Multiplexed assays of variant effect analysis with mutscan
12 September 2025
Package
mutscan 0.99.2
library(SummarizedExperiment)
library(mutscan)1 Introduction
mutscan provides functionality for processing and visualizing Multiplexed
Assays of Variant Effect (MAVE) and other similar types of data, starting from
single-end or paired-end FASTQ files.
A broad range of library designs can be processed, encompassing experiments
considering either a single protein or combinations of multiple proteins (e.g.,
aimed at studying protein-protein interactions).
The figure below provides a high-level overview of the mutscan functionality,
which will be described in more detail in the following sections.
Figure 1: Overview of the mutscan functionality
The digestFastqs() function processes each sequencing library, possibly consisting of multiple (pairs of) FASTQ files, separately, and generates an output object containing, among other things, the count table and a summary of the filtering steps. The summarizeExperiment() function takes one or more of these objects and combines them into a SummarizedExperiment object, that can then be used for downstream analysis such as plotting and statistical testing.
2 Example data
The mutscan package contains several small example FASTQ files
representing different types of experiments:
datadir <- system.file("extdata", package = "mutscan")
dir(datadir)
#> [1] "GSE102901_cis_se.rds" "cisInput_1.fastq.gz"
#> [3] "cisInput_2.fastq.gz" "cisOutput_1.fastq.gz"
#> [5] "cisOutput_2.fastq.gz" "leujunt0_1.fastq.gz"
#> [7] "leujunt0_2.fastq.gz" "multipleVariableRegions_R1.fastq.gz"
#> [9] "multipleVariableRegions_R2.fastq.gz" "multipleVariableRegions_truth.rds"
#> [11] "transInput_1.fastq.gz" "transInput_2.fastq.gz"
#> [13] "transOutput_1.fastq.gz" "transOutput_2.fastq.gz"transInput_{1,2}.fastq.gz,transOutput_{1,2}.fastq.gz- data from TRANS experiment in [@Diss2018]. The forward and reverse reads correspond to the mutated FOS and JUN sequences, respectively. Each read consists of a UMI sequence, followed by a constant sequence and the variable region.cisInput_{1,2}.fastq.gz,cisOutput_{1,2}.fastq.gz- data from CIS experiment in [@Diss2018]. The forward and reverse reads both correspond to the mutated FOS sequence. Each read consists of a UMI sequence, followed by a constant sequence and the variable region.GSE102901_cis_se.rds- aSummarizedExperimentobject obtained by processing the full CIS data from [@Diss2018].leujunt0_{1,2}.fastq.gz- unpublished data; the forward read corresponds to an unmutated sequence of one of 46 leucine zipper sequences, and the reverse read corresponds to mutated JUN sequences. Each read contains a (frame-shifted) primer sequence, followed by the variable region.
3 Processing Multiplexed Assays of Variant Effect data
3.1 Read composition specification
The main function for processing the Multiplexed Assays of Variant Effect
FASTQ files is digestFastqs(). This function requires the specification of
paths to compressed FASTQ file(s) and the composition of the reads in these
files. The composition is specified by the user, and is given in the form of
a character string indicating the parts constituting the respective read, and
an integer vector specifying the lengths of the individual parts. The
permitted components are:
S(skip) - nucleotide(s) to skipU(UMI) - UMI sequenceC(constant) - constant sequence, can be used e.g. to estimate the sequencing error rate if availableV(variable) - nucleotides corresponding to the variable regionP(primer) - a primer sequence, which may appear in a nonspecified position in the read but whose position defines the start or end position of other components.
A read can have several components of the same type (e.g., skipped nucleotides
both in the beginning or the end, or two variable regions separated by
a primer). In such cases, mutscan will concatenate all components of the
same type in the processing, while recording the lengths of the individual
components.
As an example, a read with the following composition:
[1 skipped nt] - [10 nt UMI] - [18 nt constant sequence] - [96 nt variable region]
would be specified to digestFastqs() by an elements string "SUCV",
and an element length vector c(1, 10, 18, 96).
The package can also accommodate designs with primer sequences. In this situation, the primer acts as an ‘anchor’, and the read composition before and after the primer is specified. For example, a read with the following composition:
[unknown sequence] - [10 nt primer] - [variable region, constituting the remainder of the read]
would be represented by an elements string "SPV", and an element length
vector c(-1, 10, -1), where the -1 indicates that the corresponding read part
consists of the remaining part of the read, not accounted for by any of the
other specified parts. In addition, the sequence of the primer must be
specified, and any read where the primer is not present will be discarded.
The forward and reverse reads can have different compositions. The user can also specify whether the variable parts of the forward and/or reverse reads should be reverse complemented before being processed, and whether the variable regions of the forward and reverse reads should be merged into a single consensus sequence.
3.2 Filtering
In addition to reading the FASTQ files, the digestFastqs() function will
perform a series of filtering steps, in the following order:
- Search for perfect matches to forward/reverse adapter sequences, filter out the read pair if a match is found in either the forward or reverse read.
- Search for perfect matches to forward/reverse primer sequences, filter out the read pair if not both are found.
- Filter out reads whose length is not compatible with the indicated composition.
- If forward and reverse variable regions should be merged, filter out read pairs where no valid overlap could be found.
- Filter out read pair if the average quality in the variable region is below
avePhredMinin either the forward or reverse read (or the consensus sequence if they are merged). - Filter out read pair if the number of Ns in the variable region exceeds
variableNMax. - Filter out read pair if the number of Ns in the combined forward and reverse
UMI sequence exceeds
umiNMax. - If wild type (reference) sequences are provided (via
wildTypeForwardand/orwildTypeReverse), filter out reads if there are multiple best matches among the provided wild type sequences. - Filter out read pair if any mutated base has a quality below
mutatedPhredMin. - Filter out read pair if the number of mutated codons exceeds
nbrMutatedCodonsMaxor the number of mutated nucleotides exceedsnbrMutatedBasesMax. - Filter out read pair if any of the mutated codons match any of the
codons encoded by
forbiddenMutatedCodons. - Filter out read pair if there are too many mutations in the constant sequence, or if there are multiple equally close best hits among the provided constant sequences.
Reads that are filtered out in one step will not be processed further (and thus will not contribute to the count of reads being filtered out for any other reasons).
4 Processing TRANS data
Here, we illustrate the processing of the provided TRANS experiment example
data. We filter out reads with any adapter match, an average phred quality
below 20, any Ns in the UMI or variable sequence, more than one mutated codon,
or a mutated codon ending with A or T (represented by the NNW value of the
forbiddenMutatedCodonsForward and forbiddenMutatedCodonsReverse arguments).
In order to identify the number of mutations, we have to provide the wild type
sequences of FOS and JUN (forward and reverse reads, respectively). This
will additionally name the final mutants according to the positions and
sequences of the mutated codons.
transInput <- digestFastqs(
fastqForward = file.path(datadir, "transInput_1.fastq.gz"),
fastqReverse = file.path(datadir, "transInput_2.fastq.gz"),
mergeForwardReverse = FALSE,
revComplForward = FALSE,
revComplReverse = FALSE,
adapterForward = "GGAAGAGCACACGTC",
adapterReverse = "GGAAGAGCGTCGTGT",
elementsForward = "SUCV",
elementsReverse = "SUCV",
elementLengthsForward = c(1, 10, 18, 96),
elementLengthsReverse = c(1, 8, 20, 96),
constantForward = "AACCGGAGGAGGGAGCTG",
constantReverse = "GAAAAAGGAAGCTGGAGAGA",
avePhredMinForward = 20,
avePhredMinReverse = 20,
variableNMaxForward = 0,
variableNMaxReverse = 0,
umiNMax = 0,
wildTypeForward = c(FOS = "ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"),
wildTypeReverse = c(JUN = "ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"),
nbrMutatedCodonsMaxForward = 1,
nbrMutatedCodonsMaxReverse = 1,
forbiddenMutatedCodonsForward = "NNW",
forbiddenMutatedCodonsReverse = "NNW",
mutNameDelimiter = ".",
constantMaxDistForward = -1,
constantMaxDistReverse = -1,
verbose = FALSE
)The digestFastqs() function returns a list with four elements. The
parameters list records all parameter values used during the processing, as
well as the mutscan version and time of processing.
transInput$parameters
#> $fastqForward
#> [1] "/tmp/RtmpdEPWW0/Rinst16f27f4684628c/mutscan/extdata/transInput_1.fastq.gz"
#>
#> $fastqReverse
#> [1] "/tmp/RtmpdEPWW0/Rinst16f27f4684628c/mutscan/extdata/transInput_2.fastq.gz"
#>
#> $mergeForwardReverse
#> [1] FALSE
#>
#> $minOverlap
#> [1] 0
#>
#> $maxOverlap
#> [1] 0
#>
#> $minMergedLength
#> [1] 0
#>
#> $maxMergedLength
#> [1] 0
#>
#> $maxFracMismatchOverlap
#> [1] 1
#>
#> $greedyOverlap
#> [1] TRUE
#>
#> $revComplForward
#> [1] FALSE
#>
#> $revComplReverse
#> [1] FALSE
#>
#> $elementsForward
#> [1] "SUCV"
#>
#> $elementLengthsForward
#> [1] 1 10 18 96
#>
#> $elementsReverse
#> [1] "SUCV"
#>
#> $elementLengthsReverse
#> [1] 1 8 20 96
#>
#> $adapterForward
#> [1] "GGAAGAGCACACGTC"
#>
#> $adapterReverse
#> [1] "GGAAGAGCGTCGTGT"
#>
#> $primerForward
#> [1] ""
#>
#> $primerReverse
#> [1] ""
#>
#> $wildTypeForward
#> FOS
#> "ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"
#>
#> $wildTypeReverse
#> JUN
#> "ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"
#>
#> $constantForward
#> [1] "AACCGGAGGAGGGAGCTG"
#>
#> $constantReverse
#> [1] "GAAAAAGGAAGCTGGAGAGA"
#>
#> $avePhredMinForward
#> [1] 20
#>
#> $avePhredMinReverse
#> [1] 20
#>
#> $variableNMaxForward
#> [1] 0
#>
#> $variableNMaxReverse
#> [1] 0
#>
#> $umiNMax
#> [1] 0
#>
#> $nbrMutatedCodonsMaxForward
#> [1] 1
#>
#> $nbrMutatedCodonsMaxReverse
#> [1] 1
#>
#> $nbrMutatedBasesMaxForward
#> [1] -1
#>
#> $nbrMutatedBasesMaxReverse
#> [1] -1
#>
#> $forbiddenMutatedCodonsForward
#> [1] "AAA" "AAT" "ACA" "ACT" "AGA" "AGT" "ATA" "ATT" "CAA" "CAT" "CCA" "CCT"
#> [13] "CGA" "CGT" "CTA" "CTT" "GAA" "GAT" "GCA" "GCT" "GGA" "GGT" "GTA" "GTT"
#> [25] "TAA" "TAT" "TCA" "TCT" "TGA" "TGT" "TTA" "TTT"
#>
#> $forbiddenMutatedCodonsReverse
#> [1] "AAA" "AAT" "ACA" "ACT" "AGA" "AGT" "ATA" "ATT" "CAA" "CAT" "CCA" "CCT"
#> [13] "CGA" "CGT" "CTA" "CTT" "GAA" "GAT" "GCA" "GCT" "GGA" "GGT" "GTA" "GTT"
#> [25] "TAA" "TAT" "TCA" "TCT" "TGA" "TGT" "TTA" "TTT"
#>
#> $useTreeWTmatch
#> [1] FALSE
#>
#> $collapseToWTForward
#> [1] FALSE
#>
#> $collapseToWTReverse
#> [1] FALSE
#>
#> $mutatedPhredMinForward
#> [1] 0
#>
#> $mutatedPhredMinReverse
#> [1] 0
#>
#> $mutNameDelimiter
#> [1] "."
