AMPURE

Description

Description: Placeholder for AMPURE dataset.

Usage

AMPURE

Format

A tibble/data.frame with X rows and Y columns:

Column1

Description of column1

Column2

Description of column2

...

Source

Generated by the Author

ASV_table

Description

An abundance table in long format.

Usage

ASV_table

Format

A data frame with 9 rows and 3 variables:

sample_name

Character vector, last digit includes PCR replicate

Hash

The sequence found, hashed using sha1 for consistency

nReads

Number of times that hash was found in that sample

Details

Contains the number of reads from unique ASVs across different samples.

Source

Generated by the Author

Index_PCR

Description

Placeholder description for IndexPCR template web address.

Usage

Index_PCR

Format

A character vector

Source

Generated by the Author, stored in googledrive

OTU_taxonomy

Description

Placeholder description for OTU_taxonomy dataset.

Usage

OTU_taxonomy

Format

A dataframe of DNA sequences names and their taxonomy, with 3 rows and 7 columns

Hash

Sequence identifier

Kingdom

taxon info at that rank

Phylum

taxon info at that rank

Class

taxon info at that rank

Order

taxon info at that rank

Family

taxon info at that rank

Genus

taxon info at that rank

Source

Generated by the Author

UDI_Indices

Description

Placeholder description for UDI_Indices dataset.

Usage

UDI_Indices

Format

A data frame with 384 rows and 6 columns

Index

Character vector

Bases_in_Adapter_i7

Seqs in i7

Bases_for_Sample_Sheet_i7

Seqs to include

Bases_for_Sample_Sheet_i5

Seqs to include in some illumina platoforms

Bases_for_Sample_Sheet_i5_B

Seqs to include in other illumina platoforms

Set

A, B, C, or D

Source

Generated by the Author, data from Illumina

Count stop codons or return translated sequence

Description

Given a DNA sequence (either a 'DNAString' object or a character string), this function translates it using a specified genetic code and counts the number of stop codons ('*') in the resulting amino acid sequence. Alternatively, the translated sequence itself can be returned.

Usage

count_stop_codons(
  sequence = NULL,
  format = "DNAString",
  codon = 1,
  dictionary = 5,
  return = "count"
)

Arguments

Details

The function uses [Biostrings::translate()] with the specified starting position and genetic code. Translation warnings are suppressed.

Value

If 'return = "count"', an integer giving the number of stop codons. Otherwise, a character string with the translated amino acid sequence.

Author(s)

Ramon Gallego, 2021

Examples

if (requireNamespace("Biostrings", quietly = TRUE)) {
  library(Biostrings)
  seq <- DNAString("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG")
  count_stop_codons(seq, codon = 1, dictionary = 1)
  count_stop_codons(seq, codon = 1, dictionary = 1, return = "translation")
}

Create scaled relative proportions of the number of reads of taxa in different samples

Description

This function takes a long ASV table (tibble) and creates a new column with the relative proportions scaled to their maximum, to avoid the dominance of species with better primer efficiency

Usage

eDNAindex(
  tibble,
  Sample_column = Sample,
  OTU_column = Hash,
  Counts_column = nReads,
  Biological_replicate_column = NULL,
  ...
)

Arguments

Value

A tibble with at least the columns Sample_column, OTU_column and Normalized.reads

Examples

data("training.ASV.table")
eDNAindex(training.ASV.table, Sample_column = Sample_name)

example_hashes

Description

Placeholder description for example_hashes dataset.

Usage

example_hashes

Format

A vector or data frame with X rows/columns

Source

Generated by the Author

Read FASTA or FASTQ files into a tibble

Description

Functions to read sequence files into a tidy data frame with one row per sequence.

Usage

fasta_reader(path_to_fasta)

fastq_reader(path_to_fastq, keepQ = FALSE)

Arguments

Value

- 'fasta_reader()': A tibble with columns: - 'header': sequence identifiers (without the '>'). - 'seq': nucleotide sequences.

