Comparison of whole genome sequencing approaches for Capripox viruses

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Breman, Stefan Hoffman, Andy Haegeman, Wannes Philips, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7868004/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2026 Read the published version in BMC Genomics → Version 1 posted 13 You are reading this latest preprint version Abstract Background Direct whole genome sequencing of Capripox virus genomes from diagnostic samples is not always straightforward. Low viral content in a sample, biased sequencing and subsequent assembly and mapping methods may all influence the outcome. Methods In this study we have tested and compared six next generation sequencing approaches on a homogenized skin sample from bull infected with LSDV. We compared enrichment vs. non-enriched strategies, different library preparation methods, short read Illumina sequencing with long read sequencing methods. Results We found that methods that use an unbound transposon during tagmentation produced unbalanced results and lower target read yield versus methods that use other approaches to the tagmentation step. We further find that the use of hybrid capture probes increased the number of target reads. The result of subsequent mapping and assembly steps are influenced by the choice of reference when using reference-based assembly approaches. Conclusions When, using a short read sequencing approach we advise to use a transposon free method or a method with bound transposons for DNA fragmentation. These methods outperform kits that employ free transposons for DNA fragmentation when targeting AT-rich genomes. When mapping the reads it is best to use a reference for assembly that is as closely related as possible to the sample under study. Mapping problems can be resolved by long read sequencing which we recommend for denovo whole genome sequencing. Pacific Bioscience based long read sequencing outperforms Oxford Nanopore sequencing because it is less error prone. The ONT approach used, displays the same bias as the from Illumina and is therefore less suitable when attempting (Capri)pox whole genome sequencing. Capripox lumpy skin disease virus whole genome sequencing AT-bias direct sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction 1.1.1. LSDV and context Whole genome sequencing is very important in establishing relationships within Poxvirus studies as well as reliably establishing the precise strain responsible for outbreaks [2-8]. However, many publications have rather short material and methods sections with regards to their sequencing approach. Often effective read depth (reads per position) of the target virus is lacking, the distribution of viral reads across a reference genome (breadth) is also lacking as are the particular kits and conditions applied. This makes the quality of the work hard to asses and replicate. With the recent outbreaks of poxviruses such as Mpox [9], Suipox [10], avian pox [11] and Myxoma , [12] there is a need for clear and reliable methods of genome sequence recovery. Two circulating poxviruses that recently caused outbreaks and have a large economic impact [13] for livestock holders are Lumpy Skin Disease Virus (LSDV) and Sheeppox Virus (SPPV), both belonging to the genus Capripox . Sheeppox is endemic in Asia and Africa and recent outbreaks occurred in the European Union (EU) [7]. Lumpy skin disease virus is currently distributed and dispersing across Asia and Africa [described in 14]. and additional reports of LSDV outbreaks or new strains have been published [15-18]. The SPPV and especially the LSDV outbreaks have led to a surge in research into these viruses. One domain that has especially received increased attention is the use of whole genome sequencing to gain more comprehensive insight in circulating strains and disease epidemiology. Analyses of WGS’s has led to the establishment of a phylogenetic framework for LSDV and SPPV [6,13]. These frameworks will enable research to be done in an evolutionary context and to start looking for genomic changes which influence important traits such as virulence, transmission capacity, host specificity and others. Capripox viruses are large dsDNA viruses of 150 kb and obtaining WGS can be difficult. Difficulties in obtaining complete sequences from tissue involve the high amount of non-target (host) DNA in the samples compared to the low number of target reads, and the fact that the viral genomes are AT-rich [19], which may lead to problems during sequencing when using DNA library kits with unbound transposons for DNA fragmentation [20-21]. The problem of a low number of target reads in a tissue sample can be overcome by growing the virus on a cell culture, deeper sequencing or the use of (large amplicon) PCR based methods such as developed by [22]. All of these approaches have their drawbacks. Deeper sequencing is more expensive and will not result in a better distribution of reads across a reference genome. Growing the virus in a cell culture requires a specialized high containment lab, specialized personnel, and is associated with high costs and loss of time. Growth on a culture also bears with it the risk of introducing mutations although this aspect is less important for the relatively slowly evolving poxviruses. Using PCR enrichment can be very effective, but requires multiple reactions which take time, increase costs and require skilled people to perform them successfully. Additionally, a PCR may fail if the primers site has undergone mutations, a problem more pronounced when analysing RNA viruses, but it cannot be overlooked. Ideally a method would be available that can be directly performed on a tissue sample, is fast and comes at relatively low cost. There are two sequencing approaches currently in use: ‘long read sequencing’ (LRS) and ‘short read sequencing’ (SRS) are used. The SRS methods generate sequences of no more than 300-500bp in length. Sequencing both ends of a larger fragment (so called ‘paired end sequencing’) allows for slightly longer fragments to be recovered and may convey extra information, after mapping, on the location of each read relative to a reference genome. Typically, SRS results are mapped against a reference genome after which the resulting map is evaluated and variations relative to a reference can be observed. The LRS methods are characterized by the fact that they generate reads of well over 1000 bp in length. This is highly useful for reconstruction of repetitive parts of the genome as well as the terminal repeat regions present in poxviruses [23]. These methods are also more suitable for denovo whole genome reconstruction. These LRS methods however, also suffer from drawbacks such as low output of data, high input requirements (in terms of nanograms or even micrograms of DNA), specialized protocols for sample preparation, highly specific and complex data processing methods and finally high error rates [23]. These biases and technical challenges become highly relevant when attempting to detect