#>
#> $constantMaxDistForward
#> [1] -1
#>
#> $constantMaxDistReverse
#> [1] -1
#>
#> $umiCollapseMaxDist
#> [1] 0
#>
#> $filteredReadsFastqForward
#> [1] ""
#>
#> $filteredReadsFastqReverse
#> [1] ""
#>
#> $maxNReads
#> [1] -1
#>
#> $nThreads
#> [1] 1
#>
#> $chunkSize
#> [1] 100000
#>
#> $maxReadLength
#> [1] 1024
#>
#> $processingInfo
#> [1] "Processed by mutscan v0.99.2 on 2025-09-12 18:53:05.001753"The filterSummary data.frame contains a summary of the number of reads
filtered out in each step. Note that for any given filtering step, only the
reads retained by the previous filters are considered. The numbers in the
filter column names indicate the order of the filters.
transInput$filterSummary
#> nbrTotal f1_nbrAdapter f2_nbrNoPrimer f3_nbrReadWrongLength
#> 1 1000 314 0 0
#> f4_nbrNoValidOverlap f5_nbrAvgVarQualTooLow f6_nbrTooManyNinVar
#> 1 0 7 0
#> f7_nbrTooManyNinUMI f8_nbrTooManyBestWTHits f9_nbrMutQualTooLow
#> 1 0 0 0
#> f10a_nbrTooManyMutCodons f10b_nbrTooManyMutBases f11_nbrForbiddenCodons
#> 1 392 0 8
#> f12_nbrTooManyMutConstant f13_nbrTooManyBestConstantHits nbrRetained
#> 1 0 0 279The summaryTable provides the number of reads and unique UMIs observed for
each variable sequence pair. The underscore in the strings in the sequence
column indicate the separation between the forward and reverse wild type
sequences. In addition, the table contains a column mutantName, which
provides a shorthand notation for each mutant. The format of the values in this
column is a combination of WTS.xx.NNN (separated by _), where WTS
provides the name of the closest matching wild type sequence (if only one,
unnamed wild type sequence is provided, the name will be f/r depending on
if it corresponds to the forward/reverse read, respectively). The xx part
indicates the mutated codon or nucleotide number, and NNN gives the
observed sequence for the mutated codon or nucleotide. A sequence without
mutations is named WTS.0.WT, where, again, WTS is the name of the
matching wild type sequence.
head(transInput$summaryTable)
#> mutantName
#> 1 FOS.0.WT_JUN.13.CCC
#> 2 FOS.0.WT_JUN.13.CTC
#> 3 FOS.0.WT_JUN.2.TCC
#> 4 FOS.0.WT_JUN.20.ACC
#> 5 FOS.0.WT_JUN.30.AGG
#> 6 FOS.0.WT_JUN.30.GGG
#> sequence
#> 1 ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA_ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAACCCCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT
#> 2 ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA_ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAACTCCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT
#> 3 ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA_ATCTCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT
#> 4 ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA_ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGACCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT
#> 5 ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA_ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGAGGCAGCTT
#> 6 ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA_ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGGGCAGCTT
#> nbrReads maxNbrReads nbrUmis nbrMutBases nbrMutCodons nbrMutAAs varLengths
#> 1 1 1 1 2 1 1 96_96
#> 2 1 1 1 3 1 1 96_96
#> 3 1 1 1 1 1 1 96_96
#> 4 1 1 1 1 1 1 96_96
#> 5 1 1 1 3 1 1 96_96
#> 6 1 1 1 2 1 1 96_96
#> mutantNameBase mutantNameCodon
#> 1 FOS.0.WT_JUN.37.C_JUN.39.C FOS.0.WT_JUN.13.CCC
#> 2 FOS.0.WT_JUN.37.C_JUN.38.T_JUN.39.C FOS.0.WT_JUN.13.CTC
#> 3 FOS.0.WT_JUN.4.T FOS.0.WT_JUN.2.TCC
#> 4 FOS.0.WT_JUN.58.A FOS.0.WT_JUN.20.ACC
#> 5 FOS.0.WT_JUN.88.A_JUN.89.G_JUN.90.G FOS.0.WT_JUN.30.AGG
#> 6 FOS.0.WT_JUN.89.G_JUN.90.G FOS.0.WT_JUN.30.GGG
#> mutantNameBaseHGVS mutantNameAA mutantNameAAHGVS
#> 1 FOS:c_JUN:c.37_39delinsCCC FOS.0.WT_JUN.13.P FOS:p_JUN:p.(Ala13Pro)
#> 2 FOS:c_JUN:c.37_39delinsCTC FOS.0.WT_JUN.13.L FOS:p_JUN:p.(Ala13Leu)
#> 3 FOS:c_JUN:c.4G>T FOS.0.WT_JUN.2.S FOS:p_JUN:p.(Ala2Ser)
#> 4 FOS:c_JUN:c.58T>A FOS.0.WT_JUN.20.T FOS:p_JUN:p.(Ser20Thr)
#> 5 FOS:c_JUN:c.88_90delinsAGG FOS.0.WT_JUN.30.R FOS:p_JUN:p.(Ala30Arg)
#> 6 FOS:c_JUN:c.89_90delinsGG FOS.0.WT_JUN.30.G FOS:p_JUN:p.(Ala30Gly)
#> mutationTypes
#> 1 nonsynonymous
#> 2 nonsynonymous
#> 3 nonsynonymous
#> 4 nonsynonymous
#> 5 nonsynonymous
#> 6 nonsynonymous
#> sequenceAA
#> 1 TDTLQAETDQLEDEKSALQTEIANLLKEKEKL_IARLEEKVKTLKPQNSELASTANMLREQVAQL
#> 2 TDTLQAETDQLEDEKSALQTEIANLLKEKEKL_IARLEEKVKTLKLQNSELASTANMLREQVAQL
#> 3 TDTLQAETDQLEDEKSALQTEIANLLKEKEKL_ISRLEEKVKTLKAQNSELASTANMLREQVAQL
#> 4 TDTLQAETDQLEDEKSALQTEIANLLKEKEKL_IARLEEKVKTLKAQNSELATTANMLREQVAQL
#> 5 TDTLQAETDQLEDEKSALQTEIANLLKEKEKL_IARLEEKVKTLKAQNSELASTANMLREQVRQL
#> 6 TDTLQAETDQLEDEKSALQTEIANLLKEKEKL_IARLEEKVKTLKAQNSELASTANMLREQVGQLFinally, the errorStatistics data.frame lists the number of matching and
mismatching bases in the constant sequences, stratified by the phred quality
score (from 0 to 99).
transInput$errorStatistics[rowSums(transInput$errorStatistics[, -1]) != 0, ]
#> PhredQuality nbrMatchForward nbrMismatchForward nbrMatchReverse
#> 15 14 160 11 204
#> 23 22 52 0 14
#> 28 27 302 4 474
#> 34 33 472 0 462
#> 38 37 4020 1 4405
#> nbrMismatchReverse
#> 15 17
#> 23 0
#> 28 3
#> 34 1
#> 38 0The errorStatistics output can be used to estimate the sequencing error rate:
(propErrorsConstantF <- sum(transInput$errorStatistics$nbrMismatchForward) /
(nchar(transInput$parameters$constantForward) *
transInput$filterSummary$nbrRetained))
#> [1] 0.003185982
(propErrorsConstantR <- sum(transInput$errorStatistics$nbrMismatchReverse) /
(nchar(transInput$parameters$constantReverse) *
transInput$filterSummary$nbrRetained))
#> [1] 0.0037634415 Processing CIS data
Next, we illustrate the processing of the provided CIS experiment example data.
Recall that in this case, both the forward and reverse variable sequence
correspond to the same mutated FOS sequence, and before matching to the
wild type sequence, the two components need to be merged to generate a single
variable sequence (this is specified via the mergeForwardReverse argument).
As for the TRANS data, we filter out reads with any adapter match, an average
phred quality below 20, any Ns in the UMI or variable sequence, more than one
mutated codon, or a mutated codon ending with A or T (represented by the NNW
value of the forbiddenMutatedCodonsForward argument). Since the
forward and reverse variable sequences are merged into one variable
sequence, only one wild type sequence is provided (the wildTypeReverse
argument will be ignored if specified).
cisInput <- digestFastqs(
fastqForward = file.path(datadir, "cisInput_1.fastq.gz"),
fastqReverse = file.path(datadir, "cisInput_2.fastq.gz"),
mergeForwardReverse = TRUE,
minOverlap = 0,
maxOverlap = 0,
maxFracMismatchOverlap = 1,
greedyOverlap = TRUE,
revComplForward = FALSE,
revComplReverse = TRUE,
adapterForward = "GGAAGAGCACACGTC",
adapterReverse = "GGAAGAGCGTCGTGT",
elementsForward = "SUCV",
elementsReverse = "SUCVS",
elementLengthsForward = c(1, 10, 18, 96),
elementLengthsReverse = c(1, 7, 17, 96, -1),
constantForward = "AACCGGAGGAGGGAGCTG",
constantReverse = "GAGTTCATCCTGGCAGC",
primerForward = "",
primerReverse = "",
avePhredMinForward = 20,
avePhredMinReverse = 20,
variableNMaxForward = 0,
variableNMaxReverse = 0,
umiNMax = 0,
wildTypeForward = c(FOS = "ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"),
wildTypeReverse = "",
nbrMutatedCodonsMaxForward = 1,
nbrMutatedCodonsMaxReverse = 1,
forbiddenMutatedCodonsForward = "NNW",
forbiddenMutatedCodonsReverse = "NNW",
mutNameDelimiter = ".",
constantMaxDistForward = -1,
constantMaxDistReverse = -1,
verbose = TRUE
)
#> done enumerating forbidden codons (32)
#> done enumerating forbidden codons (32)
#> start reading sequences for file pair 1 of 1...
#> 1000 read pairs processed (16.7% retained)
#> done reading sequences
#> retained 67 unique featurescisInput$parameters
#> $fastqForward
#> [1] "/tmp/RtmpdEPWW0/Rinst16f27f4684628c/mutscan/extdata/cisInput_1.fastq.gz"
#>
#> $fastqReverse
#> [1] "/tmp/RtmpdEPWW0/Rinst16f27f4684628c/mutscan/extdata/cisInput_2.fastq.gz"
#>
#> $mergeForwardReverse
#> [1] TRUE
#>
#> $minOverlap
#> [1] 0
#>
#> $maxOverlap
#> [1] 0
#>
#> $minMergedLength
#> [1] 0
#>
#> $maxMergedLength
#> [1] 0
#>
#> $maxFracMismatchOverlap
#> [1] 1
#>
#> $greedyOverlap
#> [1] TRUE
#>
#> $revComplForward
#> [1] FALSE
#>
#> $revComplReverse
#> [1] TRUE
#>
#> $elementsForward
#> [1] "SUCV"
#>
#> $elementLengthsForward
#> [1] 1 10 18 96
#>
#> $elementsReverse
#> [1] "SUCVS"
#>
#> $elementLengthsReverse
#> [1] 1 7 17 96 -1
#>
#> $adapterForward
#> [1] "GGAAGAGCACACGTC"
#>
#> $adapterReverse
#> [1] "GGAAGAGCGTCGTGT"
#>
#> $primerForward
#> [1] ""
#>
#> $primerReverse
#> [1] ""
#>
#> $wildTypeForward
#> FOS
#> "ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"
#>
#> $wildTypeReverse
#> r
#> ""
#>
#> $constantForward
#> [1] "AACCGGAGGAGGGAGCTG"
#>
#> $constantReverse
#> [1] "GAGTTCATCCTGGCAGC"
#>
#> $avePhredMinForward
#> [1] 20
#>
#> $avePhredMinReverse
#> [1] 20
#>
#> $variableNMaxForward
#> [1] 0
#>
#> $variableNMaxReverse
#> [1] 0
#>
#> $umiNMax
#> [1] 0
#>
#> $nbrMutatedCodonsMaxForward
#> [1] 1
#>
#> $nbrMutatedCodonsMaxReverse
#> [1] 1
#>
#> $nbrMutatedBasesMaxForward
#> [1] -1
#>
#> $nbrMutatedBasesMaxReverse
#> [1] -1
#>
#> $forbiddenMutatedCodonsForward
#> [1] "AAA" "AAT" "ACA" "ACT" "AGA" "AGT" "ATA" "ATT" "CAA" "CAT" "CCA" "CCT"
#> [13] "CGA" "CGT" "CTA" "CTT" "GAA" "GAT" "GCA" "GCT" "GGA" "GGT" "GTA" "GTT"
#> [25] "TAA" "TAT" "TCA" "TCT" "TGA" "TGT" "TTA" "TTT"
#>
#> $forbiddenMutatedCodonsReverse
#> [1] "AAA" "AAT" "ACA" "ACT" "AGA" "AGT" "ATA" "ATT" "CAA" "CAT" "CCA" "CCT"
#> [13] "CGA" "CGT" "CTA" "CTT" "GAA" "GAT" "GCA" "GCT" "GGA" "GGT" "GTA" "GTT"
#> [25] "TAA" "TAT" "TCA" "TCT" "TGA" "TGT" "TTA" "TTT"
#>
#> $useTreeWTmatch
#> [1] FALSE
#>
#> $collapseToWTForward
#> [1] FALSE
#>
#> $collapseToWTReverse
#> [1] FALSE
#>
#> $mutatedPhredMinForward
#> [1] 0
#>
#> $mutatedPhredMinReverse
#> [1] 0
#>
#> $mutNameDelimiter
#> [1] "."
