Documentation
Introduction
Viroids are small, single-stranded, circular non-coding RNAs, typically composed of only a few hundred nucleotides (Hull 2014; Flores et al. 2015; Gago-Zachert 2016). Lacking protein-coding ability, they hijack host plant enzymes for replication, spreading systemically within the plant and transmitting to other individuals. Because there are no effective treatments for viroid infections, all infected plants must be destroyed, posing a serious threat to agriculture. Moreover, each viroid species harbors many genetic variants, which differ in virulence—ranging from asymptomatic to causing severe stunting or even plant death.
Sequencing small RNAs from viroid-infected plants can help uncover the molecular mechanisms of infection and ultimately inform prevention strategies. A standard RNA-Seq approach involves aligning viroid-derived short reads to the viroid RNA reference to quantify expression abundance. However, since viroids are circular RNAs, alignment must account for reads that span the artificial “ends” of linearized reference sequences.
CircSeqAlignTk is an R package that provides an end-to-end solution for RNA-Seq data analysis of circular genomes, especially for viroids (Sun, Fu, and Cao 2024). It includes both command-line and graphical interfaces, covering steps from read alignment to visualization. The toolkit is user-friendly and well-documented, making it accessible to both beginners and experienced users. Additionally, it features a simulation engine that generates synthetic sequencing data resembling real RNA-Seq output, enabling robust benchmarking of alignment tools, algorithm development, and workflow testing.
Installation
To install the CircSeqAlignTk package, start R (≥ 4.2) and run the following steps:
if (!requireNamespace('BiocManager', quietly = TRUE))
install.packages('BiocManager')
BiocManager::install('CircSeqAlignTk')Note that to install the latest version of the CircSeqAlignTk package, the latest version of R is required.
Preparation of working directory
CircSeqAlignTk
is designed for end-to-end RNA-Seq data analysis of circular genome
sequences, from alignment to visualization. The whole processes will
generate many files including genome sequence indexes, and intermediate
and final alignment results. Thus, it is recommended to specify a
working directory to save these files. Here, for convenience in package
development and validation, we use a temporary folder which is
automatically arranged by the tempdir() function as the
working directory.
However, instead of using a temporary folder, users can specify a folder on the desktop or elsewhere, depending on the analysis project. For example:
Quick start
The typical workflow for analyzing RNA-Seq data from viroid-infected plants, particularly small RNA sequencing, consists of the following steps:
- filter reads to retain only those between 21 and 24 nucleotides in length, as viroid-derived small RNAs predominantly fall within this range
- align the filtered reads to viroid genome sequences
- visualize read coverage across the genome to identify regions associated with pathogenicity
This section demonstrates the complete workflow using a sample RNA-Seq dataset. It covers the full analysis pipeline, from a FASTQ file to coverage visualization, tailored for studying small RNAs derived from viroid-infected plant cells.
The FASTQ format file used in this section is included in the CircSeqAlignTk
package and can be accessed using the system.file()
function. This file contains 29,178 sequence reads, randomly sampled
from an original study in which small RNA was sequenced from a tomato
plant infected with the potato spindle tuber viroid (PSTVd) isolate
Cen-1 (FR851463) (Sun and Matsushita
2024).
The genome sequence of PSTVd isolate Cen-1 in FASTA format can be
downloaded from GenBank or ENA using the
accession number FR851463. It is also attached in the CircSeqAlignTk
package, and can be obtained using the system.file()
function.
To ensure alignment quality, trimming adapter sequences from the sequence reads is required, because most sequence reads in this FASTQ format file contain adapters with sequence “AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC”. Here, we use AdapterRemoval (Schubert, Lindgreen, and Orlando 2016) implemented in the Rbowtie2 package (Wei et al. 2018) to trim the adapters before aligning the sequence reads. Note that the length of small RNAs derived from viroids is known to be in the range of 21–24 nt. Therefore, we set an argument to remove sequence reads with lengths outside this range after adapter removal.
library(R.utils)
library(Rbowtie2)
adapter <- 'AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC'
# decompressed the gzip file for trimming to avoid errors from `remove_adapters`
gunzip(fq, destname = file.path(ws, 'srna.fq'), overwrite = TRUE, remove = FALSE)
trimmed_fq <- file.path(ws, 'srna_trimmed.fq')
params <- '--maxns 1 --trimqualities --minquality 30 --minlength 21 --maxlength 24'
remove_adapters(file1 = file.path(ws, 'srna.fq'),
adapter1 = adapter,
adapter2 = NULL,
output1 = trimmed_fq,
params,
basename = file.path(ws, 'AdapterRemoval.log'),
overwrite = TRUE)After obtaining the cleaned FASTQ format file (i.e.,
srna_trimmed.fq.gz), we build index files and perform
alignment using the build_index() and
align_reads() functions implemented in the CircSeqAlignTk
package. To precisely align the reads to the circular genome sequence of
the viroid, the alignment is performed in two stages.
ref_index <- build_index(input = genome_seq,
output = file.path(ws, 'index'))
aln <- align_reads(input = trimmed_fq,
index = ref_index,
output = file.path(ws, 'align_results'))The index files are stored in a directory specified by the
output argument of the build_index() function.
The intermediate files (e.g., FASTQ format files used as inputs) and
alignment results (e.g., BAM format files) are stored in the directory
specified by the output argument of the
align_reads() function. BAM format files with the suffixes
of .clean.t1.bam and .clean.t2.bam are the
final results obtained after alignment. Refer to the sections
@ref(generation-of-reference-sequences) and @ref(alignment) for a
detailed description of each of the files generated by each
function.
The alignment coverage can be summarized with the
calc_coverage() function. This function loads the alignment
results (i.e., *.clean.t1.bam and
*.clean.t2.bam), calculates alignment coverage from these
BAM format files, and combines them into two data frames according to
the aligned strands.
## L21 L22 L23 L24
## [1,] 11 12 1 2
## [2,] 11 12 1 2
## [3,] 11 12 1 2
## [4,] 11 13 1 2
## [5,] 11 13 1 2
## [6,] 11 13 1 2## L21 L22 L23 L24
## [1,] 7 5 0 1
## [2,] 7 5 0 1
## [3,] 7 5 0 1
## [4,] 7 5 0 1
## [5,] 7 5 0 1
## [6,] 7 5 0 1The alignment coverage can be then visualized using the
plot() function (Figure @ref(fig:quickStartVisualization)).