low copy variants, to characterize viral communities, and assess vaccine purity [24]. Samples for both LRS and SRS can be prepared in two ways. The first is shearing using sonication, the second is enzymatic shearing (‘tagmentation’) using unbound transposons. The latter method is employed in the ‘rapid kit for Oxford Nanopore sequencing and in the ‘Nextera kit’ manufactured by Illumina. The transposon-based methods are widely used but they perform sub-optimal in shearing AT-rich genomes[20-21]. The Illumina DNA prep kit also uses transposons, but these are bound to a bead. In an effort to streamline the methods and provide guidelines for successful Capripox virus whole genome sequencing, we have established a collaboration between several reference laboratories which compared seven different next generation sequencing methods. We provide an overview of the approximate effort needed to reach a reliable and (near) complete WGS for (Capri)pox viruses and guidelines for good sequencing practices. Materials and Methods 1.2.1. Experimental setup 1.2.1.1. Collaborative test The four participating laboratories were: 1) National reference laboratory of the United Kingdom (the Pirbright Institute) located in Pirbright, United Kingdom; 2) The Laboratory of the United Nations’ International Atomic Energy Agency (IAEA) located in Vienna, Austria; 3) The national reference laboratory of Australia (CSIRO) located in Canberra, Australia; and 4) the national reference laboratory of Belgium (Sciensano) located in Brussels, Belgium. Sciensano additionally serves as the European and World reference laboratory for Capripox . Each laboratory independently attempted to reconstruct a whole genome sequence from the same sample (see below) using their own methods and experience. Afterwards, the mapping and assembly of the genome sequence was redone using the results from the denovo sequencing as reference. Because time and costs are important factors when considering sequencing, an estimate of the time for sample preparation, sequencing and bio-informatics was provided, as well as the financial costs for each kit/set of reagents. 1.2.1.2. Sample used The sample used in this study was derived from a bull infected during a previous study [60] with a clade 1.2 LSDV strain which was sequenced previously and published under GenBank acc. nr. KX894508. No animal experiments were thus performed for this study. The original inoculum used for infection was grown on cell cultures and underwent four passages in OA3-T cells before being used in an animal trial [60]. The animal developed skin nodules and one of these was collected and stored We obtained one of these nodules from two of the co-authors (A. Haegeman and W. Philips). The homogenate was aliquoted in 500µl tubes and inactivated by heating the sample for 4:30h at 56°C. These aliquots were subsequently shipped to the participating laboratories. 1.2.1.3. Experiment A: Pacific Bio LRS run This experiment used the Pacific Bioscience long read sequencing approach to create a denovo WGS for the strain used in this study. DNA was extracted from four subsamples of the homogenate using the Puregene tissue core kit (Qiagen). The concentration of the combined samples was assessed using ScreenTape on Tapestation 2200.Cycle threshold (C T ) values were determined using the method developed by [25], which targets the ORF074 gene. The extract was then used as starting material for NGS and prepared according to the following methods: SMRTbell® libraries were prepared with the SMRTbell prep kit 3.0, including shearing, repair, A-tailing, ligation with barcodes and adapters, and size selection with SMRTbell clean-up beads. Pooled libraries (≥300 ng) were converted to SMRTbell libraries and sequenced on the Sequel II platform under CCS mode for 30 hours. The thus obtained read library was assembled de-novo according to the following procedure. Reads were classified using the Centrifuge classifier [26]. LSDV-matching reads were extracted and assembled de novo using Canu v2.3 [27], Flye v2.2.2 [28], and Unicycler v0.50 [29]. Alternatively, de-novo assembly was performed directly with the Improved Phased Assembly software IPA (https://github.com/PacificBiosciences/pbipa) without host removal or with Canu after removing cattle reads. Assemblies were compared, and discrepancies were assessed through mapping using minimap2 v2.26 [29-31] and visualization with IGV [32]. 1.2.1.4. Experiment B: Oxford Nanopore LRS run In this experiment we employed Oxford Nanopore technologies long read sequencing approach to obtain a sequence. DNA was extracted from one subsample of the homogenate using the Machery Nagel (MN) DNA tissue kit. The concentration of the sample was assessed using the Qubit 4.0 HS dsDNA kit. C T values were determined using the method developed by [33], which targets the D5R gene. The extracted nucleic acids were amplified using a metagenomics amplification protocol as described previously [34-35]. The library was prepared using the rapid library preparation (SQK-RBK110-96; ONT) protocol and sequenced on an R9 MinION flow cell with a GridION device. Super-accurate base calling and demultiplexing was performed using Dorado v0.7.1, [36]. Reads were filtered on a minimum Phred average quality score of 7 using Chopper v0.6.0 50]. There was insufficient data for de-novo assembly, so a reference-based assembly method was used. For this, reads were mapped to the closest matching GenBank entry (MN995838.1) using minimap2 v2.24 – [37]. A consensus genome was generated using Samtools and Medaka [38]. All positions with a depth below 3X were masked. 1.2.1.5. Experiment C) Low pass Illumina Nextera XT run In this experiment we attempted to recover the whole genome sequence using low-pass Illumina Nextera based paired end sequencing and subsequent mapping to the reference. As a pre-treatment, one subsample of the homogenate was pre-treated with Baseline Zero DNAse for 20 minutes. Subsequently the DNA was extracted from the treated subsample of the homogenate using the Machery Nagel (MN) NA tissue kit. The concentration of the sample was assessed using the Qubit 4.0 HS dsDNA kit. C T values were determined using the method developed by [33], which targets the D5R gene. Library preparation was done using the Nextera XT kit (Illumina) according to the manufacturer’s instructions and whole genome sequencing was done using the Illumina MiSeq instrument. Paired-end reads of 250bp length were generated (MiSeq V3 chemistry, i.c. Miseq v3 600c reagent cassette). Resulting data was de-multiplexed and adapters and Illumina indices were removed before continuing with downstream analyses. Read mapping was performed using the LSDV GenBank reference sequence NC_003027.1 complete genome using the BWA mapper [39-40]. Because the viral read yield was low (see the results section) no quality filtering on the reads was applied. All reads from the raw data were included in the assembly. Samtools [41] was used to create a consensus and calculate read depth and genome coverage The resulting assembly was evaluated in the program ‘Tablet’ [42] and a majority rule was applied when assessing variable indel positions. A threshold of 10% read variation per base position was used to call ambiguous (if present), non indel, sites (i.e., if >10% of the mapped reads show a consistent variant, the base was called as being ambiguous). The consensus was manually assessed and curated to verify polymorphisms and indel sites. 