#>
#> $constantMaxDistForward
#> [1] -1
#>
#> $constantMaxDistReverse
#> [1] -1
#>
#> $umiCollapseMaxDist
#> [1] 0
#>
#> $filteredReadsFastqForward
#> [1] ""
#>
#> $filteredReadsFastqReverse
#> [1] ""
#>
#> $maxNReads
#> [1] -1
#>
#> $nThreads
#> [1] 1
#>
#> $chunkSize
#> [1] 100000
#>
#> $maxReadLength
#> [1] 1024
#>
#> $processingInfo
#> [1] "Processed by mutscan v0.99.2 on 2025-09-12 18:53:05.346848"
cisInput$filterSummary
#> nbrTotal f1_nbrAdapter f2_nbrNoPrimer f3_nbrReadWrongLength
#> 1 1000 126 0 0
#> f4_nbrNoValidOverlap f5_nbrAvgVarQualTooLow f6_nbrTooManyNinVar
#> 1 0 0 44
#> f7_nbrTooManyNinUMI f8_nbrTooManyBestWTHits f9_nbrMutQualTooLow
#> 1 0 0 0
#> f10a_nbrTooManyMutCodons f10b_nbrTooManyMutBases f11_nbrForbiddenCodons
#> 1 581 0 82
#> f12_nbrTooManyMutConstant f13_nbrTooManyBestConstantHits nbrRetained
#> 1 0 0 167
cisInput$errorStatistics[rowSums(cisInput$errorStatistics[, -1]) != 0, ]
#> PhredQuality nbrMatchForward nbrMismatchForward nbrMatchReverse
#> 3 2 0 0 0
#> 15 14 51 1 41
#> 23 22 19 0 0
#> 28 27 79 1 56
#> 34 33 163 0 96
#> 38 37 2690 2 2620
#> nbrMismatchReverse
#> 3 4
#> 15 7
#> 23 0
#> 28 0
#> 34 1
#> 38 14The summaryTable now provides the number of reads and unique UMIs observed
for each variable sequence, and all values in the mutNames column will start
with FOS.
head(cisInput$summaryTable)
#> mutantName
#> 1 FOS.0.WT
#> 2 FOS.1.ACC
#> 3 FOS.1.ACG
#> 4 FOS.11.CTG
#> 5 FOS.13.GAC
#> 6 FOS.13.GAG
#> sequence
#> 1 ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA
#> 2 ACCGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA
#> 3 ACGGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA
#> 4 ACTGATACACTCCAAGCGGAGACAGACCAACTGGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA
#> 5 ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGACGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA
#> 6 ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGAGGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA
#> nbrReads maxNbrReads nbrUmis nbrMutBases nbrMutCodons nbrMutAAs varLengths
#> 1 77 77 77 0 0 0 96
#> 2 2 2 2 1 1 0 96
#> 3 1 1 1 1 1 0 96
#> 4 1 1 1 1 1 0 96
#> 5 1 1 1 1 1 0 96
#> 6 1 1 1 1 1 1 96
#> mutantNameBase mutantNameCodon mutantNameBaseHGVS mutantNameAA
#> 1 FOS.0.WT FOS.0.WT FOS:c FOS.0.WT
#> 2 FOS.3.C FOS.1.ACC FOS:c.3T>C FOS.0.WT
#> 3 FOS.3.G FOS.1.ACG FOS:c.3T>G FOS.0.WT
#> 4 FOS.33.G FOS.11.CTG FOS:c.33A>G FOS.0.WT
#> 5 FOS.39.C FOS.13.GAC FOS:c.39T>C FOS.0.WT
#> 6 FOS.39.G FOS.13.GAG FOS:c.39T>G FOS.13.E
#> mutantNameAAHGVS mutationTypes sequenceAA
#> 1 FOS:p TDTLQAETDQLEDEKSALQTEIANLLKEKEKL
#> 2 FOS:p silent TDTLQAETDQLEDEKSALQTEIANLLKEKEKL
#> 3 FOS:p silent TDTLQAETDQLEDEKSALQTEIANLLKEKEKL
#> 4 FOS:p silent TDTLQAETDQLEDEKSALQTEIANLLKEKEKL
#> 5 FOS:p silent TDTLQAETDQLEDEKSALQTEIANLLKEKEKL
#> 6 FOS:p.(Asp13Glu) nonsynonymous TDTLQAETDQLEEEKSALQTEIANLLKEKEKL6 Processing TRANS data with primers
Finally, we illustrate the processing of the provided example data, where the
first read corresponds to one of 46 leucine zipper sequences, and the second
read is a mutated JUN sequence. We first need to define the possible
sequences for the forward read. If multiple wild type sequences are provided
like here, mutscan will match each read against all of them and find the
most similar one for each read.
leu <- c(
ATF2 = "GATCCTGATGAAAAAAGGAGAAAGTTTTTAGAGCGAAATAGAGCAGCAGCTTCAAGATGCCGACAAAAAAGGAAAGTCTGGGTTCAGTCTTTAGAGAAGAAAGCTGAAGACTTGAGTTCATTAAATGGTCAGCTGCAGAGTGAAGTCACCCTGCTGAGAAATGAAGTGGCACAGCTGAAACAGCTTCTTCTGGCT",
ATF7 = "GATCCAGATGAGCGACGGCAGCGCTTTCTGGAGCGCAACCGGGCTGCAGCCTCCCGCTGCCGCCAAAAGCGAAAGCTGTGGGTGTCCTCCCTAGAGAAGAAGGCCGAAGAACTCACTTCTCAGAACATTCAGCTGAGTAATGAAGTCACATTACTACGCAATGAGGTGGCCCAGTTGAAACAGCTACTGTTAGCT",
CREB5 = "GATCCGGACGAGAGGCGGCGGAAATTTCTGGAACGGAACCGGGCAGCTGCCACCCGCTGCAGACAGAAGAGGAAGGTCTGGGTGATGTCATTGGAAAAGAAAGCAGAAGAACTCACCCAGACAAACATGCAGCTTCAGAATGAAGTGTCTATGTTGAAAAATGAGGTGGCCCAGCTGAAACAGTTGTTGTTAACA",
ATF3 = "GAAGAAGATGAAAGGAAAAAGAGGCGACGAGAAAGAAATAAGATTGCAGCTGCAAAGTGCCGAAACAAGAAGAAGGAGAAGACGGAGTGCCTGCAGAAAGAGTCGGAGAAGCTGGAAAGTGTGAATGCTGAACTGAAGGCTCAGATTGAGGAGCTCAAGAACGAGAAGCAGCATTTGATATACATGCTCAACCTT",
JDP2 = "GAGGAAGAGGAGCGAAGGAAAAGGCGCCGGGAGAAGAACAAAGTCGCAGCAGCCCGATGCCGGAACAAGAAGAAGGAGCGCACGGAGTTTCTGCAGCGGGAATCCGAGCGGCTGGAACTCATGAACGCAGAGCTGAAGACCCAGATTGAGGAGCTGAAGCAGGAGCGGCAGCAGCTCATCCTGATGCTGAACCGA",
ATF4 = "GAGAAACTGGATAAGAAGCTGAAAAAAATGGAGCAAAACAAGACAGCAGCCACTAGGTACCGCCAGAAGAAGAGGGCGGAGCAGGAGGCTCTTACTGGTGAGTGCAAAGAGCTGGAAAAGAAGAACGAGGCTCTAAAAGAGAGGGCGGATTCCCTGGCCAAGGAGATCCAGTACCTGAAAGATTTGATAGAAGAG",
ATF5 = "ACCCGAGGGGACCGCAAGCAAAAGAAGAGAGACCAGAACAAGTCGGCGGCTCTGAGGTACCGCCAGCGGAAGCGGGCAGAGGGTGAGGCCCTGGAGGGCGAGTGCCAGGGGCTGGAGGCACGGAATCGCGAGCTGAAGGAACGGGCAGAGTCCGTGGAGCGCGAGATCCAGTACGTCAAGGACCTGCTCATCGAG",
CREBZF = "AGTCCCCGGAAGGCGGCGGCGGCCGCTGCCCGCCTTAATCGACTGAAGAAGAAGGAGTACGTGATGGGGCTGGAGAGTCGAGTCCGGGGTCTGGCAGCCGAGAACCAGGAGCTGCGGGCCGAGAATCGGGAGCTGGGCAAACGCGTACAGGCACTGCAGGAGGAGAGTCGCTACCTACGGGCAGTCTTAGCCAAC",
BATF2 = "CCCAAGGAGCAACAAAGGCAGCTGAAGAAGCAGAAGAACCGGGCAGCCGCCCAGCGAAGCCGGCAGAAGCACACAGACAAGGCAGACGCCCTGCACCAGCAGCACGAGTCTCTGGAAAAAGACAACCTCGCCCTGCGGAAGGAGATCCAGTCCCTGCAGGCCGAGCTGGCGTGGTGGAGCCGGACCCTGCACGTG",
BATF3 = "GAGGATGATGACAGGAAGGTCCGAAGGAGAGAAAAAAACCGAGTTGCTGCTCAGAGAAGTCGGAAGAAGCAGACCCAGAAGGCTGACAAGCTCCATGAGGAATATGAGAGCCTGGAGCAAGAAAACACCATGCTGCGGAGAGAGATCGGGAAGCTGACAGAGGAGCTGAAGCACCTGACAGAGGCACTGAAGGAG",
CEBPE = "AAAGATAGCCTTGAGTACCGGCTGAGGCGGGAGCGCAACAACATCGCCGTGCGCAAGAGCCGAGACAAGGCCAAGAGGCGCATTCTGGAGACGCAGCAGAAGGTGCTGGAGTACATGGCAGAGAACGAGCGCCTCCGCAGCCGCGTGGAGCAGCTCACCCAGGAGCTAGACACCCTCCGCAACCTCTTCCGCCAG",
BACH1 = "CTGGATTGTATCCATGATATTCGAAGAAGAAGTAAAAACAGAATTGCTGCACAGCGCTGTCGCAAGAGAAAACTTGACTGTATACAGAATCTTGAATCAGAAATTGAGAAGCTGCAAAGTGAAAAGGAGAGCTTGTTGAAGGAAAGAGATCACATTTTGTCAACTCTGGGTGAGACAAAGCAGAACCTAACTGGA",
BACH2 = "TTAGAGTTTATTCATGATGTCCGACGGCGCAGCAAGAACCGCATCGCGGCCCAGCGCTGCCGCAAAAGGAAACTGGACTGTATTCAGAATTTAGAATGTGAAATCCGCAAATTGGTGTGTGAGAAAGAGAAACTGTTGTCAGAGAGGAATCAACTGAAAGCATGCATGGGGGAACTGTTGGACAACTTCTCCTGC",
NFE2L1 = "CTGAGCCTCATCCGAGACATCCGGCGCCGGGGCAAGAACAAGATGGCGGCGCAGAACTGCCGCAAGCGCAAGCTGGACACCATCCTGAATCTGGAGCGTGATGTGGAGGACCTGCAGCGTGACAAAGCCCGGCTGCTGCGGGAGAAAGTGGAGTTCCTGCGCTCCCTGCGACAGATGAAGCAGAAGGTCCAGAGC",
NFE2 = "CTAGCGCTAGTCCGGGACATCCGACGACGGGGCAAAAACAAGGTGGCAGCCCAGAACTGCCGCAAGAGGAAGCTGGAAACCATTGTGCAGCTGGAGCGGGAGCTGGAGCGGCTGACCAATGAACGGGAGCGGCTTCTCAGGGCCCGCGGGGAGGCAGACCGGACCCTGGAGGTCATGCGCCAACAGCTGACAGAG",
NFIL3 = "AAGAAAGATGCTATGTATTGGGAAAAAAGGCGGAAAAATAATGAAGCTGCCAAAAGATCTCGTGAGAAGCGTCGACTGAATGACCTGGTTTTAGAGAACAAACTAATTGCACTGGGAGAAGAAAACGCCACTTTAAAAGCTGAGCTGCTTTCACTAAAATTAAAGTTTGGTTTAATTAGCTCCACAGCATATGCT",
FOS = "GAAGAAGAAGAGAAAAGGAGAATCCGAAGGGAAAGGAATAAGATGGCTGCAGCCAAATGCCGCAACCGGAGGAGGGAGCTGACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTAGAGTTCATCCTGGCAGCT",
FOSB = "GAGGAAGAGGAGAAGCGAAGGGTGCGCCGGGAACGAAATAAACTAGCAGCAGCTAAATGCAGGAACCGGCGGAGGGAGCTGACCGACCGACTCCAGGCGGAGACAGATCAGTTGGAGGAAGAAAAAGCAGAGCTGGAGTCGGAGATCGCCGAGCTCCAAAAGGAGAAGGAACGTCTGGAGTTTGTGCTGGTGGCC",
FOSL1 = "GAGGAAGAGGAGCGCCGCCGAGTAAGGCGCGAGCGGAACAAGCTGGCTGCGGCCAAGTGCAGGAACCGGAGGAAGGAACTGACCGACTTCCTGCAGGCGGAGACTGACAAACTGGAAGATGAGAAATCTGGGCTGCAGCGAGAGATTGAGGAGCTGCAGAAGCAGAAGGAGCGCCTAGAGCTGGTGCTGGAAGCC",
