| Title: | A Shiny Application for Quality Control, Filtering and Trimming of FASTQ Files |
|---|---|
| Description: | An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files. |
| Authors: | Leandro Roser [aut, cre], Fernán Agüero [aut], Daniel Sánchez [aut] |
| Maintainer: | Leandro Roser <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.25.0 |
| Built: | 2024-11-29 07:12:10 UTC |
| Source: | https://github.com/bioc/FastqCleaner |
This program can remove adapters and partial
adapters from 3' and 5', using the functions
trimLRPatterns
The program extends the methodology of
the trimLRPatterns function of Biostrings,
being also capable of removing adapters present within reads and with other
additional otpions
(e.g., threshold of minimum number of bases for trimming).
For a given position in the read, the two Biostrings functions return TRUE
when a match is present between a substring of the read and the adapter.
As trimLRPatterns , adapter_filter also selects
region and goes up to the end of the sequence in the corresponding flank
as the best match. The default error rate is 0.2.
If several valid matches are found, the function removes the
largest subsequence. Adapters can be anchored or not.
When indels are allowed, the second method uses the 'edit distance' between
the subsequences and the adapter
adapter_filter( input, Lpattern = "", Rpattern = "", rc.L = FALSE, rc.R = FALSE, first = c("R", "L"), with_indels = FALSE, error_rate = 0.2, anchored = TRUE, fixed = "subject", remove_zero = TRUE, checks = TRUE, min_match_flank = 3L, ... )adapter_filter( input, Lpattern = "", Rpattern = "", rc.L = FALSE, rc.R = FALSE, first = c("R", "L"), with_indels = FALSE, error_rate = 0.2, anchored = TRUE, fixed = "subject", remove_zero = TRUE, checks = TRUE, min_match_flank = 3L, ... )
input |
|
Lpattern |
5' pattern (character or
|
Rpattern |
3' pattern (character or
|
rc.L |
Reverse complement Lpattern? default FALSE |
rc.R |
Reverse complement Rpatter? default FALSE |
first |
trim first right('R') or left ('L') side of sequences when both Lpattern and Rpattern are passed |
with_indels |
Allow indels? This feature is available only when the error_rate is not null |
error_rate |
Error rate (value in the range [0, 1] The error rate is the proportion of mismatches allowed between the adapter and the aligned portion of the subject. For a given adapter A, the number of allowed mismatches between each subsequence s of A and the subject is computed as: error_rate * L_s, where L_s is the length of the subsequence s |
anchored |
Adapter or partial adapter within sequence (anchored = FALSE, default) or only in 3' and 5' terminals? (anchored = TRUE) |
fixed |
Parameter passed to
|
remove_zero |
Remove zero-length sequences? Default TRUE |
checks |
Perform checks? Default TRUE |
min_match_flank |
Do not trim in flanks of the subject, if a match has min_match_flank of less length. Default 1L (only trim with >=2 coincidences in a flank match) |
... |
additional parameters passed to
|
Edited DNAString or
DNAStringSet object
Filtered ShortReadQ
object
Leandro Roser [email protected]
require('Biostrings') require('ShortRead') # create 6 sequences of width 43 set.seed(10) input <- random_seq(6, 43) # add adapter in 3' adapter <- "ATCGACT" input <- paste0(input, as.character(DNAString(adapter))) input <- DNAStringSet(input) # create qualities of width 50 set.seed(10) input_q <- random_qual(c(30,40), slength = 6, swidth = 50, encod = 'Sanger') # create names input_names <- seq_names(length(input)) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # trim adapter filtered <- adapter_filter(my_read, Rpattern = adapter) # look at