Package 'tidyGenR'

Description

add allele_no to genotypes

Usage

add_allele_no(gen)

Arguments

Description

Performs pairwise alignments between DNA sequence variants contained in a tidyGenR variants table and a reference FASTA database using DECIPHER::AlignPairs(). The best matching reference sequence(s) for each variant are returned and appended as additional columns to the variants table.

Usage

align_variants_ref(variants, ref_fasta, n_best = 1, reduced = TRUE, ...)

Arguments

Details

This function is intended for exploratory annotation of sequence variants, such as matching alleles against reference haplotypes, species databases, or known locus variants.

Reference sequences are read using Biostrings::readDNAStringSet(). Variant sequences are extracted from the tidyGenR variants object using tidy2sequences() and aligned against all reference sequences using pairwise alignment.

Query sequences are internally identified using MD5 hashes to facilitate unambiguous tracking across the workflow.

Value

A tidy variants table with additional columns containing alignment information for the best matching reference sequence(s).

Examples

data("variants")
ref_al <-
    system.file("extdata/reference_alleles.fasta", package = "tidyGenR")
align_variants_ref(variants, ref_al)

Description

Reads AmpliSAT results as tidy variants.

Usage

amplisas2tidy(fp)

Arguments

Details

Converts text files in folder with AmpliSAS (Sebastian et al. 2016) results into a tidy variants dataframe. MD5 is regenerated as the one in AmpliSAT does not coincide with the MD5 associated with the DNA sequence.

Value

Tidy variants (see 'variant_call()”).

References

Sebastian et al. (2016). AMPLISAS: a web server for multilocus genotyping using next‐generation amplicon sequencing data. Molecular Ecology Resources, 16(2), 498-510.

Examples

fp <- list.files(system.file("extdata", "amplisas", package = "tidyGenR"),
    full.names = TRUE
)
amplisas2tidy(fp)

Description

Check input raw FASTA/Q files

Usage

check_raw_reads(freads, rreads = NULL, low_readcount = 10)

Arguments

Details

Multiple checks on raw input FASTQ:

• Reads in FASTQ above threshold.

• Unique sample names.

• Not orphan F/R files for paired reads. For single-end data check 3 is excluded and NULL is returned in checks[4].

Value

List with 4 elements:

• 'f_reads', character vector with basename of forward reads.

• 'r_reads', character vector with basename of reads reads or NULL for single-end experiments.

• 'snames', character vector with sample names.

• 'checks', Logical vector with passed checks 1-3.

Examples

freads <-
list.files(system.file("extdata", "raw", package = "tidyGenR"),
pattern = "1.fastq.gz",
    full.names = TRUE)
check_raw_reads(freads)

Description

All FASTQ have a number of reads above threshold.

Usage

check1_lowcount(list_fp, low_readcount = low_readcount)

Arguments

Value

Logical, TRUE if test is passed. Warning if FALSE.

Description

Sample names are unique.

Usage

check2_unique_snames(snames = snames)

Arguments

Value

Logical, TRUE if test is passed. STOPs if FALSE.

Description

Not orphan F/R FASTQ files.

Usage

check3_non_orphan(list_fp, snames = snames)

Arguments

Value

Logical, TRUE if test is passed. Warning if FALSE. NULL for single end reads.

Description

Comparision between multiple tidy variants or genotypes.

Usage

compare_calls(td, dest_file = FALSE, creads = FALSE)

Arguments

Value

A list of 4 elements:

• tidy_dat: combined tidy number of reads for all elements in 'td'. If 'creads = FALSE', cells are coded as '1' (presence) and '0' (absence).

• variants: list with dataframes in wide format with all variants as columns. Variants are named 'locus_{first 6 characters of md5 of DNA seq}'. Zeroes '0' are included where no variant was found. If 'creads = FALSE', cells are coded as '1' (presence) and '0' (absence)

• comp: T/F matrix. samples x variants. TRUE, the cellij across all matrices has the same value; FALSE, any of them has a different value.

• dist: pairwise distances between matrices (analogue to Manhattan distance).

• plot1: heatmap with frequency of calls (>1 == 1) across elements compared.

• plot2: heatmap with calls for all elements compared. Black tiles indicate calls absent in the element but present in another. element.

