R/CrisprSet.R

In CrispRVariants: Tools for counting and visualising mutations in a target location

# CrisprSet class definition -----
#'@title CrisprSet class
#'@description A ReferenceClass container for holding a set of narrowed alignments,
#'each corresponding to the same target region.  Individual samples are
#'represented as CrisprRun objects.  CrisprRun objects with no on-target
#'reads are excluded.
#'CrisprSet objects are constructed with \code{\link{readsToTarget}} or
#'\code{\link{readsToTargets}}.  For most use cases, a CrisprSet object should not
#'be initialized directly.
#'@param crispr.runs A list of CrisprRun objects, typically representing individual samples
#'within an experiment
#'@param reference The reference sequence, must be the same length as the target region
#'@param target The target location (GRanges).  Variants will be counted over this region.
#'Need not correspond to the guide sequence.
#'@param rc Should the alignments be reverse complemented,
#'i.e. displayed w.r.t the reverse strand? (default: FALSE)
#'@param names A list of names for each of the samples, e.g. for displaying in plots.
#'If not supplied, the names of the crispr.runs are used, which default to the filenames
#'of the bam files if available (Default: NULL)
#'@param renumbered Should the variants be renumbered using target.loc as the zero point?
#'If TRUE, variants are described by the location of their 5'-most base with respect to the
#'target.loc.  A 3bp deletion starting 5bp 5' of the cut site would be labelled
#' as -5:3D (Default: TRUE)
#'@param target.loc The location of the Cas9 cut site with respect to the supplied target.
#'(Or some other central location).  Can be displayed on plots and used as the zero point
#'for renumbering variants. For a target region with the PAM location from bases 21-23,
#'the target.loc is base 17 (default: 17)
#'@param match.label Label for sequences with no variants (default: "no variant")
#'@param mismatch.label Label for sequences with only single nucleotide variants
#'  (default: "SNV")
#'@param bpparam A BiocParallel parameter object specifying how many cores to use.
#'The parallelisable step is calling SNVs. Parallelisation is by sample.
#' (default: SerialParam, i.e. no parallelization)
#'@param verbose If true, prints information about initialisation progress (default: TRUE)
#'@field crispr_runs A list of CrisprRun objects, typically corresponding to samples
#'of an experiment.
#'@field ref The reference sequence for the target region, as a Biostrings::DNAString object
#'@field cigar_freqs A matrix of counts for each variant
#'@field target The target location, as a GRanges object
#'@author Helen Lindsay
#'@seealso \code{\link{readsToTarget}} and \code{\link{readsToTargets}}
#'for initialising a CrisprSet, \code{\link{CrisprRun}} container for sample data.
#'@examples
#'# Load the metadata table
#'md_fname <- system.file("extdata", "gol_F1_metadata_small.txt", package = "CrispRVariants")
#'md <- read.table(md_fname, sep = "\t", stringsAsFactors = FALSE)
#'
#'# Get bam filenames and their full paths
#'bam_fnames <- sapply(md$bam.filename, function(fn){
#'  system.file("extdata", fn, package = "CrispRVariants")})
#'
#'reference <- Biostrings::DNAString("GGTCTCTCGCAGGATGTTGCTGG")
#'gd <- GenomicRanges::GRanges("18", IRanges::IRanges(4647377, 4647399), strand = "+")
#'
#'crispr_set <- readsToTarget(bam_fnames, target = gd, reference = reference,
#'                            names = md$experiment.name, target.loc = 17)
#'@export CrisprSet
#'@exportClass CrisprSet
CrisprSet = setRefClass(
  Class = "CrisprSet",
  fields = c(crispr_runs = "list",
             ref = "DNAString",
             insertion_sites = "data.frame",
             cigar_freqs = "matrix",
             target = "GRanges",
             genome_to_target = "integer",
             pars = "list")
) # -----

# CrisprSet initializer -----
CrisprSet$methods(
  initialize = function(crispr.runs, reference, target, rc = FALSE,
                        names = NULL, renumbered = TRUE, target.loc = 17,
                        match.label = "no variant", mismatch.label = "SNV",
                        bpparam = BiocParallel::SerialParam(),
                        verbose = TRUE, ...){

    # Assume: initializer not called directly, inputs checked by readsToTarget
    # To do: test if checks for empty CrisprRuns are redundant
    # Note: genomeToTargetLocs uses the strand of the target,
    # change strand of target if orientation = "positive"?
    # TO DO: REMOVE VARIANT COUNTING PARAMS FROM INITIALISER
    
    # Setting up code to allow for a Cpf1 setting in future
    enzyme <- "Cas9"

    if (isTRUE(verbose)){
      message(sprintf("Initialising CrisprSet %s:%s-%s with %s samples",
                  as.character(seqnames(target)), start(target), end(target),
                  length(crispr.runs)))
    }

    if (isTRUE(renumbered) & is.na(target.loc)){
      stop("Must specify target.loc for renumbering variant locations.\n",
           "The target.loc is the zero point with respect to the reference string.\n",
           "This is typically 17 for a 23 bp Crispr-Cas9 guide sequence")
    }

    target <<- target
    ref <<- reference
    pars <<- list("match_label" = match.label, "target.loc" = target.loc,
                  "mismatch_label" = mismatch.label, "renumbered" = renumbered,
                  "all_chimeric" = FALSE, "rc" = rc, "enzyme" = enzyme)

    crispr_runs <<- crispr.runs

    if (is.null(names)) {
      names(.self$crispr_runs) <- sapply(.self$crispr_runs, function(x) x$name)
    }else {
      names(.self$crispr_runs) <- names
    }
    
    nonempty_runs <- sapply(.self$crispr_runs, function(x) {
      ! ( length(x$alns) == 0 & length(x$chimeras) == 0 )
    })

    .self$crispr_runs <<- .self$crispr_runs[nonempty_runs]
    if (length(.self$crispr_runs) == 0) stop("no on target reads in any sample")

    is_plus <- as.character(strand(target)) %in% c("+", "*")
    
    if (unique(sapply(.self$crispr_runs, function(x) length(x$alns)) == 0)){
      pars["all_chimeric"] <<- TRUE
      dummy <- .self$.genomeToTargetLocs(target.loc, start(target),
                                end(target), plus_strand = is_plus,
                                gs = start(target), ge = end(target))
      return()
    }
                
    dummy <- .self$.genomeToTargetLocs(target.loc, start(target),
                                       end(target), plus_strand = is_plus)

