Contents

## Warning: replacing previous import 'utils::findMatches' by
## 'S4Vectors::findMatches' when loading 'AnnotationDbi'

1 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.

2 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.

ws <- tempdir()

However, instead of using a temporary folder, users can specify a folder on the desktop or elsewhere, depending on the analysis project. For example:

ws <- '~/desktop/viroid_project'

3 Quick start

Viroids are composed of 246–401 nt, single-stranded circular non-coding RNAs (Hull 2014; Flores et al. 2015; Gago-Zachert 2016). Sequencing small RNAs from viroid-infected plants could offer insights regarding the mechanisms of infection and eventually help prevent these infections in plants. The common workflow for analyzing such data involves the following steps: (i) limit read-length between 21 and 24 nt, as small RNAs derived from viroids are known to be in this range, (ii) align these reads to viroid genome sequences, and (iii) visualize the coverage of alignment to identify the pathogenic region of the viroid. This section demonstrates the workflow using a sample RNA-Seq dataset. It includes workflow from the FASTQ format file to the visualization of the analyzed results, for analyzing small RNA-seq data sequenced from viroid-infected plants.

The FASTQ format file used in this section is attached in the CircSeqAlignTk package and can be obtained using the system.file function. This FASTQ format file contains 29,178 sequence reads of small RNAs that were sequenced from a tomato plant infected with the potato spindle tuber viroid (PSTVd) isolate Cen-1 (FR851463).

fq <- system.file(package = 'CircSeqAlignTk', 'extdata', 'srna.fq.gz')

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.

genome_seq <- system.file(package = 'CircSeqAlignTk', 'extdata', 'FR851463.fa')

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 (Wei et al. 2018) package 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 4.2 and 4.3 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.

alncov <- calc_coverage(aln)
head(get_slot_contents(alncov, 'forward'))  # alignment coverage in forward strand 
##      L21 L22 L23 L24
## [1,]  13  12   1   1
## [2,]  13  12   1   1
## [3,]  13  12   1   1
## [4,]  13  13   1   1
## [5,]  13  13   1   1
## [6,]  13  13   1   1
head(get_slot_contents(alncov, 'reversed')) # alignment coverage in reversed strand 
##      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   1

The 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 reversed strands, respectively.

plot(alncov)
Alignment coverage. The alignment coverage of the case study.

Figure 1: Alignment coverage
The alignment coverage of the case study.

4 Implementation

4.1 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 (Bowtie2 and HISAT2).

Two-stage alignment process. Overview of the two-stage alignment process and the related functions in the CircSeqAlignTk package

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.

4.2 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, Bowtie2 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 Bowtie2/Rbowtie2, 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.

str(ref_index)
## Formal class 'CircSeqAlignTkRefIndex' [package "CircSeqAlignTk"] with 6 slots
##   ..@ name   : chr "FR851463"
##   ..@ seq    : chr "CGGAACTAAACTCGTGGTTCCTGTGGTTCACACCTGACCTCCTGAGCAGGAAAGAAAAAAGAATTGCGGCTCGGAGGAGCGCTTCAGGGATCCCCGGGGAAACCTGGAGCG"| __truncated__
##   ..@ length : int 361
##   ..@ fasta  : chr [1:2] "/tmp/RtmpwPBtFR/index/refseq.t1.fa" "/tmp/RtmpwPBtFR/index/refseq.t2.fa"
##   ..@ index  : chr [1:2] "/tmp/RtmpwPBtFR/index/refseq.t1" "/tmp/RtmpwPBtFR/index/refseq.t2"
##   ..@ cut_loc: num 180

The 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 get_slot_contents function.

get_slot_contents(ref_index, 'fasta')
## [1] "/tmp/RtmpwPBtFR/index/refseq.t1.fa" "/tmp/RtmpwPBtFR/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.

get_slot_contents(ref_index, 'index')
## [1] "/tmp/RtmpwPBtFR/index/refseq.t1" "/tmp/RtmpwPBtFR/index/refseq.t2"

Note that, users can simply use the slot function or @ operator to access these slot contents instead of using the get_slot_contents function. For example,

slot(ref_index, 'fasta')
slot(ref_index, 'index')

ref_index@fasta
ref_index@index

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.

get_slot_contents(ref_index, 'cut_loc')
## [1] 180

By default, Bowtie2/Rbowtie2 is used for indexing. This can be changed to HISAT2/Rhisat2 using the aligner argument.

ref_ht2index <- build_index(input = genome_seq,
                            output = file.path(ws, 'ht2index'),
                            aligner = 'hisat2')

4.3 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).