- 'fastq_reader()': A tibble with columns: - 'header': sequence identifiers (without the '@'). - 'seq': nucleotide sequences. - 'Qscores' (optional): quality scores, if 'keepQ = TRUE'.

Examples

fasta_df <- fasta_reader(system.file("extdata", "test.fasta", package="eDNAfuns"))
fastq_df <- fastq_reader(system.file("extdata", "test.fastq", package="eDNAfuns"), keepQ = TRUE)

Read FASTA or FASTQ files into a tibble

Description

Functions to read sequence files into a tidy data frame with one row per sequence.

Usage

fasta_writer(df, sequence, header, file.out)

fastq_writer(df, sequence, header, Qscores, file.out)

Arguments

Value

- 'fasta_reader()': A tibble with columns: - 'header': sequence identifiers (without the '>'). - 'seq': nucleotide sequences.

- 'fastq_reader()': A tibble with columns: - 'header': sequence identifiers (without the '@'). - 'seq': nucleotide sequences. - 'Qscores' (optional): quality scores, if 'keepQ = TRUE'.

Examples

fasta_file <- tempfile(fileext = ".fasta")

fasta_df <- fasta_reader(system.file("extdata", "test.fasta", package="eDNAfuns"))

fasta_writer(fasta_df, sequence=seq,
              header = header,
              file.out = fasta_file)
 
fastq_file <- tempfile(fileext = ".fastq")  
           
fastq_df <- fastq_reader(system.file("extdata", "test.fastq", package="eDNAfuns"), keepQ = TRUE)

fastq_writer(fastq_df, sequence=seq,
              header = header,Qscores= Qscores,
              file.out = fastq_file)

metadata

Description

Placeholder description for metadata dataset.

Usage

metadata

Format

A data frame with X rows and Y columns:

sample_name

Name of sample

Treatment

either control or treatment

Day

either 1 or 2

Source

Generated by the Author

molarity.data

Description

Placeholder description for molarity.data dataset.

Usage

molarity.data

Format

A tibble/data.frame with X rows and Y columns

Sample

Name of sample

Molarity

in nM

Amp_len

length of amplicon in bp

mass

in ng/\mul

Source

Generated by the Author

Generate mutated DNA sequences

Description

This function takes DNA sequences and generates mutated variants. Useful for testing classification algorithms on sequences with either PCR-induced or naturally occurring mutations.

Usage

mutation(sequence = NULL, format = "bin", n.mutations = NA, prob.mutation = NA)

Arguments

Value

A list of mutated sequences of the same class as the input.

Examples

data("test_seqs")
mutation(test_seqs, n.mutations = 2)
mutation(test_seqs, prob.mutation = 0.1)
seqs <- fasta_reader(system.file("extdata", "test.fasta", package="eDNAfuns"))
mutation(seqs$seq, format = "char", n.mutations = 1)

functions to translate mass into molarity and vice versa, given we are talking about double stranded DNA it requires two inputs, the mass (or molarity) and the length of the DNA fragment It works with the two most common concentrations used in Molecular Ecology labs ng/\mul for mass nM for molarity

Description

functions to translate mass into molarity and vice versa, given we are talking about double stranded DNA it requires two inputs, the mass (or molarity) and the length of the DNA fragment It works with the two most common concentrations used in Molecular Ecology labs ng/\mul for mass nM for molarity

Usage

ng2nM(ng, length_amplicon)

nM2ng(nM, length_amplicon)

Arguments

Value

Numeric. The equivalent concentration in nmoles per litre

Numeric. The equivalent concentration in ng per \muL

Examples

data("molarity.data")
ng2nM(ng=molarity.data$mass, length_amplicon = molarity.data$Amp_len)


data("molarity.data")
nM2ng(nM=molarity.data$Molarity, length_amplicon = molarity.data$Amp_len)

Plot sequence length distribution

Description

This function takes a tibble produced by 'fasta_reader()' or 'fastq_reader()' and plots the distribution of sequence lengths.

Usage

plot_seq_len_hist(Hash_tibble, length_col, binwidth = 1, label_interval = 5)

Arguments

Value

A ggplot object.