• plot3: boxplot of reads ordered by frecuency. (output only if 'creads' == TRUE).

• plot4: MDS plot (output only when length(td) > 2).

• (it writes an EXCEL file with the results to 'dest_file'.)

Examples

data("variant_calls")
compare_calls(variant_calls, creads = TRUE)

Description

Create cutadapt command

Usage

cutadapt_command(
  mode,
  interpreter,
  samples_file,
  cutadapt,
  extraArgs,
  overlap,
  e,
  g,
  G,
  o,
  p,
  readsfw,
  readsrv,
  log_out
)

Arguments

Value

cutadapt command based on the 'mode" ('pe', 'se', 'linked').

Description

Sequencing reads are demultiplexed by locus into separate FASTQ files (locus.sample.[1|2].fastq.gz) based on the locus-specific primer sequences using cutadapt.

Usage

demultiplex(
  interpreter = "/bin/bash",
  cutadapt = system2("which", "cutadapt", stdout = TRUE),
  freads,
  rreads = NULL,
  primers = NULL,
  sh_out = tempfile(fileext = ".sh"),
  outdir = tempdir(),
  log_out = tempfile(fileext = ".log"),
  temp_folder = tempdir(),
  mode = "pe",
  overlap = 15,
  e = 0.15,
  extraArgs = "",
  run = TRUE
)

Arguments

Details

This function creates a bash script to run cutadapt. By turning on running 'run = T', the function will try to execute the bash script produced (written to 'sh_out'). If the function produces any errors it is recommended to turn off running 'run = FALSE' and inspect the bash script produced to detect mis-specified cutadapt arguments or erroneous paths, and to solve them using the cutadapt documentation.

Value

Demultiplexed 'sample.locus.[12].fastq.gz'.

Examples

data("primers")
freads <-
 list.files(system.file("extdata", "raw",
                        package = "tidyGenR"),
                        pattern = "1.fastq.gz",
            full.names = TRUE)
rreads <-
 list.files(system.file("extdata", "raw",
                        package = "tidyGenR"),
                        pattern = "2.fastq.gz",
            full.names = TRUE)
demultiplex(
    freads = freads,
    rreads = rreads,
    primers = primers,
    run = FALSE)

Description

Reads FASTQ files and computes frequency of unique sequences per sample. Depth for unique sequences are organized in samples x unique sequences tables. Unique Ssequences are ordered in descending frequency.

Usage

dereplicate(
  fs,
  min_sam_fr = 2,
  min_loc_fr = 0.001,
  by = "_([a-zA-Z0-9]*_[F|R])",
  out_xlsx = NULL
)

Arguments

Details

The by parameter allows flexible grouping of files in the list. However, the results are not added within each group; individual results for each sample are always returned. For instance, given 3 files s1_loc1_F.fq, s1_loc1_R.fq and s2_loc1_F.fq:

• "([a-zA-Z0-9]*_[a-zA-Z0-9]*)", returns s1_loc1 and s2_loc2.

• "_([a-zA-Z0-9]*)_", returns loc1 and loc2.

• "([a-zA-Z0-9]*_[F|R])", returns loc1_F, loc1_R and loc2_F. The min_sam_fr and min_loc_fr filters drop data not passing the filters, so they will become zero or absent when combined. If a path to an EXCEL file is set, each element in the list is written to a different sheet in the workbook.

Value

List of extracted groups (see 'by'). Each element is the list is a dataframe with:

• column 1: 'sequence', DNA sequence of read.

• column 2: 'md5', md5 hash of DNA sequence.

• column >= 3: frequency (integers) of sequences per sample passing 'min_sam_fr' and 'min_loc_fr' filters.

Examples

fq <-
 list.files(system.file("extdata", "truncated",
                        package = "tidyGenR"),
                        pattern = "fastq.gz",
            full.names = TRUE)
dereplicate(fq)

Description

Computed as the sum of absolute difference across cells. Analogue to Manhattan distance.

Usage

dist_m(m1, m2, prop = FALSE)

Arguments

Value

Numeric distance, analogue to Manhattan distance.

Description

Requires a list of matrices in which the first column are samples, and col2...coln are numeric. Ex. number of observed variants in each cell.