1.2.1.6. Experiment D) low pass Illumina TruSeq DNA Nano run In this experiment we attempted to recover the whole genome sequence using low-pass Illumina based paired end sequencing using the TruSeq approach with subsequent mapping to the reference. As a pre-treatment the homogenate was pre-treated with Baseline Zero DNAse for 20 minutes. Subsequently the DNA was extracted from one subsample of the homogenate using the Machery Nagel (MN) NA tissue kit. The concentration of the sample was assessed using the Qubit 4.0 HS dsDNA kit. C T values were determined using the method developed by [33], which targets the D5R gene. Library preparation was done using the TruSeq DNA Nano sequencing kit (Illumina) in combination with the IDT for Illumina – TruSeq DNA UD Indexes v2 (Illumina) according to the manufacturer’s instructions following the protocol for mechanical fragmentation into 350bp fragments (100ng input). Paired-end reads of 150bp length were generated on an iSeq Sequencer (iSeq100 v2 chemistry, i.c. iSeq100 i1 Reagent v2 (300-cycle) reagent cassette.). Resulting data was de-multiplexed and adapters and Illumina indices were removed before continuing with downstream analyses. Read mapping was performed using the LSDV GenBank reference sequence NC_003027.1 complete genome using the BWA mapper [39-40]. Because the viral read yield was low (see the results section) no quality filtering on the reads was applied. All reads from the raw data were included in the assembly. A first draft consensus sequence was generated using Samtools. Read depth and coverage was also evaluated using Samtools [41]. The total assembly was evaluated in the program ‘Tablet’ [42] and a majority rule was applied when assessing variable indel positions. A threshold of 10% read variation per base position was used to call ambiguous (if present), non indel, sites (i.e., if >10% of the mapped reads show a consistent variant, the base was called as being ambiguous). The consensus was manually assessed and curated to verify polymorphisms and indel sites. 1.2.1.7. Experiment E) Illumina Nextera XT run with hybrid probe capture In this experiment we attempted hybrid probe capture of the LSDV genome and subsequent paired end sequencing using the Nextera/TruSeq based Illumina chemistry and reference based read mapping. DNA was extracted using the MagMAX Viral RNA Isolation kit (ThermoFisher) on an automated MagMAX Express 24 sample processer. C T values were determined using the capripoxvirus real-time PCR developed by [39], which targets ORF074 of the viral genome. The DNA concentration was determined using the Qubit dsDNA HS Assay Kit (Thermofisher Scientific). A probe hybridization procedure for LSDV was developed using the MyBaits Target Capture Kit (Arbor Biosciences), which uses complementary oligonucleotides for selectively binding target DNA in favour of host or environmental DNA. The oligonucleotide hybrid capture probes for enrichment were designed using representative LSDV genomes from GenBank. The final design consisted of 18,943 probes of 90 bp each with 3x depth over the LSDV genomes. Sequencing libraries for the samples were prepared using the Nextera XT DNA Library Prep Kit (Illumina) and enriched for LSDV fragments using the probes as per the manufacturer’s recommendations (Arbor Biosciences). The libraries were reamplified and sequenced using a P1 300-cycle cartridge on a NextSeq2000 instrument (Illumina). The raw reads were cleaned using Trimmomatic v.0.3911 and mapped to the Neethling Warmbaths LSDV genome (GenBank Acc. AF409137.1) using Bowtie v.2.4.412 [43]. The mapping was manually examined and edited with Geneious Prime v.2023.1.2. 1.2.1.8. Experiment F) Illumina Nextera run high output In this experiment we attempted to recover the whole genome sequence using deep Illumina Nextera based, paired end sequencing and subsequent mapping to the reference. DNA was extracted from one subsample of the homogenate using the MagMAX CORE Nucleic Acid Purification Kit (Applied Biosystems), this was then treated with BaselineZero DNAse for fifteen minutes. The concentration of the sample was assessed using Qubit 4.0 HS dsDNA kit. C T values were determined using an in house developed method, which targets the P32 gene. A total of 1ng of DNA was used as input of the Nextera XT DNA library preparation kit. Sequencing was performed using the P1 (300 cycles /100M reads) sequencing cartridge for the Illumina NextSeq 2000 sequencing platform. Initial quality control (QC) of Illumina paired-end reads was performed using FastQC, followed by trimming of low-quality bases and adapter sequences using fastp. An iterative mapping and assembly approach was then applied. Reads were first filtered by mapping to the reference genome sequence OM033705 using BBMap v39.06 (https://sourceforge.net/projects/bbmap/, 2024). The filtered reads were then de novo assembled using SPAdes, and assembly quality was evaluated with QUAST. The resulting contigs were queried against the NCBI virus database using BLASTn [44-45] to identify the closest reference sequence. The best matching reference (KY829023.3) was selected for subsequent read alignment using BWA and the resulting Alignment quality was first assessed with Qualimap, after which duplicate reads were removed using samtools markdup. The deduplicated reads were then realigned to the reference using BWA for improved accuracy, followed by a second round of alignment quality assessment with Qualimap. Variants were called with bcftools, mpileup and bcftools call, and consensus sequences were generated accordingly. All alignments, variants, and consensus sequences were visually inspected using the IGV [32]. 1.2.1.9. Experiment G) Illumina DNA-prep run In this experiment we attempted to recover the whole genome sequence using low-pass Illumina based paired end sequencing using the DNA prep approach with subsequent mapping to the reference. One subsample of the homogenate was pre-treated with Baseline Zero DNAse for 20 minutes. Subsequently the DNA was extracted from the treated subsample of the homogenate using the Machery Nagel (MN) NA tissue kit. The concentration of the sample was assessed using the Qubit 4.0 HS dsDNA kit. C T values were determined using the method developed by [33], which targets the D5R gene. Library preparation was done using the Illumina DNA Prep Library preparation kit (Illumina) according to the manufacturer’s instructions. Paired-end reads of 150bp length were generated on an iSeq™ Sequencer (iSeq100 v2 chemistry, i.c. iSeq100 i1 Reagent v2 (300-cycle)reagent cassette ). Resulting data was de-multiplexed and adapters and Illumina indices were removed before continuing with downstream analyses. Read mapping was performed using the LSDV GenBank reference sequence NC_003027.1 complete genome using the BWA mapper [39-40]. Because the viral read yield was low (see the results section) no quality filtering on the reads was applied. All reads from the raw data were included in the assembly. A first draft consensus sequence was generated using Samtools. Read depth and coverage was also evaluated using Samtools [41]. The total assembly was evaluated in the program ‘Tablet’ [42] and a majority rule was applied when assessing variable indel positions. A threshold of 10% read variation per base position was used to call ambiguous (if present), non indel, sites (i.e., if >10% of the mapped reads show a consistent variant, the base was called as being ambiguous). The consensus was manually assessed and curated to verify polymorphisms and indel sites. 