Two alignment tools (Bowtie2 and HISAT2) can be specified for sequence read alignment. By default, Bowtie2 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 the bowtie2_build or hisat2_build function implemented in Rbowtie2 or Rhisat2 packages for alignment.

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 4.2. 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.

str(aln)
## Formal class 'CircSeqAlignTkAlign' [package "CircSeqAlignTk"] with 6 slots
##   ..@ input_fastq: chr "/tmp/RtmpbD5GFz/Rinsta468816ec9089/CircSeqAlignTk/extdata/srna.fq.gz"
##   ..@ fastq      : chr [1:2] "/tmp/RtmpbD5GFz/Rinsta468816ec9089/CircSeqAlignTk/extdata/srna.fq.gz" "/tmp/RtmpwPBtFR/align_results/srna.t2.fq.gz"
##   ..@ bam        : chr [1:2] "/tmp/RtmpwPBtFR/align_results/srna.t1.bam" "/tmp/RtmpwPBtFR/align_results/srna.t2.bam"
##   ..@ clean_bam  : chr [1:2] "/tmp/RtmpwPBtFR/align_results/srna.clean.t1.bam" "/tmp/RtmpwPBtFR/align_results/srna.clean.t2.bam"
##   ..@ stats      :'data.frame':  4 obs. of  5 variables:
##   .. ..$ n_reads       : num [1:4] 29178 29012 166 30
##   .. ..$ aligned_fwd   : num [1:4] 89 22 89 21
##   .. ..$ aligned_rev   : num [1:4] 77 9 77 9
##   .. ..$ unaligned     : num [1:4] 29012 28981 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/RtmpwPBtFR/index/refseq.t1.fa" "/tmp/RtmpwPBtFR/index/refseq.t2.fa"
##   .. .. ..@ index  : chr [1:2] "/tmp/RtmpwPBtFR/index/refseq.t1" "/tmp/RtmpwPBtFR/index/refseq.t2"
##   .. .. ..@ cut_loc: num 180

The 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.

get_slot_contents(aln, 'bam')
## [1] "/tmp/RtmpwPBtFR/align_results/srna.t1.bam"
## [2] "/tmp/RtmpwPBtFR/align_results/srna.t2.bam"
get_slot_contents(aln, 'clean_bam')
## [1] "/tmp/RtmpwPBtFR/align_results/srna.clean.t1.bam"
## [2] "/tmp/RtmpwPBtFR/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.

get_slot_contents(aln, 'stats')
##                   n_reads aligned_fwd aligned_rev unaligned unsorted_reads
## srna.t1.bam         29178          89          77     29012              0
## srna.t2.bam         29012          22           9     28981              0
## srna.clean.t1.bam     166          89          77         0              0
## srna.clean.t2.bam      30          21           9         0              0

By 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 increase the alignment sensitivity, we set the maximum number of mismatches to 1 and the length of seed substrings for alignment to 20 during the process of the Bowtie2 multiseed alignment. See the Bowtie2 website to find additional parameters of Bowtie2.

aln <- align_reads(input = fq,
                   index = ref_index,
                   output = file.path(ws, 'align_results'),
                   add_args = '-L 20 -N 1')

To use HISAT2/Rhisat2, assign hisat2 to the aligner argument.

aln <- align_reads(input = fq,
                   index = ref_ht2index ,
                   output = file.path(ws, 'align_results'),
                   aligner = 'hisat2')

4.4 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.

alncov <- calc_coverage(aln)

This function returns a CircSeqAlignTkCoverage class object. Alignment coverage of the reads aligned in the forward and reversed strands are stored in the @forward and @reversed slots, respectively, as a data frame.

head(get_slot_contents(alncov, 'forward'))
##      L21 L22 L23 L24
## [1,]  12   8   1   0
## [2,]  12   8   1   0
## [3,]  12   8   1   0
## [4,]  12   9   1   0
## [5,]  13   9   1   0
## [6,]  13   9   1   0
head(get_slot_contents(alncov, 'reversed'))
##      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   0

Coverage 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 reversed strands, respectively.

plot(alncov)
Alignment coverage.