Usage

dist_m_all(ld)

Arguments

Value

Dataframe with pairwise distances between all combinations. Absolute distance 'dist_Manh' and relative 'dist_Manhp'.

Description

DADA2 is run with a set of desired parameters for a set of FASTQ files. The element 'clustering' output by dada2::dada() containing all relevant statistics from cluster formation are output in tidy format as the first element. These tidy results are plotted in 4 different ways.

Usage

explore_dada(
  fs,
  sample_locus = "(^[a-zA-Z0-9]*)_([a-zA-Z0-9]*)",
  value_na = 10,
  reduced = TRUE,
  omega_a = 0.9,
  band_size = 16,
  pool = FALSE,
  vline = NULL,
  hline_fr = NULL,
  p_titles = NULL
)

Arguments

Details

'OMEGA_A', 'BAND_SIZE' and 'pool = T/F' parameters can have a strong effect in the variants called by dada2::dada(). This function explores the relation of read count, relative frequency of the variants and the 'birth_pval' assigned to the clusters for any given values of starting parameters. Critical parameters 'pool' and 'BAND_SIZE' can be specified as arguments. Additional parameters can be set with dada2::setDadaOpt(). The 'log(-log(birth_pval))' proposed in Rosen et al. (2012) is computed and represented in plots. For represenation purposes, a virtually infinite value of 'log(-log(birth_pval)) = 10', is assigned by default to the first cluster and to 'birth_pval = 0'. Variants in each, sample/locus combination are ranked by abundance and plotted in the legend. Ranks >= 3 are named as "3". For biallelic markers, a rank >= 3 implies a likely false positive. This, visual representation can be used to decide to tune variant_call().

• omega_a: threshold for variants to be significant overabundant 'log(-log(birth_pval))' (see Rosen et al. 2012). For exploration, it is recommended to run explore_dada() with a large omega_a.

• band_size: positive numbers set a band size in Needleman-Wunsch alignments. In this context, ends free alignment is performed. Zero turns off banding, triggering full Needleman-Wunsch alignments, in which gapless alignment is performed (see issue).

• pool: calling variants pooling samples can increase sensitivity (see dicussion).

Value

List with tidy 'dada-class' clustering element and plots.

1. tidy_dada: tidy 'clustering' element from 'dada-class' merged across loci.

2. p1: plot 1, frequency of variants (sample x locus) against 'log(-log(birth_pval))'.

3. p2: plot 2, read count of variants against their frequency.

4. p3: plot 3, p1 facetted by locus.

5. p4: plot 4, p2 facetted by locus.

References

Rosen et al. (2012). Denoising PCR-amplified metagenome data. BMC Bioinformatics, 13(1).

Examples

fq <-
 list.files(system.file("extdata", "truncated",
                        package = "tidyGenR"),
                        pattern = "F_filt.fastq.gz",
            full.names = TRUE)
explore_dada(fq,
    value_na = 10,
    reduced = TRUE,
    pool = FALSE,
    vline = 2,
    hline_fr = 0.1,
    omega_a = 0.9,
    band_size = 16
)

Description

Filter variants based on frequency and depth

Usage

filter_variants(tidy_var, maf = 0, ad = 0, invert = FALSE)

Arguments

Value

Tidy tibble with filtered variants.

dataframe with filtered variants/alleles. Variants are named so column names in df are "locus", "sample", "sequence", "reads", "allele".

Examples

data("variants")
filter_variants(variants, ad = 100, maf = 0.2)

Description

Performs straight genotyping from variants based on ploidy, ADt.

Usage

genotype(variants, ploidy = 2, ADt = 10)

geno_direct(variants, ploidy, ADt)

Arguments

Details

For 'ploidy':

• '1', force haploid genotypes.

• '2', force diploid genotypes. If number of alleles is == 1, ADt criterium is applied to differentiate between "homozygotes" and "hemizygotes".

• 'poly', all variants are assumed to be real alleles. No ADt filter is applied.

Value

Tidy genotypes. Dataframe with tidy genotypes. 'sample, 'locus', 'allele', 'allele_no', 'reads', 'nt', 'md5', 'sequence'. reads, supporting allele copies are corrected: 'reads' from tidy variants are divided between resulting alleles in homozygotes and hemizygotes.

