- Research article
- Open Access
Integrated analyses using RNA-Seq data reveal viral genomes, single nucleotide variations, the phylogenetic relationship, and recombination for Apple stem grooving virus
© The Author(s). 2016
- Received: 28 April 2016
- Accepted: 3 August 2016
- Published: 9 August 2016
Next-generation sequencing (NGS) provides many possibilities for plant virology research. In this study, we performed integrated analyses using plant transcriptome data for plant virus identification using Apple stem grooving virus (ASGV) as an exemplar virus. We used 15 publicly available transcriptome libraries from three different studies, two mRNA-Seq studies and a small RNA-Seq study.
We de novo assembled nearly complete genomes of ASGV isolates Fuji and Cuiguan from apple and pear transcriptomes, respectively, and identified single nucleotide variations (SNVs) of ASGV within the transcriptomes. We demonstrated the application of NGS raw data to confirm viral infections in the plant transcriptomes. In addition, we compared the usability of two de novo assemblers, Trinity and Velvet, for virus identification and genome assembly. A phylogenetic tree revealed that ASGV and Citrus tatter leaf virus (CTLV) are the same virus, which was divided into two clades. Recombination analyses identified six recombination events from 21 viral genomes.
Taken together, our in silico analyses using NGS data provide a successful application of plant transcriptomes to reveal extensive information associated with viral genome assembly, SNVs, phylogenetic relationships, and genetic recombination.
- Apple stem grooving virus
- De novo genome assembly
- Single nucleotide variation
Apple stem grooving virus (ASGV) is a member of the genus Capillovirus in the family Betaflexiviridae [1, 2]. ASGV has been most commonly identified from apple, European pear, Japanese pear, and Citrus trees . In addition, ASGV has been identified in lily  and kiwi , and it infects several virus indicator plants, including Chenopodium, Cucumber, Nicotiana, Phaseolus, and Vigna species . ASGV infection in fruit trees is usually latent without disease symptoms ; however, ASGV sometimes causes serious viral diseases . In many cases, fruit trees are co-infected by different viruses and viroids. For instance, apple trees showing fruit deformation, leaf deformation, and mosaic, chlorosis, and rusting symptoms in India were co-infected by Apple chlorotic leaf spot virus (ACLSV), Apple mosaic virus (ApMV), ASGV, Apple stem pitting virus (ASPV), and Apple scar skin viroid (ASSVd) .
The viral particles of ASGV are flexuous filaments 620–680 nm long and 12 nm wide . ASGV has a single-stranded (ss) positive-sense monopartite RNA genome containing 5′ capping and a poly(A) tail at the 3′ region . The genome size of ASGV is about 6,495 ~ 6,597 nucleotides (nt), and it encodes two overlapping open reading frames (ORFs), ORF1 (242 kDa) and ORF2 (36 kDa) [1, 2]. ORF1 encodes a polyprotein containing a replicase and coat protein (CP), while ORF2 encodes a movement protein (MP) that is overlapped with the replicase and CP regions [1, 2]. A previous study demonstrated that ASGV mutants with a stop codon between the replicase and CP coding regions were capable of systemic infection with decrease of pathogenicity . This result revealed that expression of ASGV CP via a subgenomic RNA (sgRNA) was sufficient for viability of ASGV. Furthermore, mutational analysis revealed core promoter sequences required for the sgRNA transcription of ASGV and Potato virus T, which were conserved among viruses in the families Alphaflexiviridae and Betaflexiviridae .
Next-generation sequencing (NGS) produces huge amounts of sequencing data, which facilitate the identification of known and novel viruses and viroids in a wide range of plant species . In addition, NGS can be applied in plant virus diagnostics [6, 11] and virus ecology . Several types of NGS platforms—including HiSeq systems by Illumina, 454 FLX systems by Roche, and SOLiD systems by AB—have been developed . Each NGS system has advantages and disadvantages . The selection of proper NGS platforms is dependent on the purposes of the study. HiSeq systems produce high throughput with a relatively shorter read length, whereas 454 FLX systems generate low throughput with a longer read length. For example, the identification and diagnostics of known and novel viruses can be conducted by HiSeq systems , and viral genome sequencing using extracted viral RNAs can be performed by 454 FLX systems .
