Package 'qckitfastq'

Title:	FASTQ Quality Control
Description:	Assessment of FASTQ file format with multiple metrics including quality score, sequence content, overrepresented sequence and Kmers.
Authors:	Wenyue Xing [aut], August Guang [aut, cre]
Maintainer:	August Guang <[email protected]>
License:	Artistic-2.0
Version:	1.23.0
Built:	2024-12-19 05:39:25 UTC
Source:	https://github.com/bioc/qckitfastq

Help Index

Creates a sorted from most frequent to least frequent abundance table of adapters that are found to be present in the reads at greater than 0.1% of the reads. If output_file is selected then will save the entire set of adapters and counts. Only available for macOS/Linux due to dependency on C++14.
Compute adapter content in reads. This function is only available for macOS/Linux.
Calculate score based on Illumina format
Calculate sequece counts for each unique sequence and create a table with unique sequences and corresponding counts
Extract the number of columns and rows for a FASTQ file using seqTools.
Gets quality score encoding format from the FASTQ file. Return possibilities are Sanger(/Illumina1.8), Solexa(/Illumina1.0), Illumina1.3, and Illumina1.5. This encoding is heuristic based and may not be 100 since there is overlap in the encodings used, so it is best if you already know the format.
Calculates GC content percentage for each read in the dataset.
Calculate GC nucleotide sequence content per read of the FASTQ gzipped file
Return kmer count per sequence for the length of kmer desired
Generate overrepresented kmers of length k based on their observed to expected ratio at each position across all sequences in the dataset. The expected proportion of a length k kmer assumes site independence and is computed as the sum of the count of each base pair in the kmer times the probability of observing that base pair in the data set, i.e. P(A)count_in_kmer(A)+P(C)count_in_kmer(C)+... The observed to expected ratio is computed as log2(obs/exp). Those with obsexp_ratio > 2 are considered to be overrepresented and appear in the returned data frame along with their position in the sequence.
Sort all sequences per read by count.
Compute the mean, median, and percentiles of quality score per base. This is returned as a data frame.
Compute the mean quality score per read. per_read_quality
Creates a bar plot of the top 5 most present adapter sequences.
Generate mean GC content histogram.
Determine how to plot outliers. Heuristic used is whether their obsexp_ratio differs by more than 1 and whether they fall into the same bin or not. If for 2 outliers, obsexp_ratio differs by less than .4 and they are in the same bin, then combine into a single plotting point. NOT FULLY FUNCTIONAL
Create a box plot of the log2(observed/expected) ratio across the length of the sequence as well as top overrepresented kmers. Only ratios greater than 2 are included in the box plot. Default is 20 bins across the length of the sequence and the top 2 overrepresented kmers, but this can be changed by the user.
Plot the top 5 seqeunces
Generate a boxplot of the per position quality score.
Plot the mean quality score per sequence as a histogram. High quality sequences are those mostly distributed over 30. Low quality sequences are those mostly under 30. plot_per_read_quality
Plot the per position nucleotide content.
Plot a histogram of the number of reads with each read length.
Calculate the mean quality score per read of the FASTQ gzipped file
Compute nucleotide content per position for a single base pair. Wrapper function around seqTools.
Compute nucleotide content per position. Wrapper function around seqTools.
Creates a data frame of read lengths and the number of reads with that read length.
Will run all functions in the qckitfastq suite and save the data frames and plots to a user-provided directory. Plot names are supplied by default.

Creates a sorted from most frequent to least frequent abundance table of adapters that are found to be present in the reads at greater than 0.1% of the reads. If output_file is selected then will save the entire set of adapters and counts. Only available for macOS/Linux due to dependency on C++14.

Description

Creates a sorted from most frequent to least frequent abundance table of adapters that are found to be present in the reads at greater than 0.1% of the reads. If output_file is selected then will save the entire set of adapters and counts. Only available for macOS/Linux due to dependency on C++14.

Usage

adapter_content(infile, adapter_file = system.file("extdata",
  "adapters.txt", package = "qckitfastq"), output_file = NA)
adapter_content(infile, adapter_file = system.file("extdata",
  "adapters.txt", package = "qckitfastq"), output_file = NA)

Arguments

`infile`	the path to a gzipped FASTQ file
`adapter_file`	Path to adapters.txt file. Default from package.
`output_file`	File to save data frame to. Default NA.