Examples

data("example_hashes")
plot_seq_len_hist(example_hashes, seq_len)

ps

Description

Placeholder description for phyloseq object.

Usage

ps

Format

Formal class 'phyloseq' with five slots

Source

Generated by the Author

Read Indexing PCR Spreadsheet

Description

Reads data from a Google Sheets-based indexing PCR template. You can access the template here, create a copy in your Google Drive, and then create copies for each of your multiplexing PCR experiments.

Reads data from a Google Sheets-based PCR template. You can access the template here, create your own copy, and then create one copy per PCR reaction you want to keep track of.

Usage

read_indexing_PCR(ss)

read_step1_PCR(ss, trim = TRUE, name = TRUE)

Arguments

Details

The function extracts sample information across all plates and returns a tidy dataframe.

Captures reagent mix, cycling conditions, and sample information.

Value

A tibble with columns:

Well

Well position

Sample

Sample name

Barcode

Barcode assigned to the sample

Set

Barcode set identifier

A named list with three elements:

PCR_mix

tibble of reagents and volumes.

Cycling

tibble of PCR cycling conditions.

Samples

tibble of samples with columns 'Well', 'Sample', 'Success', 'Notes', and optionally 'PCR'.

See Also

[read_step1_PCR()] for reading normal PCR spreadsheets.

[read_indexing_PCR()] for reading multiplexing PCR spreadsheets.

Examples

# Examples are not executed because the function requires identification in Google 
## Not run: 
data("Index_PCR")
read_indexing_PCR(Index_PCR)

## End(Not run)

# Examples are not executed because the function requires identification in Google 
## Not run: 
data("template_PCR")
read_step1_PCR(template_PCR)

## End(Not run)

Read cutadapt info files

Description

Read the '–info-file' output generated by cutadapt for adapter trimming. Column structure differs depending on whether the input came from Nanopore (ONT) or Illumina sequencing.

Usage

read_info_file(file, delim = "\t", col_select = NULL, ...)

Arguments

Details

- 'read_info_file()' (Illumina) returns columns: 'Seq.id', 'n_errors', 'start_adap', 'end_adap', 'seq_before_adap', 'matching_seq', 'seq_after_adap', 'adap_name', 'QScores_seq_before', 'QScores_matching', 'QScores_after'.

Value

A tibble with the parsed 'cutadapt' information. Column names are standardized.

Examples

test_info <- system.file("extdata", "cutadapt_info_illumina.txt", package="eDNAfuns")
df_illumina <- read_info_file(test_info)

Create contingency tables with two variables

Description

This function takes a tibble and create human readable contingency tables from two variables, either by showing number of cases in each combination or weighted by the sum of a numerical variable

Usage

tally_wide(tibble, rows, cols, wt = NULL, ...)

Arguments

Value

A tibble

Examples

 df <- tibble::tibble(
  group = c("A", "A", "B", "B", "B"),
    outcome = c("yes", "no", "yes", "yes", "no")
 )
tally_wide(df, rows = group, cols = outcome)

template_PCR

Description

Placeholder description for Normal PCR template web address.

Usage

template_PCR

Format

A character vector

Source

Generated by the Author, stored in googledrive

test_seqs

Description

Placeholder description for test_seqs dataset.

Usage

test_seqs

Format

An object of class DNAbin of length 3.

Source

Generated by the Author

Convert a long tibble to a community matrix

Description

Converts a long-format table of sequence counts into a wide community matrix (samples × taxa) suitable for vegan or other community ecology tools.

Produces a distance matrix between samples using vegan's vegdist(). Optionally applies a data transformation before distance calculation.

Returns the environmental data frame (sample metadata) from a long sequence/OTU tibble by removing taxon and abundance columns.

Usage

tibble_to_comm(long.table, taxon, Abundance, sample.name)

tibble_to_dist(
  long.table,
  taxon,
  Abundance,
  sample.name,
  distance = "bray",
  transformation = NULL,
  ...
)

tibble_to_env(long.table, taxon, Abundance, sample.name, ...)

