F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.127348.1 Software Tool Article Articles CircSeqAlignTk: An R package for end-to-end analysis of RNA-seq data for circular genomes

[version 1; peer review: 1 approved, 2 approved with reservations]

 Sun Jianqiang Conceptualization Formal Analysis Funding Acquisition Investigation Methodology Project Administration Software Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-3438-3199 a 1 Fu Xi Writing – Review & Editing https://orcid.org/0000-0002-6472-6068 2 Cao Wei Writing – Review & Editing 1 Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, Tsukuba, 305-8604, Japan Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, 113-8657, Japan sun@biunit.dev

No competing interests were disclosed.

 27 10 2022 2022 11 1221 19 10 2022 Copyright: © 2022 Sun J et al. 2022 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

RNA sequencing (RNA-seq) technology has now become one of the standard tools for studying biological mechanisms at the transcriptome level. Advances in RNA-seq technology have led to the emergence of a large number of publicly available tools for RNA-seq data analysis. Most of them target linear genome sequences although it is necessary to study organisms with circular genome sequences. For example, by studying the infection mechanisms of viroids which comprise 246–401 nucleotides circular RNAs and target plants, tremendous economic and agricultural damage may be prevented. Unfortunately, using the available tools to construct workflows for the analysis of circular genome sequences is difficult, especially for non-bioinformaticians. To overcome this limitation, we present CircSeqAlignTk, an easy-to-use and richly documented R package. CircSeqAlignTk performs end-to-end RNA-seq data analysis, from alignment to the visualization of circular genome sequences, through a series of functions. Additionally, it implements a function to generate synthetic sequencing data that mimics real RNA-seq data obtained from biological experiments. CircSeqAlignTk not only provides an easy-to-use analysis interface for novice users but also allows developers to evaluate the performance of alignment tools and new workflows.

 R package alignment visualisation small RNA-seq circular genome sequence viroid. Japan Society for the Promotion of Science 21K05608 22H05179 This work was supported by JSPS KAKENHI [21K05608 and 22H05179]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction

RNA sequencing (RNA-seq) technology can offer insights into various biological mechanisms, such as gene stress responses and plant-virus infection mechanisms ( Vihervaara et al., 2018; Zanardo et al., 2019). The two essential processes for analysing RNA-seq data are aligning sequence reads to the genome sequence and summarising the alignment coverage. The widespread use of RNA-seq has encouraged the development of numerous tools for data analyses. For example, Bowtie2 ( Langmead & Salzberg, 2012) and HISAT2 ( Kim et al., 2019) are well-known tools for read alignment, whereas SAMtools ( Li et al., 2009) and BEDtools ( Quinlan & Hall, 2010) are for coverage calculation.

The application of RNA-seq technology to various organisms, including those with circular genome sequences, such as bacteria, viruses, and viroids, provides clues to solving important biological and social problems. For example, studying the infection mechanisms of viroids, the simplest known infectious agents containing single-stranded circular non-coding RNAs comprised of 246–401 nucleotides ( Hull, 2014), on plants may prevent tremendous economic and agricultural damage ( Soliman et al., 2012; Sastry, 2013). However, most existing tools are designed only for analysing RNA-seq data of organisms with linear genome sequences, such as animals and plants. Early efforts in developing tools to cater to such genomes often relied on complex workflows that involved a large number of tools written in different programming languages and were thus not user friendly, especially for non-bioinformaticians. Although several tools have recently been developed for aligning reads to circular genomes ( Ayad & Pissis, 2017; Adkar-Purushothama et al., 2021), these tools often require advanced programming skills to compensate for a lack of rich documentation and example usage.

Here, we present an easy-to-use R package, CircSeqAlignTk, which functions as a circular sequence alignment toolkit. It performs end-to-end analysis of RNA-seq data for circular genomes, mainly focusing on viroids. CircSeqAlignTk can be easily integrated with other R packages, enabling analysis to be accomplished in a uniform programming language environment.

 Methods Operation

CircSeqAlignTk is an R package registered in the Bioconductor repository with its source code available on the GitHub and archived in Zenodo ( Sun, Fu & Cao, 2022). The package requires R (≥ 4.2) and runs on most popular operating systems (OSs) including Linux, macOS X, and Windows.