Value

Sorted table of adapters and counts.

Examples

if(.Platform$OS.type != "windows") {
infile <- system.file("extdata","test.fq.gz",
    package = "qckitfastq")
adapter_content(infile)[1:5]
}
if(.Platform$OS.type != "windows") {
infile <- system.file("extdata","test.fq.gz",
    package = "qckitfastq")
adapter_content(infile)[1:5]
}

Compute adapter content in reads. This function is only available for macOS/Linux.

Description

Compute adapter content in reads. This function is only available for macOS/Linux.

Usage

calc_adapter_content(infile, adapters)
calc_adapter_content(infile, adapters)

Arguments

`infile`	filepath to fastq sequence
`adapters`	filepath to adapters

Value

map object with adapter names as the key and the number of times the adapters appears in the reads as the value

Examples

if(.Platform$OS.type != "windows") {
adapter_file <- system.file("extdata", "adapters.txt", package = "qckitfastq")
infile <- system.file("extdata", "test.fq.gz", package = "qckitfastq")
content <- calc_adapter_content(infile, adapter_file)
}
if(.Platform$OS.type != "windows") {
adapter_file <- system.file("extdata", "adapters.txt", package = "qckitfastq")
infile <- system.file("extdata", "test.fq.gz", package = "qckitfastq")
content <- calc_adapter_content(infile, adapter_file)
}

Calculate score based on Illumina format

Description

Calculate score based on Illumina format

Usage

calc_format_score(score, score_format)
calc_format_score(score, score_format)

Arguments

`score`	An ascii quality score from the fastq
`score_format`	The illumina format

Value

a string as with the best guess as to the illumina format

Examples

calc_format_score("A","Sanger")
calc_format_score("A","Sanger")

Calculate sequece counts for each unique sequence and create a table with unique sequences and corresponding counts

Description

Calculate sequece counts for each unique sequence and create a table with unique sequences and corresponding counts

Usage

calc_over_rep_seq(infile, min_size = 5L, buffer_size = 1000000L)
calc_over_rep_seq(infile, min_size = 5L, buffer_size = 1000000L)

Arguments

`infile`	A string giving the path for the fastqfile
`min_size`	An int for thhresholding over representation
`buffer_size`	An int for the number of lines to keep in memory

Value

calculate overrepresented sequence count

Examples

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
calc_over_rep_seq(infile)[seq_len(5)]
infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
calc_over_rep_seq(infile)[seq_len(5)]

Extract the number of columns and rows for a FASTQ file using seqTools.

Description

Extract the number of columns and rows for a FASTQ file using seqTools.

Usage

dimensions(fseq, sel)
dimensions(fseq, sel)

Arguments

`fseq`	an object that is the read result of the seq.read function
`sel`	'reads' for #reads/rows, 'positions' for #positions/columns

Value

a numeric value of the number of reads or the number of positions

Examples


infile <- system.file("extdata","10^5_reads_test.fq.gz",
    package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
dimensions(fseq,"reads")

infile <- system.file("extdata","10^5_reads_test.fq.gz",
    package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
dimensions(fseq,"reads")

Gets quality score encoding format from the FASTQ file. Return possibilities are Sanger(/Illumina1.8), Solexa(/Illumina1.0), Illumina1.3, and Illumina1.5. This encoding is heuristic based and may not be 100 since there is overlap in the encodings used, so it is best if you already know the format.

Description

Gets quality score encoding format from the FASTQ file. Return possibilities are Sanger(/Illumina1.8), Solexa(/Illumina1.0), Illumina1.3, and Illumina1.5. This encoding is heuristic based and may not be 100 since there is overlap in the encodings used, so it is best if you already know the format.

Usage

find_format(infile, reads_used)
find_format(infile, reads_used)

Arguments

`infile`	A string giving the path for the fastq file
`reads_used`	int, the number of reads to use to determine the encoding format.

Value

A string denoting the read format. Possibilities are Sanger, Solexa, Illumina1.3, and Illumina1.5.

Examples

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
find_format(infile,100)
infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
find_format(infile,100)

Calculates GC content percentage for each read in the dataset.

Description

Calculates GC content percentage for each read in the dataset.