1.2.1.10. Detailed methodology of sequencing methods Seven different sequencing experiments (A to G) were performed to determine the WGS of the LSDV strain present in the sample. Detailed protocols for all of the sequencing methods can be found in supplementary data (OSM1). An overview of the key steps and kits used in each of the sequencing protocols can be found in Table 1 and Figure 1. Table 1 DNA extraction methods and evaluations as well as yield and C T values for each extract and subsequent NGS sequencing yield for each resulting NGS dataset. A B C D E F G DNA extraction method Puregene tissue core kit (Qiagen) Machery Nagel DNA tissue Machery Nagel DNA tissue Machery Nagel DNA tissue MagMAX Viral RNA Isolation MagMAX CORE Nucleic Acid Purification Machery Nagel DNA tissue DNAse yes/no N N Y Y N Y Y concentration assesment method ScreenTape on Tapestation 2200 Qubit 4.0 HS dsDNA kit Qubit 4.0 HS dsDNA kit Qubit 4.0 HS dsDNA kit Qubit 4.0 HS dsDNA kit Qubit 4.0 HS dsDNA kit Qubit 4.0 HS dsDNA kit Assement of C T values [39] [52] [52] [52] [39] [*] [52] Enrichment step - - - - hybrid probe capture - - Library prep kit SMRTbell prep kit 3.0 rapid library preparation Nextera XT kit TruSeq Nano Nextera XT kit Nextera XT kit DNA prep Sequencing platform Pacific Bioscience R9 MinION GridION MiSeq iSeq NextSeq2000 NextSeq2 iSeq Bio-informatic analysis De novo reference based reference based reference based reference based reference based reference based [*] unpublished method, targeting the P32 gene. 1.2.2. Genome re-mapping of datasets B-G Datasets obtained by methods B-G were re-mapped against the dataset obtained by method A to remove any potential effect of different assembly and mapping methods. For datasets C-G the BWA mapper was used. For dataset B minimap2 v2.24 was used and no minimum value was set for read depth used to report the consensus. All assemblies were manually evaluated in Tablet. The visualization of the read depth was done via Coverage and Depth (or “CoDe”) plots prepared in R v4.2.0. [24] using ggplot2 v3.5.1 [46]. 1.2.3. Mapping evaluation and genome completeness We evaluated the final re-mapped results and compared these with regards to minimum, maximum and average read depth. We also evaluated to which extent each assembly contained regions of low (<10x), very low (<5x) or no (0) coverage. This allowed us to detect regions of low or no coverage and ignore the effect of the zero reads mapping on the flanking regions and asses the result in more detail. We then determined the standard deviation (sd) of the read depth as a reflection of the differences of depth across each genome position. To determine the completeness of our denovo reconstructed genome we evaluated the length of the terminal repeat region (TR-region). Dotplots generated by Mafft [47] and an alignment with the LSDV reference (GenBank acc. nr. NC_003027.1) were used to determine the length of the TR. Our genome reconstructions were considered complete when the length of the TR-regions reached 99%, were as long as, or longer than what is reported for the reference sequence. The consensus of experiment A was submitted to GenBank with accession number: ######. Results 1.3.1. DNA extraction, viral load and sequencing output An overview of the DNA extraction methods, concentrations and C T measurements, enrichment methods, sequencing methods as well as results of each separate WGS reconstruction as described in the paragraphs below can be found in Table 2. Table 2 DNA concentration and C T values for each extract and subsequent NGS sequencing yield for each resulting NGS dataset. A B C D E F G Concentration (ng/µl) 3.50 5.20 0.25 0.25 6.71 0.50 0.20 C T virus 20.96 18.8 20.44 20.44 18.35 25 20.44 C T host - 24.4 29.62 29.62 - - 29.62 C T ratios (target/non target) - 0.77 0.69 0.69 - - 0.69 complete genome reported Y Y N* Y Y Y Y genome coverage (%) 100 100 99 100 100 100 100 output file (10 6 reads) 8.91 6.72 3.60 3.70 30.10 9.15 7.53 genome length recovered after re-mapping 150896 150896 150855 150886 150680 150728 150891 % of LSDV associated reads 0.016 0.15 3.6 7.2 34.1 0.3 3.6 count of LSDV associated reads 1426 9937 130410 269965 3120596 176980 271316 * This sequence was not complete because the regular genomic regions were gapped. The terminal repeats were near complete as was the case for the other reconstructions. Different extraction kits were used, but the DNA concentration recovered was generally low. For the DNase treated samples the concentrations ranged from 0.20 to 0.5 ng/µl. for the untreated samples this ranged from 3.5-6.71 ng/µl. Obtained C T values for the viral target varied in the DNase treated samples were between 20.44 (dataset B & C) and 25 (dataset F). For the untreated samples the range was between 18.35 and 20.96. Experiments B and C, D and G also determined the C T values of a host gene, making that the ratio of host/virus could be determined. This ratio was 0.77 in experiment B and 0.69 in experiments C, D and G (as these datasets were all derived from the same sample (Table 2). The resulting read libraries varied in size. The smallest read-library was obtained for experiment A with 3.6 * 10 6 reads and the largest read-library was obtained for experiment E with ~30 * 10 6 reads. The total read library sizes are listed in Table 2. 