rprimer

rprimer is an R package that designs degenerate oligos and PCR assays from a multiple DNA sequence alignment of target sequences of interest. The package is specifically designed for sequence variable viruses.

Installation

To install rprimer from Bioconductor, start R (version 4.2) and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("rprimer")

Attach the package by calling:

library(rprimer)

Overview

The package contains five main functions:

• consensusProfile()
• designOligos()
• designAssays()
• checkMatch()
• plotData()

Shiny application

The package can be run through a Shiny application (a graphical user interface). To start the application, type runRprimerApp() from within R upon installing and attaching the package.

The application can also be found online, here.

Workflow

Import alignment

The first step is to import an alignment with target sequences of interest. This is done by using readDNAMultipleAlignment().

The file “example_alignment.txt” contains an alignment of 50 hepatitis E virus sequences.

infile <- system.file("extdata", "example_alignment.txt", package = "rprimer")

myAlignment <- readDNAMultipleAlignment(infile, format = "fasta")

Step 1: consensusProfile

consensusProfile() takes a DNAMultipleAlignment as input and returns all the information needed for the subsequent design process.

myConsensusProfile <- consensusProfile(myAlignment, ambiguityThreshold = 0.05)

Results (row 100-106):

position	a	c	g	t	other	gaps	majority	identity	iupac	coverage
100	0.00	1.00	0.00	0.00	0.00	0	C	1.00	C	1.00
101	1.00	0.00	0.00	0.00	0.00	0	A	1.00	A	1.00
102	0.16	0.00	0.84	0.00	0.00	0	G	0.84	R	1.00
103	0.00	0.00	1.00	0.00	0.00	0	G	1.00	G	1.00
104	0.00	0.98	0.00	0.00	0.02	0	C	1.00	C	1.00
105	0.20	0.00	0.02	0.78	0.00	0	T	0.78	W	0.98
106	0.00	0.00	1.00	0.00	0.00	0	G	1.00	G	1.00

The results can be visualized with plotData():

plotData(myConsensusProfile)

Step 2: designOligos

The next step is to design oligos. You can either use the default settings as below, or adjust them as preferred (see the package vignette or ?designOligos for more information). The default settings allow a maximum degeneracy of four, which means that only the most conserved regions of the genome will be considered as oligo binding sites.

myOligos <- designOligos(myConsensusProfile)

Results (first six rows):

type	fwd	rev	start	end	length	iupacSequence	iupacSequenceRc	identity	coverage	degeneracy	gcContentMean	gcContentRange	tmMean	tmRange	deltaGMean	deltaGRange	sequence	sequenceRc	gcContent	tm	deltaG	method	score	roiStart	roiEnd
probe	TRUE	TRUE	124	143	20	TCYGCCYTGGCGAATGCTGT	ACAGCATTCGCCARGGCRGA	0.95	0.99	4	0.60	0.10	63.17	4.33	-21.61	2.08	TCCGCCCT….	ACAGCATT….	0.65, 0…..	65.33623….	-22.6538….	ambiguous	2	1	7597
probe	FALSE	TRUE	127	146	20	GCCYTGGCGAATGCTGTGGT	ACCACAGCATTCGCCARGGC	0.98	0.99	2	0.62	0.05	63.18	1.84	-21.73	0.83	GCCCTGGC….	ACCACAGC….	0.65, 0.6	64.10586….	-22.1475….	ambiguous	3	1	7597
primer	TRUE	FALSE	128	146	19	CCYTGGCGAATGCTGTGGT	ACCACAGCATTCGCCARGG	0.97	0.99	2	0.61	0.05	61.48	1.95	-19.84	0.83	CCCTGGCG….	ACCACAGC….	0.631578….	62.45335….	-20.2570….	ambiguous	3	1	7597
primer	TRUE	FALSE	128	147	20	CCYTGGCGAATGCTGTGGTR	YACCACAGCATTCGCCARGG	0.96	0.99	4	0.60	0.10	61.61	3.37	-20.55	1.76	CCCTGGCG….	TACCACAG….	0.6, 0.5….	61.77166….	-20.5089….	ambiguous	2	1	7597
probe	TRUE	TRUE	128	146	19	CCYTGGCGAATGCTGTGGT	ACCACAGCATTCGCCARGG	0.97	0.99	2	0.61	0.05	60.45	1.94	-19.84	0.83	CCCTGGCG….	ACCACAGC….	0.631578….	61.41686….	-20.2570….	ambiguous	3	1	7597
probe	TRUE	TRUE	128	147	20	CCYTGGCGAATGCTGTGGTR	YACCACAGCATTCGCCARGG	0.96	0.99	4	0.60	0.10	60.63	3.37	-20.55	1.76	CCCTGGCG….	TACCACAG….	0.6, 0.5….	60.78676….	-20.5089….	ambiguous	2	1	7597

