Design PCR Primers

Use DECIPHER's DesignPrimers function to design the optimal set of PCR primers for targeting one group of DNA sequences in the presence of many non-target groups, as described in:
For an in-depth tutorial, see the "Design Group-Specific Primers" vignette on the Documentation page.

How do I design PCR primers?

First it is necessary to install DECIPHER and load the library in R. Next, set the "fas" variable to the path to the FASTA file of aligned sequences (e.g., "~/mySeqs.fas").

Hide output

library(DECIPHER)
> library(RSQLite)
> 
> # load sequences into a database
> fas <- "~/mySeqs.fas"
> dbConn <- dbConnect(SQLite(),
+ ":memory:")
> Seqs2DB(fas,
+ type="FASTA",
+ dbFile=dbConn,
+ identifier="")
Reading FASTA file chunk 1


118 total sequences in table Seqs.
Time difference of 0.04 secs


> 
> # identify the sequences based on their description
> x <- dbGetQuery(dbConn,
+ "select description from Seqs")$description
> ns <- unlist(lapply(strsplit(x,
+ split=" "),
+ FUN=`[`,
+ 1L))
> Add2DB(myData=data.frame(identifier=ns),
+ dbFile=dbConn)
Expression:
update `Seqs` set `identifier` = (select
`temp`.`identifier` from `temp` where
`temp`.`row_names` = `Seqs`.`row_names`) where
exists (select `temp`.`identifier` from `temp`
where `temp`.`row_names` = `Seqs`.`row_names`)


Added to table Seqs:  "identifier".


Time difference of 0.01 secs


> 
> tiles <- TileSeqs(dbConn)
  |========================================| 100%


Time difference of 13.63 secs


> head(tiles)
  row_names start end start_aligned end_aligned
1         1     1  27             1          27
2         2     2  28             2          28
3         3     3  29             3          29
4         4     4  30             4          30
5         5     5  31             5          31
6         6     6  32             6          32
  misprime width            id coverage
1    FALSE  1594 Acinetobacter        1
2    FALSE  1594 Acinetobacter        1
3    FALSE  1594 Acinetobacter        1
4    FALSE  1594 Acinetobacter        1
5    FALSE  1594 Acinetobacter        1
6    FALSE  1594 Acinetobacter        1
  groupCoverage                 target_site
1             1 AGAGTTTGATCATGGCTCAGATTGAAC
2             1 GAGTTTGATCATGGCTCAGATTGAACG
3             1 AGTTTGATCATGGCTCAGATTGAACGC
4             1 GTTTGATCATGGCTCAGATTGAACGCT
5             1 TTTGATCATGGCTCAGATTGAACGCTG
6             1 TTGATCATGGCTCAGATTGAACGCTGG
> 
> primers <- DesignPrimers(tiles,
+ identifier="Streptococcus", # target group
+ minProductSize=50, # base pairs
+ numPrimerSets=1) # candidates to design
Streptococcus (1325 candidate primers):
  |========================================| 100%


Determining Best Primer Pair:
  |========================================| 100%


Time difference of 14 secs


> head(primers)
     identifier start_forward start_reverse
1 Streptococcus           533          1289
  product_size start_aligned_forward
1          757                   553
  start_aligned_reverse permutations_forward
1                  1336                    1
  permutations_reverse score_forward
1                    1  -2.87986....
  score_reverse score_set     forward_primer.1
1  -0.00023....         0 CCGCGGTAATACGTAGGTCC
  forward_primer.2 forward_primer.3
1                          
  forward_primer.4     reverse_primer.1
1              GATTAGCTTGCCGTCACCGG
  reverse_primer.2 reverse_primer.3
1                          
  reverse_primer.4 forward_efficiency.1
1                         0.8161955
  forward_efficiency.2 forward_efficiency.3
1                   NA                   NA
  forward_efficiency.4 reverse_efficiency.1
1                   NA            0.8857364
  reverse_efficiency.2 reverse_efficiency.3
1                   NA                   NA
  reverse_efficiency.4 forward_coverage.1
1                   NA                  1
  forward_coverage.2 forward_coverage.3
1                 NA                 NA
  forward_coverage.4 reverse_coverage.1
1                 NA                  1
  reverse_coverage.2 reverse_coverage.3
1                 NA                 NA
  reverse_coverage.4 mismatches_forward
1                 NA                   
                                                  mismatches_reverse
1 Porphyromonas (0.0232%,GATTAGCTTGCCGTCACCGG/CCGGTGACTGGGGCTAAGTC) 
  mismatches_set
> 
> dbDisconnect(dbConn)