1.3.2. Target read yield and mapping results Depending on the mapping approach and reference genome used, some of the obtained consensus genomes contained gaps when compared to the sequence obtained by method A. To be able to compare the results obtained by the different methods and by the different participating laboratories we remapped the datasets against the sequence obtained via method A. The yield of target (viral) reads per dataset differed (Table 3). The lowest percentage of target reads was recovered for both LRS datasets with PacBio and ONP yielding 0.016% (method A) and 0.15% (method B) of viral reads, respectively. The difference in target read output between the same sample processed with the Nextera (method C) and DNA-prep (method G) kits vs. the TruSeq kit (method D) is twofold as they yielded 3.6% (C, G) vs 7.2% (D) target reads, respectively. Method F, representing the deep sequencing effort using the Nextera kit, yielded 0.3% of viral reads. Dataset E, combining TruSeq library preparation with target enrichment via hybrid capture probes, yielded 34.1% of viral reads. The recovered LSDV sequence contained an AT-percentage of 74.12% and a GC content of 25.88%. There were no ambiguous base positions in any assembly except in the assembly for dataset G which contains two ambiguous positions and one deletion when compared to the sequence from experiment A. The terminal repeat was 2471bp in length and was present on both the 5’ and 3’ side of the reconstructed WGS from dataset A (Figure 2 & OSM2). All datasets, except G missed 5bp of the terminal repeats compared to the sequence obtained by the PACBIO LRS in method A. Method C which used the Nextera kit even missed 36 bp at the 3’ end of the genome. Method C was also the only which resulted in a dataset with five unmapped positions in the core part of the genome and furthermore had a large number of positions (1367) only covered by less than five reads. 1.3.3. Read distribution and read depth Table 3 provides an overview of the obtained read depth and the read distribution over the genome is visualized in the CoDe plots in Figure 3. The results show that datasets C-G all had an average read depth of >100 reads per position. Dataset E showed the highest average read depth of 2559 reads per position and dataset B the lowest with 48 reads per position. The variation of the read depth over the genome however strongly differs between the methods. The read distribution for datasets generated with the transposon-based kits (B, C and F) are highly unequal (Figures 3 and 4). The sd’s for these sets are >100% of the average (Table 3). The distribution of reads for datasets A, D and G is more homogenous and the corresponding sd’s of distribution are lower. For these three datasets there is also an increased read mapping at the flanks of the genome. Dataset E also has a high sd (1659, 56.1% of the average). Dataset also has a transposon component involved but it is treated differently during sample preparation (see the discussion). Table 3 Mapping details for each experiment. A B C D E F G average read depth 58 48 268 179 2959 134 246 standard deviation 13.13 50.94 325.57 63.6 1659.55 155.11 30.8 standard deviation as % of the average 22.6 106.1 121.5 35.5 56.1 115.8 12.5 max. depth 107 575 2930 566 12756 1604 400 min. depth 17 1 0 0 0 0 0 5' missing 0 5 5 36 5 5 1 3' missing 0 5 5 5 5 5 5 number of positions at zero 0 0 5 31 0 0 1 number of positions at <5 0 653 1367 147 0 75 5 number of positions at <10 0 6092 6087 181 1 262 1 min. depth excluding the first and last 5 bp - 1 0 1 124 2 16 Discussion The use of whole genome sequences in virus studies is of increasing importance as it provides insights into the changes of the whole virus genome, not just a separate gene. Especially with relatively slow evolving viruses such as the poxviruses [48] there is need to obtain more comprehensive overview of the virus under study. But poxviruses show skewed AT-GC ratios [19] which may impact sequencing success and therefore the reliability of the WGS reconstruction. The first step of each sequencing project is the extraction of the DNA from any given sample. While this is critical there appears to be not much difference between the methods used in this study. All commercially available kits will yield approximately the same amount of DNA. The application of DNase to increase the virus to host ratio is based on the idea that the encapsulated virus (prior to extraction) may be more resistant to the treatment than the background DNA and also that there is much more background DNA than the target DNA which will thus be, relatively speaking, less affected. While this may be the case, given the slight improvement in virus to host C T ratios observed (0.77 to 0.69), there is a considerable loss of both target and non-target DNA what may impair downstream applications such as hybrid probe capture of perhaps even LRS methods. Our results show that truly random DNA fragmentation using sonication as applied in the TruSeq kit results in a more even distribution of reads over the genome when compared with the non-random tagmentation as employed in the Nextera Kit. Tagmentation is achieved by the use of a transposon. We hypothesize that the transposon used to fragment the DNA has a preference for GC-rich regions where it will attach and subsequently catalyse the fragmentation. These regions are therefore more represented in the resulting sequence read libraries. This is not a problem if AT/GC content is roughly equal within a target genome (although GC-poor regions may still be missed there), but it is not efficient in the case of Capripox genomes. When leaving out the transposon-based steps, both depth (yield) and breadth (distribution across the genome) of reads were increased. This can be seen in the case of datasets C, D and G where the yield of virus associated reads vs. host reads is almost doubled when using the TruSeq kit vs. the Nextera and DNA-prep kits (in terms of read count and percentage). This further underscores the bias introduced by the transposon-based methods and confirms what was found by [20-21]. While [49] report a minor negative AT-bias when using the Nextera kit, our results suggest a more profound effect. In this study we also tested the DNA-prep Illumina kit (experiment G), which claims to avoid the negative AT-bias by binding the transposon to beads forcing a maximum of DNA to bind to the beads irrespective of the AT/GC-ratio of the DNA [50]. Our results show that this is indeed the case, but the yield of target reads was lower than with the TruSeq based experiment (Experiment D) (Table 1, Figures 3 & 4). The use of hybrid capture probes proved to be very effective. The amount of target reads recovered increased more than 4.5 times, to >30% of the read library, when compared to the next best result of obtained with the TruSeq experiment (D). In the case of important samples or samples with low virus yield this approach may be useful and effective. Another way to overcome the low virus yield would be to use a PCR based approach. The long-range PCR protocol developed by [22] overcomes this partially. This is a very robust protocol resulting in a large amount of viral material. The downside of this procedure is that it is also time consuming and requires experienced personnel to perform. Further this protocol does not allow to reliably detect sequence variants that may occur in a virus population, which is especially of concern when evaluating vaccine sequences of live attenuated vaccines. The data processing and genome assembly is relatively straightforward, but in the case of LRS data requires powerful computing units and a good understanding of the various tools needed. The main pitfall when using the SRS data is the correct selection and use of the mapping reference sequence and proper inspection of the