Figure 3: Alignment coverage

To plot alignment coverage of the reads with a specific length, assign the targeted length to the read_lengths argument.

plot(alncov, read_lengths = c(21, 22))
Alignment coverage of reads with the specific lengths.

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:

library(ggplot2)
plot(alncov) + facet_grid(strand ~ read_length, scales = 'free_y')
Alignment coverage arranged with ggplot2.

Figure 5: Alignment coverage arranged with ggplot2

plot(alncov) + coord_polar()
Alignment coverage represented in polar coordinate system.

Figure 6: Alignment coverage represented in polar coordinate system

5 Synthetic small RNA-Seq data

5.1 Generation of synthetic sequence reads

The CircSeqAlignTk package implements the generate_fastq function to generate synthetic sequence reads in FASTQ format to mimic 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.

sim <- generate_reads(output = file.path(ws, 'synthetic_reads.fq.gz'))

This function returns a CircSeqAlignTkSim class object whose data structure can be checked with the str function, as follows:

str(sim)
## 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"
##   .. .. ..@ reversed: 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/RtmpwPBtFR/synthetic_reads.fq.gz"

The parameters for generating the peaks of alignment coverage can be checked using @peak slot.

head(get_slot_contents(sim, 'peak'))
##   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      + 0.04168474

The 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.

dim(get_slot_contents(sim, 'read_info'))
## [1] 10000     8
head(get_slot_contents(sim, 'read_info'))
##   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     24

The 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.

alncov <- get_slot_contents(sim, 'coverage')
head(get_slot_contents(alncov, 'forward'))
##      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
head(get_slot_contents(alncov, 'reversed'))
##      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
plot(alncov)
Alignment coverage of the synthetic data.

Figure 7: Alignment coverage of the synthetic data

5.2 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.

sim <- generate_reads(n = 1e3, output = file.path(ws, 'synthetic_reads.fq.gz'))

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.

sim <- generate_reads(output = file.path(ws, 'synthetic_reads.fq.gz'),
                      mismatch_prob = 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)
plot(get_slot_contents(sim, 'coverage'))
Alignment coverage of the synthetic data.

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'))
plot(get_slot_contents(sim, 'coverage'))
Alignment coverage of the synthetic data.

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.

5.3 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 <- get_slot_contents(alncov, 'forward')
fwd_true <- get_slot_contents(get_slot_contents(sim, 'coverage'), 'forward')
sqrt(sum((fwd_pred - fwd_true) ^ 2) / length(fwd_true))
## [1] 0
# coverage of reads in reversed strand
rev_pred <- get_slot_contents(alncov, 'reversed')
rev_true <- get_slot_contents(get_slot_contents(sim, 'coverage'), 'reversed')
sqrt(sum((rev_pred - rev_true) ^ 2) / length(rev_true))
## [1] 0

We found that there was no error (i.e., RMSE = 0) between the true and predicted values of alignment coverage. Thus, we confirmed that the workflow presented in the CircSeqAlignTk package works perfectly when the reads do not contain adapter sequences or 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.

sim <- generate_reads(mismatch_prob = c(0.1, 0.2),
                      output = file.path(ws, 'synthetic_reads.fq'))

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 = get_slot_contents(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 <- get_slot_contents(alncov, 'forward')
fwd_true <- get_slot_contents(get_slot_contents(sim, 'coverage'), 'forward')
sqrt(sum((fwd_pred - fwd_true) ^ 2) / length(fwd_true))
## [1] 2.510504
# coverage of reads in reversed strand
rev_pred <- get_slot_contents(alncov, 'reversed')
rev_true <- get_slot_contents(get_slot_contents(sim, 'coverage'), 'reversed')
sqrt(sum((rev_pred - rev_true) ^ 2) / length(rev_true))
## [1] 6.93215

If the sequence reads contained adapter sequences and mismatches, some sequence reads failed to align. Therefore, the coverage calculated from this alignment result (i.e., aln) showed errors from the true alignment coverage (i.e., get_slot_contents(sim, 'coverage')).

