CircSeqAlignTk: An R package for end-to-end analysis of RNA-seq data for circular genomes
25 April, 2023
Abstract
CircSeqAlignTk is designed for end-to-end RNA-Seq data analysis of circular genome sequences, from alignment to visualization. It mainly targets viroids which are composed of 246-401 nt circular RNAs. In addition, CircSeqAlignTk implements a tidy interface to generate synthetic sequencing data that mimic real RNA-Seq data, allowing developers to evaluate the performance of alignment tools, new alignment algorithms, and new workflows. This vignette provides an overview of CircSeqAlignTk and a detailed explanation of the manner in which it is used.
Package
CircSeqAlignTk 1.2.0
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.
However, instead of using a temporary folder, users can specify a folder on the desktop or elsewhere, depending on the analysis project. For example:
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).
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 (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## 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 reversed strands, respectively.
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).
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.
## 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 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 get_slot_contents function.
## [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.
## [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,
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,
Bowtie2/Rbowtie2
is used for indexing.
This can be changed to
HISAT2/Rhisat2 using
the aligner argument.
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.
## 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 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/RtmpwPBtFR/align_results/srna.t1.bam"
## [2] "/tmp/RtmpwPBtFR/align_results/srna.t2.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.
## 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 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 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.
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.
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.
## 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## 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 reversed 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
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.
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"
## .. .. ..@ 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.
## 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
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.
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.
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] 0We 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.
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] 6.93215If 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))
}## 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 3.995843The 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.
## 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 0calc_coverage and plot can be used to calculate and visualize the
alignment coverage.
## 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## 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
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
## 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
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## [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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## [73] filelock_1.0.2 MatrixGenerics_1.12.0
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## [109] bit64_4.0.5References
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Soliman, T., M. C. M. Mourits, A. G. J. M. Oude Lansink, and W. van der Werf. 2012. “Quantitative Economic Impact Assessment of an Invasive Plant Disease Under Uncertainty – a Case Study for Potato Spindle Tuber Viroid (Pstvd) Invasion into the European Union.” Crop Protection 40: 28–35. https://doi.org/https://doi.org/10.1016/j.cropro.2012.04.019.
Wei, Zheng, Wei Zhang, Huan Fang, Yanda Li, and Xiaowo Wang. 2018. “EsATAC: An Easy-to-Use Systematic Pipeline for Atac-Seq Data Analysis.” Bioinformatics. https://doi.org/10.1093/bioinformatics/bty141.
Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.