The scale of the upper and lower directions indicate alignment coverage
of the forward and reverse strands, respectively.
Alignment coverage. The alignment coverage of the case study.
Implementation
Two-stage alignment process
Circular genome sequences are generally represented as linear sequences in the FASTA format during analysis. Consequently, sequence reads obtained from organelles or organisms with circular genome sequences can be aligned anywhere, including at both ends of the sequence represented in the FASTA format. Using existing alignment tools such as Bowtie2 (Langmead and Salzberg 2012) and HISAT2 (Kim et al. 2019) to align such sequence reads onto circular sequences may fail, because these tools are designed to align sequence reads to linear genome sequences and their implementation does not assume that a single read can be aligned to both ends of a linear sequence. To solve this problem, that is, allowing reads to be aligned to both ends, the CircSeqAlignTk package implements a two-stage alignment process (Figure @ref(fig:packageImplementation)), using these existing alignment tools (i.e., Bowtie2 and HISAT2).
Two-stage alignment process. Overview of the two-stage alignment process and the related functions in the CircSeqAlignTk package
To prepare for the two-stage alignment process, two types of
reference sequences are generated from the same circular genome
sequence. The type 1 reference sequence is a linear sequence generated
by cutting a circular sequence at an arbitrary location. The type 2
reference is generated by restoring the type 1 reference sequence into a
circular sequence and cutting the circle at the opposite position to
type 1 reference sequence. The type 1 reference sequence is the input
genome sequence itself, while the type 2 reference sequence is newly
created by the build_index() function.
Once the two reference sequences are generated, the sequence reads are aligned to the two types of reference sequences in two stages: (i) aligning all sequence reads onto the type 1 reference sequences, and (ii) collecting the unaligned sequence reads and aligning them to the type 2 reference. Alignment can be performed with Bowtie2 or HISAT2 depending on the options specified by the user.
Generation of reference sequences
The build_index() function is designed to generate type
1 and type 2 reference sequences for alignment. This function has two
required arguments, input and output which are
used for specifying a file path to a genome sequence in FASTA format and
a directory path to save the generated type 1 and type 2 reference
sequences, respectively. The type 1 and type 2 reference sequences are
saved in files refseq.t1.fa and refseq.t2.fa
in FASTA format, respectively.
Following the generation of reference sequences, The
build_index() function then creates index files for each
reference sequence for alignment. The index files are saved with the
prefix refseq.t1.* and refseq.t2.*. They
correspond to the type 1 and 2 reference sequences (i.e.,
refseq.t1.fa and refseq.t2.fa), respectively.
The extension of index files depends on the alignment tool.
Two alignment tools (Bowtie2
and HISAT2) can be
specified for creating index files through the aligner
argument. If Bowtie2
is specified, then the extension is .bt2 or
.bt2l; if HISAT2 is specified,
then the extension is .ht2 or .ht2l. By
default, HISAT2 is
used.
The build_index() function first attempts to call the
specified alignment tool directly installed on the operation system;
however, if the tool is not installed, the function will then attempt to
call the bowtie2_build() or hisat2_build()
functions implemented in Rbowtie2
or Rhisat2
packages for indexing.
For example, to generate reference sequences and index files for
alignment against the viroid PSTVd isolate Cen-1 (FR851463) using HISAT2, we set the
argument input to the FASTA format file containing the
sequence of FR851463 and execute the build_index()
function. The generated index files will be saved into the folder
specified by the argument output.
genome_seq <- system.file(package = 'CircSeqAlignTk', 'extdata', 'FR851463.fa')
ref_index <- build_index(input = genome_seq, output = file.path(ws, 'index'))The function returns a CircSeqAlignTkRefIndex class
object that contains the file path to type 1 and 2 reference sequences
and corresponding index files. The data structure of
CircSeqAlignTkRefIndex can be verified using the
str() function.
## Formal class 'CircSeqAlignTkRefIndex' [package "CircSeqAlignTk"] with 6 slots
## ..@ name : chr "FR851463"
## ..@ seq : chr "CGGAACTAAACTCGTGGTTCCTGTGGTTCACACCTGACCTCCTGAGCAGGAAAGAAAAAAGAATTGCGGCTCGGAGGAGCGCTTCAGGGATCCCCGGGGAAACCTGGAGCG"| __truncated__
## ..@ length : int 361
## ..@ fasta : chr [1:2] "/tmp/RtmphrDUuQ/index/refseq.t1.fa" "/tmp/RtmphrDUuQ/index/refseq.t2.fa"
## ..@ index : chr [1:2] "/tmp/RtmphrDUuQ/index/refseq.t1" "/tmp/RtmphrDUuQ/index/refseq.t2"
## ..@ cut_loc: num 180The file path to type 1 and type 2 reference sequences,
refseq.type1.fa and refseq.type2.fa, can be
checked through the fasta slot using the
slot() function.
## [1] "/tmp/RtmphrDUuQ/index/refseq.t1.fa" "/tmp/RtmphrDUuQ/index/refseq.t2.fa"The file path (prefix) to the index files,
refseq.t1.*.bt2 and refseq.t2.*.bt2, can be
checked through index slot.
## [1] "/tmp/RtmphrDUuQ/index/refseq.t1" "/tmp/RtmphrDUuQ/index/refseq.t2"Note that, users can simply use the @ operator to access
these slot contents instead of using the slot() function.
For example,
As mentioned previously, the type 2 reference is generated by
restoring the type 1 reference sequence to a circular sequence and
cutting the circular sequence at the opposite position of type 1. The
cutting position based on the type 1 reference sequence coordinate can
be checked from the @cut_loc slot.
## [1] 180By default, HISAT2/Rhisat2
is used for indexing. This can be changed to Bowtie2/Rbowtie2
using the aligner argument, for example:
Alignment
The align_reads() function is used to align sequence
reads onto a circular genome sequence. This function requires three
arguments: input, index, and
output, which are used to specify a file path to RNA-Seq
reads in FASTQ format, a CircSeqAlignTkRefIndex class
object generated by the build_index() function, and a
directory path to save the intermediate and final results,
respectively.