resulting assembly. Use of a reference that is too variable, especially when large indels are present, may influence the result. In the case of this study, we encountered this in the case of the hybrid probe capture procedure and the Nextera deep sequencing method whereby the initially reported WGS differed from the results of the other experiments (see OSM2). Remapping revealed that this was caused by the use of a different, less related reference. Another known problem is caused by long repeat regions (>1000bp) in a genome. These cannot be covered by single reads from SRS methods and mapping will be less reliable. Long read sequencing methods hold great promise for virus studies [51] but have not been done routinely in poxviruses (e.g., 52-53]. Perhaps the fact that LRS methods might also have a bias in AT-rich genomes [54] may have discouraged people from using them. However, LRS methods avoid problems that arise from the presence of repeats in a genome and the longer reads that are generated may traverse otherwise hard to assemble parts of the genome. Further, the length of the reads also makes these methods more suitable for denovo genome reconstruction. Our results show that, while suited for denovo sequencing, LRS methods result in low virus associated read recovery. That being said, the length of the reads more than compensates for this in our results with a comprehensive mean coverage of 58 (PacBio) and 40 (ONP) reads per position. This seems sufficient to us for a reliable recovery of the virus genome, provided the coverage of the genome is homogenous [55]. While the read distribution of the PacBio result is homogenous (dataset A, Figure 3A & 4A), this was less for the ONP LRS (dataset B, Figure 3B & 4B) where the results showed similarities with those observed in the Nextera based protocols (compare Figure 3B with Figure 3C). We attribute this to the fact that the ONP procedure also involves the use of a transposon during the tagmentation step [56]. This may indicate that the employed ONP approach also suffers from a sequencing bias in AT-rich genomes and it may therefore be less suited than the PacBio technology for sequencing genomes with skewed AT/GC ratios. Based on the occurrence of restriction enzyme sites, the TR regions of Capripox were historically determined to be between 2.25 and 3.4 kb [57]. Research from [58] described the reference LSDV genome having a 2418bp inverted repeat. In the reconstruction of the LSDV genome in our sample, the TR region was ~2471 bp (Figure 2 and OSM2), which is 53bp longer than the reference. Our denovo reconstructed genome therefore probably represents the complete genome. While the TruSeq and PacBio read distributions are considered the most homogenous, an increased number of reads mapping at both flanks in the TR-region of the reconstructed genomes was observed (Figure 3A, D & G). This can be explained by unequal mapping of reads across the TR-region relative to the reference, which happens when multiple optimal mapping coordinates occur in a reference genome. The terminal repeats of LSDV are currently reported to be very variable (see the alignments produced by [14]), we are not sure whether this is really the case or if this is an artefact of low coverage of these regions in the studies publishing them or as a result of sequencing procedures. The CoDe plots used in this study are a simple to create and visually easy way to evaluate and present this. Finally, to asses genome completeness a dot plot can be reconstructed and the length of the TR-regions can be compared with the known literature and confirmed full length genome sequences present in public databases (i.e., GenBank). 1.4.1. Recommendations Based on the sequencing breadth and depth obtained for the different sequencing protocols tested we recommend to use the TruSeq kit for whole genome sequencing of Capripox viruses in samples with sufficient viral load and high enough DNA concentration as the TruSeq kit does need more input material. When it is needed to sequence a sample with very low virus content the hybrid probe capture method will be valuable to use in combination with DNA prep-based sequencing as it allows for lower concentrations of start material. Both methods can be easily implemented by any laboratory already using Illumina sequencing. For mapping and assembling we recommend to use an iterative strategy with multiple reference genomes when SRS is used. As a mis mapping occurs easily, especially in the variable regions. In order to be able to report reliable sequencing results, we recommend to strive for a read depth of 30, especially in the case of error prone methods such as ONP, as this will allow to safely capture both INDEL and nucleotide variation [59]. This furthermore strongly reduces the chance of miscalling bases due to base calling errors [55]. This is already true at 10 times coverage, but indel variation is often more difficult to capture. We also recommend to report information on read coverage and depth for each reconstructed genome so the readers can have an idea about the robustness of the reported sequence information. The latter is currently often lacking and might be the cause of difficulties during the alignment of the terminal repeat regions or regions with indels when such sequences are used as reference themselves. Declarations 1.5. Author Contributions: Conceptualization : FCB NDR. methodology ; FCB, NDR, SH. writing—original draft preparation : FCB, NDR. writing—review and editing . FCB, AH, WP, NDR, SH, SdK, , TRB, CB, CW, CEL. NGS : SH, SdK, PM MJW CB, CW, TBKS. Data analyses : FCB, MJW, CB, TRB, TBKS, CEL, CW, CB. Visualization: FCB. All authors have read and agreed to the published version of the manuscript. 1.6. Funding: This study was financed by a grant from the Bill and Melinda Gates foundation, grant nr: INV-055082 awarded Sciensano. PacBio sequencing was funded by the IAEA laboratory in Vienna. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. 1.7. Data Availability Statement: Sequence alignments are available on request. The long read sequencing result has been deposited in GenBank with accession numbers PX492334. The sequence read archives are available on request. 1.8. Informed consent statement: Not applicable 1.9. 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Additional Declarations No competing interests reported. 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As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7868004","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545291220,"identity":"93fade81-a6f6-46dd-8091-ffe36be2829a","order_by":0,"name":"Floris C. 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1","display":"","copyAsset":false,"role":"figure","size":126721,"visible":true,"origin":"","legend":"\u003cp\u003eFlow of samples per experiment. Samples indicated with a * are from the same individual tube. For the sample indicated with a + a full MiSeq cartridge was used.