    
  }, # -----

  # show -----
  show = function(){
    cat(sprintf(paste0("CrisprSet object containing %s CrisprRun samples\n",
                         "Target location:\n"), length(.self$crispr_runs)))
    print(.self$target)
    print("Most frequent variants:")
    print(.self$.getFilteredCigarTable(top.n = 6))
  }, # -----

  # .checkSamplesAndAlleles -----
  .checkSamplesAndAlleles = function(alleles, samples){
    # Not currently used
    # checks before removing alleles from particular samples 
    
    if (is.null(samples)) return()
    stopifnot(identical(length(alleles), length(samples)))
    if (is.character(samples) & ! 
        all(samples %in% names(.self$crispr_runs))){
      stop("Samples should be either sample names or numeric indices")
    }
    if (is.numeric(samples)){
      nsamples <- length(.self$crispr_runs)
      too_large <- samples > nsamples
      if (any(too_large)){
        stop(sprintf("Cannot select sample %s as there are only %s samples",
                     which(too_large)[1], nsamples))
      }
    }
    missing_alleles <- setdiff(unique(alleles), rownames(.self$cigar_freqs))
    if (any(missing_alleles)){
      warning(sprintf("Alleles not found: %s", 
                      paste(missing_alleles, sep = ", ")))
    }
  }, # -----
  
  # .setCigarLabels -----
  setCigarLabels = function(labels = NULL, label.func = NULL, verbose = FALSE, ...){
    #Optional params include:
    #split.snv Should single nucleotide variants (SNVs) be shown for
    #          reads without an insertion or deletion? (default: TRUE)
    #upstream.snv  If split.snv = TRUE, how many bases upstream of the
    #              target.loc should SNVs be shown?  (default: 8)
    #downstream.snv If split.snv = TRUE, how many bases downstream of 
    #               the target.loc should SNVs be shown? (default: 6)
    
      # To do: add checks that replacement alleles still have same indel combinations
      # or write a consensus function for alleles with different indels 
      # Make return value self instead of labels
      # (long term switching to pure S4 class)
    
      if (isTRUE(verbose)) message("Renaming cigar strings\n")
    
      if (! is.null(label.func)){
        labels <- label.func(alns(.self), ...)
      }

      if (! is.null(labels)){
        if (! all(lengths(labels) == lengths(alns(.self)))){
          stop("Lengths of labels not identical to length of alignments")
        }
        
        dummy <- lapply(seq_along(labels), function(i){
          crun <- .self$crispr_runs[[i]]
          mcols(crun$alns)$allele <- labels[[i]]
          crun$field("cigar_labels", labels[[i]])
        })
        cig_by_run <- labels
      
      } else { 
        cig_by_run <- .defaultCigarLabels(.self, ...)
      }
    
      if (isTRUE(verbose)) message("Counting variant combinations\n")
      .self$.countCigars(cig_by_run)
      .self$.getInsertions()
      invisible(cig_by_run)
  }, # -----

  # .countCigars -----
  .countCigars = function(cig_by_run = NULL, nonvar_first = TRUE){
    # Note that this function does not consider starts, two alignments starting at
    # different locations but sharing a cigar string are considered equal

    if (is.null(cig_by_run)){
      cig_by_run <- lapply(.self$crispr_runs, function(crun) crun$cigar_label)
    }

    unique_cigars <- unique(unlist(cig_by_run))

    chimera_combs <- lapply(.self$crispr_runs, function(crun) crun$chimera_combs)

    m <- matrix(unlist(lapply(cig_by_run, function(x) table(x)[unique_cigars])),
                nrow = length(unique_cigars),
                dimnames = list(unique_cigars, names(.self$crispr_runs)))

    m[is.na(m)] <- 0
    m <- m[order(rowSums(m), decreasing = TRUE),, drop = FALSE]

    if (nonvar_first){
      is_ref <- grep(.self$pars["match_label"], rownames(m))
      is_snv <- grep(.self$pars["mismatch_label"], rownames(m))
      new_order <- setdiff(1:nrow(m), c(is_ref,is_snv))
      m <- m[c(is_ref, is_snv, setdiff(1:nrow(m), c(is_ref,is_snv))),,drop = FALSE]
    }

    .self$field("cigar_freqs", m)
  }, # -----

  # filterUniqueLowQual -----
  filterUniqueLowQual = function(min_count = 2, max_n = 0, verbose = TRUE){
'
Description:
  Deletes reads containing rare variant combinations and more than
  a minimum number of ambiguity characters within the target region.
  These are assumed to be alignment errors.

Input parameters:
  min_count:    the number of times a variant combination must occur across
                all samples to keep (default: 2, i.e. a variant must occur
                at least twice in one or more samples to keep)
  max_n:        maximum number of ambiguity ("N") bases a read with a rare
                variant combination may contain.  (default: 0)
  verbose:      If TRUE, print the number of sequences removed (default: TRUE)
'
    # Find low frequency variant combinations, then find the corresponding samples
    low_freq <- .self$cigar_freqs[rowSums(.self$cigar_freqs) < min_count, , drop = FALSE]
    lf_cig_by_run <- apply(low_freq, 2, function(x) names(x)[x != 0])
    lns <- lapply(lf_cig_by_run, length)
    lf_cig_by_run <- lf_cig_by_run[lns > 0]

    # Find the corresponding reads, count the ambiguity characters
    rm_cset <- unlist(lapply(names(lf_cig_by_run), function(name){
      crun <- cset$crispr_runs[[name]]
      get_idxs <- match(lf_cig_by_run[[name]], crun$cigar_labels)
      sqs <- mcols(crun$alns[get_idxs])$seq
      to_remove <- get_idxs[as.numeric(Biostrings::letterFrequency(sqs, "N")) > max_n]
      cset_to_remove <- match(crun$cigar_labels[to_remove], rownames(cset$cigar_freqs))
      if (length(to_remove) > 0) crun$removeSeqs(to_remove)
      return(cset_to_remove)
    }))
    if (isTRUE(verbose)){
      message(sprintf("Removing %s rare sequence(s) with ambiguities\n",
                      length(rm_cset)))
    }
    if ( length(rm_cset) > 0){
      .self$field("cigar_freqs", .self$cigar_freqs[-rm_cset,,drop = FALSE])
    }
  }, # -----

