In fmicompbio/mutscan: Preprocessing and Analysis of Deep Mutational Scanning Data

knitr::opts_chunk$set(
    collapse = TRUE,
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library(SummarizedExperiment)
library(mutscan)

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.

knitr::include_graphics("mutscan-overview.001.png")

Example data

The mutscan package contains several small example FASTQ files representing different types of experiments:

datadir <- system.file("extdata", package = "mutscan")
dir(datadir)

• 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 - a SummarizedExperiment object 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• leujunt0_{1,2}.fastq.gz - unpublished data; the forward read corresponds to an unmutated sequence of one of 46 leucine zipper sequences, and the reverse read corresponds to mutated JUN sequences.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.

Processing Multiplexed Assays of Variant Effect data

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 skip
• U (UMI) - UMI sequence
• C (constant) - constant sequence, can be used e.g. to estimate the sequencing error rate if available
• V (variable) - nucleotides corresponding to the variable region
• P (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.

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 avePhredMin in 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 wildTypeForward and/or wildTypeReverse), 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 nbrMutatedCodonsMax or the number of mutated nucleotides exceeds nbrMutatedBasesMax.
• 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).

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

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

The 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)

Finally, 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, ]

The errorStatistics output can be used to estimate the sequencing error rate:

(propErrorsConstantF <- sum(transInput$errorStatistics$nbrMismatchForward) /
   (nchar(transInput$parameters$constantForward) * transInput$filterSummary$nbrRetained))
(propErrorsConstantR <- sum(transInput$errorStatistics$nbrMismatchReverse) /
   (nchar(transInput$parameters$constantReverse) * transInput$filterSummary$nbrRetained))

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
)

cisInput$parameters
cisInput$filterSummary
cisInput$errorStatistics[rowSums(cisInput$errorStatistics[, -1]) != 0, ]

The 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)

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
)

leujunt0$parameters
leujunt0$filterSummary

head(leujunt0$summaryTable)

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"))
Matrix::colSums(assay(se, "counts"))
head(rowData(se))
colData(se)

## Count type (reads or UMIs)
metadata(se)$countType

Collapsing count matrix to amino acids

The \link{\code[SummarizedExperiment]{SummarizedExperiment}} 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"))
Matrix::colSums(assay(se_collapsed, "counts"))
colData(se_collapsed)

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"))

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])

## The PPI score for the WT sequence is 1, by construction
ppis["f.0.WT", , drop = FALSE]

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. Law et al (2020), or Soneson et al (2020).

model.matrix(~ Replicate + Condition,
             data = colData(se_collapsed))

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])
## As before, the WT sequence has a logFC close to 0, by construction
edger_scores["f.0.WT", , drop = FALSE]

## 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])
## As before, the WT sequence has a logFC close to 0, by construction
limma_scores["f.0.WT", , drop = FALSE]

mutscan 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")

FAQ

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.

Session info

This vignette was compiled on the following system:

sessionInfo()

fmicompbio/mutscan documentation built on Oct. 24, 2024, 2:41 p.m.