Usage

GC_content(infile, output_file = NA)
GC_content(infile, output_file = NA)

Arguments

`infile`	the object that is the path to the FASTQ file
`output_file`	File to write results to. Default NA.

Value

Data frame with read ID and GC content of each read.

Examples


infile <- system.file("extdata", "10^5_reads_test.fq.gz",
    package = "qckitfastq")
head(GC_content(infile))

infile <- system.file("extdata", "10^5_reads_test.fq.gz",
    package = "qckitfastq")
head(GC_content(infile))

Calculate GC nucleotide sequence content per read of the FASTQ gzipped file

Description

Calculate GC nucleotide sequence content per read of the FASTQ gzipped file

Usage

gc_per_read(infile)
gc_per_read(infile)

Arguments

infile

A string giving the path for the fastqfile

Value

GC content perncentage per read

Examples

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
gc_per_read(infile)[1:10]
infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
gc_per_read(infile)[1:10]

Return kmer count per sequence for the length of kmer desired

Description

Return kmer count per sequence for the length of kmer desired

Usage

kmer_count(infile, k, output_file = NA)
kmer_count(infile, k, output_file = NA)

Arguments

`infile`	the object that is the path to gzippped FASTQ file
`k`	the length of kmer
`output_file`	File to save plot to. Default NA.

Value

kmers counts per sequence

Examples


infile <- system.file("extdata", "10^5_reads_test.fq.gz",
    package = "qckitfastq")
km<-kmer_count(infile,k=4)
km[1:20,1:10]

infile <- system.file("extdata", "10^5_reads_test.fq.gz",
    package = "qckitfastq")
km<-kmer_count(infile,k=4)
km[1:20,1:10]

Generate overrepresented kmers of length k based on their observed to expected ratio at each position across all sequences in the dataset. The expected proportion of a length k kmer assumes site independence and is computed as the sum of the count of each base pair in the kmer times the probability of observing that base pair in the data set, i.e. P(A)count_in_kmer(A)+P(C)count_in_kmer(C)+... The observed to expected ratio is computed as log2(obs/exp). Those with obsexp_ratio > 2 are considered to be overrepresented and appear in the returned data frame along with their position in the sequence.

Description

Generate overrepresented kmers of length k based on their observed to expected ratio at each position across all sequences in the dataset. The expected proportion of a length k kmer assumes site independence and is computed as the sum of the count of each base pair in the kmer times the probability of observing that base pair in the data set, i.e. P(A)count_in_kmer(A)+P(C)count_in_kmer(C)+... The observed to expected ratio is computed as log2(obs/exp). Those with obsexp_ratio > 2 are considered to be overrepresented and appear in the returned data frame along with their position in the sequence.

Usage

overrep_kmer(infile, k, output_file = NA)
overrep_kmer(infile, k, output_file = NA)

Arguments

`infile`	path to gzipped FASTQ file
`k`	the kmer length
`output_file`	File to save plot to. Default NA.

Value

Data frame with columns: Position (in read), Obsexp_ratio, & Kmer

Examples


infile <-system.file("extdata", "test.fq.gz",
    package = "qckitfastq")
overrep_kmer(infile,k=4)

infile <-system.file("extdata", "test.fq.gz",
    package = "qckitfastq")
overrep_kmer(infile,k=4)

Sort all sequences per read by count.

Description

Sort all sequences per read by count.

Usage

overrep_reads(infile, output_file = NA)
overrep_reads(infile, output_file = NA)

Arguments

`infile`	Path to gzippped FASTQ file.
`output_file`	File to save data frame to. Default NA.

Value

Table of sequences sorted by count.

Examples

infile <- system.file("extdata", "10^5_reads_test.fq.gz",
    package = "qckitfastq")
overrep_reads(infile)[1:5,]
infile <- system.file("extdata", "10^5_reads_test.fq.gz",
    package = "qckitfastq")
overrep_reads(infile)[1:5,]

Compute the mean, median, and percentiles of quality score per base. This is returned as a data frame.

Description

Compute the mean, median, and percentiles of quality score per base. This is returned as a data frame.

Usage

per_base_quality(infile, output_file = NA)
per_base_quality(infile, output_file = NA)

Arguments

`infile`	Path to a gzippped FASTQ file
`output_file`	File to write results in CSV format to. Default NA.