  # .getFilteredCigarTable -----
  .getFilteredCigarTable = function(top.n = nrow(.self$cigar_freqs),
                                    min.count = 1, min.freq = 0,
                                    include.chimeras = TRUE,
                                    include.nonindel = TRUE,
                                    exclude = NULL,
                                    type = c("counts", "proportions")){

    # Consider reordering by proportion instead of count at initialisation?

    result <- match.arg(type)

    # Add the chimeric alignments to the bottom
    if (isTRUE(include.chimeras)){
      ch_cnts <- sapply(.self$crispr_runs, function(crun) {
        length(unique(names(crun$chimeras)))
      })

      m <- .self$cigar_freqs
      if (length(m) > 0 & sum(ch_cnts) > 0){
        m_nms <- rownames(m)
        m <- rbind(m, ch_cnts)
        rownames(m) <- c(m_nms, "Other")
      } else if (length(m) == 0){
        m <- matrix(ch_cnts, nrow = 1,
                    dimnames =list("Other", names(ch_cnts)))
      }
      # If all rows should be returned, add 1 to top.n
      if (top.n == nrow(.self$cigar_freqs)) top.n <- top.n + 1
    } else {
      m <- .self$cigar_freqs
      if (nrow(m) == 0) return(NULL)
    }
    
    # Filtering takes precedence over removing nonvariants
    # and selecting top.n

    # Default freq cutoff drops "Other" if there are no chimeras
    propns <- prop.table(m,2) * 100
    # At least one column has proportion greater than cutoff
    keep_freq <- rowSums(propns >= min.freq) > 0
    keep_count <- rowSums(m >= min.count) > 0
    propns <- propns[keep_freq & keep_count,, drop = FALSE]

    # Top variants are calculated by proportional contributions
    rs <- rowSums(propns)
    topn <- rank(-rs) <= top.n

    if (result == "proportions"){
      # Return proportions
      m <- propns[topn ,, drop = FALSE]
    } else {
      # Return counts
      m <- m[keep_freq & keep_count,, drop = FALSE][topn,, drop = FALSE]
    }

    # Filtering
    if (include.nonindel == FALSE){
      nvr <- sprintf("%s|%s", .self$pars$match_label, .self$pars$mismatch_label)
      m <- m[!grepl(nvr, rownames(m)),,drop = FALSE]
    }
    if (! is.null(exclude)){
      m <- m[! rownames(m) %in% exclude,, drop = FALSE]
    }
    
    m
  }, # -----

  # .getUniqueIndelRanges -----
  .getUniqueIndelRanges = function(add_chr = TRUE, add_to_ins = TRUE){
    # Note this only gets the ranges, not the sequences, inserted sequences may differ
    # Returns a GRanges object of all insertions and deletions, with names = variant names
    # if "add_chr" == TRUE, chromosome names start with "chr"
    # if add_to_ins == TRUE, adds one to end of insertions, as required for VariantAnnotation

    cig_by_run <- lapply(.self$crispr_runs, function(crun) crun$cigar_labels)
    all_cigars <- unlist(cig_by_run)
    unique_cigars <- !duplicated(all_cigars) & 
      ! all_cigars %in% c(.self$pars$match_label, .self$pars$mismatch_label)

    x <- alns(.self)

    ir <- cigarRangesAlongReferenceSpace(unlist(cigar(x)),
                pos = unlist(GenomicAlignments::start(x)),
                ops = c("I","D"), with.ops = TRUE)
    
    ir <- ir[unique_cigars]
    el <- elementNROWS(ir)
    ir <- unlist(ir)

    if (add_to_ins){
      ins_idxs <- names(ir) == "I"
      #ins_idxs <- unlist(co[idxs] == "I")
      end(ir[ins_idxs]) <- end(ir[ins_idxs]) + 1
   }

   names(ir) <- rep(all_cigars[unique_cigars], el)

   chrom <- as.character(seqnames(.self$target))
   if (add_chr & ! grepl('^chr', chrom)){
      chrom <- paste0("chr", chrom)
   }

    GenomicRanges::GRanges(chrom, ir)
  }, # -----

  # filterVariants -----
  filterVariants = function(cig_freqs = NULL, names = NULL, columns = NULL,
                            include.chimeras = TRUE){
'
Description:
  Relabels specified variants in a table of variant allele counts as 
  non-variant, e.g. variants known to exist in control samples.
  Accepts either a size, e.g. "1D", or a specific mutation, e.g. "-4:3D".
  For alleles that include one variant to be filtered and one other variant,
  the other variant will be retained.
  If SNVs are included, these will be removed entirely, but note that SNVs
  are only called in reads that do not contain an insertion/deletion variant

Input parameters:
  cig_freqs:        A table of variant allele counts 
                    (Default: NULL, i.e. .self$cigar_freqs)
  names:            Labels of variants alleles to remove (Default: NULL)
  columns:          Indices or names of control samples.  Remove all variants that 
                    occur in these columns.  (Default: NULL)
  include.chimeras: Should chimeric reads be included? (Default: TRUE)
'

    # Potential improvements:
    # Column must include only column names from .self$cigar_freqs
    # Cannot exclude complex variants?

    if (is.null(cig_freqs)){
      cig_freqs <- .self$.getFilteredCigarTable(include.chimeras = include.chimeras)
    }
    vars <- strsplit(rownames(cig_freqs), ",")
 
    if (length(columns) > 0){
      # Select the rownames that occur in columns
      cols <- rownames(cig_freqs)[rowSums(cig_freqs[,columns, drop=FALSE]) > 0]
    } else {
      cols <- NULL
    }

    to_remove <- c(names, cols)
    rm_snv <- grepl(.self$pars$mismatch_label, to_remove)
    has_loc <- grepl(":", to_remove) & ! rm_snv
    by_loc <- to_remove[has_loc]
    by_size <- to_remove[!(has_loc|rm_snv)]
    
    # Remove SNVs - SNVS have format SNV:-1,-5, other vars -1:3D etc
    rm_snv <- gsub(".*:(.*)", "\\1", names[rm_snv])
    temp <- gsub(".*:(.*)", "\\1", unlist(vars))
    rm_snvs <- sapply(relist(temp %in% rm_snv, vars), any)
    vars[rm_snvs] <- NULL
    cig_freqs <- cig_freqs[!rm_snvs, , drop=FALSE]