 Implementation

The workflow analysis with CircSeqAlignTk ( Figure 1) begins with the preparation of two types of data. The first type is RNA-seq data in FASTQ format. This data can be obtained from biological experiments; for example, according to their research objectives, researchers may sequence small RNAs from plants which may be infected by pathogens using high-throughput sequencing platforms. Alternatively, data can be downloaded from public databases such as the Sequence Read Archive ( Leinonen et al., 2011); usually, these data are published by other researchers worldwide and can be used for re-analysis and meta-analysis. The other type of data is organism genome sequence data (e.g., the circular RNA sequence of a viroid) in FASTA format, which can be obtained from public databases such as GenBank ( Benson et al., 2013).

Overview of workflow analyses and functions implemented in the CircSeqAlignTk package.

After the preparation step, the build_index function implemented in CircSeqAlignTk builds two types of reference sequences from the input genome sequence for alignment: (i) type 1, the input genome sequence itself, and (ii) type 2, generated by restoring the type 1 reference sequence to a circular sequence by opening the circle at a position opposite to that of the type 1 reference sequence. Once the two reference sequences are built, the align_reads function is used for alignment through two stages: (i) aligning reads to the type 1 reference and (ii) collecting the unaligned reads and aligning them to the type 2 reference. The align_reads function allows users to choose either Bowtie2 ( Langmead and Salzberg, 2012) or HISAT2 ( Kim et al., 2019) for alignment. Alignment is performed by preferentially calling Bowtie2 or HISAT2, both of which are directly installed on the OS. However, if the tools are not available, align_reads will automatically call the Bioconductor packages Rbowtie2 ( Wei et al., 2018) or Rhisat2 ( Soneson, 2022) for alignment, which are installed automatically as dependencies of CircSeqAlignTk. Finally, the calc_coverage and plot functions can be used to summarise and visualise the alignment coverage according to the length and strand of the aligned reads, respectively.

Besides performing end-to-end RNA-seq data analysis, CircSeqAlignTk also implements a function to generate synthetic sequence reads to mimic RNA-seq data that are sequenced from circular genome sequences using the generate_reads function. This function is intended for developers to evaluate the performance of new alignment algorithms and analysis workflows. To generate synthetic reads, users can specify certain circular genome sequences for read sampling and then include adapter sequences and mismatches by adjusting arguments.

 Use cases

The goal of use cases is to briefly overview the basic usage of CircSeqAlignTk functions. Herein, we show two use case examples: (i) analysis of small RNA-seq data sequenced from a viroid infection experiment and (ii) analysis of synthetic small RNA-seq data generated by CircSeqAlignTk. Additionally, the detailed usages of CircSeqAlignTk are documented in the package vignette and can be accessed with the browseVignettes function.

browseVignettes('CircSeqAlignTk')

Analysis of small RNA-seq data sequenced from a viroid infection experiment

For a practical CircSeqAlignTk use case, we analysed a subset of small RNA-seq data sequenced from tomato plants that were experimentally infected with the potato spindle tuber viroid (PSTVd) isolate Cen-1. Herein, we show that aligning RNA-seq reads onto the genome sequence of PSTVd isolate Cen-1 and visualising the alignment coverage with CircSeqAlignTk. The sample RNA-seq data and the genome sequence of PSTVd isolate Cen-1 are included in CircSeqAlignTk and can be accessed with the system.file function.

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

Since most reads in this RNA-seq data contain adapters with the sequence “AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC,” we used AdapterRemoval ( Schubert et al., 2016), which was implemented in the R package Rbowtie2 ( Wei et al., 2018), to trim the adapters before analysis with CircSeqAlignTk.

library(R.utils) library(Rbowtie2) gunzip(fq, destname='srna.fq') params <- '--maxns 1 --trimqualities --minquality 30 --minlength 21 --maxlength 24' remove_adapters(file 1 = 'srna.fq',          adapter1 = 'AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC',          adapter2 = NULL,          output1 = 'srna_trimmed.fq',          params,          overwrite = TRUE)

After the adapter removal, we built indexes of PSTVd isolate Cen-1 genome sequences using the build_index function and performed alignment using the align_reads function. Thereafter, we summarised the alignment coverage using the calc_coverage function and visualised the result using the plot function ( Figure 2A).

Visualization of alignment coverage.