FOSL2 = "GAAGAGGAGGAGAAGCGTCGCATCCGGCGGGAGAGGAACAAGCTGGCTGCAGCCAAGTGCCGGAACCGACGCCGGGAGCTGACAGAGAAGCTGCAGGCGGAGACAGAGGAGCTGGAGGAGGAGAAGTCAGGCCTGCAGAAGGAGATTGCTGAGCTGCAGAAGGAGAAGGAGAAGCTGGAGTTCATGTTGGTGGCT",
MAFB = "GTGATCCGCCTGAAGCAGAAGCGGCGGACCCTGAAGAACCGGGGCTACGCCCAGTCTTGCAGGTATAAACGCGTCCAGCAGAAGCACCACCTGGAGAATGAGAAGACGCAGCTCATTCAGCAGGTGGAGCAGCTTAAGCAGGAGGTGTCCCGGCTGGCCCGCGAGAGAGACGCCTACAAGGTCAAGTGCGAGAAA",
JUN = "CAGGAGCGGATCAAGGCGGAGAGGAAGCGCATGAGGAACCGCATCGCTGCCTCCAAGTGCCGAAAAAGGAAGCTGGAGAGAATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTTAAACAGAAAGTCATGAAC",
JUNB = "CAAGAGCGCATCAAAGTGGAGCGCAAGCGGCTGCGGAACCGGCTGGCGGCCACCAAGTGCCGGAAGCGGAAGCTGGAGCGCATCGCGCGCCTGGAGGACAAGGTGAAGACGCTCAAGGCCGAGAACGCGGGGCTGTCGAGTACCGCCGGCCTCCTCCGGGAGCAGGTGGCCCAGCTCAAACAGAAGGTCATGACC",
JUND = "CAGGAGCGCATCAAGGCGGAGCGCAAGCGGCTGCGCAACCGCATCGCCGCCTCCAAGTGCCGCAAGCGCAAGCTGGAGCGCATCTCGCGCCTGGAAGAGAAAGTGAAGACCCTCAAGAGTCAGAACACGGAGCTGGCGTCCACGGCGAGCCTGCTGCGCGAGCAGGTGGCGCAGCTCAAGCAGAAAGTCCTCAGC",
CREB3 = "GAACAAATTCTGAAACGTGTGCGGAGGAAGATTCGAAATAAAAGATCTGCTCAAGAGAGCCGCAGGAAAAAGAAGGTGTATGTTGGGGGTTTAGAGAGCAGGGTCTTGAAATACACAGCCCAGAATATGGAGCTTCAGAACAAAGTACAGCTTCTGGAGGAACAGAATTTGTCCCTTCTAGATCAACTGAGGAAA",
HLF = "CTGAAGGATGACAAGTACTGGGCAAGGCGCAGAAAGAACAACATGGCAGCCAAGCGCTCCCGCGACGCCCGGAGGCTGAAAGAGAACCAGATCGCCATCCGGGCCTCGTTCCTGGAGAAGGAGAACTCGGCCCTCCGCCAGGAGGTGGCTGACTTGAGGAAGGAGCTGGGCAAATGCAAGAACATACTTGCCAAG",
MAFG = "ATCGTCCAGCTGAAGCAGCGCCGGCGCACGCTCAAGAACCGCGGCTACGCTGCCAGCTGCCGCGTGAAGCGGGTGACGCAGAAGGAGGAGCTGGAGAAGCAGAAGGCGGAGCTGCAGCAGGAGGTGGAGAAGCTGGCCTCAGAGAACGCCAGCATGAAGCTGGAGCTCGACGCGCTGCGCTCCAAGTACGAGGCG",
MAFK = "GTGACCCGCCTGAAGCAGCGTCGGCGCACACTCAAGAACCGCGGCTACGCGGCCAGCTGCCGCATCAAGCGGGTGACGCAGAAGGAGGAGCTGGAGCGGCAGCGCGTGGAGCTGCAGCAGGAGGTGGAGAAGCTGGCGCGTGAGAACAGCAGCATGCGGCTGGAGCTGGACGCCCTGCGCTCCAAGTACGAGGCG",
XBP1 = "AGCCCCGAGGAGAAGGCGCTGAGGAGGAAACTGAAAAACAGAGTAGCAGCTCAGACTGCCAGAGATCGAAAGAAGGCTCGAATGAGTGAGCTGGAACAGCAAGTGGTAGATTTAGAAGAAGAGAACCAAAAACTTTTGCTAGAAAATCAGCTTTTACGAGAGAAAACTCATGGCCTTGTAGTTGAGAACCAGGAG",
ATF6 = "ATTGCTGTGCTAAGGAGACAGCAACGTATGATAAAAAATCGAGAATCCGCTTGTCAGTCTCGCAAGAAGAAGAAAGAATATATGCTAGGGTTAGAGGCGAGATTAAAGGCTGCCCTCTCAGAAAACGAGCAACTGAAGAAAGAAAATGGAACACTGAAGCGGCAGCTGGATGAAGTTGTGTCAGAGAACCAGAGG",
ATF6B = "GCAAAGCTGCTGAAGCGGCAGCAGCGAATGATCAAGAACCGGGAGTCAGCCTGCCAGTCCCGGAGAAAGAAGAAAGAGTATCTGCAGGGACTGGAGGCTCGGCTGCAAGCAGTACTGGCTGACAACCAGCAGCTCCGCCGAGAGAATGCTGCCCTCCGGCGGCGGCTGGAGGCCCTGCTGGCTGAAAACAGCGAG",
CEBPA = "AAGAACAGCAACGAGTACCGGGTGCGGCGCGAGCGCAACAACATCGCGGTGCGCAAGAGCCGCGACAAGGCCAAGCAGCGCAACGTGGAGACGCAGCAGAAGGTGCTGGAGCTGACCAGTGACAATGACCGCCTGCGCAAGCGGGTGGAACAGCTGAGCCGCGAACTGGACACGCTGCGGGGCATCTTCCGCCAG",
CEBPB = "AAGCACAGCGACGAGTACAAGATCCGGCGCGAGCGCAACAACATCGCCGTGCGCAAGAGCCGCGACAAGGCCAAGATGCGCAACCTGGAGACGCAGCACAAGGTCCTGGAGCTCACGGCCGAGAACGAGCGGCTGCAGAAGAAGGTGGAGCAGCTGTCGCGCGAGCTCAGCACCCTGCGGAACTTGTTCAAGCAG",
CEBPD = "CGCGGCAGCCCCGAGTACCGGCAGCGGCGCGAGCGCAACAACATCGCCGTGCGCAAGAGCCGCGACAAGGCCAAGCGGCGCAACCAGGAGATGCAGCAGAAGTTGGTGGAGCTGTCGGCTGAGAACGAGAAGCTGCACCAGCGCGTGGAGCAGCTCACGCGGGACCTGGCCGGCCTCCGGCAGTTCTTCAAGCAG",
CEBPG = "CGAAACAGTGACGAGTATCGGCAACGCCGAGAGAGGAACAACATGGCTGTGAAAAAGAGCCGGTTGAAAAGCAAGCAGAAAGCACAAGACACACTGCAGAGAGTCAATCAGCTCAAAGAAGAGAATGAACGGTTGGAAGCAAAAATCAAATTGCTGACCAAGGAATTAAGTGTACTCAAAGATTTGTTTCTTGAG",
CREB1 = "GAAGCAGCACGAAAGAGAGAGGTCCGTCTAATGAAGAACAGGGAAGCAGCTCGAGAGTGTCGTAGAAAGAAGAAAGAATATGTGAAATGTTTAGAAAACAGAGTGGCAGTGCTTGAAAATCAAAACAAGACATTGATTGAGGAGCTAAAAGCACTTAAGGACCTTTACTGCCACAAATCAGAT",
CREB3L1 = "GAGAAGGCCTTGAAGAGAGTCCGGAGGAAAATCAAGAACAAGATCTCAGCCCAGGAGAGCCGTCGTAAGAAGAAGGAGTATGTGGAGTGTCTAGAAAAGAAGGTGGAGACATTTACATCTGAGAACAATGAACTGTGGAAGAAGGTGGAGACCCTGGAGAATGCCAACAGGACCCTGCTCCAGCAGCTGCAGAAA",
CREB3L2 = "GAGAAGGCCCTGAAGAAAATTCGGAGGAAGATCAAGAATAAGATTTCTGCTCAGGAAAGTAGGAGAAAGAAGAAAGAATACATGGACAGCCTGGAGAAAAAAGTGGAGTCTTGTTCAACTGAGAACTTGGAGCTTCGGAAGAAGGTAGAGGTTCTAGAGAACACTAATAGGACTCTCCTTCAGCAACTCCAGAAG",
CREB3L3 = "GAGCGAGTGCTGAAAAAAATCCGCCGGAAAATCCGGAACAAGCAGTCGGCGCAAGAAAGCAGGAAGAAGAAGAAGGAATATATCGATGGCCTGGAGACTCGGATGTCAGCTTGCACTGCTCAGAATCAGGAGTTACAGAGGAAAGTCTTGCATCTCGAGAAGCAAAACCTGTCCCTCTTGGAGCAACTGAAGAAA",
CREB3L4 = "GAGAGGGTCCTCAAGAAGGTCAGGAGGAAAATCCGTAACAAGCAGTCAGCTCAGGACAGTCGGCGGCGGAAGAAGGAGTACATTGATGGGCTGGAGAGCAGGGTGGCAGCCTGTTCTGCACAGAACCAAGAATTACAGAAAAAAGTCCAGGAGCTGGAGAGGCACAACATCTCCTTGGTAGCTCAGCTCCGCCAG",
CREBL2 = "CCAGCCAAAATTGACTTGAAAGCAAAACTTGAGAGGAGCCGGCAGAGTGCAAGAGAATGCCGAGCCCGAAAAAAGCTGAGATATCAGTATTTGGAAGAGTTGGTATCCAGTCGAGAAAGAGCTATATGTGCCCTCAGAGAGGAACTGGAAATGTACAAGCAGTGGTGCATGGCAATGGACCAAGGAAAAATCCCT",
CREBRF = "CCCTTAACAGCCCGACCAAGGTCAAGGAAGGAAAAAAATAAGCTGGCTTCCAGAGCTTGTCGGTTAAAGAAGAAAGCCCAGTATGAAGCTAATAAAGTGAAATTATGGGGCCTCAACACAGAATATGATAATTTATTGTTTGTAATCAACTCCATCAAGCAAGAGATTGTAAACCGGGTACAGAATCCAAGAGAT",
DBP = "CAGAAGGATGAGAAATACTGGAGCCGGCGGTACAAGAACAACGAGGCAGCCAAGCGGTCCCGTGACGCCCGGCGGCTCAAGGAGAACCAGATATCGGTGCGGGCGGCCTTCCTGGAGAAGGAGAACGCCCTGCTGCGGCAGGAAGTTGTGGCCGTGCGCCAGGAGCTGTCCCACTACCGCGCCGTGCTGTCCCGA",
NFE2L2 = "CTTGCATTAATTCGGGATATACGTAGGAGGGGTAAGAATAAAGTGGCTGCTCAGAATTGCAGAAAAAGAAAACTGGAAAATATAGTAGAACTAGAGCAAGATTTAGATCATTTGAAAGATGAAAAAGAAAAATTGCTCAAAGAAAAAGGAGAAAATGACAAAAGCCTTCACCTACTGAAAAAACAACTCAGCACC",
NFE2L3 = "GTCTCACTTATCCGTGACATCAGACGAAGAGGGAAAAATAAAGTTGCTGCGCAGAACTGTCGTAAACGCAAATTGGACATAATTTTGAATTTAGAAGATGATGTATGTAACTTGCAAGCAAAGAAGGAAACTCTTAAGAGAGAGCAAGCACAATGTAACAAAGCTATTAACATAATGAAACAGAAACTGCATGAC",
TEF = "CAGAAGGATGAAAAGTACTGGACAAGACGCAAGAAGAACAACGTGGCAGCTAAACGGTCACGGGATGCCCGGCGCCTGAAAGAGAATCAGATCACCATCCGGGCAGCCTTCCTGGAGAAGGAGAACACAGCCCTGCGGACGGAGGTGGCCGAGCTACGCAAGGAGGTGGGCAAGTGCAAGACCATCGTGTCCAAG"
)Next we process the data, not allowing any mismatches in the forward read, but 1 mismatching codon in the reverse read. Now, we assume that the variable sequence starts immediately after the provided primers, and hence we don’t specify any UMI/constant sequence lengths. For the forward read, the variable region is taken to be the remainder of the read (after the primer), whereas for the reverse read, we specify the variable sequence length to 96.
leujunt0 <- digestFastqs(
fastqForward = file.path(datadir, "leujunt0_1.fastq.gz"),
fastqReverse = file.path(datadir, "leujunt0_2.fastq.gz"),
mergeForwardReverse = FALSE,
revComplForward = FALSE,
revComplReverse = FALSE,
elementsForward = "SPV",
elementsReverse = "SPVS",
elementLengthsForward = c(-1, 19, -1),
elementLengthsReverse = c(-1, 20, 96, -1),
constantForward = "",
constantReverse = "",
adapterForward = "",
adapterReverse = "",
primerForward = "GTCAGGTGGAGGCGGATCC",
primerReverse = "GAAAAAGGAAGCTGGAGAGA",
avePhredMinForward = 20,
avePhredMinReverse = 20,
variableNMaxForward = 0,
variableNMaxReverse = 0,
umiNMax = 0,
wildTypeForward = leu,
wildTypeReverse = c(JUN = "ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"),
nbrMutatedCodonsMaxForward = 0,
nbrMutatedCodonsMaxReverse = 1,
forbiddenMutatedCodonsForward = "",
forbiddenMutatedCodonsReverse = "NNW",
mutatedPhredMinForward = 0.0,
mutatedPhredMinReverse = 0.0,
mutNameDelimiter = ".",
verbose = TRUE
)
#> done enumerating forbidden codons (0)
#> done enumerating forbidden codons (32)
#> start reading sequences for file pair 1 of 1...