    # Reclassify indel variants as non-variant
    loc_mask <- rep(TRUE, length(unlist(vars)))
    size_mask <- loc_mask

    # Remove by location
    if (length(by_loc) > 0){
      loc_mask <- !grepl(paste(by_loc, collapse = "|"), unlist(vars))
    }
    # Remove by size - cannot be mutation combinations
    if (length(by_size) > 0){
      size_mask <- !(gsub(".*:", "", unlist(vars)) %in% by_size)
    }
    mask <- relist(loc_mask & size_mask, vars)
    vars <- as.list(IRanges::CharacterList(vars)[mask])
    vars <- lapply(vars, base::paste, sep = ",", collapse = ",")

    vars[vars == ""] <- .self$pars$match_label
    cig_freqs <- rowsum(cig_freqs, unlist(vars))
    cig_freqs
  }, # -----

  # .getSNVs -----
  .getSNVs = function(min.freq = 0.25, include.chimeras = TRUE){
    # Potential improvements:
    # Inconsistency between input (0-1) and output (0-100)
    
    cig_fqs <- .self$.getFilteredCigarTable(include.chimeras = include.chimeras)
    snv <- .self$pars["mismatch_label"]
    snv_nms <- rownames(cig_fqs)[grep(snv, rownames(cig_fqs))]
    all_snv_locs <- strsplit(gsub(sprintf("%s|:", snv), "", snv_nms), ",")
    snv_locs <- unique(unlist(all_snv_locs))
    asv <- unlist(all_snv_locs)
    total_count <- colSums(cig_fqs)
    
    snv_fqs <- structure(vector(length = length(snv_locs)), names = snv_locs)
    for (snv_loc in snv_locs){
      # Check if this SNV location occurs within the allele
      nms <- snv_nms[sapply(relist(asv == snv_loc, all_snv_locs), any)]
      snv_count <- colSums(cig_fqs[nms,, drop = FALSE])
      fq <- snv_count/total_count * 100
      snv_fqs[snv_loc] <- max(fq)
    }
    snv_fqs[snv_fqs >= min.freq*100]
  }, # -----

  # mutationEfficiency -----
  mutationEfficiency = function(snv = c("non_variant", "include","exclude"),
                                include.chimeras = TRUE,
                                exclude.cols = NULL, group = NULL,
                                filter.vars = NULL, filter.cols = NULL,
                                count.alleles = FALSE, per.sample = TRUE,
                                min.freq = 0){
'
Description:
  Calculates summary statistics for the mutation efficiency, i.e.
  the percentage of reads that contain a variant.  Reads that do not
  contain and insertion or deletion, but do contain a single nucleotide
  variant (snv) can be considered as mutated, non-mutated, or not
  included in efficiency calculations as they are ambiguous.
  Note: mutationEfficiency does not treat partial alignments differently


Input parameters:
  snv:    One of "include" (consider reads with mismatches to be mutated),
          "exclude" (do not include reads with snvs in efficiency calculations),
          and "non_variant" (consider reads with mismatches to be non-mutated).
  include.chimeras: Should chimeras be counted as variants?  (Default: TRUE)
  exclude.cols:   A list of column names to exclude from calculation, e.g. if one
                  sample is a control (default: NULL, i.e. include all columns)
  group:          A grouping variable.  Efficiency will be calculated per group,
                  instead of for individual.
                  Cannot be used with exclude.cols.
  filter.vars:    Variants that should not be counted as mutations.
  filter.cols:    Column names to be considered controls.  Variants occuring in
                  a control sample will not be counted as mutations.
  count.alleles:  If TRUE, also report statistics about the number of alleles
                  per sample/per group. (Default:  FALSE)
  per.sample:     Return efficiencies for each sample (Default: TRUE)
  min.freq:       Minimum frequency for counting alleles.  Does not apply to
                  calculating efficiency.  To filter when calculating 
                  efficiency, first use "variantCounts".
                  (Default: 0, i.e. no filtering)
Return value:
  A vector of efficiency statistics per sample and overall, or a
  matrix if a group is supplied.

'
    if (! is.null(group) && ! is.null(exclude.cols)){
      stop("Only one of group or exclude.cols may be supplied")
    }

    snv <- match.arg(snv)
    freqs <- .self$cigar_freqs
    if (isTRUE(include.chimeras)){
      freqs <- .self$.getFilteredCigarTable(include.chimeras = include.chimeras)
    }

    # Exclude unwanted columns, e.g. controls
    exclude.idxs <- match(exclude.cols, colnames(freqs))
    if (any(is.na(exclude.idxs))){
      nf <- exclude.cols[is.na(exclude.idxs)]
      stop(sprintf("Column(s) %s not found in variant counts table",
                   paste(nf, collapse = " ")))
    }

    if (length(filter.vars) > 0 | length(filter.cols) > 0){
      freqs <- .self$filterVariants(cig_freqs = freqs, names = filter.vars,
                                    columns = filter.cols)
    }

    if (length(exclude.idxs) > 0){
      freqs <- freqs[,-exclude.idxs, drop = FALSE]
    }

    is_snv <- grep(.self$pars$mismatch_label, rownames(freqs))

    if (snv == "exclude"){
      if (length(is_snv) > 0) freqs <- freqs[-is_snv,,drop = FALSE]
    }

    total_seqs <- colSums(freqs)
    names(total_seqs) <- colnames(freqs)
    not_mutated <- grep(.self$pars$match_label, rownames(freqs))
    if (snv == "non_variant") not_mutated <- c(not_mutated, is_snv)

    if (length(not_mutated) > 0) freqs <- freqs[-not_mutated,,drop = FALSE]

    if (! is.null(group)){
      if (! class(group) == "factor") group <- as.factor(group)
      result <- lapply(levels(group), function(g){
        eff <- .self$.calculateEfficiency(freqs[,group == g, drop = FALSE],
                                          total_seqs, count.alleles,
                                          per.sample, min.freq)
        
      })
      result <- do.call(rbind, result)
      rownames(result) <- levels(group)
      return(result)
    }

    .self$.calculateEfficiency(freqs, total_seqs, count.alleles, 
                               per.sample, min.freq)
  }, # -----

