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Selection of Pseudo-random primers

Selection of Pseudo-random primers against rRNA

Human rRNA reference sequences

We extracted the human rRNA sequences from the reference locus U13369 in a file called hsu13369.fasta, produced with the command extractfeat -type rRNA U13369.gb (from the EMBOSS package).

HSU13369_3657_5527 [rRNA] Human ribosomal DNA complete repeating unit.
HSU13369_6623_6779 [rRNA] Human ribosomal DNA complete repeating unit.
HSU13369_7935_12969 [rRNA] Human ribosomal DNA complete repeating unit.

Mitochondrial rRNA

We extracted human mitochondrial rRNA sequences in the same way, from the reference locus NC_012920.

NC_012920_648_1601 [rRNA] Homo sapiens mitochondrion, complete genome.
NC_012920_1671_3229 [rRNA] Homo sapiens mitochondrion, complete genome.

Combination

The reference sequences were then reverse-complemented and combined in a plain text file with the following command.

(cat nc_012920.fasta hsu13369.fasta | revseq -filter | grep -v '>' | perl -pe chomp ; echo) > ribo.txt

Selection in R

Reverse-complement of the linker sequence from Harbers et al., 2013, but without barcode and fingerprints.

LINKER <- 'CCCTATAAGATCGGAAGAGCGGTTCGGAGACCTTCAGTTCGACTA'

Barcode sequences from Poulain et al., 2016 (in press). Note that when barcode sequences are introduced after the reverse-transcription, it is not necessary to filter them out.

BARCODES <- scan('barcodes.txt', what='character')

rRNA sequence (see above for details)

RIBO <- scan('ribo.txt', what='character')

Creation of a table representing every possible hexamer.

acgt <- c('A', 'C', 'G', 'T')
hexamers <- apply(expand.grid(acgt, acgt, acgt, acgt, acgt, acgt), 1, paste, collapse='')
hexamers <- data.frame(row.names=hexamers)
hexamers[,c('LINKER_0', 'LINKER_1', 'LINKER_2', 'LINKER_3', 'RIBO_0', 'RIBO_1', 'BARCODE')] <- c(rep(FALSE, 7))

matchHexamers <- function(reference, mismatches) {
  names( unlist( sapply( rownames(hexamers)
                       , function(X) {agrep( X
                                           , reference
                                           , mismatches
                                           , ignore.case = TRUE)}
                       )
               )
        )
}

hexamers[matchHexamers(LINKER, 0), "LINKER_0"] <- TRUE
hexamers[matchHexamers(LINKER, 1), "LINKER_1"] <- TRUE
hexamers[matchHexamers(LINKER, 2), "LINKER_2"] <- TRUE
hexamers[matchHexamers(LINKER, 3), "LINKER_3"] <- TRUE
hexamers[matchHexamers(RIBO, 0),   "RIBO_0"]   <- TRUE
hexamers[matchHexamers(RIBO, 1),   "RIBO_1"]   <- TRUE
hexamers[BARCODES,                 "BARCODE"]  <- TRUE

Here, barcodes are filtered out by matching row names directly, since in our nanoCAGE method they are hexamers. Note that the matchHexamers function does not expect line breaks, so passing the a list of longer barcodes (like Illumina/Nextera indexes) will not produce the expected results.

summary(hexamers)
##   LINKER_0        LINKER_1        LINKER_2       LINKER_3         RIBO_0         RIBO_1       
##  Mode :logical   Mode :logical   Mode :logical   Mode:logical   Mode :logical   Mode:logical  
##  FALSE:4056      FALSE:3082      FALSE:259       TRUE:4096      FALSE:719       TRUE:4096     
##  TRUE :40        TRUE :1014      TRUE :3837      NA's:0         TRUE :3377      NA's:0        
##  NA's :0         NA's :0         NA's :0                        NA's :0                       
##   BARCODE       
##  Mode :logical  
##  FALSE:4000     
##  TRUE :96       
##  NA's :0

with(hexamers, rownames(hexamers)[! (LINKER_2 | RIBO_0 | BARCODE)])
##  [1] "GCCAAA" "AGCAAA" "AAACAA" "ACACAA" "TGCCAA" "CAAACA" "CACACA" "TGCACA" "GTCACA" "TAGCCA"
## [11] "GTGGCA" "TGTTTA" "ATTTTA" "CAAAAC" "CACAAC" "GCTAAC" "AACCAC" "CTACCC" "TACCCC" "CTAGCC"
## [21] "CTGGCC" "TGTGCC" "ATTGCC" "CTACGC" "TATGGC" "TTGTGC" "ACCACG" "CACAGG" "ACTGTG" "TGCCAT"
## [31] "TGGCAT" "GTGCAT" "TTGTAT" "ATTTAT" "TTTTAT" "TGGCGT" "TGTTGT" "ATTTGT" "TTGCTT" "TGTCTT"