#> 1000 read pairs processed (60% retained)
#> done reading sequences
#> retained 587 unique featuresleujunt0$parameters
#> $fastqForward
#> [1] "/tmp/RtmpdEPWW0/Rinst16f27f4684628c/mutscan/extdata/leujunt0_1.fastq.gz"
#>
#> $fastqReverse
#> [1] "/tmp/RtmpdEPWW0/Rinst16f27f4684628c/mutscan/extdata/leujunt0_2.fastq.gz"
#>
#> $mergeForwardReverse
#> [1] FALSE
#>
#> $minOverlap
#> [1] 0
#>
#> $maxOverlap
#> [1] 0
#>
#> $minMergedLength
#> [1] 0
#>
#> $maxMergedLength
#> [1] 0
#>
#> $maxFracMismatchOverlap
#> [1] 1
#>
#> $greedyOverlap
#> [1] TRUE
#>
#> $revComplForward
#> [1] FALSE
#>
#> $revComplReverse
#> [1] FALSE
#>
#> $elementsForward
#> [1] "SPV"
#>
#> $elementLengthsForward
#> [1] -1 19 -1
#>
#> $elementsReverse
#> [1] "SPVS"
#>
#> $elementLengthsReverse
#> [1] -1 20 96 -1
#>
#> $adapterForward
#> [1] ""
#>
#> $adapterReverse
#> [1] ""
#>
#> $primerForward
#> [1] "GTCAGGTGGAGGCGGATCC"
#>
#> $primerReverse
#> [1] "GAAAAAGGAAGCTGGAGAGA"
#>
#> $wildTypeForward
#> ATF2
#> "GATCCTGATGAAAAAAGGAGAAAGTTTTTAGAGCGAAATAGAGCAGCAGCTTCAAGATGCCGACAAAAAAGGAAAGTCTGGGTTCAGTCTTTAGAGAAGAAAGCTGAAGACTTGAGTTCATTAAATGGTCAGCTGCAGAGTGAAGTCACCCTGCTGAGAAATGAAGTGGCACAGCTGAAACAGCTTCTTCTGGCT"
#> ATF7
#> "GATCCAGATGAGCGACGGCAGCGCTTTCTGGAGCGCAACCGGGCTGCAGCCTCCCGCTGCCGCCAAAAGCGAAAGCTGTGGGTGTCCTCCCTAGAGAAGAAGGCCGAAGAACTCACTTCTCAGAACATTCAGCTGAGTAATGAAGTCACATTACTACGCAATGAGGTGGCCCAGTTGAAACAGCTACTGTTAGCT"
#> CREB5
#> "GATCCGGACGAGAGGCGGCGGAAATTTCTGGAACGGAACCGGGCAGCTGCCACCCGCTGCAGACAGAAGAGGAAGGTCTGGGTGATGTCATTGGAAAAGAAAGCAGAAGAACTCACCCAGACAAACATGCAGCTTCAGAATGAAGTGTCTATGTTGAAAAATGAGGTGGCCCAGCTGAAACAGTTGTTGTTAACA"
#> ATF3
#> "GAAGAAGATGAAAGGAAAAAGAGGCGACGAGAAAGAAATAAGATTGCAGCTGCAAAGTGCCGAAACAAGAAGAAGGAGAAGACGGAGTGCCTGCAGAAAGAGTCGGAGAAGCTGGAAAGTGTGAATGCTGAACTGAAGGCTCAGATTGAGGAGCTCAAGAACGAGAAGCAGCATTTGATATACATGCTCAACCTT"
#> JDP2
#> "GAGGAAGAGGAGCGAAGGAAAAGGCGCCGGGAGAAGAACAAAGTCGCAGCAGCCCGATGCCGGAACAAGAAGAAGGAGCGCACGGAGTTTCTGCAGCGGGAATCCGAGCGGCTGGAACTCATGAACGCAGAGCTGAAGACCCAGATTGAGGAGCTGAAGCAGGAGCGGCAGCAGCTCATCCTGATGCTGAACCGA"
#> ATF4
#> "GAGAAACTGGATAAGAAGCTGAAAAAAATGGAGCAAAACAAGACAGCAGCCACTAGGTACCGCCAGAAGAAGAGGGCGGAGCAGGAGGCTCTTACTGGTGAGTGCAAAGAGCTGGAAAAGAAGAACGAGGCTCTAAAAGAGAGGGCGGATTCCCTGGCCAAGGAGATCCAGTACCTGAAAGATTTGATAGAAGAG"
#> ATF5
#> "ACCCGAGGGGACCGCAAGCAAAAGAAGAGAGACCAGAACAAGTCGGCGGCTCTGAGGTACCGCCAGCGGAAGCGGGCAGAGGGTGAGGCCCTGGAGGGCGAGTGCCAGGGGCTGGAGGCACGGAATCGCGAGCTGAAGGAACGGGCAGAGTCCGTGGAGCGCGAGATCCAGTACGTCAAGGACCTGCTCATCGAG"
#> CREBZF
#> "AGTCCCCGGAAGGCGGCGGCGGCCGCTGCCCGCCTTAATCGACTGAAGAAGAAGGAGTACGTGATGGGGCTGGAGAGTCGAGTCCGGGGTCTGGCAGCCGAGAACCAGGAGCTGCGGGCCGAGAATCGGGAGCTGGGCAAACGCGTACAGGCACTGCAGGAGGAGAGTCGCTACCTACGGGCAGTCTTAGCCAAC"
#> BATF2
#> "CCCAAGGAGCAACAAAGGCAGCTGAAGAAGCAGAAGAACCGGGCAGCCGCCCAGCGAAGCCGGCAGAAGCACACAGACAAGGCAGACGCCCTGCACCAGCAGCACGAGTCTCTGGAAAAAGACAACCTCGCCCTGCGGAAGGAGATCCAGTCCCTGCAGGCCGAGCTGGCGTGGTGGAGCCGGACCCTGCACGTG"
#> BATF3
#> "GAGGATGATGACAGGAAGGTCCGAAGGAGAGAAAAAAACCGAGTTGCTGCTCAGAGAAGTCGGAAGAAGCAGACCCAGAAGGCTGACAAGCTCCATGAGGAATATGAGAGCCTGGAGCAAGAAAACACCATGCTGCGGAGAGAGATCGGGAAGCTGACAGAGGAGCTGAAGCACCTGACAGAGGCACTGAAGGAG"
#> CEBPE
#> "AAAGATAGCCTTGAGTACCGGCTGAGGCGGGAGCGCAACAACATCGCCGTGCGCAAGAGCCGAGACAAGGCCAAGAGGCGCATTCTGGAGACGCAGCAGAAGGTGCTGGAGTACATGGCAGAGAACGAGCGCCTCCGCAGCCGCGTGGAGCAGCTCACCCAGGAGCTAGACACCCTCCGCAACCTCTTCCGCCAG"
#> BACH1
#> "CTGGATTGTATCCATGATATTCGAAGAAGAAGTAAAAACAGAATTGCTGCACAGCGCTGTCGCAAGAGAAAACTTGACTGTATACAGAATCTTGAATCAGAAATTGAGAAGCTGCAAAGTGAAAAGGAGAGCTTGTTGAAGGAAAGAGATCACATTTTGTCAACTCTGGGTGAGACAAAGCAGAACCTAACTGGA"
#> BACH2
#> "TTAGAGTTTATTCATGATGTCCGACGGCGCAGCAAGAACCGCATCGCGGCCCAGCGCTGCCGCAAAAGGAAACTGGACTGTATTCAGAATTTAGAATGTGAAATCCGCAAATTGGTGTGTGAGAAAGAGAAACTGTTGTCAGAGAGGAATCAACTGAAAGCATGCATGGGGGAACTGTTGGACAACTTCTCCTGC"
#> NFE2L1
#> "CTGAGCCTCATCCGAGACATCCGGCGCCGGGGCAAGAACAAGATGGCGGCGCAGAACTGCCGCAAGCGCAAGCTGGACACCATCCTGAATCTGGAGCGTGATGTGGAGGACCTGCAGCGTGACAAAGCCCGGCTGCTGCGGGAGAAAGTGGAGTTCCTGCGCTCCCTGCGACAGATGAAGCAGAAGGTCCAGAGC"
#> NFE2
#> "CTAGCGCTAGTCCGGGACATCCGACGACGGGGCAAAAACAAGGTGGCAGCCCAGAACTGCCGCAAGAGGAAGCTGGAAACCATTGTGCAGCTGGAGCGGGAGCTGGAGCGGCTGACCAATGAACGGGAGCGGCTTCTCAGGGCCCGCGGGGAGGCAGACCGGACCCTGGAGGTCATGCGCCAACAGCTGACAGAG"
#> NFIL3
#> "AAGAAAGATGCTATGTATTGGGAAAAAAGGCGGAAAAATAATGAAGCTGCCAAAAGATCTCGTGAGAAGCGTCGACTGAATGACCTGGTTTTAGAGAACAAACTAATTGCACTGGGAGAAGAAAACGCCACTTTAAAAGCTGAGCTGCTTTCACTAAAATTAAAGTTTGGTTTAATTAGCTCCACAGCATATGCT"
#> FOS
#> "GAAGAAGAAGAGAAAAGGAGAATCCGAAGGGAAAGGAATAAGATGGCTGCAGCCAAATGCCGCAACCGGAGGAGGGAGCTGACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTAGAGTTCATCCTGGCAGCT"
#> FOSB
#> "GAGGAAGAGGAGAAGCGAAGGGTGCGCCGGGAACGAAATAAACTAGCAGCAGCTAAATGCAGGAACCGGCGGAGGGAGCTGACCGACCGACTCCAGGCGGAGACAGATCAGTTGGAGGAAGAAAAAGCAGAGCTGGAGTCGGAGATCGCCGAGCTCCAAAAGGAGAAGGAACGTCTGGAGTTTGTGCTGGTGGCC"
#> FOSL1
#> "GAGGAAGAGGAGCGCCGCCGAGTAAGGCGCGAGCGGAACAAGCTGGCTGCGGCCAAGTGCAGGAACCGGAGGAAGGAACTGACCGACTTCCTGCAGGCGGAGACTGACAAACTGGAAGATGAGAAATCTGGGCTGCAGCGAGAGATTGAGGAGCTGCAGAAGCAGAAGGAGCGCCTAGAGCTGGTGCTGGAAGCC"
#> FOSL2
#> "GAAGAGGAGGAGAAGCGTCGCATCCGGCGGGAGAGGAACAAGCTGGCTGCAGCCAAGTGCCGGAACCGACGCCGGGAGCTGACAGAGAAGCTGCAGGCGGAGACAGAGGAGCTGGAGGAGGAGAAGTCAGGCCTGCAGAAGGAGATTGCTGAGCTGCAGAAGGAGAAGGAGAAGCTGGAGTTCATGTTGGTGGCT"
#> MAFB
#> "GTGATCCGCCTGAAGCAGAAGCGGCGGACCCTGAAGAACCGGGGCTACGCCCAGTCTTGCAGGTATAAACGCGTCCAGCAGAAGCACCACCTGGAGAATGAGAAGACGCAGCTCATTCAGCAGGTGGAGCAGCTTAAGCAGGAGGTGTCCCGGCTGGCCCGCGAGAGAGACGCCTACAAGGTCAAGTGCGAGAAA"
#> JUN
#> "CAGGAGCGGATCAAGGCGGAGAGGAAGCGCATGAGGAACCGCATCGCTGCCTCCAAGTGCCGAAAAAGGAAGCTGGAGAGAATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTTAAACAGAAAGTCATGAAC"
#> JUNB
#> "CAAGAGCGCATCAAAGTGGAGCGCAAGCGGCTGCGGAACCGGCTGGCGGCCACCAAGTGCCGGAAGCGGAAGCTGGAGCGCATCGCGCGCCTGGAGGACAAGGTGAAGACGCTCAAGGCCGAGAACGCGGGGCTGTCGAGTACCGCCGGCCTCCTCCGGGAGCAGGTGGCCCAGCTCAAACAGAAGGTCATGACC"
#> JUND