the filtered sequences sread(filtered)require('Biostrings') require('ShortRead') # create 6 sequences of width 43 set.seed(10) input <- random_seq(6, 43) # add adapter in 3' adapter <- "ATCGACT" input <- paste0(input, as.character(DNAString(adapter))) input <- DNAStringSet(input) # create qualities of width 50 set.seed(10) input_q <- random_qual(c(30,40), slength = 6, swidth = 50, encod = 'Sanger') # create names input_names <- seq_names(length(input)) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # trim adapter filtered <- adapter_filter(my_read, Rpattern = adapter) # look at the filtered sequences sread(filtered)
Check quality encoding
check_encoding(x = NULL, custom = NULL)check_encoding(x = NULL, custom = NULL)
x |
Quality values |
custom |
custom encoding from the following: 'Sanger' ——–> expected range: [0, 40] 'Illumina1.8' ——–> expected range: [0, 41] 'Illumina1.5' ——–> expected range: [0, 40] 'Illumina1.3' ——–> expected range: [3, 40] 'Solexa' ——–> expected range: [-5, 40] |
List with encoding information
Leandro Roser [email protected]
require(Biostrings) x <- list(PhredQuality(0:40), SolexaQuality(-5:40), IlluminaQuality(3:40)) x <- lapply(x, function(i)utf8ToInt(as.character(i)[1])) lapply(x, check_encoding) SolexaQuality(0:40) IlluminaQuality(0:40)require(Biostrings) x <- list(PhredQuality(0:40), SolexaQuality(-5:40), IlluminaQuality(3:40)) x <- lapply(x, function(i)utf8ToInt(as.character(i)[1])) lapply(x, check_encoding) SolexaQuality(0:40) IlluminaQuality(0:40)
The program removes low complexity sequences, computing the entropy with the observed frequency of dinucleotides.
complex_filter(input, threshold = 0.5, referenceEntropy = 3.908135)complex_filter(input, threshold = 0.5, referenceEntropy = 3.908135)
input |
|
threshold |
A threshold value computed as the relation of the H of the sequences and the reference H. Default is 0.5 |
referenceEntropy |
Reference entropy. By default, the program uses a value of 3.908, that corresponds to the entropy of the human genome in bits |
Filtered ShortReadQ
object
Leandro Roser [email protected]
require('Biostrings') require('ShortRead') # create sequences of different width set.seed(10) input <- lapply(c(0, 6, 10, 16, 20, 26, 30, 36, 40), function(x) random_seq(1, x)) # create repetitive 'CG' sequences with length adequante # for a total length: # input + CG = 40 set.seed(10) CG <- lapply(c(20, 17, 15, 12, 10, 7, 5, 2, 0), function(x) paste(rep('CG', x), collapse = '')) # concatenate input and CG input <- mapply('paste', input, CG, sep = '') input <- DNAStringSet(input) # plot relative entropy (E, Shannon 1948) freq <- dinucleotideFrequency(input) freq <- freq /rowSums(freq) H <- -rowSums(freq * log2(freq), na.rm = TRUE) H_max <- 3.908135 # max entropy plot(H/H_max, type='b', xlab = 'Sequence', ylab= 'E') # create qualities of width 40 set.seed(10) input_q <- random_qual(c(30,40), slength = 9, swidth = 40, encod = 'Sanger') # create names input_names <- seq_names(9) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # apply the filter filtered <- complex_filter(my_read) # look at the filtered sequences sread(filtered)require('Biostrings') require('ShortRead') # create sequences of different width set.seed(10) input <- lapply(c(0, 6, 10, 16, 20, 26, 30, 36, 40), function(x) random_seq(1, x)) # create repetitive 'CG' sequences with length adequante # for a total length: # input + CG = 40 set.seed(10) CG <- lapply(c(20, 17, 15, 12, 10, 7, 5, 2, 0), function(x) paste(rep('CG', x), collapse = '')) # concatenate input and CG input <- mapply('paste', input, CG, sep = '') input <- DNAStringSet(input) # plot relative entropy (E, Shannon 1948) freq <- dinucleotideFrequency(input) freq <- freq /rowSums(freq) H <- -rowSums(freq * log2(freq), na.rm = TRUE) H_max <- 3.908135 # max entropy plot(H/H_max, type='b', xlab = 'Sequence', ylab= 'E') # create qualities of width 40 set.seed(10) input_q <- random_qual(c(30,40), slength = 9, swidth = 40, encod = 'Sanger') # create names input_names <- seq_names(9) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # apply the filter filtered <- complex_filter(my_read) # look at the filtered sequences sread(filtered)