  # .calculateEfficiency -----
  .calculateEfficiency = function(freqs, total_seqs, count.alleles, 
                                  per.sample, min.freq = 0){
     mutants <- colSums(freqs)
     ts <- total_seqs[colnames(freqs)]
     mutant_efficiency = mutants/ts * 100
     average <- mean(mutant_efficiency)
     median <- median(mutant_efficiency)
     overall <- sum(mutants)/ sum(ts) * 100
     sds <- sd(mutants)
     result <- round(c(mutant_efficiency, average, median, overall, sds, 
                       sum(ts)),2)
     names(result) <- c(colnames(freqs), "Average","Median","Overall",
                        "StDev","ReadCount")
     
     if (isTRUE(count.alleles)){
       result <- c(result, .self$.countVariantAlleles(freqs, ts,
                                        min.freq))
     }
     if (! isTRUE(per.sample)){
       result <- result[!names(result) %in% colnames(freqs)]
     }
     result
  }, # -----

  # .countVariantAlleles ----
  .countVariantAlleles = function(freqs, total_seqs, min.freq){
    # how many nonzero entries per sample (absolute and percentage)
    # This step removes variants that never occur in this sample group
    
    freqs/total_seqs * 100
    
    if (min.freq > 0){
      propn <- freqs/total_seqs * 100
      freqs[propn < min.freq] <- 0
    }
    freqs <- freqs[rowSums(freqs) > 0,, drop = FALSE]
    
    if (nrow(freqs) == 0) return(vector())
    alleles <- colSums(freqs > 0)
    alleles_pc <- alleles/nrow(freqs)*100
    
    # Rate of new alleles (how many sequences per allele)
    # Produces "NA" if any samples have 0 alleles
    allele_rate <- colSums(freqs)/alleles
    nvars <- nrow(freqs)
    
    result <- c(mean(alleles), mean(alleles_pc), mean(allele_rate), min(alleles),
                max(alleles), nvars)
    result <- sapply(result, round, 2)
    names(result) <- c("AvAlleles","AvPctAlleles","SeqsPerAllele", 
                       "MinAlleles", "MaxAlleles","TotalAlleles")
    result
  }, # -----

  # classifyVariantsByType -----
  classifyVariantsByType = function(...){
'
Description:
  Classifies variants as insertions, deletions, or complex (combinations).
  In development
Input parameters:
  ...   Optional arguments to "variantCounts" for filtering variants
        before classification
Return value:
  A named vector classifying variant alleles as insertions, deletions, etc
'
   
    filter_pars <- dispatchDots(.self$.getFilteredCigarTable, ...) 
    nms <- rownames(do.call(.self$.getFilteredCigarTable, filter_pars))
    vars <- rep(NA, length(nms))
    names(vars) <- nms
    is_snv <- grepl(.self$pars$mismatch_label, nms)
    is_ref <- grepl(.self$pars$match_label, nms)
    is_ins <- grepl("I", nms)
    is_del <- grepl("D", nms)
    is_complex <- grepl(",", nms)
    ins_and_del <- is_ins & is_del
    vars[is_ref] <- .self$pars$match_label
    vars[is_snv] <- .self$pars$mismatch_label
    vars[which(nms == "Other")] <- "Other"
    vars[is_ins] <- "insertion"
    vars[is_del] <- "deletion"
    vars[ins_and_del] <- "insertion/deletion"
    vars[is_complex & is_ins & ! ins_and_del] <- "multiple insertions"
    vars[is_complex & is_del & ! ins_and_del] <- "multiple deletions"
    
    vars
  }, # -----

  # classifyVariantsByLoc -----
  classifyVariantsByLoc = function(txdb, add_chr = TRUE, verbose = TRUE, ...){
  '
Description:
  Uses the VariantAnnotation package to look up the location of the
  variants.  VariantAnnotation allows multiple classification tags per variant,
  this function returns a single tag.  The following preference order is used:
  spliceSite > coding > intron > fiveUTR > threeUTR > promoter > intergenic

Input parameters:
  txdb:     A BSgenome transcription database
  add_chr:  Add "chr" to chromosome names to make compatible with UCSC (default: TRUE)
  verbose:  Print progress (default: TRUE)
  ...:      Filtering arguments for variantCounts

Return value:
  A vector of classification tags, matching the rownames of .self$cigar_freqs
  (the variant count table)
  '

    if (isTRUE(verbose)) message("Looking up variant locations\n")

    stopifnot(requireNamespace("VariantAnnotation"))

    gr <- .self$.getUniqueIndelRanges(add_chr)
    locs <- VariantAnnotation::locateVariants(gr, txdb, VariantAnnotation::AllVariants())
    if (isTRUE(verbose)) message("Classifying variants\n")

    locs_codes <- paste(seqnames(locs), start(locs), end(locs), sep = "_")
    # Note that all indels have the same range
    indel_codes <- paste(seqnames(gr), start(gr), end(gr), sep = "_")
    indel_to_loc <- lapply(indel_codes, function(x) locs$LOCATION[which(locs_codes == x)])

    var_levels <- c("spliceSite","coding","intron","fiveUTR",
                    "threeUTR","promoter", "intergenic")
    result <- unlist(lapply(indel_to_loc, function(x){
      y <- factor(x,levels = var_levels)
      if (length(y) == 0) return(NA)
      var_levels[min(as.numeric(y))]}))
    names(result) <- names(gr)

    filter_pars <- dispatchDots(.self$.getFilteredCigarTable, ...)
    vars <- rownames(do.call(.self$.getFilteredCigarTable, filter_pars))
    classification <- rep("", length(vars))
    names(classification) <- vars

    no_var <- grep(.self$pars$match_label, vars)
    classification[no_var] <- .self$pars$match_label
    snv <- grep(.self$pars$mismatch_label, vars)
    classification[snv] <- .self$pars$mismatch_label
    classification[which(vars == "Other")] <- "Other"

    result <- result[names(result) %in% vars]
    ord <- match(names(result), vars)
    classification[ord] <- result
    classification
  }, # -----