Value

A dataframe of the mean, median and quantiles of the FASTQ file

Author(s)

Wenyue Xing, [email protected]

August Guang, [email protected]

Examples

per_base_quality(system.file("extdata", "10^5_reads_test.fq.gz",
    package = "qckitfastq"))
per_base_quality(system.file("extdata", "10^5_reads_test.fq.gz",
    package = "qckitfastq"))

Compute the mean quality score per read. `per_read_quality`

Description

Compute the mean quality score per read. per_read_quality

Usage

per_read_quality(infile, output_file = NA)
per_read_quality(infile, output_file = NA)

Arguments

`infile`	Path to FASTQ file
`output_file`	File to write plot to. Will not write to file if NA. Default NA.

Value

Data frame of mean quality score per read

Examples

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
prq <- per_read_quality(infile)

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
prq <- per_read_quality(infile)

Creates a bar plot of the top 5 most present adapter sequences.

Description

Creates a bar plot of the top 5 most present adapter sequences.

Usage

plot_adapter_content(ac_sorted, output_file = NA)
plot_adapter_content(ac_sorted, output_file = NA)

Arguments

`ac_sorted`	Sorted table of adapters and counts.
`output_file`	File to save data frame to. Default NA.

Value

Barplot of top 5 most frequent adapter sequences.

Examples

if(.Platform$OS.type != "windows") {
infile <- system.file("extdata", "test.fq.gz", package = "qckitfastq")
ac_sorted <- adapter_content(infile)
plot_adapter_content(ac_sorted)
}
if(.Platform$OS.type != "windows") {
infile <- system.file("extdata", "test.fq.gz", package = "qckitfastq")
ac_sorted <- adapter_content(infile)
plot_adapter_content(ac_sorted)
}

Generate mean GC content histogram.

Description

Generate mean GC content histogram.

Usage

plot_GC_content(gc_df, output_file = NA)
plot_GC_content(gc_df, output_file = NA)

Arguments

`gc_df`	the object that is the GC content vectors generated from GC content function
`output_file`	File to write plot to. Will not write to file if NA. Default NA.

Value

A histogram of mean GC content.

Examples


infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
gc_df<-GC_content(infile)
plot_GC_content(gc_df)

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
gc_df<-GC_content(infile)
plot_GC_content(gc_df)

Determine how to plot outliers. Heuristic used is whether their obsexp_ratio differs by more than 1 and whether they fall into the same bin or not. If for 2 outliers, obsexp_ratio differs by less than .4 and they are in the same bin, then combine into a single plotting point. NOT FULLY FUNCTIONAL

Description

Determine how to plot outliers. Heuristic used is whether their obsexp_ratio differs by more than 1 and whether they fall into the same bin or not. If for 2 outliers, obsexp_ratio differs by less than .4 and they are in the same bin, then combine into a single plotting point. NOT FULLY FUNCTIONAL

Usage

plot_outliers(overkm, top_num)
plot_outliers(overkm, top_num)

Arguments

`overkm`	data frame with columns pos, obsexp_ratio, and kmer that has already been reordered by descending obsexp_ratio
`top_num`	number of most overrepresented kmers to plot. Default is 5.

Value

currently 0 as function is not fully working.

Create a box plot of the log2(observed/expected) ratio across the length of the sequence as well as top overrepresented kmers. Only ratios greater than 2 are included in the box plot. Default is 20 bins across the length of the sequence and the top 2 overrepresented kmers, but this can be changed by the user.

Description

Create a box plot of the log2(observed/expected) ratio across the length of the sequence as well as top overrepresented kmers. Only ratios greater than 2 are included in the box plot. Default is 20 bins across the length of the sequence and the top 2 overrepresented kmers, but this can be changed by the user.

Usage

plot_overrep_kmer(overkm, bins = 20, top_num = 2, output_file = NA)
plot_overrep_kmer(overkm, bins = 20, top_num = 2, output_file = NA)

Arguments

`overkm`	data frame with columns pos, obsexp_ratio, and kmer
`bins`	number of intervals across the length of the sequence
`top_num`	number of most overrepresented kmers to plot
`output_file`	File to write plot to. Will not write to file if NA. Default NA.