#> "CAGGAGCGCATCAAGGCGGAGCGCAAGCGGCTGCGCAACCGCATCGCCGCCTCCAAGTGCCGCAAGCGCAAGCTGGAGCGCATCTCGCGCCTGGAAGAGAAAGTGAAGACCCTCAAGAGTCAGAACACGGAGCTGGCGTCCACGGCGAGCCTGCTGCGCGAGCAGGTGGCGCAGCTCAAGCAGAAAGTCCTCAGC"
#> CREB3
#> "GAACAAATTCTGAAACGTGTGCGGAGGAAGATTCGAAATAAAAGATCTGCTCAAGAGAGCCGCAGGAAAAAGAAGGTGTATGTTGGGGGTTTAGAGAGCAGGGTCTTGAAATACACAGCCCAGAATATGGAGCTTCAGAACAAAGTACAGCTTCTGGAGGAACAGAATTTGTCCCTTCTAGATCAACTGAGGAAA"
#> HLF
#> "CTGAAGGATGACAAGTACTGGGCAAGGCGCAGAAAGAACAACATGGCAGCCAAGCGCTCCCGCGACGCCCGGAGGCTGAAAGAGAACCAGATCGCCATCCGGGCCTCGTTCCTGGAGAAGGAGAACTCGGCCCTCCGCCAGGAGGTGGCTGACTTGAGGAAGGAGCTGGGCAAATGCAAGAACATACTTGCCAAG"
#> MAFG
#> "ATCGTCCAGCTGAAGCAGCGCCGGCGCACGCTCAAGAACCGCGGCTACGCTGCCAGCTGCCGCGTGAAGCGGGTGACGCAGAAGGAGGAGCTGGAGAAGCAGAAGGCGGAGCTGCAGCAGGAGGTGGAGAAGCTGGCCTCAGAGAACGCCAGCATGAAGCTGGAGCTCGACGCGCTGCGCTCCAAGTACGAGGCG"
#> MAFK
#> "GTGACCCGCCTGAAGCAGCGTCGGCGCACACTCAAGAACCGCGGCTACGCGGCCAGCTGCCGCATCAAGCGGGTGACGCAGAAGGAGGAGCTGGAGCGGCAGCGCGTGGAGCTGCAGCAGGAGGTGGAGAAGCTGGCGCGTGAGAACAGCAGCATGCGGCTGGAGCTGGACGCCCTGCGCTCCAAGTACGAGGCG"
#> XBP1
#> "AGCCCCGAGGAGAAGGCGCTGAGGAGGAAACTGAAAAACAGAGTAGCAGCTCAGACTGCCAGAGATCGAAAGAAGGCTCGAATGAGTGAGCTGGAACAGCAAGTGGTAGATTTAGAAGAAGAGAACCAAAAACTTTTGCTAGAAAATCAGCTTTTACGAGAGAAAACTCATGGCCTTGTAGTTGAGAACCAGGAG"
#> ATF6
#> "ATTGCTGTGCTAAGGAGACAGCAACGTATGATAAAAAATCGAGAATCCGCTTGTCAGTCTCGCAAGAAGAAGAAAGAATATATGCTAGGGTTAGAGGCGAGATTAAAGGCTGCCCTCTCAGAAAACGAGCAACTGAAGAAAGAAAATGGAACACTGAAGCGGCAGCTGGATGAAGTTGTGTCAGAGAACCAGAGG"
#> ATF6B
#> "GCAAAGCTGCTGAAGCGGCAGCAGCGAATGATCAAGAACCGGGAGTCAGCCTGCCAGTCCCGGAGAAAGAAGAAAGAGTATCTGCAGGGACTGGAGGCTCGGCTGCAAGCAGTACTGGCTGACAACCAGCAGCTCCGCCGAGAGAATGCTGCCCTCCGGCGGCGGCTGGAGGCCCTGCTGGCTGAAAACAGCGAG"
#> CEBPA
#> "AAGAACAGCAACGAGTACCGGGTGCGGCGCGAGCGCAACAACATCGCGGTGCGCAAGAGCCGCGACAAGGCCAAGCAGCGCAACGTGGAGACGCAGCAGAAGGTGCTGGAGCTGACCAGTGACAATGACCGCCTGCGCAAGCGGGTGGAACAGCTGAGCCGCGAACTGGACACGCTGCGGGGCATCTTCCGCCAG"
#> CEBPB
#> "AAGCACAGCGACGAGTACAAGATCCGGCGCGAGCGCAACAACATCGCCGTGCGCAAGAGCCGCGACAAGGCCAAGATGCGCAACCTGGAGACGCAGCACAAGGTCCTGGAGCTCACGGCCGAGAACGAGCGGCTGCAGAAGAAGGTGGAGCAGCTGTCGCGCGAGCTCAGCACCCTGCGGAACTTGTTCAAGCAG"
#> CEBPD
#> "CGCGGCAGCCCCGAGTACCGGCAGCGGCGCGAGCGCAACAACATCGCCGTGCGCAAGAGCCGCGACAAGGCCAAGCGGCGCAACCAGGAGATGCAGCAGAAGTTGGTGGAGCTGTCGGCTGAGAACGAGAAGCTGCACCAGCGCGTGGAGCAGCTCACGCGGGACCTGGCCGGCCTCCGGCAGTTCTTCAAGCAG"
#> CEBPG
#> "CGAAACAGTGACGAGTATCGGCAACGCCGAGAGAGGAACAACATGGCTGTGAAAAAGAGCCGGTTGAAAAGCAAGCAGAAAGCACAAGACACACTGCAGAGAGTCAATCAGCTCAAAGAAGAGAATGAACGGTTGGAAGCAAAAATCAAATTGCTGACCAAGGAATTAAGTGTACTCAAAGATTTGTTTCTTGAG"
#> CREB1
#> "GAAGCAGCACGAAAGAGAGAGGTCCGTCTAATGAAGAACAGGGAAGCAGCTCGAGAGTGTCGTAGAAAGAAGAAAGAATATGTGAAATGTTTAGAAAACAGAGTGGCAGTGCTTGAAAATCAAAACAAGACATTGATTGAGGAGCTAAAAGCACTTAAGGACCTTTACTGCCACAAATCAGAT"
#> CREB3L1
#> "GAGAAGGCCTTGAAGAGAGTCCGGAGGAAAATCAAGAACAAGATCTCAGCCCAGGAGAGCCGTCGTAAGAAGAAGGAGTATGTGGAGTGTCTAGAAAAGAAGGTGGAGACATTTACATCTGAGAACAATGAACTGTGGAAGAAGGTGGAGACCCTGGAGAATGCCAACAGGACCCTGCTCCAGCAGCTGCAGAAA"
#> CREB3L2
#> "GAGAAGGCCCTGAAGAAAATTCGGAGGAAGATCAAGAATAAGATTTCTGCTCAGGAAAGTAGGAGAAAGAAGAAAGAATACATGGACAGCCTGGAGAAAAAAGTGGAGTCTTGTTCAACTGAGAACTTGGAGCTTCGGAAGAAGGTAGAGGTTCTAGAGAACACTAATAGGACTCTCCTTCAGCAACTCCAGAAG"
#> CREB3L3
#> "GAGCGAGTGCTGAAAAAAATCCGCCGGAAAATCCGGAACAAGCAGTCGGCGCAAGAAAGCAGGAAGAAGAAGAAGGAATATATCGATGGCCTGGAGACTCGGATGTCAGCTTGCACTGCTCAGAATCAGGAGTTACAGAGGAAAGTCTTGCATCTCGAGAAGCAAAACCTGTCCCTCTTGGAGCAACTGAAGAAA"
#> CREB3L4
#> "GAGAGGGTCCTCAAGAAGGTCAGGAGGAAAATCCGTAACAAGCAGTCAGCTCAGGACAGTCGGCGGCGGAAGAAGGAGTACATTGATGGGCTGGAGAGCAGGGTGGCAGCCTGTTCTGCACAGAACCAAGAATTACAGAAAAAAGTCCAGGAGCTGGAGAGGCACAACATCTCCTTGGTAGCTCAGCTCCGCCAG"
#> CREBL2
#> "CCAGCCAAAATTGACTTGAAAGCAAAACTTGAGAGGAGCCGGCAGAGTGCAAGAGAATGCCGAGCCCGAAAAAAGCTGAGATATCAGTATTTGGAAGAGTTGGTATCCAGTCGAGAAAGAGCTATATGTGCCCTCAGAGAGGAACTGGAAATGTACAAGCAGTGGTGCATGGCAATGGACCAAGGAAAAATCCCT"
#> CREBRF
#> "CCCTTAACAGCCCGACCAAGGTCAAGGAAGGAAAAAAATAAGCTGGCTTCCAGAGCTTGTCGGTTAAAGAAGAAAGCCCAGTATGAAGCTAATAAAGTGAAATTATGGGGCCTCAACACAGAATATGATAATTTATTGTTTGTAATCAACTCCATCAAGCAAGAGATTGTAAACCGGGTACAGAATCCAAGAGAT"
#> DBP
#> "CAGAAGGATGAGAAATACTGGAGCCGGCGGTACAAGAACAACGAGGCAGCCAAGCGGTCCCGTGACGCCCGGCGGCTCAAGGAGAACCAGATATCGGTGCGGGCGGCCTTCCTGGAGAAGGAGAACGCCCTGCTGCGGCAGGAAGTTGTGGCCGTGCGCCAGGAGCTGTCCCACTACCGCGCCGTGCTGTCCCGA"
#> NFE2L2
#> "CTTGCATTAATTCGGGATATACGTAGGAGGGGTAAGAATAAAGTGGCTGCTCAGAATTGCAGAAAAAGAAAACTGGAAAATATAGTAGAACTAGAGCAAGATTTAGATCATTTGAAAGATGAAAAAGAAAAATTGCTCAAAGAAAAAGGAGAAAATGACAAAAGCCTTCACCTACTGAAAAAACAACTCAGCACC"
#> NFE2L3
#> "GTCTCACTTATCCGTGACATCAGACGAAGAGGGAAAAATAAAGTTGCTGCGCAGAACTGTCGTAAACGCAAATTGGACATAATTTTGAATTTAGAAGATGATGTATGTAACTTGCAAGCAAAGAAGGAAACTCTTAAGAGAGAGCAAGCACAATGTAACAAAGCTATTAACATAATGAAACAGAAACTGCATGAC"
#> TEF
#> "CAGAAGGATGAAAAGTACTGGACAAGACGCAAGAAGAACAACGTGGCAGCTAAACGGTCACGGGATGCCCGGCGCCTGAAAGAGAATCAGATCACCATCCGGGCAGCCTTCCTGGAGAAGGAGAACACAGCCCTGCGGACGGAGGTGGCCGAGCTACGCAAGGAGGTGGGCAAGTGCAAGACCATCGTGTCCAAG"
#>
#> $wildTypeReverse
#> JUN
#> "ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"
#>
#> $constantForward
#> [1] ""
#>
#> $constantReverse
#> [1] ""
#>
#> $avePhredMinForward
#> [1] 20
#>
#> $avePhredMinReverse
#> [1] 20
#>
#> $variableNMaxForward
#> [1] 0
#>
#> $variableNMaxReverse
#> [1] 0
#>
#> $umiNMax
#> [1] 0
#>
#> $nbrMutatedCodonsMaxForward
#> [1] 0
#>
#> $nbrMutatedCodonsMaxReverse
#> [1] 1
#>
#> $nbrMutatedBasesMaxForward
#> [1] -1
#>
#> $nbrMutatedBasesMaxReverse
#> [1] -1
#>
#> $forbiddenMutatedCodonsForward
#> character(0)
#>
#> $forbiddenMutatedCodonsReverse
#> [1] "AAA" "AAT" "ACA" "ACT" "AGA" "AGT" "ATA" "ATT" "CAA" "CAT" "CCA" "CCT"
#> [13] "CGA" "CGT" "CTA" "CTT" "GAA" "GAT" "GCA" "GCT" "GGA" "GGT" "GTA" "GTT"
#> [25] "TAA" "TAT" "TCA" "TCT" "TGA" "TGT" "TTA" "TTT"
#>
#> $useTreeWTmatch
#> [1] FALSE
#>
#> $collapseToWTForward
#> [1] FALSE
#>
#> $collapseToWTReverse
#> [1] FALSE
#>
#> $mutatedPhredMinForward
#> [1] 0
#>
#> $mutatedPhredMinReverse
#> [1] 0
#>
#> $mutNameDelimiter
#> [1] "."