Moreover, NGS systems are useful for virus–host interaction studies. For instance, sRNA-Seq has been used for virus-derived siRNAs (vsiRNAs) of ASGV from ASGV-infected samples . This study showed an increase in siRNA production towards the 3′ end of ASGV and several tRNA-derived sRNAs were differentially regulated by ASGV infection. A previous study identified 149 conserved and 141 novel miRNAs of pear associated with ASGV infection and found several miRNAs in response to high temperature, which was used to reduce ASGV titers in the shoot meristem tip . Pear transcriptome analysis between ASGV-infected and ASGV-free apple samples has been conducted and identified 184 up-regulated and 136 down-regulated genes in ASGV infected shoot culture as compared to ASGV-free shoot culture .
Several approaches to detect ASGV have been developed, such as long-distance PCR (LD PCR) to amplify the complete genome of ASGV , multiplex reverse transcriptase (RT)-PCR for major apple viruses [17–19] and pear viruses , and immunochromatographic assays by monoclonal antibodies specific for CP . Moreover, using available genome sequences for ASGV, two phylogenetic groups and four recombinants of 16 ASGV isolates have been identified , and the molecular evolution of subgenomic RNA of ASGV has been studied .
Several recent studies have demonstrated that many plant transcriptomes contain viral sequences that could be applied to studies associated with virus identification and viral genome assembly [23, 24].
In this study, we conducted in silico analyses using publicly available transcriptome data for viral genome assembly and identification using ASGV as an exemplar virus. We showed the application of transcriptome data for the analysis of single nucleotide variations (SNVs) on the ASGV genome. Moreover, the two viral genomes obtained were successfully applied in the phylogenetic and recombination analyses of known ASGV genomes.
Identification and de novo assembly of ASGV genome from ASGV-infected apple mRNA transcriptome
Of the known viruses, we selected ASGV, which mostly infects fruit trees, including apple (Malus domestica) and pear (Pyrus pyrifolia). Due to the clonal propagation of fruit trees, the possibility of virus infection is very high. We screened several apple and pear transcriptomes and selected the transcriptomes infected by ASGV for further study (data not shown). Of the several previously reported apple transcriptomes in response to ASGV infection, we selected two studies: one that performed mRNA sequencing (mRNA-Seq)  and one that performed sRNA-Seq . Both sets of samples included ASGV-infected and ASGV-free apple plants (Additional file 1).
Blast results to identify ASGV-associated contigs from ASGV-infected apple mRNA-Seq data
It is well known that RNA viruses have a quasispecies nature with a high mutation rate within infected hosts. Thus, we analyzed the SNVs of ASGV in the ASGV-infected sample. We mapped raw data on the genome of ASGV isolate Fuji, and interestingly, reads were highly mapped on the regions for CP and MP (Fig. 1b). Using the SAMtools program, we identified 90 SNVs. In particular, many SNVs were identified in the 5′ and 3′ regions of the ASGV genome (Fig. 1c and Additional file 4).
In many previous studies, the assembled contigs or transcripts were frequently used to identify viruses or viroids in the host transcriptome . Although the assembled contigs did not contain any viral sequences in the ASGV-free sample, it is possible that the raw sequence data contained viral sequences. The single- or paired-end mRNA sequencing by HiSeq2000 produces raw sequence data up to 101 bp in size. Therefore, the raw data can also be successfully applied to identify viral sequences in the host transcriptome data. We aligned a raw FASTQ file from the ASGV-free sample on the genome of ASGV isolate Fuji using the BWA program. As shown in Fig. 1d, 41 sequenced reads were mapped on the genome of ASGV isolate Fuji. To confirm the alignment results, we blasted the FASTA converted sequences against the ASGV genome. We found that 30 sequenced reads were aligned along the ASGV genome (Fig. 1e). The mapping and blast results using sequenced raw data clearly demonstrated the presence of ASGV viral sequences in the ASGV-free sample.
Identification and de novo genome assembly of ASGV from ASGV-infected sRNA transcriptomes
Previous studies have demonstrated that both mRNA-Seq and sRNA-Seq are useful for virus identification [26, 27]. To validate the utility of sRNA-Seq data for the de novo assembly of the ASGV genome, we used sRNA data from a previous study that conducted apple leaf sRNA sequencing using samples from the apple cultivar Golden Delicious (GD) . The data were composed of 12 libraries from ASGV-infected and ASGV-free samples (Additional file 1). Moreover, two different types of libraries were generated according to size fraction .