This function aligns sequence reads within the two-stage alignment process
described above. Thus, it (i) aligns reads to the type 1 reference
sequence (i.e., refseq.t1.fa) and (ii) collects the
unaligned reads and aligns them with the type 2 reference sequence
(i.e., refseq.t2.fa).
By default, HISAT2 is used, and
it can be changed with the alinger argument. Similar to the
build_index() function, the align_reads()
function first attempts to call the specified alignment tool directly
installed on the operation system; however, if the tool is not
installed, the function then attempts to call alignment functions
implemented in Rbowtie2
or Rhisat2
packages.
The following example is aligning RNA-Seq reads in FASTQ format
(fq) on the reference index (ref_index) of
PSTVd isolate Cen-1 (FR851463) which was generated at the section
@ref(generation-of-reference-sequences). The alignment results will be
stored into the folder specified by the argument
output.
fq <- system.file(package = 'CircSeqAlignTk', 'extdata', 'srna.fq.gz')
# trimming the adapter sequences if needed before alignment, omitted here.
aln <- align_reads(input = fq,
index = ref_index,
output = file.path(ws, 'align_results'))This function returns a CircSeqAlignTkAlign class object
containing the path to the intermediate files and final alignment
results.
## Formal class 'CircSeqAlignTkAlign' [package "CircSeqAlignTk"] with 6 slots
## ..@ input_fastq: chr "/tmp/RtmpgSyMeM/Rinst19eb6af5efac/CircSeqAlignTk/extdata/srna.fq.gz"
## ..@ fastq : chr [1:2] "/tmp/RtmpgSyMeM/Rinst19eb6af5efac/CircSeqAlignTk/extdata/srna.fq.gz" "/tmp/RtmphrDUuQ/align_results/srna.t2.fq.gz"
## ..@ bam : chr [1:2] "/tmp/RtmphrDUuQ/align_results/srna.t1.bam" "/tmp/RtmphrDUuQ/align_results/srna.t2.bam"
## ..@ clean_bam : chr [1:2] "/tmp/RtmphrDUuQ/align_results/srna.clean.t1.bam" "/tmp/RtmphrDUuQ/align_results/srna.clean.t2.bam"
## ..@ stats :'data.frame': 4 obs. of 5 variables:
## .. ..$ n_reads : num [1:4] 29178 29007 171 29
## .. ..$ aligned_fwd : num [1:4] 93 20 93 20
## .. ..$ aligned_rev : num [1:4] 78 9 78 9
## .. ..$ unaligned : num [1:4] 29007 28978 0 0
## .. ..$ unsorted_reads: num [1:4] 0 0 0 0
## ..@ reference :Formal class 'CircSeqAlignTkRefIndex' [package "CircSeqAlignTk"] with 6 slots
## .. .. ..@ name : chr "FR851463"
## .. .. ..@ seq : chr "CGGAACTAAACTCGTGGTTCCTGTGGTTCACACCTGACCTCCTGAGCAGGAAAGAAAAAAGAATTGCGGCTCGGAGGAGCGCTTCAGGGATCCCCGGGGAAACCTGGAGCG"| __truncated__
## .. .. ..@ length : int 361
## .. .. ..@ fasta : chr [1:2] "/tmp/RtmphrDUuQ/index/refseq.t1.fa" "/tmp/RtmphrDUuQ/index/refseq.t2.fa"
## .. .. ..@ index : chr [1:2] "/tmp/RtmphrDUuQ/index/refseq.t1" "/tmp/RtmphrDUuQ/index/refseq.t2"
## .. .. ..@ cut_loc: num 180The alignment results are saved as BAM format files in the specified
directory with the suffixes of *.t1.bam and
*.t2.bam. The original alignment results may contain
mismatches. Hence, this function performs filtering to remove alignment
with the mismatches over the specified value from the BAM format file.
Filtering results for *.t1.bam and *.t2.bam
are saved as *.clean.t1.bam and
*.clean.t2.bam, respectively. The path to the original and
filtered BAM format files can be checked using @bam and
@clean_bam slots, respectively.
## [1] "/tmp/RtmphrDUuQ/align_results/srna.t1.bam"
## [2] "/tmp/RtmphrDUuQ/align_results/srna.t2.bam"## [1] "/tmp/RtmphrDUuQ/align_results/srna.clean.t1.bam"
## [2] "/tmp/RtmphrDUuQ/align_results/srna.clean.t2.bam"The alignment statistics (for example, number of input sequence
reads, number of aligned reads) can be checked using the
@stats slot.
## n_reads aligned_fwd aligned_rev unaligned unsorted_reads
## srna.t1.bam 29178 93 78 29007 0
## srna.t2.bam 29007 20 9 28978 0
## srna.clean.t1.bam 171 93 78 0 0
## srna.clean.t2.bam 29 20 9 0 0By default, the align_read() function allows a single
mismatch in the alignment of each read (i.e.,
n_mismatch = 1). To forbid a mismatch or allow more
mismatches, assign 0 or a large number to the
n_mismatch argument.
aln <- align_reads(input = fq,
index = ref_index,
output = file.path(ws, 'align_results'),
n_mismatch = 0)The number of threads for alignment can be specified using the
n_threads argument. Setting a large number of threads (but
not exceeding the computer limits) can accelerate the speed of
alignment.
aln <- align_reads(input = fq,
index = ref_index,
output = file.path(ws, 'align_results'),
n_threads = 4)Additional arguments to be directly passed on to the alignment tool
can be specified with the add_args argument. For example,
to disable spliced alignment for HISAT2, we set
--no-spliced-alignment for HISAT alinger as follows.
aln <- align_reads(input = fq,
index = ref_index,
output = file.path(ws, 'align_results'),
add_args = '--no-spliced-alignment')For additional parameters available with HISAT2, refer to the HISAT2 Online Manual.
Alternatively, user can use Bowtie2
for alignment by setting the aligner argument to
"bowtie2". Similar to HISAT2, user can specify additional
alignment parameters using the appropriate arguments. For a full list of
Bowtie2 options, refer to the Bowtie2
Online Manual.