  # classifyCodingBySize -----
  classifyCodingBySize = function(var_type, cutoff = 10){
'
Description:
    This is a naive classification of variants as frameshift or in-frame
    Coding indels are summed, and indels with sum divisible by 3 are
    considered frameshift.  Note that this may not be correct for variants
    that span an intron-exon boundary
Input paramters:
    var_type:   A vector of var_type. Only variants with var_type == "coding" 
                are considered.  Intended to work with classifyVariantsByLoc
    cutoff:     Variants are divided into those less than and greater 
                than "cutoff" (Default: 10)
Result:
    A character vector with a classification for each variant allele
'

    is_coding <- var_type == "coding" & ! is.na(var_type)

    indels <- .self$.getFilteredCigarTable()[is_coding,,drop = FALSE]
    if (length(indels) > 0){
      temp <- lapply(rownames(indels), function(x) strsplit(x, ",")[[1]])
      indel_grp <- rep(c(1:nrow(indels)), elementNROWS(temp))
      indel_ln <- rowsum(as.numeric(gsub("^.*:([0-9]+)[DI]", "\\1", unlist(temp))),
                         indel_grp)

      inframe <- indel_ln %% 3 == 0
      is_short <- indel_ln < cutoff

      indel_grp <- rep(sprintf("inframe indel < %s", cutoff), nrow(indels))
      indel_grp[is_short &! inframe] <- sprintf("frameshift indel < %s", cutoff)
      indel_grp[!is_short & inframe] <- sprintf("inframe indel > %s", cutoff)
      indel_grp[!is_short & !inframe] <- sprintf("frameshift indel > %s", cutoff)
      var_type[is_coding] <- indel_grp
    }

    var_type
  }, # -----

  # heatmapCigarFreqs -----
  heatmapCigarFreqs = function(as.percent = TRUE, x.size = 8, y.size = 8,
                               x.axis.title = NULL, x.angle = 90,
                               min.freq = 0, min.count = 0,
                               top.n = nrow(.self$cigar_freqs),
                               type = c("counts", "proportions"),
                               header = c("default", "counts", "efficiency"),
                               order = NULL, alleles = NULL, 
                               create.plot = TRUE, ...){

    '
Description:
    Internal method for CrispRVariants:plotFreqHeatmap, optionally filters the table
    of variants, then a table of variant counts, coloured by counts or proportions.

Input parameters:
    as.percent:   Should colours represent the percentage of reads per sample 
                  (TRUE) or the actual counts (FALSE)?  (Default: TRUE)
    x.size:       Font size for x axis labels (Default: 8)
    y.size:       Font size for y axis labels (Default: 8)
    x.axis.title: Title for x axis
    min.freq:     Include only variants with frequency at least min.freq in at 
                  least one sample
    min.count:    Include only variants with count at least min.count in at 
                  least one sample
    top.n:        Include only the n most common variants
    type:         Should labels show counts or proportions?  (Default: counts)
    header:       What should be displayed in the header of the heatmap.
                  Default: total count for type = "counts" or proportion of
                  reads shown in the matrix for type = "proportions".
                  If "counts" is selected, total counts will be shown for both
                  types.  "efficiency" shows the mutation efficiency
                  (calculated with default settings)
    order:        Reorder the columns according to this order (Default: NULL)
    alleles:      Names of alleles to include.  Selection of alleles takes
                  place after filtering (Default: NULL).
    exclude:      Names of alleles to exclude (Default: NULL)
    create.plot:  Should the plot be created (TRUE, default), or the data used in
                  the plot returned.
    ...:          Extra filtering or plotting options

Return value:
    A ggplot2 plot object.  Call "print(obj)" to display

See also:
    CrispRVariants::plotFreqHeatmap
    '

    filter_fun <- .self$.getFilteredCigarTable
    filter.args <- suppressWarnings(dispatchDots(filter_fun,
                           top.n = top.n, min.count = min.count,
                           min.freq = min.freq, type = type, ...))
    
    cig_freqs <- do.call(filter_fun, filter.args)

    # Alleles determines row order and alleles included
    if (! is.null(alleles)){
      # Move "Other" to the end for consistency with plotAlignments
      is_other <- which(alleles == "Other")
      alleles <- alleles[c(setdiff(seq_along(alleles), is_other), is_other)]
      row_ord <- match(alleles, rownames(cig_freqs))
      if (any(is.na(row_ord))){
        warning(sprintf("After filtering, alleles %s not found.", 
                        paste(alleles[is.na(row_ord)], collapse = ", ")))
      }
      cig_freqs <- cig_freqs[na.omit(row_ord),, drop = FALSE]
    }
    
    header_type <- match.arg(header)
    header_name <- "Total"
    type <- match.arg(type)
    
    if (header_type == "default" | header_type == "counts"){
      header <- colSums(.self$.getFilteredCigarTable())
    } else if (header_type == "efficiency"){
      header <- .self$mutationEfficiency()[1:ncol(cig_freqs)]
      header_name <- "Mutation efficiency"
    }

    if (type == "proportions"){
      # If type == proportions, default means proportions not counts
      if (header_type == "default") header <- round(colSums(cig_freqs), 2)
      cig_freqs <- round(cig_freqs, 2)
      col_sums <- rep(100, ncol(cig_freqs))
    } else if (type == "counts"){
      col_sums <- colSums(.self$.getFilteredCigarTable())
    }

    # Order the columns
    if (! is.null(order)){
      cig_freqs <- cig_freqs[,order, drop = FALSE]
      header <- header[order]
      col_sums <- col_sums[order]
    }

    freq_heatmap_args <- list(obj = cig_freqs, header = header,
                              header.name = header_name,
                              col.sums = col_sums, as.percent = as.percent,
                              x.size = x.size, y.size = y.size,
                              x.axis.title = x.axis.title,
                              x.angle = x.angle, add.other = TRUE)
    
    freq_heatmap_args <- modifyList(freq_heatmap_args, list(...))
    
    if (! isTRUE(create.plot)){
       return(freq_heatmap_args)
    }
    
    p <- plotFreqHeatmap(cig_freqs, header = header, header.name = header_name,
                         col.sums = col_sums, as.percent = as.percent,
                         x.size = x.size, y.size = y.size,
                         x.axis.title = x.axis.title,
                         x.angle = x.angle, add.other = TRUE, ...)
    return(p)
  }, # -----

  # plotVariants -----
  plotVariants = function(min.freq = 0, min.count = 0,
                   top.n = nrow(.self$cigar_freqs), alleles = NULL,
                   renumbered = .self$pars["renumbered"],
                   add.other = TRUE, create.plot = TRUE,
                   plot.regions = NULL, allow.partial = TRUE, ...){
'
Description:
  Internal method for CrispRVariants:plotAlignments, optionally filters the table
  of variants, then plots variants with respect to the reference sequence,
  collapsing insertions and displaying insertion sequences below the plot.