The program removes a given number of bases from the 3' or 5' regions of the sequences contained in a ShortReadQ object
fixed_filter(input, trim3 = NA, trim5 = NA)fixed_filter(input, trim3 = NA, trim5 = NA)
input |
|
trim3 |
Number of bases to remove from 3' |
trim5 |
Number of bases to remove from 5' |
Filtered ShortReadQ
object
Leandro Roser [email protected]
require('Biostrings') require('ShortRead') # create 6 sequences of width 20 set.seed(10) input <- random_seq(6, 20) # create qualities of width 20 set.seed(10) input_q <- random_qual(c(30,40), slength = 6, swidth = 20, encod = 'Sanger') # create names input_names <- seq_names(6) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # apply the filter filtered3 <- fixed_filter(my_read, trim5 = 5) filtered5 <- fixed_filter(my_read, trim3 = 5) filtered3and5 <- fixed_filter(my_read, trim3 = 10, trim5 = 5) # look at the trimmed sequences sread(filtered3) sread(filtered5) sread(filtered3and5)require('Biostrings') require('ShortRead') # create 6 sequences of width 20 set.seed(10) input <- random_seq(6, 20) # create qualities of width 20 set.seed(10) input_q <- random_qual(c(30,40), slength = 6, swidth = 20, encod = 'Sanger') # create names input_names <- seq_names(6) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # apply the filter filtered3 <- fixed_filter(my_read, trim5 = 5) filtered5 <- fixed_filter(my_read, trim3 = 5) filtered3and5 <- fixed_filter(my_read, trim3 = 10, trim5 = 5) # look at the trimmed sequences sread(filtered3) sread(filtered5) sread(filtered3and5)
Inject a letter in a set of sequences at random positions
inject_letter_random( my_seq, how_many_seqs = NULL, how_many_letters = NULL, letter = "N" )inject_letter_random( my_seq, how_many_seqs = NULL, how_many_letters = NULL, letter = "N" )
my_seq |
character vector with sequences to inject |
how_many_seqs |
How many sequences pick to inject Ns. An interval [min_s, max_s] with min_s minimum and max_s maximum sequences can be passed. In this case, a value is picked from the interval. If NULL, a random value within the interval [1, length(my_seq)] is picked. |
how_many_letters |
How many times inject the letter in the i sequences that are going to be injected. An interval [min_i max_i] can be passed. In this case, a value is randomly picked for each sequence i. This value represents the number of times that the letter will be injected in the sequence i. If NULL, a random value within the interval [1, width(my_seq[i])] is picked for each sequence i. |
letter |
Letter to inject. Default: 'N' |
character vector
Leandro Roser [email protected]
# For reproducible examples, make a call to set.seed before # running each random function set.seed(10) s <- random_seq(slength = 10, swidth = 20) set.seed(10) s <- inject_letter_random(s, how_many_seqs = 1:30, how_many= 2:10)# For reproducible examples, make a call to set.seed before # running each random function set.seed(10) s <- random_seq(slength = 10, swidth = 20) set.seed(10) s <- inject_letter_random(s, how_many_seqs = 1:30, how_many= 2:10)
Launch FastqCleaner application
launch_fqc(launch.browser = TRUE, ...)launch_fqc(launch.browser = TRUE, ...)