Summarization and visualization of alignment results
Summarization and visualization of the alignment results can be
performed with the calc_coverage() and plot()
functions, respectively. The calc_coverage() function
calculates alignment coverage from the two BAM files,
*.clean.t1.bam and *.clean.t2.bam, generated
by the align_reads() function.
This function returns a CircSeqAlignTkCoverage class
object. Alignment coverage of the reads aligned in the forward and
reverse strands are stored in the @forward and
@reverse slots, respectively, as a data frame.
## L21 L22 L23 L24
## [1,] 10 8 1 1
## [2,] 10 8 1 1
## [3,] 10 8 1 1
## [4,] 10 9 1 1
## [5,] 10 9 1 1
## [6,] 10 9 1 1## L21 L22 L23 L24
## [1,] 5 4 0 0
## [2,] 5 4 0 0
## [3,] 5 4 0 0
## [4,] 5 4 0 0
## [5,] 5 4 0 0
## [6,] 5 4 0 0Coverage can be visualized with an area chart using the
plot() function. In the chart, the upper and lower
directions of the y-axis represent the alignment coverage of reads with
forward and reverse strands, respectively.
Alignment coverage.
To plot alignment coverage of the reads with a specific length,
assign the targeted length to the read_lengths
argument.
Alignment coverage of reads with the specific lengths.
As the plot() function returns a ggplot2 class object,
we can use additional functions implemented in the ggplot2
package (Wickham 2016) to decorate the
chart, for example:
Alignment coverage arranged with ggplot2.
Alignment coverage represented in polar coordinate system.
Synthetic small RNA-Seq data
Generation of synthetic sequence reads
The CircSeqAlignTk
package implements the generate_fastq() function to
generate synthetic sequence reads in FASTQ format to simulate RNA-Seq
data sequenced from organelles or organisms with circular genome
sequences. This function is intended for the use of developers, to help
them evaluate the performance of alignment tools, new alignment
algorithms, and new workflows.
To generate synthetic sequence reads with default parameters and save
them into a file named synthetic_reads.fq.gz in
GZIP-compressed FASTQ format, run the following command. By default, it
generates 10,000 reads.
This function returns a CircSeqAlignTkSim class object
whose data structure can be checked with the str()
function, as follows:
## Formal class 'CircSeqAlignTkSim' [package "CircSeqAlignTk"] with 6 slots
## ..@ seq : chr "CGGAACTAAACTCGTGGTTCCTGTGGTTCACACCTGACCTCCTGAGCAGGAAAGAAAAAAGAATTGCGGCTCGGAGGAGCGCTTCAGGGATCCCCGGGGAAACCTGGAGCG"| __truncated__
## ..@ adapter : chr "AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC"
## ..@ read_info:'data.frame': 10000 obs. of 8 variables:
## .. ..$ mean : num [1:10000] 341 74 227 65 341 239 341 239 239 341 ...
## .. ..$ std : num [1:10000] 4 4 4 4 4 3 4 3 3 4 ...
## .. ..$ strand: chr [1:10000] "+" "+" "+" "+" ...
## .. ..$ prob : num [1:10000] 0.108 0.195 0.11 0.15 0.108 ...
## .. ..$ start : num [1:10000] 703 436 589 422 701 602 699 598 598 701 ...
## .. ..$ end : num [1:10000] 726 458 609 443 724 625 722 621 621 722 ...
## .. ..$ sRNA : chr [1:10000] "TGGAACCGCAGTTGGTTCCTCGGA" "AGGAGCGCTTCAGGGATCCCCGG" "CCCTCGCCCCCTTTGCGCTGT" "GAATTGCGGCTCGGAGGAGCGC" ...
## .. ..$ length: num [1:10000] 24 23 21 22 24 24 24 24 24 22 ...
## ..@ peak :'data.frame': 8 obs. of 4 variables:
## .. ..$ mean : num [1:8] 74 324 341 239 227 23 75 65
## .. ..$ std : num [1:8] 4 3 4 3 4 5 3 4
## .. ..$ strand: chr [1:8] "+" "+" "+" "+" ...
## .. ..$ prob : num [1:8] 0.1947 0.0762 0.1079 0.1342 0.1105 ...
## ..@ coverage :Formal class 'CircSeqAlignTkCoverage' [package "CircSeqAlignTk"] with 3 slots
## .. .. ..@ forward : num [1:361, 1:4] 56 30 10 3 0 0 0 0 0 0 ...
## .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. ..$ : NULL
## .. .. .. .. ..$ : chr [1:4] "L21" "L22" "L23" "L24"
## .. .. ..@ reverse : num [1:361, 1:4] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. ..$ : NULL
## .. .. .. .. ..$ : chr [1:4] "L21" "L22" "L23" "L24"
## .. .. ..@ .figdata:'data.frame': 2888 obs. of 4 variables:
## .. .. .. ..$ position : int [1:2888] 1 2 3 4 5 6 7 8 9 10 ...
## .. .. .. ..$ read_length: Factor w/ 4 levels "21","22","23",..: 1 1 1 1 1 1 1 1 1 1 ...
## .. .. .. ..$ coverage : num [1:2888] 56 30 10 3 0 0 0 0 0 0 ...
## .. .. .. ..$ strand : chr [1:2888] "+" "+" "+" "+" ...
## ..@ fastq : chr "/tmp/RtmphrDUuQ/synthetic_reads.fq.gz"The parameters for generating the peaks of alignment coverage can be
checked using @peak slot.
## mean std strand prob
## 1 74 4 + 0.19467997
## 2 324 3 + 0.07618699
## 3 341 4 + 0.10791345
## 4 239 3 + 0.13421258
## 5 227 4 + 0.11047903
## 6 23 5 + 0.04168474The parameters for sequence-read sampling can be checked using the
@read_info slot. The first four columns (i.e.,
mean, std, strand, and
prob) represent peak information used for sampling sequence
reads; the next two columns (i.e., start and
end) are the exact start and end position of the sampled
sequence reads, respectively; and the last two columns (i.e.,
sRNA and length) are the nucleotides and
length of the sampled sequence reads.