#>
#> $constantMaxDistForward
#> [1] -1
#>
#> $constantMaxDistReverse
#> [1] -1
#>
#> $umiCollapseMaxDist
#> [1] 0
#>
#> $filteredReadsFastqForward
#> [1] ""
#>
#> $filteredReadsFastqReverse
#> [1] ""
#>
#> $maxNReads
#> [1] -1
#>
#> $nThreads
#> [1] 1
#>
#> $chunkSize
#> [1] 100000
#>
#> $maxReadLength
#> [1] 1024
#>
#> $processingInfo
#> [1] "Processed by mutscan v0.99.2 on 2025-09-12 18:53:05.576733"
leujunt0$filterSummary
#> nbrTotal f1_nbrAdapter f2_nbrNoPrimer f3_nbrReadWrongLength
#> 1 1000 0 126 0
#> f4_nbrNoValidOverlap f5_nbrAvgVarQualTooLow f6_nbrTooManyNinVar
#> 1 0 0 76
#> f7_nbrTooManyNinUMI f8_nbrTooManyBestWTHits f9_nbrMutQualTooLow
#> 1 0 0 0
#> f10a_nbrTooManyMutCodons f10b_nbrTooManyMutBases f11_nbrForbiddenCodons
#> 1 195 0 3
#> f12_nbrTooManyMutConstant f13_nbrTooManyBestConstantHits nbrRetained
#> 1 0 0 600head(leujunt0$summaryTable)
#> mutantName
#> 1 ATF2.0.WT_JUN.13.CCC
#> 2 ATF2.0.WT_JUN.20.TGG
#> 3 ATF2.0.WT_JUN.32.CGG
#> 4 ATF2.0.WT_JUN.4.ATG
#> 5 ATF2.0.WT_JUN.5.ATG
#> 6 ATF2.0.WT_JUN.6.GAG
#> sequence
#> 1 GATCCTGATGAAAAAAGGAGAAAGTTTTTAGAGCGAAATAGAGCAGCAGCTTCAAGATGCCGACAAAAAAGGAAAGTCTGGGTTCAGTCTTTAGAGAAGAAAGCT_ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAACCCCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT
#> 2 GATCCTGATGAAAAAAGGAGAAAGTTTTTAGAGCGAAATAGAGCAGCAGCTTCAAGATGCCGACAAAAAAGGAAAGTCTGGGTTCAGTCTTTAGAGAAGAAAG_ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTGGACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT
#> 3 GATCCTGATGAAAAAAGGAGAAAGTTTTTAGAGCGAAATAGAGCAGCAGCTTCAAGATGCCGACAAAAAAGGAAAGTCTGGGTTCAGTCTTTAGAGAAGAAAGC_ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCGG
#> 4 GATCCTGATGAAAAAAGGAGAAAGTTTTTAGAGCGAAATAGAGCAGCAGCTTCAAGATGCCGACAAAAAAGGAAAGTCTGGGTTCAGTCTTTAGAGAAGAAAGC_ATCGCCCGGATGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT
#> 5 GATCCTGATGAAAAAAGGAGAAAGTTTTTAGAGCGAAATAGAGCAGCAGCTTCAAGATGCCGACAAAAAAGGAAAGTCTGGGTTCAGTCTTTAGAGAAGAAAGCT_ATCGCCCGGCTGATGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT
#> 6 GATCCTGATGAAAAAAGGAGAAAGTTTTTAGAGCGAAATAGAGCAGCAGCTTCAAGATGCCGACAAAAAAGGAAAGTCTGGGTTCAGTCTTTAGAGAAGAAAGCT_ATCGCCCGGCTGGAGGAGAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT
#> nbrReads maxNbrReads nbrUmis nbrMutBases nbrMutCodons nbrMutAAs varLengths
#> 1 1 1 0 2 1 1 105_96
#> 2 1 1 0 2 1 1 103_96
#> 3 1 1 0 2 1 1 104_96
#> 4 1 1 0 1 1 1 104_96
#> 5 1 1 0 2 1 1 105_96
#> 6 1 1 0 1 1 0 105_96
#> mutantNameBase mutantNameCodon mutantNameBaseHGVS
#> 1 ATF2.0.WT_JUN.37.C_JUN.39.C ATF2.0.WT_JUN.13.CCC ATF2:c_JUN:c.37_39delinsCCC
#> 2 ATF2.0.WT_JUN.59.G_JUN.60.G ATF2.0.WT_JUN.20.TGG ATF2:c_JUN:c.59_60delinsGG
#> 3 ATF2.0.WT_JUN.95.G_JUN.96.G ATF2.0.WT_JUN.32.CGG ATF2:c_JUN:c.95_96delinsGG
#> 4 ATF2.0.WT_JUN.10.A ATF2.0.WT_JUN.4.ATG ATF2:c_JUN:c.10C>A
#> 5 ATF2.0.WT_JUN.13.A_JUN.14.T ATF2.0.WT_JUN.5.ATG ATF2:c_JUN:c.13_14delinsAT
#> 6 ATF2.0.WT_JUN.18.G ATF2.0.WT_JUN.6.GAG ATF2:c_JUN:c.18A>G
#> mutantNameAA mutantNameAAHGVS mutationTypes
#> 1 ATF2.0.WT_JUN.13.P ATF2:p_JUN:p.(Ala13Pro) nonsynonymous
#> 2 ATF2.0.WT_JUN.20.W ATF2:p_JUN:p.(Ser20Trp) nonsynonymous
#> 3 ATF2.0.WT_JUN.32.R ATF2:p_JUN:p.(Leu32Arg) nonsynonymous
#> 4 ATF2.0.WT_JUN.4.M ATF2:p_JUN:p.(Leu4Met) nonsynonymous
#> 5 ATF2.0.WT_JUN.5.M ATF2:p_JUN:p.(Glu5Met) nonsynonymous
#> 6 ATF2.0.WT_JUN.0.WT ATF2:p_JUN:p silent
#> sequenceAA
#> 1 DPDEKRRKFLERNRAAASRCRQKRKVWVQSLEKKA_IARLEEKVKTLKPQNSELASTANMLREQVAQL
#> 2 DPDEKRRKFLERNRAAASRCRQKRKVWVQSLEKK_VARLEEKVKTLKAQNSELAWTANMLREQVAQL
#> 3 DPDEKRRKFLERNRAAASRCRQKRKVWVQSLEKK_EARLEEKVKTLKAQNSELASTANMLREQVAQR
#> 4 DPDEKRRKFLERNRAAASRCRQKRKVWVQSLEKK_EARMEEKVKTLKAQNSELASTANMLREQVAQL
#> 5 DPDEKRRKFLERNRAAASRCRQKRKVWVQSLEKKA_IARLMEKVKTLKAQNSELASTANMLREQVAQL
#> 6 DPDEKRRKFLERNRAAASRCRQKRKVWVQSLEKKA_IARLEEKVKTLKAQNSELASTANMLREQVAQL7 Combining samples
The digestFastqs() function processes each sample (pair of FASTQ files)
separately. In order to prepare the data for downstream statistical analysis
and plotting, the summarizeExperiment() function takes a named list
of outputs from digestFastqs(), and a data.frame with sample annotations,
and generates a SummarizedExperiment object,
with an assay containing either UMI or read counts. To illustrate this, we
process also the output sample for the TRANS experiment for which we
processed the input sample above, and feed both outputs to
summarizeExperiment().
transOutput <- digestFastqs(
fastqForward = file.path(datadir, "transOutput_1.fastq.gz"),
fastqReverse = file.path(datadir, "transOutput_2.fastq.gz"),
mergeForwardReverse = FALSE,
revComplForward = FALSE,
revComplReverse = FALSE,
adapterForward = "GGAAGAGCACACGTC",
adapterReverse = "GGAAGAGCGTCGTGT",
elementsForward = "SUCV",
elementsReverse = "SUCV",
elementLengthsForward = c(1, 10, 18, 96),
elementLengthsReverse = c(1, 8, 20, 96),
constantForward = "AACCGGAGGAGGGAGCTG",
constantReverse = "GAAAAAGGAAGCTGGAGAGA",
primerForward = "",
primerReverse = "",
avePhredMinForward = 20,
avePhredMinReverse = 20,
variableNMaxForward = 0,
variableNMaxReverse = 0,
umiNMax = 0,
wildTypeForward = c(FOS = "ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"),
wildTypeReverse = c(JUN = "ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"),
nbrMutatedCodonsMaxForward = 1,
nbrMutatedCodonsMaxReverse = 1,
forbiddenMutatedCodonsForward = "NNW",
forbiddenMutatedCodonsReverse = "NNW",
mutNameDelimiter = ".",
constantMaxDistForward = -1,
constantMaxDistReverse = -1,
verbose = FALSE
)
## Generate SummarizedExperiment object
se <- summarizeExperiment(
x = list(sample1 = transInput,
sample2 = transOutput),
coldata = data.frame(Name = c("sample1", "sample2"),
Condition = c("input", "output"),
Replicate = c("R1", "R1"),
OD = c(0.05, 1.5))
)
## The SummarizedExperiment contains a count matrix, and annotations
## for samples and variants in the `colData` and `rowData`,
## respectively.
head(assay(se, "counts"))
#> 6 x 2 sparse Matrix of class "dgCMatrix"
#> sample1 sample2
#> FOS.0.WT_JUN.0.WT . 1
#> FOS.0.WT_JUN.13.CCC 1 .
#> FOS.0.WT_JUN.13.CTC 1 .
#> FOS.0.WT_JUN.13.GCG . 1
#> FOS.0.WT_JUN.2.TCC 1 .
#> FOS.0.WT_JUN.20.ACC 1 .
Matrix::colSums(assay(se, "counts"))
#> sample1 sample2
#> 279 285
head(rowData(se))
#> DataFrame with 6 rows and 19 columns
#> mutantName sequence nbrMutBases
#> <character> <character> <character>
#> FOS.0.WT_JUN.0.WT FOS.0.WT_JUN.0.WT ACTGATACACTCCAAGCGGA.. 0
#> FOS.0.WT_JUN.13.CCC FOS.0.WT_JUN.13.CCC ACTGATACACTCCAAGCGGA.. 2
#> FOS.0.WT_JUN.13.CTC FOS.0.WT_JUN.13.CTC ACTGATACACTCCAAGCGGA.. 3
#> FOS.0.WT_JUN.13.GCG FOS.0.WT_JUN.13.GCG ACTGATACACTCCAAGCGGA.. 1
#> FOS.0.WT_JUN.2.TCC FOS.0.WT_JUN.2.TCC ACTGATACACTCCAAGCGGA.. 1
#> FOS.0.WT_JUN.20.ACC FOS.0.WT_JUN.20.ACC ACTGATACACTCCAAGCGGA.. 1
#> minNbrMutBases maxNbrMutBases nbrMutCodons minNbrMutCodons
#> <integer> <integer> <character> <integer>
#> FOS.0.WT_JUN.0.WT 0 0 0 0
#> FOS.0.WT_JUN.13.CCC 2 2 1 1
#> FOS.0.WT_JUN.13.CTC 3 3 1 1
#> FOS.0.WT_JUN.13.GCG 1 1 1 1
#> FOS.0.WT_JUN.2.TCC 1 1 1 1
#> FOS.0.WT_JUN.20.ACC 1 1 1 1
#> maxNbrMutCodons nbrMutAAs minNbrMutAAs maxNbrMutAAs
#> <integer> <character> <integer> <integer>
#> FOS.0.WT_JUN.0.WT 0 0 0 0
#> FOS.0.WT_JUN.13.CCC 1 1 1 1
#> FOS.0.WT_JUN.13.CTC 1 1 1 1
#> FOS.0.WT_JUN.13.GCG 1 0 0 0
#> FOS.0.WT_JUN.2.TCC 1 1 1 1
#> FOS.0.WT_JUN.20.ACC 1 1 1 1
#> mutantNameBase mutantNameBaseHGVS
#> <character> <character>
#> FOS.0.WT_JUN.0.WT FOS.0.WT_JUN.0.WT FOS:c_JUN:c
#> FOS.0.WT_JUN.13.CCC FOS.0.WT_JUN.37.C_JU.. FOS:c_JUN:c.37_39del..
#> FOS.0.WT_JUN.13.CTC FOS.0.WT_JUN.37.C_JU.. FOS:c_JUN:c.37_39del..
#> FOS.0.WT_JUN.13.GCG FOS.0.WT_JUN.39.G FOS:c_JUN:c.39T>G
#> FOS.0.WT_JUN.2.TCC FOS.0.WT_JUN.4.T FOS:c_JUN:c.4G>T
#> FOS.0.WT_JUN.20.ACC FOS.0.WT_JUN.58.A FOS:c_JUN:c.58T>A
#> mutantNameCodon mutantNameAA
#> <character> <character>
#> FOS.0.WT_JUN.0.WT FOS.0.WT_JUN.0.WT FOS.0.WT_JUN.0.WT
#> FOS.0.WT_JUN.13.CCC FOS.0.WT_JUN.13.CCC FOS.0.WT_JUN.13.P
#> FOS.0.WT_JUN.13.CTC FOS.0.WT_JUN.13.CTC FOS.0.WT_JUN.13.L
#> FOS.0.WT_JUN.13.GCG FOS.0.WT_JUN.13.GCG FOS.0.WT_JUN.0.WT
#> FOS.0.WT_JUN.2.TCC FOS.0.WT_JUN.2.TCC FOS.0.WT_JUN.2.S
#> FOS.0.WT_JUN.20.ACC FOS.0.WT_JUN.20.ACC FOS.0.WT_JUN.20.T
#> mutantNameAAHGVS sequenceAA mutationTypes
#> <character> <character> <character>
#> FOS.0.WT_JUN.0.WT FOS:p_JUN:p TDTLQAETDQLEDEKSALQT..
#> FOS.0.WT_JUN.13.CCC FOS:p_JUN:p.(Ala13Pro) TDTLQAETDQLEDEKSALQT.. nonsynonymous
#> FOS.0.WT_JUN.13.CTC FOS:p_JUN:p.(Ala13Leu) TDTLQAETDQLEDEKSALQT.. nonsynonymous
#> FOS.0.WT_JUN.13.GCG FOS:p_JUN:p TDTLQAETDQLEDEKSALQT.. silent
#> FOS.0.WT_JUN.2.TCC FOS:p_JUN:p.(Ala2Ser) TDTLQAETDQLEDEKSALQT.. nonsynonymous
#> FOS.0.WT_JUN.20.ACC FOS:p_JUN:p.(Ser20Thr) TDTLQAETDQLEDEKSALQT.. nonsynonymous
#> varLengths
#> <character>
#> FOS.0.WT_JUN.0.WT 96_96
#> FOS.0.WT_JUN.13.CCC 96_96
#> FOS.0.WT_JUN.13.CTC 96_96
#> FOS.0.WT_JUN.13.GCG 96_96
#> FOS.0.WT_JUN.2.TCC 96_96
#> FOS.0.WT_JUN.20.ACC 96_96
colData(se)
#> DataFrame with 2 rows and 20 columns
#> Name Condition Replicate OD nbrTotal f1_nbrAdapter
#> <character> <character> <character> <numeric> <integer> <integer>
#> sample1 sample1 input R1 0.05 1000 314
#> sample2 sample2 output R1 1.50 1000 366
#> f2_nbrNoPrimer f3_nbrReadWrongLength f4_nbrNoValidOverlap
#> <integer> <integer> <integer>
#> sample1 0 0 0
#> sample2 0 0 0
#> f5_nbrAvgVarQualTooLow f6_nbrTooManyNinVar f7_nbrTooManyNinUMI
#> <integer> <integer> <integer>
#> sample1 7 0 0
#> sample2 6 0 0
#> f8_nbrTooManyBestWTHits f9_nbrMutQualTooLow f10a_nbrTooManyMutCodons
#> <integer> <integer> <integer>
#> sample1 0 0 392
#> sample2 0 0 338
#> f10b_nbrTooManyMutBases f11_nbrForbiddenCodons
#> <integer> <integer>
#> sample1 0 8
#> sample2 0 5
#> f12_nbrTooManyMutConstant f13_nbrTooManyBestConstantHits nbrRetained
#> <integer> <integer> <integer>
#> sample1 0 0 279
#> sample2 0 0 285
## Count type (reads or UMIs)
metadata(se)$countType
#> [1] "umis"8 Collapsing count matrix to amino acids
The object generated
by summarizeExperiment() contains one row for each observed variant
(combination). This can be further collapsed by replacing the mutated codon by
the corresponding amino acid, and aggregating the counts corresponding to the
same mutated amino acid (combination).