launch.browser |
Launch in browser? Default TRUE |
... |
Additional parameters passed to |
Launch the application, without return value
Leandro Roser [email protected]
# Uncomment and paste in te console to launch the application: # launch_fqc() NULL# Uncomment and paste in te console to launch the application: # launch_fqc() NULL
The program removes from a ShortReadQ object those sequences with a length lower than rm.min or/and higher than rm.max
length_filter(input, rm.min = NA, rm.max = NA)length_filter(input, rm.min = NA, rm.max = NA)
input |
|
rm.min |
Threshold value for the minimun number of bases |
rm.max |
Threshold value for the maximum number of bases |
Filtered ShortReadQ
object
Leandro Roser [email protected]
require('Biostrings') require('ShortRead') # create ShortReadQ object width widths between 1 and 100 set.seed(10) input <- random_length(100, widths = 1:100) # apply the filter, removing sequences length < 10 or length > 80 filtered <- length_filter(input, rm.min = 10, rm.max = 80) # look at the filtered sequences sread(filtered)require('Biostrings') require('ShortRead') # create ShortReadQ object width widths between 1 and 100 set.seed(10) input <- random_length(100, widths = 1:100) # apply the filter, removing sequences length < 10 or length > 80 filtered <- length_filter(input, rm.min = 10, rm.max = 80) # look at the filtered sequences sread(filtered)
This program is a wrapper to
nFilter.
It removes the sequences with a number of N's above
a threshold value 'rm.N'.
All the sequences with a number of N > rm.N (N >= rm.N) will be removed
n_filter(input, rm.N)n_filter(input, rm.N)
input |
|
rm.N |
Threshold value of N's to remove a sequence from the output (sequences with number of Ns > threshold are removed) For example, if rm.N is 3, all the sequences with a number of Ns > 3 (Ns >= 4) will be removed |
Filtered ShortReadQ
object
Leandro Roser [email protected]
require('Biostrings') require('ShortRead') # create 6 sequences of width 20 set.seed(10) input <- random_seq(50, 20) # inject N's set.seed(10) input <- inject_letter_random(input, how_many_seqs = 1:30, how_many = 1:10) input <- DNAStringSet(input) # watch the N's frequency hist(letterFrequency(input, 'N'), breaks = 0:10, main = 'Ns Frequency', xlab = '# Ns') # create qualities of width 20 set.seed(10) input_q <- random_qual(50, 20) # create names input_names <- seq_names(50) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # apply the filter filtered <- n_filter(my_read, rm.N = 3) # watch the filtered sequences sread(filtered) # watch the N's frequency hist(letterFrequency(sread(filtered), 'N'), main = 'Ns distribution', xlab = '')require('Biostrings') require('ShortRead') # create 6 sequences of width 20 set.seed(10) input <- random_seq(50, 20) # inject N's set.seed(10) input <- inject_letter_random(input, how_many_seqs = 1:30, how_many = 1:10) input <- DNAStringSet(input) # watch the N's frequency hist(letterFrequency(input, 'N'), breaks = 0:10, main = 'Ns Frequency', xlab = '# Ns') # create qualities of width 20 set.seed(10) input_q <- random_qual(50, 20) # create names input_names <- seq_names(50) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # apply the filter filtered <- n_filter(my_read, rm.N = 3) # watch the filtered sequences sread(filtered) # watch the N's frequency hist(letterFrequency(sread(filtered), 'N'), main = 'Ns distribution', xlab = '')
The program removes the sequences with a quality lower the 'minq' threshold
qmean_filter(input, minq, q_format = NULL, check.encod = TRUE)qmean_filter(input, minq, q_format = NULL, check.encod = TRUE)
input |
|
minq |
Quality threshold |
q_format |
Quality format used for the file, as returned by check.encoding |
check.encod |
Check the encoding of the sequence? This argument is incompatible with q_format |