## [1] 10000 8## mean std strand prob start end sRNA length
## 1 341 4 + 0.1079135 703 726 TGGAACCGCAGTTGGTTCCTCGGA 24
## 2 74 4 + 0.1946800 436 458 AGGAGCGCTTCAGGGATCCCCGG 23
## 3 227 4 + 0.1104790 589 609 CCCTCGCCCCCTTTGCGCTGT 21
## 4 65 4 + 0.1496360 422 443 GAATTGCGGCTCGGAGGAGCGC 22
## 5 341 4 + 0.1079135 701 724 CTTGGAACCGCAGTTGGTTCCTCG 24
## 6 239 3 + 0.1342126 602 625 TGCGCTGTCGCTTCGGCTACTACC 24The alignment coverage of the synthetic sequence reads are stored in
the @coverage slot as a CircSeqAlignTkCoverage
class object. This can be visualized using the plot()
function.
## L21 L22 L23 L24
## [1,] 56 125 212 446
## [2,] 30 88 166 376
## [3,] 10 43 116 291
## [4,] 3 14 63 206
## [5,] 0 1 28 122
## [6,] 0 0 6 44## L21 L22 L23 L24
## [1,] 0 0 0 0
## [2,] 0 0 0 0
## [3,] 0 0 0 0
## [4,] 0 0 0 0
## [5,] 0 0 0 0
## [6,] 0 0 0 0
Alignment coverage of the synthetic data.
Examples of sequence read generation with additional paramaters
To change the number of sequence reads that need to be generated, use
the n argument in the generate_reads()
function.
By default, the generate_reads() function generates
sequence reads from the genome sequence of the viroid PSTVd isolate
Cen-1 (FR851463). To
change the seed genome sequence for sequence read sampling, users can
specify a sequence as characters or as a file path to the FASTA format
file containing a sequence using the seq argument.
genome_seq <- 'CGGAACTAAACTCGTGGTTCCTGTGGTTCACACCTGACCTCCTGACAAGAAAAGAAAAAAGAAGGCGGCTCGGAGGAGCGCTTCAGGGATCCCCGGGGAAACCTGGAGCGAACTGGCAAAAAAGGACGGTGGGGAGTGCCCAGCGGCCGACAGGAGTAATTCCCGCCGAAACAGGGTTTTCACCCTTCCTTTCTTCGGGTGTCCTTCCTCGCGCCCGCAGGACCACCCCTCGCCCCCTTTGCGCTGTCGCTTCGGCTACTACCCGGTGGAAACAACTGAAGCTCCCGAGAACCGCTTTTTCTCTATCTTACTTGCTCCGGGGCGAGGGTGTTTAGCCCTTGGAACCGCAGTTGGTTCCT'
sim <- generate_reads(seq = genome_seq,
output = file.path(ws, 'synthetic_reads.fq.gz'))By default, generate_reads() function generates sequence
reads with the adapter sequence “AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC”. To
change the adapter sequence, specify a sequence as characters or as a
file path to a FASTA format file containing a adapter sequence using the
adapter argument. For example, the following scripts
generate reads with 150 nt, containing the adapter sequence
“AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA”.
adapter <- 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'
sim <- generate_reads(adapter = adapter,
output = file.path(ws, 'synthetic_reads.fq.gz'),
read_length = 150)In contrast, to generate sequence reads without adapter sequences,
run the generate_reads() function with
adapter = NA.
sim <- generate_reads(adapter = NA,
output = file.path(ws, 'synthetic_reads.fq.gz'),
read_length = 150)The generate_reads() function also implements a process
that introduces several mismatches into the reads after sequence-read
sampling. To introduce a single mismatch for each sequence read with a
probability of 0.05, set the mismatch_prob argument to
0.05.
To allow a single sequence read to have multiple mismatches, assign
multiple values to the mismatch_prob argument. For example,
using the following scripts, the function first generates 10,000 reads.
Then, introduce the first mismatches against all sequence reads with the
probability of 0.05. This will generate approximately 500 (i.e., 10,000
x 0.05) sequence reads containing a mismatch. Next, the function
introduces a second mismatch against the sequence reads with a single
mismatch with the probability of 0.1. Thus, this will generate
approximately 50 (i.e., 500 x 0.1) sequence reads containing two
mismatches.
sim <- generate_reads(output = file.path(ws, 'synthetic_reads.fq.gz'),
mismatch_prob = c(0.05, 0.1))In addition, the generate_reads() provide some
groundbreaking arguments, srna_length and
peaks, to specify the length and strand of sequence reads
and the positions of peaks of the alignment coverage, respectively.
Using these arguments allows users to generate synthetic sequence reads
that are very close to the real small RNA-Seq data sequenced from
viroid-infected plants. The following is an example of how to use these
arguments:
peaks <- data.frame(
mean = c( 0, 25, 70, 90, 150, 240, 260, 270, 330, 350),
std = c( 5, 5, 5, 5, 10, 5, 5, 1, 2, 1),
strand = c( '+', '+', '-', '-', '+', '+', '-', '+', '+', '-'),
prob = c(0.10, 0.10, 0.18, 0.05, 0.03, 0.18, 0.15, 0.10, 0.06, 0.05)
)
srna_length <- data.frame(
length = c( 21, 22, 23, 24),
prob = c(0.45, 0.40, 0.10, 0.05)
)
sim <- generate_reads(n = 1e4,
output = file.path(ws, 'synthetic_reads.fq.gz'),
srna_length = srna_length,
peaks = peaks)
Alignment coverage of the synthetic data.