The six libraries from ASGV-infected samples were subjected to de novo transcriptome assembly using the Trinity program followed by a blast search to identify viral contigs. However, we obtained only 209 contigs with 425 bp of N50 value, and no ASGV-associated contigs were identified by the blast search. It seems that the Trinity program was not optimal for de novo transcriptome assembly using sRNA data. Thus, we used the Velvet program, which is well known for sRNA transcriptome assembly . The Velvet assembler assembled a total of 28,690 contigs, which were blasted against a plant viral database identifying 30 contigs associated with ASGV (Additional file 5). We mapped the identified ASGV-associated contigs on the reference genome of ASGV (NC_001749.2). The 30 contigs covered about 30 % of the ASGV genome and displayed many gaps along the genome. In order to confirm that sRNA reads cannot cover the complete genome of ASGV, we mapped sRNA raw data on the ASGV reference genome (Fig. 1f). We found that several regions of ASGV were not mapped by sRNA sequences. Based on the mapping results, we also identified 69 SNVs from sRNA data by the SAM Toolkit (Fig. 1g and Additional file 6).
Identification and de novo genome assembly of ASGV from pear mRNA transcriptome
We used pear transcriptome data from a previous study that did not include any information on the virus infection. The transcriptome data (accession number SRX532394) was derived from a mixture of nine different fruit developmental stages of the Pyrus pyrifolia cultivar Cuiguan. The transcriptome was initially assembled by SOAPdenovo2; however, we performed de novo transcriptome assembly again using the Trinity program. A total of 33,858 transcripts were assembled (Additional file 7). Assembled sequences were subjected to a blast search against a viral reference database. We found nine contigs associated with ASGV ranging from 222 bp to 6,513 bp (Additional file 8). Of the nine contigs associated with ASGV, a single contig with 6,513 bp was a nearly complete genome sequence of ASGV. After removing poly(A) tails from the contig, we obtained a sequence with 6,488 nt referred to as ASGV isolate Cuiguan (accession number: KR185346).
Identification of viruses from raw mRNA-Seq data of pear transcriptome by BLAST search
Name of virus
Size of genome
Apple stem grooving virus
Potato leafroll virus
Prunus virus T isolate Aze239
Apple green crinkle associated virus
Apple stem pitting virus
Apricot latent virus
Grapevine fleck virus
Rupestris stem pitting associated virus-1
Apple chlorotic leaf spot virus
Grapevine Pinot gris virus
Zucchini yellow mosaic virus
We examined SNVs for ASGV isolate Cuiguan within the pear transcriptome after alignment of the raw data on the ASGV isolate Cuiguan (Fig. 1h). We found 28 SNVs in the whole ASGV genome (Additional file 9). Interestingly, SNVs were only identified in the replicase region containing helicase, RdRP (Fig. 1i). However, SNVs were not detected in the region of MP or CP. Of the identified nucleotide changes, C to T (10 SNVs) was dominant followed by T to C (6 SNVs), G to A (6 SNVs), and A to G (6 SNVs).
Comparison of de novo sequence assemblers for viral genome assembly
Comparison of de novo transcriptome assemblers for assembly of viral contigs
No. of total contigs
No. of viral contigs
% of viral contigs
Phylogenetic analysis of ASGV isolates
Several previous studies have reported that ASGV is closely related to citrus tatter leaf virus (CTLV) . To confirm previous results, we blast identified two ASGV genomes in this study against the NCBI nucleotide database. The blast results confirmed that CTLV is closely grouped with ASGV isolates in the genus Capillovirus. From the GenBank, we retrieved all ASGV-associated sequences as well as CTLV-associated sequences. After removing partial sequences, we collected a total of 21 genomes of ASGV and CTLV isolates, including two ASGV isolates in this study. The host ranges of CTLV were mostly from Citrus species as well as Lilium species (Additional file 10). Pear black necrotic leaf spot virus (PBNLSV) isolated from pear was an isolate of ASGV according to the annotation in GenBank . Most ASGV isolates were isolated from apple and pear, and some isolates, such as ASGV isolates Matsuco and Li-23, were identified from Citrus tamurana and Lily, respectively. To reveal phylogenetic relationships, we aligned genome sequences displaying high sequence similarity of ASGV and CTLV. Sequence alignment and a phylogenetic tree using genome sequences of ASGV and CTLV identified two largely divided clades (Fig. 2e). The first clade contained 19 genomes, while the second clade included only PBNLSV and ASGV isolate KFP. The first clade could be further divided into three groups. Group A consisted of six CTLV isolates and a single ASGV isolate, while Group B contained only ASGV isolates. Group C was the largest, including seven ASGV isolates and two CTLV isolates.