Value

A box plot of the log2(observed/expected ratio) across the length of the sequence

Examples

infile <- system.file("extdata", "test.fq.gz",
    package = "qckitfastq")
over_km <- overrep_kmer(infile,k=4)
plot_overrep_kmer(over_km)
infile <- system.file("extdata", "test.fq.gz",
    package = "qckitfastq")
over_km <- overrep_kmer(infile,k=4)
plot_overrep_kmer(over_km)

Plot the top 5 seqeunces

Description

Plot the top 5 seqeunces

Usage

plot_overrep_reads(overrep_reads, output_file = NA)
plot_overrep_reads(overrep_reads, output_file = NA)

Arguments

`overrep_reads`	the table that sorts the sequence content and corresponding counts in descending order
`output_file`	File to save plot to. Will not write to file if NA. Default NA.

Value

plot of the top 5 overrepresented sequences

Examples


infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
overrep_df <- overrep_reads(infile)
plot_overrep_reads(overrep_df)

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
overrep_df <- overrep_reads(infile)
plot_overrep_reads(overrep_df)

Generate a boxplot of the per position quality score.

Description

Generate a boxplot of the per position quality score.

Usage

plot_per_base_quality(per_base_quality, output_file = NA)
plot_per_base_quality(per_base_quality, output_file = NA)

Arguments

`per_base_quality`	a data frame of the mean, median and quantiles of sequence quality per base. Most likely generated with the 'per_base_quality' function.
`output_file`	File to save plot to. Will not write to file if NA. Default NA.

Value

A boxplot of per position quality score distribution.

Examples


pbq <- per_base_quality(system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq"))
plot_per_base_quality(pbq)
pbq <- per_base_quality(system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq"))
plot_per_base_quality(pbq)

Plot the mean quality score per sequence as a histogram. High quality sequences are those mostly distributed over 30. Low quality sequences are those mostly under 30. `plot_per_read_quality`

Description

Plot the mean quality score per sequence as a histogram. High quality sequences are those mostly distributed over 30. Low quality sequences are those mostly under 30. plot_per_read_quality

Usage

plot_per_read_quality(prq, output_file = NA)
plot_per_read_quality(prq, output_file = NA)

Arguments

`prq`	Data frame from per_read_quality function
`output_file`	File to write plot to. Will not write to file if NA. Default NA.

Value

Plot of mean quality score per read

Examples

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
prq <- per_read_quality(infile)
plot_per_read_quality(prq)
infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
prq <- per_read_quality(infile)
plot_per_read_quality(prq)

Plot the per position nucleotide content.

Description

Plot the per position nucleotide content.

Usage

plot_read_content(read_content, output_file = NA)
plot_read_content(read_content, output_file = NA)

Arguments

`read_content`	Data frame produced by read_content function.
`output_file`	File to save plot to. Will not write to file if NA. Default NA.

Value

ggplot line plot of all nucleotide content inclding A, T, G, C and N

Examples

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
read_content <- read_content(fseq)
plot_read_content(read_content)
infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
read_content <- read_content(fseq)
plot_read_content(read_content)

Plot a histogram of the number of reads with each read length.

Description

Plot a histogram of the number of reads with each read length.

Usage

plot_read_length(read_len, output_file = NA)
plot_read_length(read_len, output_file = NA)

Arguments

`read_len`	Data frame of read lengths and number of reads with that length.
`output_file`	File to save plot to. Default is NA, i.e. do not write to file.

Value

A histogram of the read length distribution.

Author(s)

Wenyue Xing, [email protected], August Guang, [email protected]

Examples


infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
read_len <- read_length(fseq)
plot_read_length(read_len)

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
read_len <- read_length(fseq)
plot_read_length(read_len)

Calculate the mean quality score per read of the FASTQ gzipped file

Description

Calculate the mean quality score per read of the FASTQ gzipped file

Usage

qual_score_per_read(infile)
qual_score_per_read(infile)

Arguments

infile

A string giving the path for the fastqfile

Value

mean quality per read

Examples

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
qual_score_per_read(infile)$q50_per_position[1:10]
infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
qual_score_per_read(infile)$q50_per_position[1:10]

Compute nucleotide content per position for a single base pair. Wrapper function around seqTools.