In the synthetic data generated by the generate_reads()
function, every peak contains a relatively equal proportion of sequence
reads with different sequence read lengths (Figure
@ref(fig:simSetLengthAndPeaks)). However, in real data, composition of
the reads differs from peak to peak. The CircSeqAlignTk
package provides a merge function to generate such
synthetic data. This feature can be used, to first generate multiple
synthetic data with various features with the
generate_reads() function and then merge these synthetic
data with the merge() function.
peaks_1 <- data.frame(
mean = c( 100, 150, 250, 300),
std = c( 5, 5, 5, 5),
strand = c( '+', '+', '-', '-'),
prob = c(0.25, 0.25, 0.40, 0.05)
)
srna_length_1 <- data.frame(
length = c( 21, 22),
prob = c(0.45, 0.65)
)
sim_1 <- generate_reads(n = 1e4,
output = file.path(ws, 'synthetic_reads_1.fq.gz'),
srna_length = srna_length_1,
peaks = peaks_1)
peaks_2 <- data.frame(
mean = c( 50, 200, 300),
std = c( 5, 5, 5),
strand = c( '+', '-', '+'),
prob = c(0.80, 0.10, 0.10)
)
srna_length_2 <- data.frame(
length = c( 21, 22, 23),
prob = c(0.10, 0.10, 0.80)
)
sim_2 <- generate_reads(n = 1e3,
output = file.path(ws, 'synthetic_reads_2.fq.gz'),
srna_length = srna_length_2,
peaks = peaks_2)
peaks_3 <- data.frame(
mean = c( 80, 100, 220, 270),
std = c( 5, 5, 1, 2),
strand = c( '-', '+', '+', '-'),
prob = c( 0.20, 0.30, 0.20, 0.30)
)
srna_length_3 <- data.frame(
length = c( 19, 20, 21, 22),
prob = c(0.30, 0.30, 0.20, 0.20)
)
sim_3 <- generate_reads(n = 5e3,
output = file.path(ws, 'synthetic_reads_3.fq.gz'),
srna_length = srna_length_3,
peaks = peaks_3)
# merge the three data sets
sim <- merge(sim_1, sim_2, sim_3,
output = file.path(ws, 'synthetic_reads.fq.gz'))
Alignment coverage of the synthetic data.
From Figure @ref(fig:simMergeMultiSimObjects), it can be seen that the lengths of the sequence reads that constitute the peaks vary from peak to peak. For example, the first peak of the forward strand is mainly composed of sequence reads with a length of 23 nt, and the third peak of the forward strand is mainly composed of sequence reads with lengths of 21 nt and 22 nt.
Performance evaluation with the synthetic data
Here we show how to use the CircSeqAlignTk
package to evaluate the performance of the workflow, from aligning
sequence reads to calculating alignment coverage, as shown in the Quick Start section. First, to validate that the
workflow is working correctly, we generate sequence reads without
adapter sequences and mismatches using the generate_reads()
function and apply the workflow to these synthetic reads.
sim <- generate_reads(adapter = NA,
mismatch_prob = 0,
output = file.path(ws, 'synthetic_reads.fq.gz'))
genome_seq <- system.file(package = 'CircSeqAlignTk', 'extdata', 'FR851463.fa')
ref_index <- build_index(input = genome_seq,
output = file.path(ws, 'index'))
aln <- align_reads(input = file.path(ws, 'synthetic_reads.fq.gz'),
index = ref_index,
output = file.path(ws, 'align_results'))
alncov <- calc_coverage(aln)The true alignment coverage of this synthetic data is stored in the
@coverage slot of the sim variable, whereas
the predicted alignment coverage is stored in the alncov
variable. Here, we can calculate the root mean squared error (RMSE)
between the true and predicted values for validation.
# coverage of reads in forward strand
fwd_pred <- slot(alncov, 'forward')
fwd_true <- slot(slot(sim, 'coverage'), 'forward')
sqrt(sum((fwd_pred - fwd_true) ^ 2) / length(fwd_true))## [1] 0# coverage of reads in reverse strand
rev_pred <- slot(alncov, 'reverse')
rev_true <- slot(slot(sim, 'coverage'), 'reverse')
sqrt(sum((rev_pred - rev_true) ^ 2) / length(rev_true))## [1] 1.34597We observed only a small discrepancy between the true and predicted alignment coverage when the simulated dataset does not contain adapter sequences and mismatches.
Next, we evaluate the performance of this workflow under conditions similar to those of real RNA-Seq data by concatenating adapter sequences and introducing mismatches into the reads. We first generate synthetic sequence reads with a length of 150 nt that contain at most two mismatches and have adapter sequences.
Next, we follow the Quick Start chapter to trim the adapter sequences, perform alignment, and calculate the alignment coverage.
library(R.utils)
library(Rbowtie2)
# quality control
params <- '--maxns 1 --trimqualities --minquality 30 --minlength 21 --maxlength 24'
remove_adapters(file1 = file.path(ws, 'synthetic_reads.fq'),
adapter1 = slot(sim, 'adapter'),
adapter2 = NULL,
output1 = file.path(ws, 'srna_trimmed.fq'),
params,
basename = file.path(ws, 'AdapterRemoval.log'),
overwrite = TRUE)
# alignment
genome_seq <- system.file(package = 'CircSeqAlignTk', 'extdata', 'FR851463.fa')
ref_index <- build_index(input = genome_seq,
output = file.path(ws, 'index'))
aln <- align_reads(input = file.path(ws, 'srna_trimmed.fq'),
index = ref_index,
output = file.path(ws, 'align_results'),
n_mismatch = 2)
# calculate alignment coverage
alncov <- calc_coverage(aln)We then calculate the RMSE between the true and the predicted values of the alignment coverage.
# coverage of reads in forward strand
fwd_pred <- slot(alncov, 'forward')
fwd_true <- slot(slot(sim, 'coverage'), 'forward')
sqrt(sum((fwd_pred - fwd_true) ^ 2) / length(fwd_true))## [1] 14.25471# coverage of reads in reverse strand
rev_pred <- slot(alncov, 'reverse')
rev_true <- slot(slot(sim, 'coverage'), 'reverse')
sqrt(sum((rev_pred - rev_true) ^ 2) / length(rev_true))## [1] 29.16496When sequence reads contained adapter sequences and mismatches, some
reads failed to align. As a result, the coverage derived from the
alignment output (aln) showed slightly larger deviations
from the true alignment coverage
(slot(sim, 'coverage')).