Recombination analysis for 21 ASGV isolates
Recombination analysis of 21 ASGV genomes using RDP4 program
In recombinant sequence
Relative to CTLV_Lily
Minor parental sequence(s)
Major parental sequence(s)
Unknown (ASGV_HH), Unknown (ASGV_CHN)
ASGV_HH, ASGV_CHN, CTLV_MTH[P]
Unknown (CTLV_Pk), Unknown (CTLV_Kumquat1), Unknown (CTLV_LCd-NA-1), Unknown (CTLV_SO), Unknown (CTLV_XHC), Unknown (ASGV_Matsuco)
CTLV_SO, CTLV_Kumquat1, CTLV_XHC
The rapid development of NGS is enabling virologists to find viruses from numerous species [10, 31]. NGS-based approaches have identified not only known viruses but also novel viruses [32, 33]. In fact, many horticultural plants are frequently infected by viruses and viroids [11, 24, 34, 35]. In particular, fruit trees usually propagated by grafting and cuttage are reservoirs of various plant viruses and viroids [24, 34]. In addition, the big data produced by NGS techniques has prompted virus identification in silico [23, 24]. Here, we discussed the library types, sequencing methods, and de novo assembler for virus identification and viral genome assembly.
The majority of plant viruses are composed of RNA genomes, and DNA viruses also replicate via an RNA intermediate . Thus, RNA-based transcriptome libraries are preferable to DNA-based genome libraries for virus identification. In the current study, we used published plant transcriptome data. To enrich viral RNAs, ribosome-deleted libraries are usually prepared using extracted total RNAs from virus-infected samples . However, we demonstrated that the mRNA libraries using oligo d(T) were successfully applied for virus identification. Of course, RNA viruses with poly(A) tails such as ASGV are also easily identified by mRNA libraries. Similarly, several polyadenylated RNA viruses have been identified from sweet potato transcriptomes . Several recent studies have also demonstrated that ribosome-deleted RNA libraries as well as plant mRNA libraries are suitable for the identification of viruses without poly(A) tails or viroids [23, 24, 39]. Therefore, it might be ideal to use ribosome-deleted libraries for studies only focused on viruses. In the case of studies of both viruses and host plants, mRNA libraries can be usefully applied .
In this study, we used data from two different library types, including mRNA and sRNA libraries that were single-end sequenced by the HiSeq2000 system. According to many recent studies, viral genomes have been de novo assembled from mRNA as well as sRNA data [24, 33]. In our study, we assembled nearly complete genomes of two ASGV isolates from the mRNA data; however, the sRNA data could cover only 30 % of the ASGV genome. We compared the numbers of sequencing reads between the mRNA and sRNA data. However, the numbers of sequence reads between mRNA and sRNA were very similar, indicating that the sequencing amount is not an important factor for viral genome assembly. In fact, when the number of sequencing reads is increased, the number of viral-associated reads is increased. Therefore, the quantity of the sequenced data might play an important role in de novo genome assembly. The number of pear (3,524,264,028 bases) transcriptomes was about ten times that of apple transcriptomes (364,090,972 bases). The sequence reads associated with ASGV were 7,668 viral reads out of 7,430,428 reads for the apple sample and 4,274 viral reads out of 97,896,223 reads for the pear sample. Although the number of total sequence reads in the apple sample was much smaller than that in the pear sample, the number of sequence reads associated with ASGV was about 1.8 times higher. This result suggests the amount of viral replication in the host might be also an important factor in de novo viral genome assembly. The portion of viral nucleic acids in the sample infected by virus is often low suggesting enrichment of virions prior to NGS . For example, purification of double-stranded (ds) RNAs from the Prunus species followed by 454 pyrosequencing enabled to assemble four complete genomes of Asian prunus virus 1 (APV1), APV2, and APV3 . This study demonstrated successful application of dsRNA purification for virus genome assembly using NGS technique.
In the case of sRNA, two different types were prepared based on size fraction . The libraries without size fraction contain a large number of ASGV-associated reads, but the libraries with size fraction contain very few reads associated with ASGV. Of course, the sRNA libraries were targeted for the identification of viral sRNAs. We suppose that the small number of sRNAs might be related to the ability of the RNA silencing machinery in the host. In any case, a sufficient number of viral-associated reads is necessary for viral de novo genome assembly.