Filtered ShortReadQ
object
Leandro Roser [email protected]
require(ShortRead) set.seed(10) # create 30 sequences of width 20 input <- random_seq(30, 20) # create qualities of width 20 ## high quality (15 sequences) set.seed(10) my_qual <- random_qual(c(30,40), slength = 15, swidth = 20, encod = 'Sanger') ## low quality (15 sequences) set.seed(10) my_qual_2 <- random_qual(c(5,30), slength = 15, swidth = 20, encod = 'Sanger') # concatenate vectors input_q<- c(my_qual, my_qual_2) # create names input_names <- seq_names(30) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # watch the average qualities alphabetScore(my_read) / width(my_read) # apply the filter filtered <- qmean_filter(my_read, minq = 30) # watch the average qualities alphabetScore(my_read) / width(my_read) # watch the filtered sequences sread(filtered)require(ShortRead) set.seed(10) # create 30 sequences of width 20 input <- random_seq(30, 20) # create qualities of width 20 ## high quality (15 sequences) set.seed(10) my_qual <- random_qual(c(30,40), slength = 15, swidth = 20, encod = 'Sanger') ## low quality (15 sequences) set.seed(10) my_qual_2 <- random_qual(c(5,30), slength = 15, swidth = 20, encod = 'Sanger') # concatenate vectors input_q<- c(my_qual, my_qual_2) # create names input_names <- seq_names(30) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # watch the average qualities alphabetScore(my_read) / width(my_read) # apply the filter filtered <- qmean_filter(my_read, minq = 30) # watch the average qualities alphabetScore(my_read) / width(my_read) # watch the filtered sequences sread(filtered)
Create a ShortReadQ
object with random sequences and qualities
random_length( n, widths, random_widths = TRUE, replace = TRUE, len_prob = NULL, seq_prob = c(0.25, 0.25, 0.25, 0.25), q_prob = NULL, nuc = c("DNA", "RNA"), qual = NULL, encod = c("Sanger", "Illumina1.8", "Illumina1.5", "Illumina1.3", "Solexa"), base_name = "s", sep = "_" )random_length( n, widths, random_widths = TRUE, replace = TRUE, len_prob = NULL, seq_prob = c(0.25, 0.25, 0.25, 0.25), q_prob = NULL, nuc = c("DNA", "RNA"), qual = NULL, encod = c("Sanger", "Illumina1.8", "Illumina1.5", "Illumina1.3", "Solexa"), base_name = "s", sep = "_" )
n |
number of sequences |
widths |
width of the sequences |
random_widths |
width must be picked at random from the passed parameter 'widths', considering the value as an interval where any integer can be picked. Default TRUE. Otherwise, widths are picked only from the vector passed. |
replace |
sample widths with replacement? Default TRUE. |
len_prob |
vector with probabilities for each width value. Default NULL (equiprobability) |
seq_prob |
a vector of four probabilities values to set the frequency of the nucleotides 'A', 'C', 'G', 'T', for DNA, or 'A', 'C', 'G', 'U', for RNA. For example = c(0.25, 0.25, 0.5, 0). Default is = c(0.25, 0.25, 0.25, 0.25) (equiprobability for the 4 bases). If the sum of the probabilities is > 1, the values will be nomalized to the range [0, 1]. |
q_prob |
a vector of range = range(qual), with probabilities to set the frequency of each quality value. Default is equiprobability. If the sum of the probabilities is > 1, the values will be nomalized to the range [0, 1]. |
nuc |
create sequences of DNA (nucleotides = c('A', 'C', 'G', 'T')) or RNA (nucleotides = c('A, 'C', 'G', 'U'))?. Default: 'DNA' |
qual |
quality range for the sequences. It must be a range included in the selected encoding: 'Sanger' = [0, 40] 'Illumina1.8' = [0, 41] 'Illumina1.5' = [0, 40] 'Illumina1.3' = [3, 40] 'Solexa' = [-5, 40] example: for a range from 20 to 30 in Sanger encoding, pass the argument = c(20, 30) |
encod |
sequence encoding |
base_name |
Base name for strings |
sep |
Character separing base names and the read number. Default: '_' |