A. Alignment coverage of RNA-seq data from viroid-infected tomato plants. B. Alignment coverage of synthetic RNA-seq data generated by the CircSeqAlignTk functions.

ref_index <- build_index(input = genome_seq,                output = 'index') aln <- align_reads(input = 'srna_trimmed.fq',            index = ref_index,            output = 'align_results') alncov <- calc_coverage(aln) plot(alncov)

Analysis of synthetic small RNA-seq data

One of the notable functions of CircSeqAlignTk is generating synthetic small RNA-seq data that mimics real RNA-seq data obtained from biological experiments. Herein, we used the generate_reads function to generate 10,000 small RNA-seq reads with 150 nucleotides and the adapter sequence “AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC” to mimic real RNA-seq reads from plants infected by PSTVd isolate Cen-1. Additionally, we introduced two mismatches for each read with the probabilities of 0.1 and 0.01, respectively.

set.seed(1) genome_seq <- system.file(package = 'CircSeqAlignTk', 'extdata', 'FR851463.fa') sim <- generate_reads(n = 5000,              seq = genome_seq,              adapter = 'AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC',              output = 'synthetic_reads.fq.gz',              read_length = 150,              mismatch_prob = c(0.1, 0.1 * 0.1))

The above function generates synthetic reads by repeating the following operation: randomly cutting substrings from the whole genome sequence of PSTVd isolate Cen-1, adding the adapter, and introducing two mismatches with the specified probability. The location of random cutting and the length of reads can be stored into a variable, allowing users check these information as well as visualise the ground truth of alignment coverage of these synthetic reads ( Figure 2B).

head (slot (sim, 'read_info')) ##   mean std strand    prob  start end            sRNA   length ## 1  341   4    +  0.1079135   704 727 GGAACCGCAGTTGGTTCCTCGGAA     24 ## 2   74   4    +  0.1946800   431 454 CTCGGAGGAGCGCTTCAGGGATCC     24 ## 3  227   4    +  0.1104790   588 611 CCCCTCGCCCCCTTTGCGCTGTCG     24 ## 4   65   4    +  0.1496360   425 445 TTGCGGCCCGGAGGAGCGCTT      21 ## 5  341   4    +  0.1079135   702 724 TTGGAACCGCAGTTGGTTCCGCG     23 ## 6  239   3    +  0.1342126   599 622 CTTTGCGCTGTCGCTTCGGCTACT     24 alncov <- slot (sim, 'coverage') plot(alncov)

The generated reads are saved in FASTQ format. Users can use these reads to evaluate the performance of the workflow analysis by calculating the root mean squared error between the ground truth and outputs by the workflow.

gunzip('synthetic_reads.fq.gz', destname='synthetic_reads.fq') params <- '--maxns 1 --trimqualities --minquality 30 --minlength 21 --maxlength 24' remove_adapters(file 1 = 'synthetic_reads.fq',           adapter1 = 'AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC',           adapter2 = NULL,           output1 = 'synthetic_reads_trimmed.fq',           params,           overwrite = TRUE) ref_index <- build_index(input = genome_seq,               output = 'index') aln <- align_reads(input = 'synthetic_reads_trimmed.fq',            index = ref_index,            output = 'align_results') alncov <- calc_coverage(aln) plot(alncov)

# coverage of reads in forward strand fwd_pred <- slot (alncov, 'forward') fwd_true <- slot (slot (sim, 'coverage'), 'forward') sqrt (sum((fwd_pred - fwd_true) ^ 2) / length (fwd_true)) ## [1] 0.2201737

# coverage of reads in reversed strand rev_pred <- slot (alncov, 'reversed') rev_true <- slot (slot (sim, 'coverage'), 'reversed') sqrt (sum((rev_pred - rev_true) ^ 2) / length (rev_true)) ## [1] 0.1262061

Conclusions

The R package CircSeqAlignTk has promising potential for end-to-end analysis of RNA-seq data for circular genomes including bacteria, viruses, and viroids. In addition, it can also be extended to other organisms and organelles with circular genomes, such as mitochondria and chloroplasts. Given its easy installation, straightforward usage, and detailed documentation, the package will dramatically reduce barriers to analysing such RNA-seq data.

 Software availability

Software available from: https://doi.org/doi:10.18129/B9.bioc.CircSeqAlignTk

Source code available from: https://github.com/jsun/CircSeqAlignTk

Archived source code at time of publication: https://doi.org/10.5281/zenodo.7218032 ( Sun, Fu & Cao, 2022).