Examples

data("variants")
genotype(variants[1:100,],
    ADt = 10, ploidy = 2
)

Description

Conversion of genotype data between formats.

• gen_tidy2compact, from tidy to compact.

• gen_compact2wide, from compact to wide.

• gen_tidy2wide, from tidy into wide.

• gen_wide2structure, from wide to STRUCTURE-formatted text file.

• gen_tidy2genalex, from tidy to GENALEX-formatted xlsx or text file.

• gen_tidy2integers, recode 'allele' in tidy genotypes as integers.

Usage

gen_tidy2compact(gen, delim = "/")

gen_compact2wide(gen)

gen_tidy2wide(gen)

gen_wide2structure(gen, write_out, popdata = FALSE, delim = "/")

gen_tidy2integers(gen)

gen_tidy2genalex(gen, popdata, write_out, data_name = "dataset1")

Arguments

Details

Genotypes from genotype() are returned as tidy data. Tidy data implies one data point per row. Each row from tidy genotypes represents an allele call for a given sample and locus. Rows with missing data are excluded. Thus, tidy genotypes contain at least the three columns: sample, locus and allele; bit often contain more columns (eg, reads, allele_no, nt, sequence, md5, population, etc.). The tidy format can be expanded column-wise and row-wise without altering the data structure, and it can be easily manipulated. Some of the more handy functions here-included involve genotype conversion between different formats:

• compact, each row correspond to the genotype call for a given locus in a sample. Therefore, it only contains three columns, sample, locus and genotype. Default separator for alleles under genotype is '/'. For diploid data, hemizygotes are recoded as "allele1" instead of "allele1/NA". All other column metadata is lost.

• wide, genotypes are recoded in samples (rows) x loci (columns) dataframe. The first column samples, contain sample names. All other columns contain loci names. Each cell contains genotypes in the A/B format. Diploid genotypes are coded as A/B. Cells with missing data have NA_character.

For the STRUCTURE format, '-9' are introduced as missing data in STRUCTURE output. In hemizygous calls A, the missing allele is encoded as missing data -9. Ploidy is retrieved from attributes. Only, diploid genotypes are allowed.

Value

• gen_tidy2compact, compact genotypes with at least 'sample' ,'locus' and 'genotype' columns.

• gen_compact2wide, wide genotypes.

• gen_tidy2wide, wide genotypes.

• gen_wide2structure, plain text file formatted for STRUCTURE.

• gen_tidy2genalex, 'xls' file with genotype data for GENALEX.

• gen_tidy2integers, tidy genotypes with alleles recoded as integers (1..n).

Examples

data("genotypes")
gencom <- gen_tidy2compact(genotypes)
gen_compact2wide(gencom)
gen_tidy2wide(genotypes)
gen_tidy2integers(genotypes)
# read metadata for sample populations
gen_str <- tempfile(fileext = ".str")
meta <-
    read.csv(system.file("extdata/metadata.csv", package = "tidyGenR"))
gen_wide2structure(gen_tidy2wide(genotypes),
    write_out = gen_str, popdata = meta
)
genalex_txt <- tempfile(fileext = ".txt")
gen_tidy2genalex(genotypes,
    popdata = meta,
    write_out = genalex_txt,
)

Description

Filtered genotypes of 20 loci and 91 samples of Rattus baluensis used in Camacho-Sanchez et al. (2026).

Format

tidy dataframe or tibble.

sample

Sample name.

locus

Locus name.

allele

Allele name.

allele_no

Allele ordinal number.

reads

Number of reads supporting allele.

nt

Length in number of nucleotides of allele sequence.

md5

MD5 hash of allele sequence.

sequence

DNA sequence of allele.

Description

Plot MDS from distances between compared calls

Usage

mds_comp(df)

Arguments

Description

It takes a multiple sequence alignment and data to create a NEXUS file which can be read by PopART (https://popart.maths.otago.ac.nz).

Usage

out_popart(
  msa,
  x,
  blocks = c("DATA", "TRAITS"),
  sname,
  xgroups,
  outnex = tempfile(fileext = ".nex")
)

Arguments

Value

No return value. Called for its side effect of writing a NEXUS file to outnex. Invisibly returns NULL.