Case studies
A simulation study to evaluate the performance of the workflow
Synthetic sequence reads for various scenarios can be generated by
repeating the generate_reads() function. These synthetic
sequence reads can be used to evaluate the workflow, from aligning reads
to calculating alignment coverage as shown in the Quick Start chapter, more reliably. Given below
is an example for generating 10 sets of synthetic sequence reads,
performing alignment, and calculating alignment coverage for performance
evaluation. Note that two mismatches are introduced here with
probabilities of 0.1 and 0.2, respectively; and adapter sequences are
added until the length of the reads reaches 150 nt.
library(R.utils)
library(Rbowtie2)
params <- '--maxns 1 --trimqualities --minquality 30 --minlength 21 --maxlength 24'
genome_seq <- system.file(package = 'CircSeqAlignTk', 'extdata', 'FR851463.fa')
ref_index <- build_index(input = genome_seq,
output = file.path(ws, 'index'))
fwd_rmse <- rev_rmse <- rep(NA, 10)
for (i in seq(fwd_rmse)) {
# prepare file names and directory to store the simulation results
simset_dpath <- file.path(ws, paste0('sim_tries_', i))
dir.create(simset_dpath)
syn_fq <- file.path(simset_dpath, 'synthetic_reads.fq')
trimmed_syn_fq <- file.path(simset_dpath, 'srna_trimmed.fq')
align_result <- file.path(simset_dpath, 'align_results')
fig_coverage <- file.path(simset_dpath, 'alin_coverage.png')
# generate synthetic reads
set.seed(i)
sim <- generate_reads(mismatch_prob = c(0.1, 0.2),
output = syn_fq)
# quality control
remove_adapters(file1 = syn_fq,
adapter1 = slot(sim, 'adapter'),
adapter2 = NULL,
output1 = trimmed_syn_fq,
params,
basename = file.path(ws, 'AdapterRemoval.log'),
overwrite = TRUE)
# alignment
aln <- align_reads(input = trimmed_syn_fq,
index = ref_index,
output = align_result,
n_mismatch = 2)
# calculate alignment coverage
alncov <- calc_coverage(aln)
# calculate RMSE
fwd_pred <- slot(alncov, 'forward')
fwd_true <- slot(slot(sim, 'coverage'), 'forward')
fwd_rmse[i] <- sqrt(sum((fwd_pred - fwd_true) ^ 2) / length(fwd_true))
rev_pred <- slot(alncov, 'reverse')
rev_true <- slot(slot(sim, 'coverage'), 'reverse')
rev_rmse[i] <- sqrt(sum((rev_pred - rev_true) ^ 2) / length(rev_true))
}## forward reverse
## 1 25.67048 9.505721
## 2 30.96219 8.047771
## 3 23.38621 13.727033
## 4 23.77073 13.673376
## 5 14.50012 27.662013
## 6 12.06891 22.706684
## 7 19.44217 23.387306
## 8 11.46840 22.131626
## 9 21.24969 15.588346
## 10 18.25235 16.705221The RMSE between the true (i.e., simulation condition) and predicted coverage for the sequence reads in forward strand and reverse strand are 20.08 ± 6.23 and 17.31 ± 6.43, respectively. The result indicates that performance of this workflow is worse when the sequence reads contain up to two mismatches as compared with no mismatches (i.e., RMSE shown in the section @ref(performance-evaluation-with-the-synthetic-data)). To examine detailed changes in performance, users can change the number of mismatches and the probabilities of mismatches to estimate how the performance changes.
Analysis of small RNA-Seq data from vioid-infected tomato plants
The damage caused by viroids to plants is thought to occur during the replication process of the viroid that infects the plants. Sequencing of small RNAs, including viroid-derived small RNAs (vd-sRNAs), siRNAs, and miRNAs from viroid-infected plants could offer insights regarding the mechanism of infection and eventually help in preventing plant damage. The common workflow for analyzing such sequencing data is to (i) limit the read-length (e.g., between 21 and 24 nt), (ii) align these reads to viroid genome sequences, and (iii) visualize coverage of alignment to identify the pathogenic region in the viroid.
Adkar-Purushothama et al. reported viroid-host interactions by infecting potato spindle tuber viroid (PSTVd) RG1 variant in tomato plants (Adkar-Purushothama, Iyer, and Perreault 2017). In their study, small RNAs were sequenced from viroid-infected tomato plants to investigate the expression profiles (i.e., alignment coverage) of vd-sRNAs. In this case study, we demonstrate the manner in which such expression profiles can be calculated using the CircSeqAlignTk package.
First, we prepare a directory to store the initial data,
intermediate, and final results. Then, we use the
download.file function to download the genome sequence of
PSTVd RG1 and small RNA-Seq data of viroid-infected tomato plants that
are registered in GenBank with accession number U23058 and gene
expression omnibus (GEO) with accession number GSE70166,
respectively. The downloaded genome sequence is saved as
U23058.fa and the small RNA-Seq data is saved as
GSM1717894_PSTVd_RG1.txt.gz by running the following
scripts:
library(utils)
project_dpath <- tempdir()
dir.create(project_dpath)
options(timeout = 60 * 10)
download.file(url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=nucleotide&id=U23058&rettype=fasta&retmode=text',
destfile = file.path(project_dpath, 'U23058.fa'))
download.file(url = 'https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE70166&format=file',
destfile = file.path(project_dpath, 'GSE70166.tar'))
untar(file.path(project_dpath, 'GSE70166.tar'), exdir = project_dpath)Following the preparation, we specify the genome sequence of the
viroid (i.e., U23058.fa) to build index files, and align
the reads of the small RNA-Seq data
(GSM1717894_PSTVd_RG1.txt.gz) against the viroid genome.
Note that this process may take a few minutes, depending on machine
power.
genome_seq <- file.path(project_dpath, 'U23058.fa')
fq <- file.path(project_dpath, 'GSM1717894_PSTVd_RG1.txt.gz')
ref_index <- build_index(input = genome_seq,
output = file.path(project_dpath, 'index'))
aln <- align_reads(input = fq, index = ref_index,
output = file.path(project_dpath, 'align_results'))The number of sequence reads that can align with the viroid genome sequences can be checked using the following script. From the alignment results saved in the cleaned BAM format file, we can see that the numbers of 63,862 + 11,614 = 75,476 and 11,046 + 1,370 = 12,416 reads in forward and reverse strands that were successfully aligned to the viroid genome sequences, respectively.