6 Case studies

6.1 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 = get_slot_contents(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 <- get_slot_contents(alncov, 'forward')
    fwd_true <- get_slot_contents(get_slot_contents(sim, 'coverage'), 'forward')
    fwd_rmse[i] <- sqrt(sum((fwd_pred - fwd_true) ^ 2) / length(fwd_true))
    rev_pred <- get_slot_contents(alncov, 'reversed')
    rev_true <- get_slot_contents(get_slot_contents(sim, 'coverage'), 'reversed')
    rev_rmse[i] <- sqrt(sum((rev_pred - rev_true) ^ 2) / length(rev_true))
}
rmse <- data.frame(forward = fwd_rmse, reversed = rev_rmse)
rmse
##     forward reversed
## 1  4.598115 2.356990
## 2  5.325811 2.220373
## 3  4.912320 3.166108
## 4  5.074036 3.010140
## 5  3.031689 6.275585
## 6  2.821195 4.571834
## 7  4.332925 4.563874
## 8  2.726956 4.652837
## 9  4.185369 3.121390
## 10 4.105516 3.995843

The RMSE between the true (i.e., simulation condition) and predicted coverage for the sequence reads in forward strand and reversed strand are 4.11 ± 0.95 and 3.79 ± 1.25, 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 = 0 as shown in the section 5.3). To examine detailed changes in performance, users can change the number of mismatches and the probabilities of mismatches to estimate how the performance changes.

6.2 Analysis of small RNA-Seq data from vioid-infected tomato plants

Viroids are the smallest infectious pathogens. Most variants of viroids infect plants without being toxic, while some variants occasionally result in extensive damage such as stunting, leaf deformation, leaf necrosis, fruit distortion, and even plant death (Flores et al. 2005). A single outbreak of toxic viroid infection can cause tremendous economic and agricultural damage (Soliman et al. 2012; Sastry 2013).

The damage caused by viroids to plants is thought to occur during the replication process of the viroid that infects the plants. Viroids are composed of 246–401 nt single-stranded circular non-coding RNAs (Hull 2014; Flores et al. 2015; Gago-Zachert 2016). Replication of viroids depends on their host plants through an RNA-based rolling circle process. This process generates double-stranded RNAs (dsRNAs) as intermediate products. The dsRNAs are cut into 21–24 nt fragments called small interfering RNAs (siRNAs) or microRNAs (miRNAs) by Dicer, a bidentate RNase III-like enzyme. siRNAs or miRNAs are then transferred to the RNA-induced silencing complex (RISC), which acts as a functional intermediate for RNA interference (RNAi). Consequently, RNAi causes mRNA cleavage and translational attenuation, resulting in a disease in the host plant.

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 71,545 + 15,500 = 87,045 and 11,994 + 1,571 = 13,565 reads in forward and reversed strands that were successfully aligned to the viroid genome sequences, respectively.

get_slot_contents(aln, 'stats')
##                                       n_reads aligned_fwd aligned_rev unaligned
## GSM1717894_PSTVd_RG1.txt.t1.bam        730499       71586       12000    646913
## GSM1717894_PSTVd_RG1.txt.t2.bam        646913       15504        1572    629837
## GSM1717894_PSTVd_RG1.txt.clean.t1.bam   83539       71545       11994         0
## GSM1717894_PSTVd_RG1.txt.clean.t2.bam   17071       15500        1571         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              0

calc_coverage and plot can be used to calculate and visualize the alignment coverage.

alncov <- calc_coverage(aln)
head(get_slot_contents(alncov, 'forward'))
##        L21  L22 L23 L24
## [1,] 10832 4201 119 351
## [2,] 10832 4201 119 351
## [3,] 10849 4204 125 354
## [4,] 11029 4827 127 363
## [5,] 14050 4852 135 404
## [6,] 14061 4887 142 414
head(get_slot_contents(alncov, 'reversed'))
##      L21 L22 L23 L24
## [1,] 866 610  36  59
## [2,] 866 610  36  59
## [3,] 868 610  36  59
## [4,] 870 611  36  60
## [5,] 869 609  36  60
## [6,] 862 609  35  60
plot(alncov)
Alignment coverage of small RNA-Seq data obtained from the viroid-infected tomato plants.

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.