In addition, sequencing methods are important for virus identification and viral genome assembly. In this study, all transcriptome data were single-end sequenced by HiSeq2000. As compared to single-end sequencing, paired-end sequencing provides sequences from both ends of a fragment and generates high-quality and alignable sequence data. The advantages of paired-end sequencing have been previously reported . Thus, paired-end sequencing was far superior for the identification and genome assembly of the target virus.
For virus identification, assembled contigs are frequently used. Therefore, the choice of de novo assembler affects the quality and quantity of virus identification. For instance, mRNA data were very efficiently assembled by Trinity; however, few and low-quality contigs were assembled from the sRNA data by Trinity. Our comparative studies between the two de novo assemblers suggest Trinity and Velvet for de novo assembly of mRNA data and sRNA data, respectively. The obtained viral contigs assembled by Trinity from mRNA data were low in number but long in length, while the viral contigs assembled by Velvet were high in number but short in length. For the de novo assembly of a target virus with high-quality mRNA data, Trinity is ideal. Velvet cannot assemble a nearly complete viral genome, but it assembled many contigs, which enabled us to identify additional viruses, for example, viruses in the pear transcriptomes. Recently, several programs IVA, PRICE, and VICUNA for de novo assembly of RNA virus genome have been developed [43–45]. The choice of optimal de novo assembler might be dependent on researchers and purposes.
It is well known that RNA viruses have a quasispecies nature within the host . However, to date, most studies have shown the variants and mutation rates of target viruses using cloning-based Sanger sequencing methods . In this study, we successfully demonstrated the usefulness of plant transcriptome data for revealing the SNVs of ASGV. In fact, it is quite difficult to find virus variants using transcriptome data, while cloning-based sequencing methods might reveal variants. However, the cloning-based approaches require a RT-PCR amplification procedure to amplify full-length viral genomes. Practically, the amplification of full-length viral genomes is not easy even though plant viruses are relatively small. We showed the presence of ASGV variants in the transcriptome by comparing the ASGV genome from the cultivar Fuji derived from the Sanger-sequencing method and de novo assembly. We did not judge which ASGV genome was the dominant ASGV genome; however, it is highly likely that the de novo-assembled ASGV was a consensus genome sequence of ASGV. The mutation rates of identified ASGV genomes were varied: 1.38 % (90 SNVs) in the Fuji, 1 % (69 SNVs) in the GD, and 0.43 % (28 SNVs) in the Cuiguan. We suppose that several factors—including hosts, viral replication, and environmental cues—might affect the mutation rates. The association of viral mutation rate with other factors will be an interesting subject for further study .
Taken together, our study showed the successful application of plant transcriptome data for virus identification, viral genome assembly, and viral mutation rates. In addition, we discussed several factors, including library preparation, NGS systems, de novo assemblers, and sample conditions for virus identification and genome assembly.
Detailed information for plant materials can be found in the previous studies [5, 25]. In brief, RNA-Seq data were derived from three different plant materials including Malus x domestica cultivar Fuji (SRP034943), M. x domestica cv. Golden Delicious seedlings, grafted onto MM.109 rootstocks (SRP035543), and Pyrus pyrifolia cultivar Cuiguan (SRP041640).
Raw data processing and de novo transcriptome assembly
In this study, we used RNA-Seq data from three different projects. The first study employed mRNA-Seq data composed of two libraries derived from ASGV-infected and ASGV-free apple samples . The second study employed sRNA-Seq data composed of 12 libraries derived from ASGV-infected and ASGV-free apple samples . The third study employed mRNA-Seq data composed of a single library from pear samples without information on the ASGV infection. Information on the plant materials and library preparation were described in detail in the previous studies. Detailed information on the raw data can be found in Additional file 1: Table S1. All data were single-end sequenced by HiSeq2000. All bioinformatics analyses were performed in the Linux (Linux Mint version 17) installed workstation (four 16-core CPUs and 256 GB ram). We downloaded raw data for 15 libraries with respective accession numbers from the sequence read archive (SRA) database using the SRA toolkit . The raw SRA data were converted to FASTQ files using the SRA toolkit. For the de novo assembly of transcriptomes, we used two different programs, Trinity version 2.0.6 and Velvet version 1.2.10 [28, 50]. De novo transcriptome assembly was performed according to the manuals provided by developers with default parameters.