se_collapsed <- collapseMutantsByAA(se)
head(assay(se_collapsed, "counts"))
#> sample1 sample2
#> FOS.0.WT_JUN.0.WT 1 3
#> FOS.0.WT_JUN.10.S 0 1
#> FOS.0.WT_JUN.12.L 0 1
#> FOS.0.WT_JUN.13.K 1 0
#> FOS.0.WT_JUN.13.L 1 0
#> FOS.0.WT_JUN.13.P 1 0
Matrix::colSums(assay(se_collapsed, "counts"))
#> sample1 sample2
#> 279 285
colData(se_collapsed)
#> DataFrame with 2 rows and 20 columns
#> Name Condition Replicate OD nbrTotal f1_nbrAdapter
#> <character> <character> <character> <numeric> <integer> <integer>
#> sample1 sample1 input R1 0.05 1000 314
#> sample2 sample2 output R1 1.50 1000 366
#> f2_nbrNoPrimer f3_nbrReadWrongLength f4_nbrNoValidOverlap
#> <integer> <integer> <integer>
#> sample1 0 0 0
#> sample2 0 0 0
#> f5_nbrAvgVarQualTooLow f6_nbrTooManyNinVar f7_nbrTooManyNinUMI
#> <integer> <integer> <integer>
#> sample1 7 0 0
#> sample2 6 0 0
#> f8_nbrTooManyBestWTHits f9_nbrMutQualTooLow f10a_nbrTooManyMutCodons
#> <integer> <integer> <integer>
#> sample1 0 0 392
#> sample2 0 0 338
#> f10b_nbrTooManyMutBases f11_nbrForbiddenCodons
#> <integer> <integer>
#> sample1 0 8
#> sample2 0 5
#> f12_nbrTooManyMutConstant f13_nbrTooManyBestConstantHits nbrRetained
#> <integer> <integer> <integer>
#> sample1 0 0 279
#> sample2 0 0 2859 Diagnostic plots
mutscan contains functionality for
generating a variety of diagnostic plots. Here we illustrate these using the
full CIS data set from [@Diss2018], which has been processed using
digestFastqs() as illustrated above, and summarized in a
SummarizedExperiment object provided with the package.
se <- readRDS(file.path(datadir, "GSE102901_cis_se.rds"))First, we can plot a summary of the filtering
process, indicating the number of reads that were filtered out by
(or retained after) each step of the mutscan filtering.
plotFiltering(se, valueType = "reads", onlyActiveFilters = TRUE,
plotType = "remaining", facetBy = "sample", numberSize = 3)
plotFiltering(se, valueType = "fractions", onlyActiveFilters = TRUE,
plotType = "filtered", facetBy = "step", numberSize = 3)
We can also generate a pairs plot displaying the correlation among the samples in the data set.
plotPairs(se, selAssay = "counts")
Additional plot functions can be used to visualize the total count per sample, across all variants, or the distribution of variant counts per sample.
plotTotals(se, selAssay = "counts")
plotDistributions(se, selAssay = "counts", plotType = "density",
pseudocount = 1)
Finally, we can create a full QC report as follows:
generateQCReport(se, outFile = tempfile(fileext = ".html"))10 Calculating fitness scores
The function calculateFitnessScore() can be used to calculate fitness scores
as described in [@Diss2018]. The function requires the user to specify a
pairingCol, containing the replicate ID for each sample; one or more
ODCols, containing the optical density for each sample, and a
comparison, which is a character vector of length 3 specifying the
comparison to perform, of the form (groupColumn, numerator,
denominator). Here, groupColumn is the name of the column in colData(se)
that contains the grouping information, and numerator and denominator
specify the values of this column representing the two groups to be compared.
Here, we illustrate the application of calculateFitnessScore() on the
SummarizedExperiment object containing all the three CIS
replicates from [@Diss2018].
se_collapsed <- collapseMutantsByAA(se)
ppis <- calculateFitnessScore(
se = se_collapsed, pairingCol = "Replicate",
ODCols = c("OD1", "OD2"),
comparison = c("Condition", "cis_output", "cis_input"),
WTrows = "f.0.WT"
)
head(ppis[order(abs(rowMeans(ppis)), decreasing = TRUE), , drop = FALSE])
#> cis_output_vs_cis_input_repl1 cis_output_vs_cis_input_repl2
#> f.8.M 1.079682 1.077555
#> f.8.A 1.077780 1.087804
#> f.2.Q 1.093753 1.065724
#> f.3.M 1.084323 1.078138
#> f.9.K 1.085896 1.037838
#> f.13.K 1.043217 1.046260
#> cis_output_vs_cis_input_repl3
#> f.8.M 1.117395
#> f.8.A 1.103149
#> f.2.Q 1.065259
#> f.3.M 1.050588
#> f.9.K 1.062279
#> f.13.K 1.095404
## The PPI score for the WT sequence is 1, by construction
ppis["f.0.WT", , drop = FALSE]
#> cis_output_vs_cis_input_repl1 cis_output_vs_cis_input_repl2
#> f.0.WT 1 1
#> cis_output_vs_cis_input_repl3
#> f.0.WT 111 Scoring mutations with edgeR or limma
As an alternative to the fitness scoring, mutscan can be used to model the
observed counts using a generalized linear model (with edgeR) or a general
linear model (with limma) and estimate a logFC and a p-value for the
enrichment of each variant betwen two conditions (or more generally, in
association with any predictor), compared to one or more WT sequences. Note
that for this, at least two replicates are required.
We start by looking at the design matrix, in order to determine which of the
coefficients to specify for the testing in calculateRelativeFC(). For more
information about how to set up and interpret design matrices in edgeR or
limma, see e.g.Â
Law et al (2020),
or Soneson et al (2020).
model.matrix(~ Replicate + Condition,
data = colData(se_collapsed))
#> (Intercept) Replicate Conditioncis_output
#> SRR5952435 1 1 0
#> SRR5952436 1 2 0
#> SRR5952437 1 3 0
#> SRR5952438 1 1 1
#> SRR5952439 1 2 1
#> SRR5952440 1 3 1
#> attr(,"assign")
#> [1] 0 1 2
#> attr(,"contrasts")
#> attr(,"contrasts")$Condition
#> [1] "contr.treatment"Next, we apply either edgeR or limma to extract the logFCs of the mutants,
compared to the wildtype sequence.
## edgeR
edger_scores <- calculateRelativeFC(
se = se_collapsed,
design = model.matrix(~ Replicate + Condition,
data = colData(se_collapsed)),
coef = "Conditioncis_output", pseudocount = 1, WTrows = "f.0.WT",
method = "edgeR")
head(edger_scores[order(edger_scores$PValue), , drop = FALSE])
#> logFC logCPM F PValue FDR logFC_shrunk
#> f.27.* -4.503529 11.12474 15282.12 1.382896e-23 5.691459e-21 -4.502824
#> f.7.G -4.184471 11.14499 14270.70 2.286048e-23 5.691459e-21 -4.183994
#> f.28.* -4.387484 11.14116 13973.55 2.667871e-23 5.691459e-21 -4.386944
#> f.7.* -4.460187 10.87738 13025.46 4.468224e-23 5.883624e-21 -4.459450
#> f.14.* -4.373749 10.90447 12836.43 4.974299e-23 5.883624e-21 -4.372984
#> f.18.* -4.823126 10.52294 12485.43 6.097352e-23 5.883624e-21 -4.821931
#> df.total df.prior df.test
#> f.27.* 14.69484 11.69481 1
#> f.7.G 14.69484 11.69481 1
#> f.28.* 14.69484 11.69481 1
#> f.7.* 14.69484 11.69481 1
#> f.14.* 14.69484 11.69481 1
#> f.18.* 14.69481 11.69481 1
## As before, the WT sequence has a logFC close to 0, by construction
edger_scores["f.0.WT", , drop = FALSE]
#> logFC logCPM F PValue FDR logFC_shrunk df.total df.prior
#> f.0.WT 8.76768e-16 19.30433 0 1 1 -2.393544e-15 14.69484 11.69481
#> df.test
#> f.0.WT 1
## limma
limma_scores <- calculateRelativeFC(
se = se_collapsed,
design = model.matrix(~ Replicate + Condition,
data = colData(se_collapsed)),
coef = "Conditioncis_output", pseudocount = 1, WTrows = "f.0.WT",
method = "limma")
head(limma_scores[order(limma_scores$P.Value), , drop = FALSE])
#> logFC CI.L CI.R AveExpr t P.Value
#> f.27.* -4.503210 -4.622021 -4.384400 9.963039 -84.07624 5.299650e-16
#> f.4.F -2.897232 -2.974240 -2.820225 11.116524 -83.45582 5.720907e-16
#> f.28.* -4.387941 -4.505027 -4.270855 10.031042 -83.13096 5.956021e-16
#> f.7.G -4.183176 -4.296040 -4.070312 10.124077 -82.21606 6.677075e-16
#> f.21.G -4.080241 -4.196485 -3.963998 10.116854 -77.86148 1.171161e-15
#> f.29.* -4.469138 -4.596492 -4.341784 10.036584 -77.84277 1.174071e-15
#> adj.P.Val B se.logFC df.total df.prior
#> f.27.* 1.068332e-13 27.23208 0.05356103 10.34072 7.340722
#> f.4.F 1.068332e-13 27.37901 0.03471576 10.34072 7.340722
#> f.28.* 1.068332e-13 27.16244 0.05278347 10.34072 7.340722
#> f.7.G 1.068332e-13 27.10387 0.05088028 10.34072 7.340722
#> f.21.G 1.077888e-13 26.58967 0.05240385 10.34072 7.340722
#> f.29.* 1.077888e-13 26.53937 0.05741237 10.34072 7.340722
## As before, the WT sequence has a logFC close to 0, by construction
limma_scores["f.0.WT", , drop = FALSE]
#> logFC CI.L CI.R AveExpr t P.Value
#> f.0.WT 2.834537e-08 -0.02280783 0.02280789 19.29458 2.75679e-06 0.9999979
#> adj.P.Val B se.logFC df.total df.prior
#> f.0.WT 0.9999979 -10.20615 0.01028202 10.34072 7.340722mutscan also contains functions for plotting the results of the statistical
testing - in particular, MA (mean-difference) plots and volcano plots can be
easily generated.
plotMeanDiff(edger_scores, pointSize = "large")
plotVolcano(edger_scores, pointSize = "large")
12 FAQ
12.0.1 Can digestFastqs process a sample where the reads are spread across multiple (pairs of) FASTQ files?
Yes, you can specify a vector of FASTQ files to fastqForward/fastqReverse.
In this case, the reads from all (pairs of) files will be analyzed as if they
came from a single FASTQ file. In case of paired-end data, take care to specify
the forward and reverse FASTQ files in the same order.
13 Session info
This vignette was compiled on the following system:
sessionInfo()
#> R version 4.5.1 Patched (2025-08-23 r88802)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] mutscan_0.99.2 SummarizedExperiment_1.39.1
#> [3] Biobase_2.69.0 GenomicRanges_1.61.2
#> [5] Seqinfo_0.99.2 IRanges_2.43.0
#> [7] S4Vectors_0.47.0 BiocGenerics_0.55.1
#> [9] generics_0.1.4 MatrixGenerics_1.21.0
#> [11] matrixStats_1.5.0 BiocStyle_2.37.1
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 xfun_0.53 bslib_0.9.0
#> [4] ggplot2_4.0.0 htmlwidgets_1.6.4 ggrepel_0.9.6
#> [7] GGally_2.4.0 lattice_0.22-7 vctrs_0.6.5
#> [10] tools_4.5.1 bitops_1.0-9 parallel_4.5.1
#> [13] tibble_3.3.0 pkgconfig_2.0.3 Matrix_1.7-4
#> [16] RColorBrewer_1.1-3 S7_0.2.0 lifecycle_1.0.4
#> [19] compiler_4.5.1 farver_2.1.2 Biostrings_2.77.2
#> [22] Rsamtools_2.25.3 tinytex_0.57 statmod_1.5.0
#> [25] codetools_0.2-20 htmltools_0.5.8.1 sass_0.4.10
#> [28] yaml_2.3.10 pillar_1.11.0 crayon_1.5.3
#> [31] jquerylib_0.1.4 tidyr_1.3.1 DT_0.34.0
#> [34] BiocParallel_1.43.4 limma_3.65.4 DelayedArray_0.35.2
#> [37] cachem_1.1.0 magick_2.9.0 abind_1.4-8
#> [40] ggstats_0.10.0 metapod_1.17.0 locfit_1.5-9.12
#> [43] tidyselect_1.2.1 digest_0.6.37 dplyr_1.1.4
#> [46] purrr_1.1.0 bookdown_0.44 labeling_0.4.3
#> [49] fastmap_1.2.0 grid_4.5.1 cli_3.6.5
#> [52] SparseArray_1.9.1 magrittr_2.0.4 S4Arrays_1.9.1
#> [55] dichromat_2.0-0.1 withr_3.0.2 edgeR_4.7.4
#> [58] scales_1.4.0 rmarkdown_2.29 XVector_0.49.0
#> [61] evaluate_1.0.5 knitr_1.50 rlang_1.1.6
#> [64] Rcpp_1.1.0 glue_1.8.0 BiocManager_1.30.26
#> [67] csaw_1.43.1 jsonlite_2.0.0 R6_2.6.1