License: MIT

Data availability

Zenodo: CircSeqAlignTk. https://doi.org/10.5281/zenodo.7218032 ( Sun, Fu & Cao, 2022).

The datasets analysed in this manuscript are stored in the inst/extdata directory of CircSeqAlignTk package.

 Acknowledgements

We thank Yosuke Matsushita for insightful discussions and inputs.

 References Adkar-Purushothama CR Sridharan lyer P Sano T : sRNA Profiler: a user-focused interface for small RNA mapping and profiling. Cells. 2021;10(7):1771. 34359940 10.3390/cells10071771 Ayad LAK Pissis SP : MARS: improving multiple circular sequence alignment using refined sequences. BMC Genomes. 2017;18:86. 28088189 10.1186/s12864-016-3477-5 Benson DA Cavanaugh M Clark K : GenBank. Nucleic Acids Res. 2013;41:D36–D42. 23193287 10.1093/nar/gks1195 Hull R : Plant Virology (fifth edition). Plant virology. Cambridge, Massachusetts, US: Academic Press;2014. 10.1016/C2010-0-64974-1 Kim D Paggi JM Park C : Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 2019;37:907–915. 31375807 10.1038/s41587-019-0201-4 Langmead B Salzberg S : Fast gapped-read alignment with Bowtie 2. Nat. Methods. 2012;9:357–359. 22388286 10.1038/nmeth.1923 Leinonen R Sugawara H Shumway M : and on behalf of the International Nucleotide Sequence Database Collaboration. The sequence read archive. Nucleic Acids Res. 2011;39:D19–D21. 21062823 10.1093/nar/gkq1019 Li H Handsaker B Wysoker A : The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–2079. 19505943 10.1093/bioinformatics/btp352 Quinlan AR Hall IM : BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–842. 20110278 10.1093/bioinformatics/btq033 Sastry KS : Impact of virus and viroid diseases on crop yields. Plant virus and viroid diseases in the tropics. Springer; Dordrecht:2013. 10.1007/978-94-007-6524-5_3 Schubert M Lindgreen S Orlando L : (2016) AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC. Res. Notes. 2016;9:88. 26868221 10.1186/s13104-016-1900-2 Soliman T Mourits MCM Oude Lansink AGJM : 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 Prot. 2012;40:28–35. 10.1016/j.cropro.2012.04.019 Soneson C : Rhisat2: R wrapper for HISAT2 aligner. R package version 1.12.0. GitHub.[October 6, 2022 accessed]. Reference Source Sun J Fu X Cao W : CircSeqAlignTk. Zenodo.[Dataset].2022. 10.5281/zenodo.7218032 Vihervaara A Duarte FM Lis JT : Molecular mechanisms driving transcriptional stress responses. Nat. Rev. Genet. 2018;19:385–397. 29556092 10.1038/s41576-018-0001-6 Wei Z Zhang W Fang H : esATAC: and easy-to-use systematic pipeline for ATAC-seq data analysis. Bioinformatics. 2018;34(15):2664–2665. 29522192 10.1093/bioinformatics/bty141 Zanardo LG Souza GB de Alves MS : Transcriptomics of plant–virus interactions: a review. Theor. Exp. Plant Physiol. 2019;31:103–125. 10.1007/s40626-019-00143-z 10.5256/f1000research.139847.r267951 Reviewer response for version 1 Zelikovsky Alexander 1 Referee https://orcid.org/0000-0003-4424-4691 Sahoo Bikram 1 Co-referee https://orcid.org/0000-0001-6481-2583 Department of Computer Science, Georgia State University, Atlanta, Georgia, USA

Competing interests: No competing interests were disclosed.

 28 4 2024 Copyright: © 2024 Zelikovsky A and Sahoo B 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

The article provides a comprehensive overview of the urgent need for packages tailored for non-bioinformaticians, addressing the challenges in analyzing circular genome data. However, there are some minor comments:

The authors primarily focus on the use of Bowtie2 and HISAT2 for alignment, neglecting to mention pseudoalignment algorithms like Kalisto. Including a brief description of such tools in the introduction would enhance the readers' understanding. Additionally, a comparative analysis of different alignment algorithms, along with the option to choose between them, would augment the robustness of the tool.

While the package utilizes SAMtools and BEDtools for coverage computation, incorporating downstream analysis tools compatible with circular genomes would enrich the utility of the tool for users.

