the set of forward and reverse primers that minimizes potential false positive overlap is returned. This will also initiate a thorough search through all target sites upstream and downstream of the expected binding sites to ensure that the primers do not bind to nearby positions. Lowering the maxSearchSize
will speed up the thorough search at the expense of potentially missing an unexpected target site. The number of possible primer sets assessed is increased with the size of numPrimers
.
AmplifyDNA
, CalculateEfficiencyPCR
, DesignSignatures
, TileSeqs
db <- system.file("extdata", "Bacteria_175seqs.sqlite", package="DECIPHER")
# not run (must have OligoArrayAux installed first):
## Not run: tiles <- TileSeqs(db, identifier=c("Enterobacteriales","Pseudomonadales"))
## Not run: primers <- DesignPrimers(tiles, identifier="Enterobacteriales", start=280, end=420,
# minProductSize=50, numPrimerSets=1)## End(Not run)
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