ShortReadQ object
Leandro Roser [email protected]
# For reproducible examples, make a call to set.seed before # running each random function set.seed(10) s1 <- random_seq(slength = 10, swidth = 20) s1 set.seed(10) s2 <- random_seq(slength = 10, swidth = 20, prob = c(0.6, 0.1, 0.3, 0)) s2# For reproducible examples, make a call to set.seed before # running each random function set.seed(10) s1 <- random_seq(slength = 10, swidth = 20) s1 set.seed(10) s2 <- random_seq(slength = 10, swidth = 20, prob = c(0.6, 0.1, 0.3, 0)) s2
Create a BStringSet
object
with random qualities
random_qual( slength, swidth, qual = NULL, encod = c("Sanger", "Illumina1.8", "Illumina1.5", "Illumina1.3", "Solexa"), prob = NULL )random_qual( slength, swidth, qual = NULL, encod = c("Sanger", "Illumina1.8", "Illumina1.5", "Illumina1.3", "Solexa"), prob = NULL )
slength |
number of sequences |
swidth |
width of the sequences |
qual |
quality range for the sequences. It must be a range included in the selected encoding: 'Sanger' = [0, 40] 'Illumina1.8' = [0, 41] 'Illumina1.5' = [0, 40] 'Illumina1.3' = [3, 40] 'Solexa' = [-5, 40] example: for a range from 20 to 30 in Sanger encoding, pass the argument = c(20, 30) |
encod |
sequence encoding |
prob |
a vector of range = range(qual), with probabilities to set the frequency of each quality value. Default is equiprobability. If the sum of the probabilities is > 1, the values will be nomalized to the range [0, 1]. |
BStringSet object
Leandro Roser [email protected]
q <- random_qual(30, 20) qq <- random_qual(30, 20) q
Create a
DNAStringSet object
with random sequences
random_seq( slength, swidth, nuc = c("DNA", "RNA"), prob = c(0.25, 0.25, 0.25, 0.25) )random_seq( slength, swidth, nuc = c("DNA", "RNA"), prob = c(0.25, 0.25, 0.25, 0.25) )
slength |
Number of sequences |
swidth |
Width of the sequences |
nuc |
Create sequences of DNA (nucleotides = c('A', 'C', 'G', 'T')) or RNA (nucleotides = c('A, 'C', 'G', 'U'))?. Default: 'DNA' |
prob |
A vector of four probability values used to set the frequency of the nucleotides 'A', 'C', 'G', 'T', for DNA, or 'A', 'C', 'G', 'U', for RNA. For example = c(0.25, 0.25, 0.5, 0). Default is = c(0.25, 0.25, 0.25, 0.25) (equiprobability for the 4 bases). If the sum of the probabilities is > 1, the values will be nomalized to the range [0, 1]. |
DNAStringSet object
Leandro Roser [email protected]
# For reproducible examples, make a call to set.seed before # running each random function set.seed(10) s1 <- random_seq(slength = 10, swidth = 20) s1 set.seed(10) s2 <- random_seq(slength = 10, swidth = 20, prob = c(0.6, 0.1, 0.3, 0)) s2# For reproducible examples, make a call to set.seed before # running each random function set.seed(10) s1 <- random_seq(slength = 10, swidth = 20) s1 set.seed(10) s2 <- random_seq(slength = 10, swidth = 20, prob = c(0.6, 0.1, 0.3, 0)) s2
Removes a set of sequences
seq_filter(input, rm.seq)seq_filter(input, rm.seq)
input |
|
rm.seq |
Ccharacter vector with sequences to remove |
Filtered ShortReadQ
object
Leandro Roser [email protected]
require(ShortRead) set.seed(10) input <- random_length(30, 3:7) rm.seq = c('TGGTC', 'CGGT', 'GTTCT', 'ATA') # verify that some sequences match match_before <- unlist(lapply(rm.seq, function(x) grep(x, as.character(sread(input))))) filtered <- seq_filter(input,rm.seq = rm.seq) # verify that matching sequences were removed match_after <- unlist(lapply(rm.seq, function(x) grep(x, as.character(sread(filtered)))))require(ShortRead) set.seed(10) input <- random_length(30, 3:7) rm.seq = c('TGGTC', 'CGGT', 'GTTCT', 'ATA') # verify that some sequences match match_before <- unlist(lapply(rm.seq, function(x) grep(x, as.character(sread(input))))) filtered <- seq_filter(input,rm.seq = rm.seq) # verify that matching sequences were removed match_after <- unlist(lapply(rm.seq, function(x) grep(x, as.character(sread(filtered)))))