Each figure in the article requires thorough explanation to aid interpretation, as they may be challenging to decipher at first glance. For instance, the coverage plot's significance is not easy to comprehend.

To attract non-computational users, it would be advantageous for the package to feature an easy-to-use graphical user interface (GUI), aligning with the intended accessibility for this audience.

 Overall, these suggestions aim to enhance the comprehensiveness and usability of the CircSeqAlignTk package for a wider range of users.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Partly

Is the rationale for developing the new software tool clearly explained?

Yes

Is the description of the software tool technically sound?

Yes

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly

Reviewer Expertise:

Computational genomics, RNA-seq and DNA-seq data analysis

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Sun Jianqiang National Agriculture and Food Research Organization, Japan, Japan

Competing interests: No competing interests were disclosed.

 29 4 2024

Thank you for reviewing our manuscript and for your constructive comments. According to reviewers’ comments, we have largely revised the manuscript and updated the software. Please find our point-by-point detailed responses to the reviewers’ comments, below.

We will update the software to support kallisto and update the documentation of this package in the next scheduled release. Thank you for your suggestion.

We plan to add functions for downstream analysis based on future needs.

We modified the captions of figures in the revised manuscript.

Thank you and other reviewers’ suggestions, we have already implemented the GUI and released it.

10.5256/f1000research.139847.r254597 Reviewer response for version 1 Dong Xueyi 1 2 Referee https://orcid.org/0000-0003-1136-3117 Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia ACRF Cancer Biology and Stem Cells Division Institution, Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia

Competing interests: No competing interests were disclosed.

 20 3 2024 Copyright: © 2024 Dong X 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

In the manuscript "CircSeqAlignTk: An R package for end-to-end analysis of RNA-seq data for circular genomes" by Jianqiang Sun, Xi Fu and Wei Cao, the authors introduced an R package CircSeqAlignTk for RNA-seq data analysis related to circular genome, including read alignment, coverage visualization and data simulation. The authors have clearly introduced the rationale for developing this package, how the alignment to circular genome works, and how to use this package. I only have several minor comments:

The authors should add more details on the definition of alignment coverage and explain how it is calculated in this package. More descriptions should be added to the captions of Figure 2 to help the readers interpret this figure.

While the analyses are based on circular genomes, the visualization of alignment coverage was still on linear axes. I found it a bit hard to determine the end of the sequence coordinate in Figure 2 (i.e. are the blank space on the right end of x-axis regions with no coverage, or are they outside of the range of the genome?). Have you considered using circular plots?

 If applicable, the performance of CircSeqAlignTk should be compared to other tools for the same or similar tasks.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Partly

Is the rationale for developing the new software tool clearly explained?

Yes

Is the description of the software tool technically sound?

Yes

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly

Reviewer Expertise:

bioinformatics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Sun Jianqiang National Agriculture and Food Research Organization, Japan, Japan

Competing interests: The authors have no competing interests to disclose.

 22 4 2024

Thank you for reviewing our manuscript and for your constructive comments. We have largely revised the manuscript according to your and other reviewers’ comments. Please check the revised manuscript. Additionally, below are our responses to the individual comments.

The coverage is calculated separately for forward and reverse aligned-reads from the BAM file using the `IRanges::coverage` function which counts the number of reads covering each position of viroid-genome. Thank you for pointing this out. We have revised the manuscript and Figure 2.

Because the expression profile of viroids’ small RNA is often visualised in linear axes,  this package visualises in a linear fashion by default. However, the vignette of this package introduces an example of visualisation with circular axes ( https://www.bioconductor.org/packages/release/bioc/html/CircSeqAlignTk.html).

Thank you for the constructive comments. Although many alignment tools have been developed for circular genomes, few specialise in viroid expression analysis. Among these, the sRNA Profiler (Adkar-Purushothama et al) is an alignment tool with similar purpose to CircSeqAlignTk. However, neither by checking the manual nor by reading the code we could not find the functionality to perform alignment and visualisation from FASTQ files. Thus, comparative studies were not possible due to the lack of software on the same basis as CircSeqAlignTk.

10.5256/f1000research.139847.r228243 Reviewer response for version 1 Soler Eric 1 Referee https://orcid.org/0000-0003-0521-7463 Salma Mohammad 1 Co-referee University Montpellier & Université de Paris, Paris & Montpellier, France

Competing interests: No competing interests were disclosed.