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7868004/v1/9bd5ff535539fad105f0e758.png"},{"id":96268580,"identity":"de803a56-38be-44ec-ae8c-1b9a31146a88","added_by":"auto","created_at":"2025-11-19 09:01:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151280,"visible":true,"origin":"","legend":"\u003cp\u003eDot plots of the WGS based on dataset alpha. A displays a comparison of the sequence with itself. The blue lines in the topleft and lower richt corners indicate the presence of the terminal repeat region. B displays a representation of the first and last 10000 basepairs of dataset A. The blue lines indicate the terminal repeat region. The length of the region is displayed at the top left corner.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7868004/v1/dbf83a92b35bbc1fc8d556fc.png"},{"id":96364864,"identity":"63fe1733-9597-487c-a076-0cbfb16ee506","added_by":"auto","created_at":"2025-11-20 10:09:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":367005,"visible":true,"origin":"","legend":"\u003cp\u003eCoverage and Depth plots (“CoDe”)plots for each dataset. Panels A \u0026amp; B represent the long-read sequencing based datasets A and B. Panels CB-G represent the Illumina based datasets C-G respectively. The dashed green line denotes the average read depth for each dataset based on all positions.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7868004/v1/696d6f5e6dac11223adcd129.png"},{"id":96268582,"identity":"67e14765-b2b5-4ed2-a8b1-db42d632e456","added_by":"auto","created_at":"2025-11-19 09:01:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77717,"visible":true,"origin":"","legend":"\u003cp\u003eboxplots displaying distribution of read depth around the average for datasets A,B,C,D,F,G. Mapping results for LRS are in blue, for SRS in pink (\u003cstrong\u003eA\u003c/strong\u003e). Boxplot displaying distribution of read depth around the average for dataset E with a different scale (\u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7868004/v1/ae59083912ad55cfa850ca76.png"},{"id":100069051,"identity":"035318b7-c406-43b1-b474-1877de00f2fb","added_by":"auto","created_at":"2026-01-12 16:07:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1856525,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7868004/v1/1f86c90f-e851-48ab-8aa0-60a679f4f0ff.pdf"},{"id":96268581,"identity":"94ea27e9-4cb2-4ea9-89da-9fd78815b6a1","added_by":"auto","created_at":"2025-11-19 09:01:09","extension":"fas","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":905632,"visible":true,"origin":"","legend":"","description":"","filename":"OSM2finalremappingsequences.fas","url":"https://assets-eu.researchsquare.com/files/rs-7868004/v1/38982bf858ae0531ad6dc07e.fas"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComparison of whole genome sequencing approaches for \u003cem\u003eCapripox\u003c/em\u003e viruses\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003ch4\u003e1.1.1. LSDV and context\u003c/h4\u003e\n\u003cp\u003eWhole genome sequencing is very important in establishing relationships within Poxvirus studies as well as reliably establishing the precise strain responsible for outbreaks [2-8]. However, many publications have rather short material and methods sections with regards to their sequencing approach. Often effective read depth (reads per position) of the target virus is lacking, the distribution of viral reads across a reference genome (breadth) is also lacking as are the particular kits and conditions applied. This makes the quality of the work hard to asses and replicate. With the recent outbreaks of poxviruses such as \u003cem\u003eMpox\u003c/em\u003e [9], \u003cem\u003eSuipox\u003c/em\u003e [10], avian pox [11] and \u003cem\u003eMyxoma\u003c/em\u003e, [12] there is a need for clear and reliable methods of genome sequence recovery.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo circulating poxviruses that recently caused outbreaks and have a large economic impact [13] for livestock holders are Lumpy Skin Disease Virus (LSDV) and Sheeppox Virus (SPPV), both belonging to the genus \u003cem\u003eCapripox\u003c/em\u003e. Sheeppox is endemic in Asia and Africa and recent outbreaks occurred in the European Union (EU) [7]. Lumpy skin disease virus is currently distributed and dispersing across Asia and Africa [described in 14]. and additional reports of LSDV outbreaks or new strains have been published [15-18]. The SPPV and especially the LSDV outbreaks have led to a surge in research into these viruses. One domain that has especially received increased attention is the use of whole genome sequencing to gain more comprehensive insight in circulating strains and disease epidemiology. Analyses of WGS\u0026rsquo;s has led to the establishment of a phylogenetic framework for LSDV and SPPV [6,13]. These frameworks will enable research to be done in an evolutionary context and to start looking for genomic changes which influence important traits such as virulence, transmission capacity, host specificity and others. \u003cem\u003eCapripox\u003c/em\u003e viruses are large dsDNA viruses of 150 kb and obtaining WGS can be difficult. Difficulties in obtaining complete sequences from tissue involve the high amount of non-target (host) DNA in the samples compared to the low number of target reads, and the fact that the viral genomes are AT-rich [19], which may lead to problems during sequencing when using DNA library kits with unbound transposons for DNA fragmentation [20-21]. The problem of a low number of target reads in a tissue sample can be overcome by growing the virus on a cell culture, deeper sequencing or the use of (large amplicon) PCR based methods such as developed by [22]. All of these approaches have their drawbacks. Deeper sequencing is more expensive and will not result in a better distribution of reads across a reference genome. Growing the virus in a cell culture requires a specialized high containment lab, specialized personnel, and is associated with high costs and loss of time. Growth on a culture also bears with it the risk of introducing mutations although this aspect is less important for the relatively slowly evolving poxviruses. Using PCR enrichment can be very effective, but requires multiple reactions which take time, increase costs and require skilled people to perform them successfully. Additionally, a PCR may fail if the primers site has undergone mutations, a problem more pronounced when analysing RNA viruses, but it cannot be overlooked. Ideally a method would be available that can be directly performed on a tissue sample, is fast and comes at relatively low cost. There are two sequencing approaches currently in use: \u0026lsquo;long read sequencing\u0026rsquo; (LRS) and \u0026lsquo;short read sequencing\u0026rsquo; (SRS) are used. The SRS methods generate sequences of no more than 300-500bp in length. Sequencing both ends of a larger fragment (so called \u0026lsquo;paired end sequencing\u0026rsquo;) allows for slightly longer fragments to be recovered and may convey extra information, after mapping, on the location of each read relative to a reference genome. Typically, SRS results are mapped against a