Examples

data("genotypes")
x <-
    dplyr::filter(genotypes, locus == "abcg8")
nr <- seq_len(nrow(x))
y <-
    dplyr::mutate(x, sname = paste0("seq_", nr))
msa <-
    tidy2sequences(y, fasta_header = "{sname}") |>
    DECIPHER::AlignSeqs()
out_popart(msa, y,
    sname = "sname",
    xgroups = "sample",
    blocks = c("DATA", "TRAITS")
)

Description

A dataframe containing 27 primer pairs used to amplify intron loci in Rattus baluensis (Igea et al. 2010; Camacho-Sanchez et al. 2018).

Format

A data frame with 27 rows and 3 variables.

locus

Locus name.

fw

5' to 3' forward primer sequence.

rv

5' to 3' reverse primer sequence.

Source

Igea et al. (2010); Camacho-Sanchez et al. (2018).

References

Camacho-Sanchez et al. (2018). Interglacial refugia on tropical mountains: novel insights from the summit rat (Rattus baluensis), a Borneo mountain endemic. Diversity and Distributions.

Igea et al. (2010) Novel intron markers to study the phylogeny of closely related mammalian species. BMC Evolutionary Biology.

Description

Creates dataframe with reads distribution across samples (rows) and loci (columns).

Usage

reads_loci_samples(
  path,
  pattern_fq = "1.fastq.gz",
  sample_locus = "(^[a-zA-Z0-9]*).([a-zA-Z0-9]*)",
  all.variants = FALSE,
  var_id = "md5"
)

Arguments

Value

Dataframe with columns being loci and rows being samples. Samples are in first column.

Examples

reads_loci_samples(
     path = system.file("extdata", "truncated",
                       package = "tidyGenR"),
     pattern_fq = "F_filt.fastq.gz")
# with variants dataframe
data("variants")
reads_loci_samples(path = variants)

Description

It generates a dataframe with tracked reads per samples per locus. through the pipeline, from dataframes or FASTQ files.

Usage

reads_track(l, sample_pattern = "^[A-Za-z0-9]*")

Arguments

Details

'l' is a named list. It can be either a dataframe with two mandatory fields ('sample' and 'reads'), or a character vector with 2 elements: 1, the path to a directory containing FASTQ files; and ,2, a pattern matching desired FASTQ files. By, default, sample names matching 'sample_pattern' '^[A-Za-z0-9]*' are extracted from FASTQ files. 'left_join()' is used iteratively from the first to the last element in 'l', so the elements in 'l' need to be nested from element 1 to the last.

Value

Dataframe with tracked reads through the pipeline. The first column is named 'sample' and the following

Examples

# from folder with FASTQ files
path2truncated <-
   system.file("extdata", "truncated",
             package = "tidyGenR")
l <- list(
      truncated = c(path2truncated, "F_filt.fastq.gz"))
reads_track(l)

Description

Removes all rows with hemizygous alleles from tidy genotypes.

Usage

remove_hemizygotes(gen)

Arguments

Details

If "A/B" -> "A/B"; if "A" -> NULL. It only is applicable to diploid data.

Value

Tidy genotypes without hemizygous alleles.

Examples

data("genotypes")
remove_hemizygotes(genotypes)

Description

Removes monomorphic (or polymorphic) loci from tidy genotypes.

Usage

remove_monomorphic(gen, invert = FALSE)

Arguments

Value

Tidy genotypes with mono/poly-morphic loci.

Examples

data(genotypes)
remove_monomorphic(genotypes)

Description

Removes FASTA/FASTQ files with a number of reads less than or equal to min_reads from a specified directory.

Usage

remove_poor_fastq(path2fastq, pattern = "fastq.gz", min_reads = 0)

Arguments

Value

No return value. Called for its side effects: FASTA/FASTQ files in path2fastq with a number of reads less than or equal to min_reads are removed from disk. Invisibly returns NULL.

Examples

tmp <- tempdir()
fq <- file.path(tmp, "no_reads.fastq")
file.create(fq)
remove_poor_fastq(tmp, pattern = "fastq", min_reads = 0)

Description

Rename alleles in a dataframe based on reference alleles

Usage

rename_alleles(df_alleles, ref_alleles, replacements = FALSE)

Arguments

Value

Dataframe as 'df_alleles' with renamed alleles as in 'ref_alleles'.