## n_reads aligned_fwd aligned_rev unaligned
## GSM1717894_PSTVd_RG1.txt.t1.bam 730499 63862 11046 655591
## GSM1717894_PSTVd_RG1.txt.t2.bam 655591 11614 1370 642607
## GSM1717894_PSTVd_RG1.txt.clean.t1.bam 74908 63862 11046 0
## GSM1717894_PSTVd_RG1.txt.clean.t2.bam 12984 11614 1370 0
## unsorted_reads
## GSM1717894_PSTVd_RG1.txt.t1.bam 0
## GSM1717894_PSTVd_RG1.txt.t2.bam 0
## GSM1717894_PSTVd_RG1.txt.clean.t1.bam 0
## GSM1717894_PSTVd_RG1.txt.clean.t2.bam 0The calc_coverage() and plot() functions
can be used to calculate and visualize the alignment coverage.
## L21 L22 L23 L24
## [1,] 8795 3898 120 335
## [2,] 8831 3898 120 335
## [3,] 8847 3901 125 337
## [4,] 8975 4436 129 344
## [5,] 11358 4483 138 379
## [6,] 11427 4532 144 387## L21 L22 L23 L24
## [1,] 760 565 34 60
## [2,] 760 565 34 60
## [3,] 761 567 34 60
## [4,] 761 567 34 61
## [5,] 761 566 34 61
## [6,] 755 566 33 61
Alignment coverage of small RNA-Seq data obtained from the viroid-infected tomato plants.
We can confirm that the results with the CircSeqAlignTk package are the same as the results shown in Figure 1B of the original paper (Adkar-Purushothama, Iyer, and Perreault 2017) based on the above figure @ref(fig:tutorialViroidCoverage).
GUI mode of CircSeqAlignTk
The CircSeqAlignTk package also provides a graphical user interface (GUI) mode. To use the GUI mode, we can run the following script:
In the GUI mode, users are required to set a working directory (default is the current directory), a FASTQ file for small RNA-Seq data, and a FASTA file of the viroid genome sequence. After setting up the working directory and files, users can click the “Run” buttons to start the FASTQ quality control and alignment process. If required, the parameters of quality control and alignment can be adjusted. The results will be displayed in the application and saved in the working directory.
Session Information
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_4.0.1 Rbowtie2_2.17.0 R.utils_2.13.0
## [4] R.oo_1.27.1 R.methodsS3_1.8.2 CircSeqAlignTk_1.13.0
## [7] BiocStyle_2.39.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 sys_3.4.3
## [3] jsonlite_2.0.0 magrittr_2.0.4
## [5] GenomicFeatures_1.63.1 farver_2.1.2
## [7] rmarkdown_2.30 fs_1.6.6
## [9] BiocIO_1.21.0 vctrs_0.7.1
## [11] memoise_2.0.1 Rsamtools_2.27.0
## [13] RCurl_1.98-1.17 htmltools_0.5.9
## [15] S4Arrays_1.11.1 progress_1.2.3
## [17] curl_7.0.0 SparseArray_1.11.10
## [19] sass_0.4.10 shinyFiles_0.9.3
## [21] bslib_0.10.0 htmlwidgets_1.6.4
## [23] httr2_1.2.2 plotly_4.12.0
## [25] cachem_1.1.0 buildtools_1.0.0
## [27] GenomicAlignments_1.47.0 igraph_2.2.1
## [29] mime_0.13 lifecycle_1.0.5
## [31] pkgconfig_2.0.3 Matrix_1.7-4
## [33] R6_2.6.1 fastmap_1.2.0
## [35] MatrixGenerics_1.23.0 shiny_1.12.1
## [37] digest_0.6.39 ShortRead_1.69.2
## [39] AnnotationDbi_1.73.0 S4Vectors_0.49.0
## [41] GenomicRanges_1.63.1 RSQLite_2.4.5
## [43] hwriter_1.3.2.1 labeling_0.4.3
## [45] filelock_1.0.3 httr_1.4.7
## [47] abind_1.4-8 compiler_4.5.2
## [49] withr_3.0.2 bit64_4.6.0-1
## [51] S7_0.2.1 BiocParallel_1.45.0
## [53] DBI_1.2.3 biomaRt_2.67.1
## [55] Rhisat2_1.27.0 rappdirs_0.3.4
## [57] DelayedArray_0.37.0 rjson_0.2.23
## [59] tools_4.5.2 otel_0.2.0
## [61] httpuv_1.6.16 glue_1.8.0
## [63] restfulr_0.0.16 promises_1.5.0
## [65] grid_4.5.2 generics_0.1.4
## [67] gtable_0.3.6 tidyr_1.3.2
## [69] data.table_1.18.2.1 hms_1.1.4
## [71] XVector_0.51.0 BiocGenerics_0.57.0
## [73] pillar_1.11.1 stringr_1.6.0
## [75] later_1.4.5 dplyr_1.1.4
## [77] BiocFileCache_3.1.0 lattice_0.22-7
## [79] rtracklayer_1.71.3 bit_4.6.0
## [81] deldir_2.0-4 tidyselect_1.2.1
## [83] maketools_1.3.2 Biostrings_2.79.4
## [85] knitr_1.51 IRanges_2.45.0
## [87] Seqinfo_1.1.0 SummarizedExperiment_1.41.0
## [89] stats4_4.5.2 xfun_0.56
## [91] Biobase_2.71.0 matrixStats_1.5.0
## [93] stringi_1.8.7 UCSC.utils_1.7.1
## [95] lazyeval_0.2.2 yaml_2.3.12
## [97] evaluate_1.0.5 codetools_0.2-20
## [99] cigarillo_1.1.0 interp_1.1-6
## [101] tibble_3.3.1 BiocManager_1.30.27
## [103] cli_3.6.5 xtable_1.8-4
## [105] jquerylib_0.1.4 Rcpp_1.1.1
## [107] GenomeInfoDb_1.47.2 dbplyr_2.5.1
## [109] png_0.1-8 XML_3.99-0.20
## [111] RUnit_0.4.33.1 parallel_4.5.2
## [113] blob_1.3.0 prettyunits_1.2.0
## [115] latticeExtra_0.6-31 jpeg_0.1-11
## [117] SGSeq_1.45.0 bitops_1.0-9
## [119] pwalign_1.7.0 txdbmaker_1.7.3
## [121] viridisLite_0.4.2 scales_1.4.0
## [123] purrr_1.2.1 crayon_1.5.3
## [125] rlang_1.1.7 KEGGREST_1.51.1
## [127] shinyjs_2.1.1