 13 2 2024 Copyright: © 2024 Soler E and Salma M 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

The manuscript titled “CircSeqAlignTk: An R package for end-to-end analysis of RNA-seq data for circular genomes” by Jianqiang Sun, Xi Fu, and Wei Cao describes a new tool dedicated to circular genome mapping in deep sequencing applications. While this tool could be of interest to scientists dealing with (small) circular genome sequencing, its accessibility to non-bioinformaticians/non-specialists could be improved (see comments below). We provide several suggestions to enhance the content of the manuscript.

Minor comments:

Comparative Analysis: A comparative analysis of CircSeqAlignTk with existing tools would greatly enhance the manuscript. This comparison could focus on performance metrics, user-friendliness, and specific advantages of CircSeqAlignTk. Such analysis would provide a clearer picture of the tool’s place in the current landscape of bioinformatics software.

Discussion of Limitations: A balanced discussion of any potential limitations of CircSeqAlignTk, or scenarios where it might not be the optimal choice, would provide a more comprehensive view of the tool. This discussion could guide users in making informed decisions about when to use this package.

Future Work and Enhancements: Suggestions for future enhancements would be beneficial. This could include potential areas of expansion or integration with other bioinformatics tools and workflows.

Implementation of an R Shiny Interface: Lastly, we strongly recommend the implementation of an R Shiny interface for CircSeqAlignTk completed by Docker integration. An R Shiny interface would significantly enhance the accessibility of the tool, especially for researchers who are not bioinformaticians. This user-friendly interface would allow a broader range of scientists to engage with and benefit from the tool, making it not just a powerful resource but also an accessible one. The ability to interact with CircSeqAlignTk through a graphical interface would streamline the analysis process and potentially increase the adoption and impact of the tool in the research community. Incorporating Docker into this solution would offer several advantages. It would not only make CircSeqAlignTk more accessible but also ensure its robustness, reproducibility, and compatibility with diverse computational environments. This approach could significantly expand the user base of CircSeqAlignTk and enhance its overall utility in the scientific community.

 In conclusion, the manuscript presents a valuable tool for the analysis of RNA-seq data from circular genomes. The implementation of the suggested enhancements would, in our opinion, greatly increase the manuscript’s impact and the utility of CircSeqAlignTk to a broader scientific community.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Partly

Is the rationale for developing the new software tool clearly explained?

Yes

Is the description of the software tool technically sound?

Yes

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes

Reviewer Expertise:

Genomics, epigenetics, bioinformatics

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.

Sun Jianqiang National Agriculture and Food Research Organization, Japan, Japan

Competing interests: The authors have no competing interests to disclose.

 22 4 2024

Thank you for reviewing our manuscript and for your constructive comments. We have largely revised the manuscript according to your and other reviewers’ comments. Please check the revised manuscript. Additionally, below are our responses to the individual comments.

Thank you for the constructive comments. Although many alignment tools have been developed for circular genomes, few specialise in viroid expression analysis. Among these, the sRNA Profiler (Adkar-Purushothama et al) is an alignment tool with similar purpose to CircSeqAlignTk. However, neither by checking the manual nor by reading the code we could not find the functionality to perform alignment and visualisation from FASTQ files. Thus, comparative studies were not possible due to the lack of software on the same basis as CircSeqAlignTk.

We added the discussion about the limitations of CircSeqAlignTk in the revised manuscript. In brief, although CircSeqAlignTk provides a user-friendly analysis tool and therefore offers a way to adjust important parameters that may affect the analysis results, some minor parameter adjustments are not possible.

Currently, research on viroid expression profiling focuses on mapping viroid-derived sRNAs to viroids and visualising their coverage. Our package covers all these operations and is promising enough to be stand-alone at the moment. However, since CircSeqAlignTk is one of Bioconductor packages, it can easily be used together with other R/Bioconductor packages. Additionally, we intend to update the package according to the direction of development and the needs of future research.

Thanks for the constructive remarks, we have implemented the Shiny interface to access CircSeqAlignTk for end-to-end data analysis. Users can now create an instance with the `build_app` function and easily start the GUI with the `runApp` function. Additionally, as you know, the size of RNA-Seq data is generally large and using Docker requires mounting the directory, which is quite inconvenient for users unfamiliar with CUI. For this reason, we decided not to provide a Docker image at this stage.