Arguments

Value

A numeric matrix with taxa as columns and samples as row names.

A dist object.

A tibble of unique sample-level metadata.

Examples

data("ASV_table")
 tibble_to_comm(ASV_table, 
                taxon = Hash,
                 Abundance = nReads,
                  sample.name = sample_name)
data("ASV_table")
tibble_to_dist(ASV_table,
               taxon = Hash,
               Abundance = nReads,
               sample.name = sample_name,
               distance = "bray",
               transformation = "hellinger")

data("ASV_table")
data("metadata")
dplyr::inner_join(ASV_table,metadata) |> 
tibble_to_env(taxon = Hash,
              Abundance = nReads, 
              sample.name = sample_name)

Convert a tidy ASV table to a phyloseq object

Description

This function converts a tidy ASV table, along with optional taxonomy and metadata, into a 'phyloseq' object.

Extracts ASV counts, taxonomy, and metadata from a 'phyloseq' object into tidy data frames.

Usage

tidy2phyloseq(
  ASV_table,
  OTU_taxonomy = NULL,
  metadata = NULL,
  Taxa = "sseqid",
  Sample = "sample_name",
  Reads = "nr",
  tree = NULL
)

phyloseq2tidy(phylo_obj, Taxa = "sseqid", Sample = "sample_name", Reads = "nr")

Arguments

Value

A 'phyloseq' object combining OTU table, taxonomy, metadata, and optionally a tree.

A list with three tibbles:

ASV_table

Long-format tibble of counts: Sample, Taxa, Reads.

taxonomy

Taxonomy table as tibble (NULL if none in phyloseq).

metadata

Sample metadata as tibble (NULL if none in phyloseq).

Examples

data("ASV_table")
data("metadata")
data("OTU_taxonomy")
ps <- tidy2phyloseq(ASV_table = ASV_table,
                    OTU_taxonomy = OTU_taxonomy,
                    metadata = metadata,
                    Taxa = "Hash",
                    Reads = "nReads")


data("ps")
tidy_list <- phyloseq2tidy(ps)

training.ASV.table

Description

Placeholder description for training.ASV.table dataset.

Usage

training.ASV.table

Format

A data frame with 1909 rows and 4 variables:

Sample_name

Character vector, last digit includes PCR replicate

Locus

Which locus the sequences are from, numeric

Hash

The sequence found, hashed using sha1 for consistency

nReads

Number of times that hash was found in that sample

Source

Generated by the Author

training.metadata

Description

Placeholder description for training.metadata dataset.

Usage

training.metadata

Format

A data frame with 95 rows and 8 columns

Sample_name

Character vector, last digit includes PCR replicate

eDNA.sample

Biological sample

rep

PCR replicate

Transect

Transect of origin

position

Stop in Transect

depth

in the water column

lat

latitude in decimal degrees

lon

longitude in decimal degrees

Source

Generated by the Author

tree

Description

Placeholder description for tree dataset.

Usage

tree

Format

An object of class phylo or similar

Source

Generated by the Author

Write Indexing PCR Spreadsheet

Description

Creates a new Google Sheet for indexing PCRs from a template, fills in the sample information, and writes it into the correct ranges of the sheet.

Usage

write_indexing_PCR(
  data,
  name,
  ss_template = "1naS-F_dj4SNmND5nJ5TKhMX3TikmRS00ILKjfg_Ucgc"
)

Arguments

Details

This function: 1. Creates a new Google Sheet with the given 'name'. 2. Copies the template sheet into it. 3. Splits the input 'data' by plate column. 4. Writes each split dataframe into its designated range of the sheet.

Value

A Google Sheet object (as returned by [googlesheets4::gs4_create()]) with the data written into the correct plate layout.

Examples

#Examples are not executed because the function requires identification in Google 
## Not run: 
my_data <- tibble::tibble(
 Well = c("A1","A2"),
  Sample = c("Sample1","Sample2"),
  Column = c(1,2)
)
write_indexing_PCR(my_data, "PCR_001")

## End(Not run)