Description

Compute nucleotide content per position for a single base pair. Wrapper function around seqTools.

Usage

read_base_content(fseq, content)
read_base_content(fseq, content)

Arguments

`fseq`	a seqTools::fastqq object
`content`	nucleotide. Options are "A","T","G","C","N"(either capital or lower case)

Value

Nucleotide sequence content per position.

Author(s)

Wenyue Xing, [email protected], August Guang [email protected]

Examples

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
read_base_content(fseq,"A")
infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
read_base_content(fseq,"A")

Compute nucleotide content per position. Wrapper function around seqTools.

Description

Compute nucleotide content per position. Wrapper function around seqTools.

Usage

read_content(fseq, output_file = NA)
read_content(fseq, output_file = NA)

Arguments

`fseq`	a seqTools::fastqq object
`output_file`	File to write results in CSV format to. Will not write to file if NA. Default NA.

Value

Data frame of nucleotide sequence content per position

Examples

infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
read_content(fseq)
infile <- system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
read_content(fseq)

Creates a data frame of read lengths and the number of reads with that read length.

Description

Creates a data frame of read lengths and the number of reads with that read length.

Usage

read_length(fseq, output_file = NA)
read_length(fseq, output_file = NA)

Arguments

`fseq`	a seqTools object produced by seqTools::fastqq on the raw FASTQ file
`output_file`	File to save data frame to. Default NA.

Value

Data frame of read lengths and number of reads with that length.

Examples

infile <- system.file("extdata","test.fq.gz",
    package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
read_len <- read_length(fseq)
infile <- system.file("extdata","test.fq.gz",
    package = "qckitfastq")
fseq <- seqTools::fastqq(infile,k=6)
read_len <- read_length(fseq)

Will run all functions in the qckitfastq suite and save the data frames and plots to a user-provided directory. Plot names are supplied by default.

Description

Will run all functions in the qckitfastq suite and save the data frames and plots to a user-provided directory. Plot names are supplied by default.

Usage

run_all(infile, dir)
run_all(infile, dir)

Arguments

`infile`	Path to gzipped FASTQ file
`dir`	Directory to save results to

Value

Generate files from all functions

Examples

infile <- system.file("extdata", "test.fq.gz",
    package = "qckitfastq")
testfolder <- tempdir()
run_all(infile, testfolder)
infile <- system.file("extdata", "test.fq.gz",
    package = "qckitfastq")
testfolder <- tempdir()
run_all(infile, testfolder)

Package 'qckitfastq'

Help Index

Creates a sorted from most frequent to least frequent abundance table of adapters that are found to be present in the reads at greater than 0.1% of the reads. If output_file is selected then will save the entire set of adapters and counts. Only available for macOS/Linux due to dependency on C++14.

Description

Usage

Arguments

Value

Examples

Compute adapter content in reads. This function is only available for macOS/Linux.

Description

Usage

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Value

Examples

Calculate score based on Illumina format

Description

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Value

Examples

Calculate sequece counts for each unique sequence and create a table with unique sequences and corresponding counts

Description

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Value

Examples

Extract the number of columns and rows for a FASTQ file using seqTools.

Description

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Value

Examples

Gets quality score encoding format from the FASTQ file. Return possibilities are Sanger(/Illumina1.8), Solexa(/Illumina1.0), Illumina1.3, and Illumina1.5. This encoding is heuristic based and may not be 100 since there is overlap in the encodings used, so it is best if you already know the format.

Description

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Value

Examples

Calculates GC content percentage for each read in the dataset.

Description

Usage

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Value

Examples

Calculate GC nucleotide sequence content per read of the FASTQ gzipped file

Description

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Value

Examples

Return kmer count per sequence for the length of kmer desired

Description

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Value

Examples

Description

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Value

Examples

Sort all sequences per read by count.

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Value

Examples

Compute the mean, median, and percentiles of quality score per base. This is returned as a data frame.

Description

Usage

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Value

Author(s)

Examples

Compute the mean quality score per read. per_read_quality

Description

Usage

Arguments

Value

Examples

Compute the mean quality score per read. `per_read_quality`

Plot the mean quality score per sequence as a histogram. High quality sequences are those mostly distributed over 30. Low quality sequences are those mostly under 30. `plot_per_read_quality`