Input parameters:
  min.freq:         i(%) include variants that occur in at least i% of reads
                    in at least one sample
  min.count         i (integer) include variants that occur at leas i times in
                    at least one sample
  top.n:            n (integer) Plot only the n most frequent variants
                    (default: plot all)
                    Note that if there are ties in variant ranks,
                    top.n only includes ties with all members ranking <= top.n
  alleles:          Alleles to include after filtering.  Default NULL means use all
                    alleles that pass filtering.
  renumbered:       If TRUE, the x-axis is numbered with respect to the target
                    (cut) site.  If FALSE, x-axis shows genomic locations.
                    (default: TRUE)
  add.other         Add a blank row named "Other" for chimeric alignments,
                    if there are any (Default: TRUE)
  create.plot       Data is plotted if TRUE and returned without if FALSE.
                    (Default: TRUE)
  plot.regions      Subregion of the target to plot (Default: NULL)
  allow.partial     Should partial alignments be allowed?  (Default: TRUE)
  ...               additional arguments for plotAlignments

Return value:
  A ggplot2 plot object.  Call "print(obj)" to display
'
    dots <- list(...)
    
    # Potential improvement: user supplied filter_fun
    # Filter by frequency, count etc
    filter_fun <- .self$.getFilteredCigarTable  
    filter.args <- suppressWarnings(dispatchDots(filter_fun,
                                top.n = top.n, min.count = min.count,
                                min.freq = min.freq, ...))
    cig_freqs <- do.call(filter_fun, filter.args)
    
    # Filter by name specify a row order
    if (! is.null(alleles)){
      row_ord <- match(alleles, rownames(cig_freqs))
      if (any(is.na(row_ord))){
        warning(sprintf("After filtering, alleles %s not found.", 
                        paste(alleles[is.na(row_ord)], collapse = ", ")))
      }
      cig_freqs <- cig_freqs[na.omit(row_ord),, drop = FALSE]
    }
    
    # Remove "Other" alignments (if there are any) before making alignment
    # strings
    if ("Other" %in% rownames(cig_freqs)){
      cig_freqs <- cig_freqs[rownames(cig_freqs) != "Other",, drop = FALSE]
    } else {
      add.other <- FALSE
    }

    # Make the alignment strings to be displayed in the plot
    alns <- .self$makePairwiseAlns(cig_freqs, allow_partial = allow.partial)

    # Set the location for the vertical line indicating the zero point
    tloc <- ifelse(is.na(.self$pars$target.loc), 17, .self$pars$target.loc)
        
    ins.sites <- .self$insertion_sites

    # If necessary, reverse complement the inserted sequences
    if (isTRUE(.self$pars$rc)){
      temp <- seq_along(1:width(.self$target))
      starts <- rev(temp) +1 # +1 because considering the leftmost point
      names(starts) <- temp
      ins.sites$start <- starts[ins.sites$start]
      ins.sites$seq <- as.character(reverseComplement(DNAStringSet(ins.sites$seq)))
    }
    
    # Get coordinates with respect to target instead of genome
    if (isTRUE(renumbered)){
      genomic_coords <- c(start(.self$target):end(.self$target))
      target_coords <- .self$genome_to_target[as.character(genomic_coords)]
      if (isTRUE(.self$pars$rc)){
        target_coords <- rev(target_coords)
      }
      #xbreaks = which(target_coords %% 5 == 0 | abs(target_coords) == 1)
      #target_coords <- target_coords[xbreaks]
      #args <- list(obj = .self$ref, alns = alns, ins.sites = ins.sites,
      #             xtick.labs = target_coords, xtick.breaks = xbreaks,
      #             target.loc =  tloc, add.other = add.other)
      
      xtcks <- .getAxisCoords(seq_along(target_coords), target_coords)
      
      args <- list(obj = .self$ref, alns = alns, ins.sites = ins.sites,
                  xtick.labs = xtcks$tick_labs, xtick.breaks = xtcks$tick_locs,
                  target.loc =  tloc, add.other = add.other)
      
    } else {
      args <- list(obj = .self$ref, alns = alns, ins.sites = ins.sites,
                   target.loc = tloc, add.other = add.other)
    }
    
    args <- modifyList(args, dots)
    
    # If displaying the reverse complement, target loc is to the left
    strd <- as.character(strand(.self$target))

    reverse_tloc <- (strd == "+" & isTRUE(.self$pars$rc)) |
                    (strd == "-" & ! isTRUE(.self$pars$rc))
    
    if (isTRUE(reverse_tloc)){
        tloc <- width(.self$target) - tloc
        args["target.loc"] <- tloc
        args["pam.start"] <- tloc - 5
        args["pam.end"] <- tloc - 3
        args[["guide.loc"]] <- IRanges::IRanges(tloc - 5, tloc + 17)
    }

    args <- modifyList(args, dots)
    
    if (! is.null(plot.regions)){
      args <- .self$.truncatePlot(args, plot.regions, target_coords)
      if (! "plot.text.size" %in% args){ args$plot.text.size <- 3 }
    }
    
    if (! isTRUE(create.plot)) return(args)
    
    p <- do.call(plotAlignments, args)
    return(p)
  }, # -----

  # .truncatePlot -----
  .truncatePlot = function(plot_args, keep, target_coords){
    # To do: fix the box location if it is truncated

    
    trunc_result <- selectAlnRegions(alns = plot_args$alns,
                                     reference = plot_args$obj,
                                     target = .self$target,
                                     keep)
    
    new_starts <- .adjustRelativeInsLocs(target = .self$target,
                                         keep = trunc_result$keep,
                                         starts = plot_args$ins.sites$start,
                                         trunc_result$nchar_join)
    
    lb <- c(start(keep)[2:length(keep)], end(keep)[1:length(keep)-1])
    keep_tcks <- ! trunc_result$indices == 0
    xtcks <- .getAxisCoords(trunc_result$indices[keep_tcks],
                            target_coords[keep_tcks],
                            loc.boundaries = trunc_result$indices[lb])
    
     
    plot_args$alns <- trunc_result$alns
    plot_args$obj <- trunc_result$ref
    plot_args$ins.sites$start <- new_starts
    plot_args$xtick.labs <- xtcks$tick_labs
    plot_args$xtick.breaks <- xtcks$tick_locs
    
    # guide.loc, target.loc, pam.start, pam.end
    plot_args
  }, # -----

  # .getInsertions -----
  .getInsertions = function(){
    # Used by plotVariants for getting a table of insertions

      all_ins <- lapply(seq_along(.self$crispr_runs), function(i){
          ins <- .self$crispr_runs[[i]]$getInsertionSeqs(.self$target)
          if (! is.null(ins)) ins$sample <- i
          ins  
      })
    
      all_ins <- do.call(rbind, all_ins)
      if (is.null(all_ins)){
        all_ins <- data.frame()
      } else {
        all_ins <- all_ins[order(all_ins$start),]
        all_ins$cigar_label <- as.character(all_ins$cigar_label)
      }      
      