7 Session Information

sessionInfo()
## R version 4.3.0 RC (2023-04-13 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## 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_3.4.2        Rbowtie2_2.6.0       R.utils_2.12.2      
## [4] R.oo_1.25.0          R.methodsS3_1.8.2    CircSeqAlignTk_1.2.0
## [7] BiocStyle_2.28.0    
## 
## loaded via a namespace (and not attached):
##   [1] DBI_1.1.3                   bitops_1.0-7               
##   [3] deldir_1.0-6                biomaRt_2.56.0             
##   [5] rlang_1.1.0                 magrittr_2.0.3             
##   [7] Rhisat2_1.16.0              matrixStats_0.63.0         
##   [9] compiler_4.3.0              RSQLite_2.3.1              
##  [11] GenomicFeatures_1.52.0      png_0.1-8                  
##  [13] vctrs_0.6.2                 stringr_1.5.0              
##  [15] pkgconfig_2.0.3             crayon_1.5.2               
##  [17] fastmap_1.1.1               magick_2.7.4               
##  [19] dbplyr_2.3.2                XVector_0.40.0             
##  [21] labeling_0.4.2              utf8_1.2.3                 
##  [23] Rsamtools_2.16.0            rmarkdown_2.21             
##  [25] purrr_1.0.1                 bit_4.0.5                  
##  [27] xfun_0.39                   zlibbioc_1.46.0            
##  [29] cachem_1.0.7                GenomeInfoDb_1.36.0        
##  [31] jsonlite_1.8.4              progress_1.2.2             
##  [33] blob_1.2.4                  highr_0.10                 
##  [35] DelayedArray_0.26.0         BiocParallel_1.34.0        
##  [37] jpeg_0.1-10                 parallel_4.3.0             
##  [39] prettyunits_1.1.1           R6_2.5.1                   
##  [41] bslib_0.4.2                 stringi_1.7.12             
##  [43] RColorBrewer_1.1-3          rtracklayer_1.60.0         
##  [45] GenomicRanges_1.52.0        jquerylib_0.1.4            
##  [47] Rcpp_1.0.10                 bookdown_0.33              
##  [49] SummarizedExperiment_1.30.0 knitr_1.42                 
##  [51] SGSeq_1.34.0                IRanges_2.34.0             
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##  [55] tidyselect_1.2.0            yaml_2.3.7                 
##  [57] codetools_0.2-19            RUnit_0.4.32               
##  [59] hwriter_1.3.2.1             curl_5.0.0                 
##  [61] lattice_0.21-8              tibble_3.2.1               
##  [63] withr_2.5.0                 Biobase_2.60.0             
##  [65] ShortRead_1.58.0            KEGGREST_1.40.0            
##  [67] evaluate_0.20               BiocFileCache_2.8.0        
##  [69] xml2_1.3.3                  Biostrings_2.68.0          
##  [71] pillar_1.9.0                BiocManager_1.30.20        
##  [73] filelock_1.0.2              MatrixGenerics_1.12.0      
##  [75] stats4_4.3.0                generics_0.1.3             
##  [77] RCurl_1.98-1.12             S4Vectors_0.38.0           
##  [79] hms_1.1.3                   munsell_0.5.0              
##  [81] scales_1.2.1                glue_1.6.2                 
##  [83] tools_4.3.0                 interp_1.1-4               
##  [85] BiocIO_1.10.0               GenomicAlignments_1.36.0   
##  [87] XML_3.99-0.14               grid_4.3.0                 
##  [89] tidyr_1.3.0                 latticeExtra_0.6-30        
##  [91] AnnotationDbi_1.62.0        colorspace_2.1-0           
##  [93] GenomeInfoDbData_1.2.10     restfulr_0.0.15            
##  [95] cli_3.6.1                   rappdirs_0.3.3             
##  [97] fansi_1.0.4                 dplyr_1.1.2                
##  [99] gtable_0.3.3                sass_0.4.5                 
## [101] digest_0.6.31               BiocGenerics_0.46.0        
## [103] farver_2.1.1                rjson_0.2.21               
## [105] memoise_2.0.1               htmltools_0.5.5            
## [107] lifecycle_1.0.3             httr_1.4.5                 
## [109] bit64_4.0.5

References

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