Sequence mapping and identification of viral contigs
For sequence alignment on the reference viral genome, we used Burrows-Wheeler Aligner (BWA) software with default parameters  Standalone BLAST version 2.1.19 was installed in the Linux system. To identify viral sequences in the assembled contigs, we used MEGABLAST, which is optimized for highly similar sequences against complete reference sequences for viruses and viroids (http://www.ncbi.nlm.nih.gov/genome/viruses/) with Evalue 1e-5 as a cutoff. In addition, all raw data were converted to FASTA files using the SRA toolkit and subjected to a MEGABLAST search against the viral reference database with Evalue 1e-5 as a cutoff.
De novo assembly of ASGV genomes
The viral contigs identified by the BLAST search were retrieved by the BLASTCMD program in the standalone BLAST system. To assemble ASGV genomes, the identified viral contigs were aligned against the ASGV reference genome (NC_001749.2) using ClustalW implemented in the MEGA6 program . The nearly complete genome of ASGV was manually obtained. The poly(A) tail at the 3′ end of ASGV was removed. We obtained nearly complete genomes for ASGV isolate Fuji (accession number KU500890) and ASGV isolate Cuiguan (accession number KR185346) from apple and pear transcriptomes. In the case of ASGV isolate GD, the obtained contigs covered only 30 % of the ASGV complete genome. Therefore, ASGV genome isolate GD was not obtained by the in silico approach.
Analysis of SNVs in transcriptomes
In order to analyze SNVs of ASGV genomes, the raw data were aligned on each identified viral genome using the BWA program with default parameters. In the case of ASGV isolate Fuji and ASGV isolate Cuiguan, the de novo-assembled genomes were used. For ASGV isolate GD, the ASGV reference genome sequence was used for alignment. The aligned SAM files by BWA were converted into BAM files by SAMtools . For SNV calling, we sorted the BAM files and then generated the VCF file format using mpileup function of SAMtools . BCFtools implemented in SAMtools was finally used to call SNVs. The positions of identified SNVs on the ASGV genome were visualized by the Tablet program .
Phylogenetic and recombination analyses of ASGV genomes
To retrieve the ASGV genome sequences, we first retrieved all sequences related to ASGV from the nucleotide database in GenBank (http://www.ncbi.nlm.nih.gov/genbank/). After eliminating partial sequences, only complete or nearly complete genome sequences for ASGV and CTLV isolates were identified. A total of 21 genome sequences including two isolates in this study were aligned by the ClustalW program with default parameters. After alignment, we deleted unnecessary sequences and poly(A) tails at the 5′ and 3′ regions, respectively. The manually edited aligned sequences were subjected to the construction of a phylogenetic tree using the MEGA6 program. The phylogenetic tree was constructed by the neighbor-joining method with 1,000 bootstrap replicates and Kimura 2-parameter distance.
We used Recombination Detection Program (RDP) version 4.66 . To identify recombinants in the 21 ASGV genomes, the sequences aligned by ClustalW were exported into MEGA file format using the MEGA6 program. We searched recombination events by nine different algorithms in the RDP4 program, and only recombination events supported by at least five algorithms were finally identified.
This work is dedicated to the memory of my father, Tae Jin Cho (1946–2015).
This work was carried out with the support of the “Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ01186102)” conducted by the Rural Development Administration, Republic of Korea.
Availability of data and materials
Raw sequencing data used in this study are available with following accession numbers in SRA database (SRS525152, SRS525150, SRS539610, SRS539601, SRS539598, SRS539592, SRS539584, SRS539484, SRS539610, SRS539601, SRS539598, SRS539592, SRS539584, SRS539484, and SRS598509). The analyzed data associated with ASGV and supporting information are available in additional files. The nearly complete genome sequences for ASGV isolate Fuji (accession number KU500890) and ASGV isolate Cuiguan (accession number KR185346) from apple and pear transcriptomes were deposited in GenBank with respective accession number.
WKC and YJ designed the research; YJ, HC, SMK, SLK, and BCL performed the research; YJ, HC, SMK, SLK, BCL, and WKC analysed the data; and YJ, HC, SMK, SLK, BCL, and WKC wrote the paper. All authors read and approved the final manuscript.
The authors declare that they have no competing interest.
Consent for publication
Ethics approval and consent to participate
This study did not include the use of any animals, human or otherwise, so did not require ethical approval.
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