The results can be visualized as a dashboard, using plotData():

plotData(myOligos)

Step 3: designAssays

designAssays() finds pairs of forward and reverse primers and combine them with probes, if probes are present in the input dataset. You can either use the default settings as below, or adjust the design constraints (see the package vignette or ?designAssays for more information).

myAssays <- designAssays(myOligos)

Results (first six rows):

start	end	length	totalDegeneracy	score	startFwd	endFwd	lengthFwd	iupacSequenceFwd	identityFwd	coverageFwd	degeneracyFwd	gcContentMeanFwd	gcContentRangeFwd	tmMeanFwd	tmRangeFwd	deltaGMeanFwd	deltaGRangeFwd	sequenceFwd	gcContentFwd	tmFwd	deltaGFwd	methodFwd	startRev	endRev	lengthRev	iupacSequenceRev	identityRev	coverageRev	degeneracyRev	gcContentMeanRev	gcContentRangeRev	tmMeanRev	tmRangeRev	deltaGMeanRev	deltaGRangeRev	sequenceRev	gcContentRev	tmRev	deltaGRev	methodRev	plusPr	minusPr	startPr	endPr	lengthPr	iupacSequencePr	iupacSequenceRcPr	identityPr	coveragePr	degeneracyPr	gcContentMeanPr	gcContentRangePr	tmMeanPr	tmRangePr	deltaGMeanPr	deltaGRangePr	sequencePr	sequenceRcPr	gcContentPr	tmPr	deltaGPr	methodPr	roiStart	roiEnd
5605	5673	69	6	2.00	5605	5624	20	GGCRGTGGTTTCTGGGGTGA	0.98	1	2	0.62	0.05	62.84	2.51	-20.86	1.25	GGCAGTGG….	0.6, 0.65	61.57995….	-20.2350….	ambiguous	5654	5673	20	GTTGGTTGGATGAASATAGG	1	1	2	0.4	0	50.71	1.1	-15.27	0.52	GTTGGTTG….	0.4, 0.4	50.15469….	-15.0078….	ambiguous	TRUE	FALSE	5625	5642	18	CMGGGTTGATTCTCAGCC	GGCTGAGAATCAACCCKG	0.97	0.99	2	0.58	0.06	55.14	2.79	-17.06	1.25	CAGGGTTG….	GGCTGAGA….	0.555555….	53.74554….	-16.4324….	ambiguous	1	7597
5605	5673	69	6	2.33	5605	5624	20	GGCRGTGGTTTCTGGGGTGA	0.98	1	2	0.62	0.05	62.84	2.51	-20.86	1.25	GGCAGTGG….	0.6, 0.65	61.57995….	-20.2350….	ambiguous	5654	5673	20	GTTGGTTGGATGAASATAGG	1	1	2	0.4	0	50.71	1.1	-15.27	0.52	GTTGGTTG….	0.4, 0.4	50.15469….	-15.0078….	ambiguous	TRUE	FALSE	5625	5643	19	CMGGGTTGATTCTCAGCCC	GGGCTGAGAATCAACCCKG	0.97	0.99	2	0.61	0.05	57.63	2.64	-18.54	1.25	CAGGGTTG….	GGGCTGAG….	0.578947….	56.30713….	-17.9185….	ambiguous	1	7597
5605	5673	69	6	2.00	5605	5624	20	GGCRGTGGTTTCTGGGGTGA	0.98	1	2	0.62	0.05	62.84	2.51	-20.86	1.25	GGCAGTGG….	0.6, 0.65	61.57995….	-20.2350….	ambiguous	5654	5673	20	GTTGGTTGGATGAASATAGG	1	1	2	0.4	0	50.71	1.1	-15.27	0.52	GTTGGTTG….	0.4, 0.4	50.15469….	-15.0078….	ambiguous	TRUE	TRUE	5625	5644	20	CMGGGTTGATTCTCAGCCCT	AGGGCTGAGAATCAACCCKG	0.97	1.00	2	0.58	0.05	58.87	2.55	-19.43	1.25	CAGGGTTG….	AGGGCTGA….	0.55, 0.6	57.59836….	-18.8035….	ambiguous	1	7597
5605	5673	69	6	1.67	5605	5624	20	GGCRGTGGTTTCTGGGGTGA	0.98	1	2	0.62	0.05	62.84	2.51	-20.86	1.25	GGCAGTGG….	0.6, 0.65	61.57995….	-20.2350….	ambiguous	5654	5673	20	GTTGGTTGGATGAASATAGG	1	1	2	0.4	0	50.71	1.1	-15.27	0.52	GTTGGTTG….	0.4, 0.4	50.15469….	-15.0078….	ambiguous	TRUE	TRUE	5625	5645	21	CMGGGTTGATTCTCAGCCCTT	AAGGGCTGAGAATCAACCCKG	0.98	1.00	2	0.55	0.05	59.21	2.43	-20.08	1.25	CAGGGTTG….	AAGGGCTG….	0.523809….	57.99472….	-19.4553….	ambiguous	1	7597
5605	5673	69	6	2.00	5605	5624	20	GGCRGTGGTTTCTGGGGTGA	0.98	1	2	0.62	0.05	62.84	2.51	-20.86	1.25	GGCAGTGG….	0.6, 0.65	61.57995….	-20.2350….	ambiguous	5654	5673	20	GTTGGTTGGATGAASATAGG	1	1	2	0.4	0	50.71	1.1	-15.27	0.52	GTTGGTTG….	0.4, 0.4	50.15469….	-15.0078….	ambiguous	TRUE	FALSE	5625	5646	22	CMGGGTTGATTCTCAGCCCTTC	GAAGGGCTGAGAATCAACCCKG	0.98	0.99	2	0.57	0.05	59.91	2.28	-21.11	1.25	CAGGGTTG….	GAAGGGCT….	0.545454….	58.77533….	-20.4881….	ambiguous	1	7597
5605	5673	69	6	2.00	5605	5624	20	GGCRGTGGTTTCTGGGGTGA	0.98	1	2	0.62	0.05	62.84	2.51	-20.86	1.25	GGCAGTGG….	0.6, 0.65	61.57995….	-20.2350….	ambiguous	5654	5673	20	GTTGGTTGGATGAASATAGG	1	1	2	0.4	0	50.71	1.1	-15.27	0.52	GTTGGTTG….	0.4, 0.4	50.15469….	-15.0078….	ambiguous	TRUE	FALSE	5626	5643	18	MGGGTTGATTCTCAGCCC	GGGCTGAGAATCAACCCK	0.97	0.99	2	0.58	0.06	55.71	1.65	-17.21	0.94	AGGGTTGA….	GGGCTGAG….	0.555555….	54.88806….	-16.7409….	ambiguous	1	7597