    .self$field("insertion_sites", all_ins)
  }, # -----

  # consensusAlleles -----
  consensusAlleles = function(cig_freqs = .self$cigar_freqs, return_nms = TRUE,
                              match.ops = c("M","X","=")){
'
Description:
Get variants by their cigar string, make the pairwise alignments for the consensus
sequence for each variant allele

Input parameters:
cig_freqs:  A table of variant allele frequencies (by default: .self$cigar_freqs,
but could also be filtered)
return_nms: If true, return a list of sequences and labels (Default:FALSE)
match.ops:  CIGAR operations for 1-1 alignments

Return:
A DNAStringSet of the consensus sequences for the specified alleles,
or a list containing the consensus sequences and names for the labels
if return_nms = TRUE
'
    
    # Note: with old pac bio data, reads can have different cigars
    # but same label for insertions and deletions
    
    alns <- unname(unlist(alns(.self)))
    cigs <- cigar(alns)
    cig_labels <- mcols(alns)$allele
    
    # calling by name with duplicates returns the first match
    names(cigs) <- cig_labels
  
    # Get read indices per label, match row order of cig_freqs
    splits <- split(seq_along(cig_labels), cig_labels)
    splits <- splits[match(rownames(cig_freqs), names(splits))]
    
    # Convert names to cigar strings 
    splits_labels <- names(splits)
    names(splits) <- cigs[names(splits)]
    
    # Find alignments where a deletion spans the target region
    grep_str <- paste(match.ops, collapse = "|")
    all_d <- grep(grep_str, names(splits), invert = TRUE)
    
    all_alns <- unlist(alns(.self), use.names = FALSE)
    
    seqs <- c()
    starts <- c()
    
    for (i in seq_along(splits)){
      idxs <- splits[[i]]
      start <- start(all_alns[idxs])
      sqs <- mcols(all_alns[idxs])$seq
      
      if (i %in% all_d){
        seqs[i] <- ""
        starts[i] <- start[1]
      }
      else {
        if (length(unique(start)) > 1 | 
            length(unique(width(sqs))) > 1){

          warning("Sequences do not all have the same start")
          tt <- table(start)
          starts[i] <- as.numeric(names(tt))[order(tt, decreasing = TRUE)][1]
          cigs <- cigar(all_alns[idxs])
          
          result <- GenomicAlignments::sequenceLayer(sqs, cigs,
                                                     to = "pairwise",
                                                     D.letter = "-",
                                                     N.letter = "N")
          shifts <- start - GenomicAlignments::start(.self$target)
          
          # CHECK WITH MORE COMPLEX EXAMPLE
          aln_sqs <- Biostrings::stackStrings(result, shift = shifts,
                                            from = 1,
                                            to = max(nchar(result)),
                                            Lpadding.letter = "+",
                                            Rpadding.letter = ".")
          
          cm <- consensusMatrix(aln_sqs)
          sq <- rownames(cm)[apply(cm, 2, which.max)]
          sq <- paste0(sq[! sq %in% c("-", "+", ".")], collapse = "")
          seqs[i] <- sq
          
        }  else {
          cig <- names(splits[[i]])
          seqs[i] <- consensusString(sqs)
          starts[i] <- start[1]
        }
      }
    }
    
    seqs <- Biostrings::DNAStringSet(seqs)
    
    if (isTRUE(return_nms)){
      mcols(seqs) <- data.frame(cigar = names(splits), 
                      label = splits_labels, start = starts)
    }

    names(seqs) <- splits_labels
    seqs
  }, # -----


  # makePairwiseAlns -----
  makePairwiseAlns = function(cig_freqs = .self$cigar_freqs, ...){
'
Description:
Get variants by their cigar string, make the pairwise alignments for the consensus
sequence for each variant allele

Input parameters:
cig_freqs:  A table of variant allele frequencies (by default: .self$cigar_freqs,
            but could also be filtered)
...:        Extra arguments for CrispRVariants::seqsToAln, e.g. which symbol
            should be used for representing deleted bases
'

    # The short cigars (not renumbered) do not have enough information,
    # use the full cigars for sorting
    # Do this just for the alns to be displayed?
    temp <- .self$consensusAlleles(cig_freqs, return_nms = TRUE)
    
    alns <- seqsToAln(mcols(temp)$cigar, temp, target = .self$target,
                 aln_start = mcols(temp)$start, 
                 reverse_complement = .self$pars$rc, ...)

    names(alns) <- mcols(temp)$label
    alns
  }, # -----

   # .genomeToTargetLocs -----
  .genomeToTargetLocs = function(target.loc, target_start, target_end, plus_strand, 
                                 gs = NULL, ge = NULL){
    # target.loc should be relative to the start of the target sequence, even if the
    # target is on the negative strand
    # target.loc is the left side of the cut site (Will be numbered -1)
    # target_start and target_end are genomic coordinates, with target_start < target_end
    # plus_strand: is the target on the positive strand wrt the reference?
    # returns a vector of genomic locations and target locations

    # Example:  target.loc = 5
    # Before: 1  2  3  4  5  6  7  8
    # After: -5 -4 -3 -2 -1  1  2  3
    # Left =  original - target.loc - 1
    # Right = original - target.loc

    stopifnot(isTRUEorFALSE(plus_strand))
    if (all(lengths(alns(.self))) == 0) return(NULL)
    
    if (is.null(gs) | is.null(ge)){
      gs <- min(sapply(.self$crispr_runs, function(x) min(start(x$alns))))
      ge <- max(sapply(.self$crispr_runs, function(x) max(end(x$alns))))
    }
    
    if (! isTRUE(plus_strand)){
      tg <- target_end - (target.loc - 1)
      new_numbering <- rev(c(seq(-1*(ge - (tg -1)),-1), c(1:(tg - gs))))

    } else {
      tg <- target_start + target.loc - 1
      new_numbering <- c(seq(-1*(tg - (gs-1)),-1), c(1:(ge - tg)))
    }
    
    names(new_numbering) <- c(gs:ge)

    .self$field("genome_to_target", new_numbering)
    new_numbering
  } # -----
)