Documentation
Updated: 2025-05-09. Compiled: 2025-05-13
Abstract
CircSeqAlignTk is an end-to-end toolkit for the analysis of RNA-Seq data derived from circular genome sequences, with a primary focus on viroids, circular RNAs typically consisting of a few hundred nucleotides. In addition to analysis capabilities, CircSeqAlignTk provides a streamlined interface for generating synthetic RNA-Seq data that simulate real datasets. This functionality enables developers to benchmark alignment tools, test novel alignment algorithms, and validate new workflows.
Package
CircSeqAlignTk 1.11.2
Contents
1 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.
2 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.
3 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:
4 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
5.2 and 5.3
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 1).
The scale of the upper and lower directions indicate alignment coverage of
the forward and reverse strands, respectively.
Figure 1: Alignment coverage
The alignment coverage of the case study.
5 Implementation
5.1 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 2), using these existing alignment tools (i.e., Bowtie2 and HISAT2).
Figure 2: 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.
5.2 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/RtmpbqPHcC/index/refseq.t1.fa" "/tmp/RtmpbqPHcC/index/refseq.t2.fa"
## ..@ index : chr [1:2] "/tmp/RtmpbqPHcC/index/refseq.t1" "/tmp/RtmpbqPHcC/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/RtmpbqPHcC/index/refseq.t1.fa" "/tmp/RtmpbqPHcC/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/RtmpbqPHcC/index/refseq.t1" "/tmp/RtmpbqPHcC/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:
5.3 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 5.2.
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/RtmpD756Qy/Rinst208a7f27c28dea/CircSeqAlignTk/extdata/srna.fq.gz"
## ..@ fastq : chr [1:2] "/tmp/RtmpD756Qy/Rinst208a7f27c28dea/CircSeqAlignTk/extdata/srna.fq.gz" "/tmp/RtmpbqPHcC/align_results/srna.t2.fq.gz"
## ..@ bam : chr [1:2] "/tmp/RtmpbqPHcC/align_results/srna.t1.bam" "/tmp/RtmpbqPHcC/align_results/srna.t2.bam"
## ..@ clean_bam : chr [1:2] "/tmp/RtmpbqPHcC/align_results/srna.clean.t1.bam" "/tmp/RtmpbqPHcC/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/RtmpbqPHcC/index/refseq.t1.fa" "/tmp/RtmpbqPHcC/index/refseq.t2.fa"
## .. .. ..@ index : chr [1:2] "/tmp/RtmpbqPHcC/index/refseq.t1" "/tmp/RtmpbqPHcC/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/RtmpbqPHcC/align_results/srna.t1.bam"
## [2] "/tmp/RtmpbqPHcC/align_results/srna.t2.bam"## [1] "/tmp/RtmpbqPHcC/align_results/srna.clean.t1.bam"
## [2] "/tmp/RtmpbqPHcC/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.
5.4 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.
Figure 3: Alignment coverage
To plot alignment coverage of the reads with a specific length,
assign the targeted length to the read_lengths argument.
Figure 4: 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:
Figure 5: Alignment coverage arranged with ggplot2
Figure 6: Alignment coverage represented in polar coordinate system
6 Synthetic small RNA-Seq data
6.1 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/RtmpbqPHcC/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
Figure 7: Alignment coverage of the synthetic data
6.2 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)
Figure 8: 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 8).
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'))
Figure 9: Alignment coverage of the synthetic data
From Figure 9, 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.
6.3 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')).
7 Case studies
7.1 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 6.3). To examine detailed changes in performance, users can change the number of mismatches and the probabilities of mismatches to estimate how the performance changes.
7.2 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
Figure 10: 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 10.
8 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.
9 Session Information
## R version 4.5.0 (2025-04-11)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.5.2 Rbowtie2_2.15.0 R.utils_2.13.0
## [4] R.oo_1.27.1 R.methodsS3_1.8.2 CircSeqAlignTk_1.11.2
## [7] BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_2.0.0
## [3] magrittr_2.0.3 magick_2.8.6
## [5] GenomicFeatures_1.61.2 farver_2.1.2
## [7] rmarkdown_2.29 fs_1.6.6
## [9] BiocIO_1.19.0 vctrs_0.6.5
## [11] memoise_2.0.1 Rsamtools_2.25.0
## [13] RCurl_1.98-1.17 tinytex_0.57
## [15] htmltools_0.5.8.1 S4Arrays_1.9.0
## [17] progress_1.2.3 curl_6.2.2
## [19] SparseArray_1.9.0 sass_0.4.10
## [21] shinyFiles_0.9.3 bslib_0.9.0
## [23] htmlwidgets_1.6.4 httr2_1.1.2
## [25] plotly_4.10.4 cachem_1.1.0
## [27] GenomicAlignments_1.45.0 igraph_2.1.4
## [29] mime_0.13 lifecycle_1.0.4
## [31] pkgconfig_2.0.3 Matrix_1.7-3
## [33] R6_2.6.1 fastmap_1.2.0
## [35] MatrixGenerics_1.21.0 shiny_1.10.0
## [37] digest_0.6.37 ShortRead_1.67.0
## [39] AnnotationDbi_1.71.0 S4Vectors_0.47.0
## [41] GenomicRanges_1.61.0 RSQLite_2.3.11
## [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_4.5.0
## [49] bit64_4.6.0-1 withr_3.0.2
## [51] BiocParallel_1.43.0 DBI_1.2.3
## [53] biomaRt_2.65.0 Rhisat2_1.25.0
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## [61] restfulr_0.0.15 promises_1.3.2
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## [67] data.table_1.17.2 hms_1.1.3
## [69] xml2_1.3.8 XVector_0.49.0
## [71] BiocGenerics_0.55.0 pillar_1.10.2
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## [93] lazyeval_0.2.2 yaml_2.3.10
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## [107] png_0.1-8 XML_3.99-0.18
## [109] RUnit_0.4.33 parallel_4.5.0
## [111] blob_1.2.4 prettyunits_1.2.0
## [113] latticeExtra_0.6-30 jpeg_0.1-11
## [115] SGSeq_1.43.0 bitops_1.0-9
## [117] pwalign_1.5.0 txdbmaker_1.5.3
## [119] viridisLite_0.4.2 scales_1.4.0
## [121] purrr_1.0.4 crayon_1.5.3
## [123] rlang_1.1.6 KEGGREST_1.49.0
## [125] shinyjs_2.1.0References
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