The assays can be visualized using plotData():

plotData(myAssays)

Additional step: checkMatch

checkMatch() shows the proportion and names of the target sequences in the input alignment that match with the generated oligos or assays. See the package vignette or ?checkMatch for more information.

## Randomly select six oligos to illustrate an example 
selection <- sample(seq_len(nrow(myOligos)), size = 6)

matchTableOligos <- checkMatch(myOligos[selection, ], target = myAlignment)

Results:

iupacSequence	perfectMatch	idPerfectMatch	oneMismatch	idOneMismatch	twoMismatches	idTwoMismatches	threeMismatches	idThreeMismatches	fourOrMoreMismatches	idFourOrMoreMismatches	offTargetMatch	idOffTargetMatch
MGGGTTGATTCTCAGCCCT	0.90	AB073912….	0.10	AB481228….	0		0		0		0
TGACMGGGTTGATTCTCA	0.92	AB073912….	0.08	AB481228….	0		0		0		0
GTGACMGGGTTGATTCTCA	0.92	AB073912….	0.08	AB481228….	0		0		0		0
CCCCTATWTTCATCCAACCAA	1.00	AB073912….	0.00		0		0		0		0
CCCCTATWTTCATCCAACC	1.00	AB073912….	0.00		0		0		0		0
TCAGCCCTTCGCMMTCCCCTAT	0.94	AB073912….	0.06	AB481228….	0		0		0		0

The match table can be visualized using plotData():

plotData(matchTableOligos)

More information

Please see the package vignette for more information on how to use the package.

Citation

Persson S., Larsson C., Simonsson M., Ellström P. (2022) rprimer: an R/bioconductor package for design of degenerate oligos for sequence variable viruses. BMC Bioinformatics 23:239

The publication describes version 1.1.0 of the package.