Examples

data("genotypes")
ref_al <-
    system.file("extdata/reference_alleles.fasta", package = "tidyGenR")
rename_alleles(df_alleles = genotypes, ref_alleles = ref_al)

Description

Detect N strings and remove them from aligned BStringSet MSA.

Usage

rm_n_msa(
  msa,
  pattern_n = paste0(rep("N", 10), collapse = ""),
  outfasta = FALSE
)

Arguments

Details

When using 'mergePairs(justConcatenate = T)', a string of 10 Ns is introduced between paired-end concatenated reads. This function detects the coordinates of the N string in the MSA and removes it. It uses 'matchPattern()' to detect patterns. WARNING: it has not been tested for multiple hits of the pattern. It could potentially work with other patterns in addition to N strings, but it has not been tested.

Value

Filtered BStringSet MSA.

Description

Write FASTA and/or return BioString object with selected sequences.

Usage

tidy2sequences(
  td,
  fasta_header = "{locus}_{allele}",
  filename = FALSE,
  seq = "sequence"
)

Arguments

Details

'fasta_header' is a flexible selector and constructor for FASTA headers. The variables included are used to filter distinct rows in 'td'. Examples of outputs:

• "{locus}_{allele}", all different allele sequences per locus.

• "{sample}{locus}{variant}", all sequences for all the variants for all samples, thus repeated sequences from the same variant corresponding to different samples.

• "{md5}", all different DNA sequences.

• "{sample}", one sequence per sample. Since one sample matches to many sequences, the first occurrence in the dataframe is selected.

Value

'DNAStringSet' object with selected sequences.

Examples

data("genotypes")
tidy2sequences(
    td = genotypes,
    fasta_header = "{locus}_{allele}",
    filename = FALSE
)

Description

Truncation lengths for forward and reverse reads for the dataset containing 30 primer pairs used to amplify intron loci in Rattus baluensis (Camacho-Sanchez et al. 2026).

Format

Data frame with three columns:

locus

Locus name.

trunc_f

Truncation length for forward reads.

trunc_r

Truncation length for reverse reads.

Description

Truncates reads using 'filterAndTrim()' from DADA2. It automatically detects single-end or paired-end sequencing files.

Usage

trunc_amp(
  loci = NULL,
  in_dir,
  trunc_fr,
  fw_pattern,
  rv_pattern = NULL,
  outdir = tempdir(),
  filt_name = "_F_filt.fastq.gz",
  mode_trun = "pe",
  multithread = FALSE,
  max_ee = 3,
  trunc_q = 2
)

Arguments

Value

List with dataframes of number of reads before and after truncation. It writes truncated sequence to "outdir"

Examples

dem <-
 system.file("extdata", "demultiplexed", package = "tidyGenR")
# single end
trunc_amp(
    mode_trun = "se",
    in_dir = dem,
    fw_pattern = "1.fastq.gz",
    trunc_fr = 200
)
# paired-end
data("trunc_fr")
trunc_amp(
    mode_trun = "se",
    loci = c("chrna9", "nfkbia"),
    in_dir = dem,
    fw_pattern = "1.fastq.gz",
    rv_pattern = "2.fastq.gz",
    trunc_fr = trunc_fr,
    max_ee = 3,
    trunc_q = 2
)

Description

Convert sequence-based variants to length-based

Usage

var_seq2len(variants)

Arguments

Details

Recodes dataframe with sequence-based variants, such as the output from 'variant_call' to length-based variants. The sequence length 'nt', instead of the nucleotide sequence, is used for determining variants. Each variant is named after its number of nucleotides. Number of reads from previous variants with equal lengths are aggreated. Legacy for microsatellite data.

Value

Dataframe with length-based variants and the variables: 'locus' 'sample' 'variant' 'reads'.

Examples

data("variants")
var_seq2len(variants)

Description

'variant_call' Call variants for multiple loci.