Selection of PS primers against hemoglobin

Hemoglobin sequences

Combined in one file Hb.txt (without FASTA headers).

R Code

acgt <- c('A', 'C', 'G', 'T')
Hb <- scan('Hb.txt', what='character')
hexamers <- apply(expand.grid(acgt, acgt, acgt, acgt, acgt, acgt), 1, paste, collapse='')
hexamers <- data.frame(row.names=hexamers)
hexamers[,c('Hb_0', 'Hb_1', 'Hb_2')] <- c(rep(FALSE,3 ))

hexamers[matchHexamers(Hb, 0), "Hb_0"] <- TRUE
hexamers[matchHexamers(Hb, 1), "Hb_1"] <- TRUE
hexamers[matchHexamers(Hb, 2), "Hb_2"] <- TRUE
summary(hexamers)
##     Hb_0            Hb_1           Hb_2        
##  Mode :logical   Mode :logical   Mode:logical  
##  FALSE:3154      FALSE:33        TRUE:4096     
##  TRUE :942       TRUE :4063      NA's:0        
##  NA's :0         NA's :0

with(hexamers, rownames(hexamers)[! (Hb_1)])
##  [1] "GTTAAA" "CGACAA" "GGATAA" "GTATAA" "CTACGA" "TATCGA" "CGAATA" "GATATA" "CGTATA" "GTACTA"
## [11] "TACCTA" "ATCGTA" "CTCGTA" "TCGTTA" "TAAAAC" "TACAAC" "ATTTAC" "AAACCC" "TAATGC" "ATCTGC"
## [21] "CTAATC" "ATTCCG" "CTATCG" "GATTCG" "TACGAT" "ATCGAT" "ATCTAT" "TCGTAT" "CTAATT" "TCCATT"
## [31] "CCGATT" "TCGATT" "CGATTT"

Selection of 40 random hexamers

acgt <- c('A', 'C', 'G', 'T')
hexamers <- apply(expand.grid(acgt, acgt, acgt, acgt, acgt, acgt), 1, paste, collapse='')
set.seed(1)
sample(hexamers,40)
##  [1] "TTTAAC" "TATTCC" "CGGACG" "CCAGGT" "CGTATA" "TGCCGT" "TCCATT" "GTAGGG" "TGAAGG" "ATTTAA"
## [11] "CGACTA" "CACTGA" "CCTTGG" "AAGAGC" "GCACAT" "TTGTTC" "TTGCTG" "CTATTT" "CTAAGC" "CAGCAT"
## [21] "CAGTGT" "AAGCTA" "GTCCGG" "TTTTCA" "AAACAC" "TAGAGC" "GCTAAA" "TACAGC" "CACTCT" "AGGCCC"
## [31] "AGGGTC" "CCAGCG" "CCCTTC" "ACTTGA" "AAGACT" "GGCGGG" "AGCGAT" "GCTGCA" "AGTCTG" "ACAGGC"

rm(.Random.seed)

In our paper, the result of the random selection was:

 [1] "CCAGTC" "CCCTTC" "TTTTTT" "CTGTAC" "TGACCG" "TGTGAT" "AACCCT" "AGGCGG"
 [9] "TCGTCT" "CTACAA" "GTACGC" "CAGAAG" "GTGTCT" "GTGTGC" "AAGACT" "CGGGTA"
[17] "AAGAGA" "GAGGTG" "GCTCTT" "GGTGTG" "GCACGT" "TGAACT" "GGGGCG" "GAGAGG" 
[25] "CCTCAG" "TAAGTT" "ATCTGC" "ACTTAA" "CACAGC" "AGATGA" "GGTAGC" "AAGGCC" 
[33] "CGCAGG" "AACCTC" "CAGTTG" "ATTCCC" "AGATGG" "GCGGAC" "CTGGCG" "CTTCAC"