Create BStringSet
object with names
seq_names(n, base_name = "s", sep = "_")seq_names(n, base_name = "s", sep = "_")
n |
Number of reads |
base_name |
Base name for strings |
sep |
Character separing base names and the read number. Default: '_ |
BStringSet object
snames <- seq_names(10) snames snames2 <- seq_names(10, base_name = 's', sep = '.') snames2snames <- seq_names(10) snames snames2 <- seq_names(10, base_name = 's', sep = '.') snames2
The program removes from the 3' tails of the sequences a set of nucleotides showing a quality < a threshold value in a ShortReadQ object
trim3q_filter( input, rm.3qual, q_format = NULL, check.encod = TRUE, remove_zero = TRUE )trim3q_filter( input, rm.3qual, q_format = NULL, check.encod = TRUE, remove_zero = TRUE )
input |
|
rm.3qual |
Quality threshold for 3' tails |
q_format |
Quality format used for the file, as returned by check_encoding |
check.encod |
Check the encoding of the sequence? This argument is incompatible with q_format. Default TRUE |
remove_zero |
Remove zero-length sequences? |
Filtered ShortReadQ
object
Leandro Roser [email protected]
require('Biostrings') require('ShortRead') # create 6 sequences of width 20 set.seed(10) input <- random_seq(6, 20) # create qualities of width 15 and paste to qualities # of length 5 used for the tails. # for two of the sequences, put low qualities in tails set.seed(10) my_qual <- random_qual(c(30,40), slength = 6, swidth = 15, encod = 'Sanger') set.seed(10) tails <- random_qual(c(30,40), slength = 6, swidth = 5, encod = 'Sanger') set.seed(10) tails[2:3] <- random_qual(c(3, 20), slength = 2, swidth = 5, encod = 'Sanger') my_qual <- paste0(my_qual, tails) input_q <- BStringSet(my_qual) # create names input_names <- seq_names(6) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # apply the filter filtered <- trim3q_filter(my_read, rm.3qual = 28) # look at the trimmed sequences sread(filtered)require('Biostrings') require('ShortRead') # create 6 sequences of width 20 set.seed(10) input <- random_seq(6, 20) # create qualities of width 15 and paste to qualities # of length 5 used for the tails. # for two of the sequences, put low qualities in tails set.seed(10) my_qual <- random_qual(c(30,40), slength = 6, swidth = 15, encod = 'Sanger') set.seed(10) tails <- random_qual(c(30,40), slength = 6, swidth = 5, encod = 'Sanger') set.seed(10) tails[2:3] <- random_qual(c(3, 20), slength = 2, swidth = 5, encod = 'Sanger') my_qual <- paste0(my_qual, tails) input_q <- BStringSet(my_qual) # create names input_names <- seq_names(6) # create ShortReadQ object my_read <- ShortReadQ(sread = input, quality = input_q, id = input_names) # apply the filter filtered <- trim3q_filter(my_read, rm.3qual = 28) # look at the trimmed sequences sread(filtered)
This program is a wrapper to
occurrenceFilter.
It removes the duplicated sequences of a FASTQ file.
unique_filter(input)unique_filter(input)
input |
|
Filtered ShortReadQ object
Leandro Roser [email protected]
require('Biostrings') require('ShortRead') set.seed(10) s <- random_seq(10, 10) s <- sample(s, 30, replace = TRUE) q <- random_qual(30, 10) n <- seq_names(30) my_read <- ShortReadQ(sread = s, quality = q, id = n) # check presence of duplicates isUnique(as.character(sread(my_read))) # apply the filter filtered <- unique_filter(my_read) isUnique(as.character(sread(filtered)))require('Biostrings') require('ShortRead') set.seed(10) s <- random_seq(10, 10) s <- sample(s, 30, replace = TRUE) q <- random_qual(30, 10) n <- seq_names(30) my_read <- ShortReadQ(sread = s, quality = q, id = n) # check presence of duplicates isUnique(as.character(sread(my_read))) # apply the filter filtered <- unique_filter(my_read) isUnique(as.character(sread(filtered)))