Usage

variant_call(
  loci = NULL,
  in_dir,
  fw_pattern = "_F_filt.fastq.gz",
  rv_pattern = NULL,
  sample_locus = "(^[a-zA-Z0-9]*).([a-zA-Z0-9]*)",
  c_unmerged = FALSE,
  pool = FALSE,
  error_function = loessErrfun,
  multithread = FALSE,
  chim_rm = "consensus",
  ad = 1,
  maf = 0,
  omega_a_f = getDadaOpt()$OMEGA_A,
  omega_a_r = getDadaOpt()$OMEGA_A,
  band_size = getDadaOpt()$BAND_SIZE
)

Arguments

Details

Allows single-end and paired-end data. Be careful with the use of 'c_unmerged'. It will trigger the 'justConcatenate' argument in 'mergePairs', and 10 N's will be used to concatenate non-overlapping F and R reads. Use 'c_unmerged' carefully, as it will generate artificial variant sequences. Default is deactivated. Variants are reformatted and to a "tibble" and filtered according to 'maf' and 'ad', which are added as attributes to output. Variables of output are 'sample', 'locus', 'sequence' (DNA sequence), 'variant' (name of variant), 'reads' (number of reads supporting that variant), 'nt' (sequence length), 'md5' (md5 checksum).

Value

tidy tibble with locus, sample, sequence, variant (name), nt(sequence length), md5.

Examples

# truncated fastq
truncated <-
 system.file("extdata", "truncated", package = "tidyGenR")
# variant calling
variant_call(in_dir = truncated)

Description

• 'variant_call_dada()' Call variants for a single locus using DADA2. It is nested in 'variant_call'.

• 'loess_err_mod4()' Replaces default 'loessErrfun' when estimating error matrices for NovaSeq binned qualities.

• 'chimera_removal()' Wrapping function to remove chimeras using 'removeBimeraDenovo()'.

Usage

variant_call_dada(
  locus,
  in_dir,
  fw_pattern = "_F_filt.fastq.gz",
  rv_pattern = NULL,
  sample_locus = "(^[a-zA-Z0-9]*)_([a-zA-Z0-9]*)",
  c_unmerged = FALSE,
  pool = FALSE,
  error_function = loessErrfun,
  multithread = FALSE,
  chim_rm = "consensus",
  omega_a_f = getDadaOpt()$OMEGA_A,
  omega_a_r = getDadaOpt()$OMEGA_A,
  band_size = getDadaOpt()$BAND_SIZE
)

loess_err_mod4(trans)

chimera_removal(seqtab, chim_rm)

dada2list(dada_object, names)

Arguments

Details

• 'variant_call_dada()' Allows single-end and paired-end data. 'c_unmerged' will trigger the 'justConcatenate' argument in 'mergePairs', and 10 N's will be used to concatenate non-overlapping F and R reads. Use 'concateNotMerged' carefully, as it will generate artificial variant sequences. Default is deactivated.

• 'loess_err_mod4()' This is a beta function which has been shown to work in my experience and other users' experience. See discussion in https://github.com/benjjneb/dada2/issues/1307

• 'dada2list()' DADA2 functions return a dataframe whenever the length of the object is == 1 or a list if length > 1. To smooth prevent from bugs, this function standardizes the output of dada2 to lists when length == 1. Unaltered when input is a list.

Value

• 'variant_call_dada()' Numeric array. Column names are variant sequences; row names are sample names and each number in the array indicate the number of reads supporting variant 'i' in sample 'j'.

• 'chimera_removal()' 'seqtab' like object (see dada2).

• 'dada2list()' List with dada2 object/s.

Description

List of 5 tidy variants dataframes with variant calls using different strategies.

Format

A list of 5 dataframes.

ind_bs16

pool = F; band_size = 16

ind_bs0

pool = F; band_size = 0

pool_bs16

pool = T; band_size = 16

pool_bs0

pool = T; band_size = 0

amplisas

AmpliSAT

Description

Filtered tidy variants of Rattus baluensis from Trusmadi (Camacho-Sanchez et al. 2026), corresponding to 27 loci from 44 samples.

Format

tidy dataframe or tibble.

sample

Sample name.

locus

Locus name.

variant

Variant name.

reads

Number of reads supporting allele.

nt

Length in number of nucleotides of allele sequence.

md5

MD5 checksum of allele sequence.

sequence

DNA sequence of allele.