reference genome after which the resulting map is evaluated and variations relative to a reference can be observed. The LRS methods are characterized by the fact that they generate reads of well over 1000 bp in length. This is highly useful for reconstruction of repetitive parts of the genome as well as the terminal repeat regions present in poxviruses [23]. These methods are also more suitable for denovo whole genome reconstruction. These LRS methods however, also suffer from drawbacks such as low output of data, high input requirements (in terms of nanograms or even micrograms of DNA), specialized protocols for sample preparation, highly specific and complex data processing methods and finally high error rates [23]. These biases and technical challenges become highly relevant when attempting to detect low copy variants, to characterize viral communities, and assess vaccine purity [24]. Samples for both LRS and SRS can be prepared in two ways. The first is shearing using sonication, the second is enzymatic shearing (\u0026lsquo;tagmentation\u0026rsquo;) using unbound transposons. The latter method is employed in the \u0026lsquo;rapid kit for Oxford Nanopore sequencing and in the \u0026lsquo;Nextera kit\u0026rsquo; manufactured by Illumina. The transposon-based methods are widely used but they perform sub-optimal in shearing AT-rich genomes[20-21]. The Illumina DNA prep kit also uses transposons, but these are bound to a bead.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn an effort to streamline the methods and provide guidelines for successful \u003cem\u003eCapripox\u0026nbsp;\u003c/em\u003evirus whole genome sequencing, we have established a collaboration between several reference laboratories which compared seven different next generation sequencing methods. We provide an overview of the approximate effort needed to reach a reliable and (near) complete WGS for (Capri)pox viruses and guidelines for good sequencing practices.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch4\u003e1.2.1. Experimental setup\u003c/h4\u003e\n\u003ch5\u003e1.2.1.1. Collaborative test\u003c/h5\u003e\n\u003cp\u003eThe four participating laboratories were: 1) National reference laboratory of the United Kingdom (the Pirbright Institute) located in Pirbright, United Kingdom; 2) The Laboratory of the United Nations\u0026rsquo; International Atomic Energy Agency (IAEA) located in Vienna, Austria; 3) The national reference laboratory of Australia (CSIRO) located in Canberra, Australia; and 4) the national reference laboratory of Belgium (Sciensano) located in Brussels, Belgium. Sciensano additionally serves as the European and World reference laboratory for \u003cem\u003eCapripox\u003c/em\u003e. Each laboratory independently attempted to reconstruct a whole genome sequence from the same sample (see below) using their own methods and experience. Afterwards, the mapping and assembly of the genome sequence was redone using the results from the denovo sequencing as reference. Because time and costs are important factors when considering sequencing, an estimate of the time for sample preparation, sequencing and bio-informatics was provided, as well as the financial costs for each kit/set of reagents.\u003c/p\u003e\n\u003ch5\u003e1.2.1.2. Sample used\u003c/h5\u003e\n\u003cp\u003eThe sample used in this study was derived from a bull infected during a previous study [60] with a clade 1.2 LSDV strain which was sequenced previously and published \u0026nbsp;under GenBank acc. nr. KX894508. No animal experiments were thus performed for this study. The original inoculum used for infection was grown on cell cultures and underwent four passages in OA3-T cells before being used in an animal trial [60]. The animal developed skin nodules and one of these was collected and stored We obtained one of these nodules from two of the co-authors (A. Haegeman and W. Philips). The homogenate was aliquoted in 500\u0026micro;l tubes and inactivated by heating the sample for 4:30h at 56\u0026deg;C. These aliquots were subsequently shipped to the participating laboratories.\u0026nbsp;\u003c/p\u003e\n\u003ch5\u003e1.2.1.3.\u0026nbsp;Experiment A:\u0026nbsp;Pacific Bio LRS run\u003c/h5\u003e\n\u003cp\u003eThis experiment used the Pacific Bioscience long read sequencing approach to create a denovo WGS for the strain used in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDNA was extracted from four subsamples of the homogenate using the Puregene tissue core kit (Qiagen). The concentration of the combined samples was assessed using ScreenTape on Tapestation 2200.Cycle threshold (C\u003csub\u003eT\u003c/sub\u003e) values were determined using the method developed by [25], which targets the ORF074 gene.\u003c/p\u003e\n\u003cp\u003eThe extract was then used as starting material for NGS and prepared according to the following methods: SMRTbell\u0026reg; libraries were prepared with the SMRTbell prep kit 3.0, including shearing, repair, A-tailing, ligation with barcodes and adapters, and size selection with SMRTbell clean-up beads. Pooled libraries (\u0026ge;300 ng) were converted to SMRTbell libraries and sequenced on the Sequel II platform under CCS mode for 30 hours.\u003c/p\u003e\n\u003cp\u003eThe thus obtained read library was assembled de-novo according to the following procedure. Reads were classified using the Centrifuge classifier [26]. LSDV-matching reads were extracted and assembled de novo using Canu v2.3 [27], Flye v2.2.2 [28], and Unicycler v0.50 [29]. Alternatively, de-novo assembly was performed directly with the Improved Phased Assembly software IPA (https://github.com/PacificBiosciences/pbipa) without host removal or with Canu after removing cattle reads. Assemblies were compared, and discrepancies were assessed through mapping using minimap2 v2.26 [29-31] and visualization with IGV [32].\u003c/p\u003e\n\u003ch5\u003e1.2.1.4. Experiment B: Oxford Nanopore LRS run\u003c/h5\u003e\n\u003cp\u003eIn this experiment we employed Oxford Nanopore technologies long read sequencing approach to obtain a sequence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDNA was extracted from one subsample of the homogenate using the Machery Nagel (MN) DNA tissue kit. The concentration of the sample was assessed using the Qubit 4.0 HS dsDNA kit. C\u003csub\u003eT\u003c/sub\u003e values were determined using the method developed by [33], which targets the D5R gene.\u003c/p\u003e\n\u003cp\u003eThe extracted nucleic acids were amplified using a metagenomics amplification protocol as described previously [34-35]. The library was prepared using the rapid library preparation (SQK-RBK110-96; ONT) protocol and sequenced on an R9 MinION flow cell with a GridION device. Super-accurate base calling and demultiplexing was performed using Dorado v0.7.1, [36]. Reads were filtered on a minimum Phred average quality score of 7 using Chopper v0.6.0 50].\u003c/p\u003e\n\u003cp\u003eThere was insufficient data for de-novo assembly, so a reference-based assembly method was used. For this, reads were mapped to the closest matching GenBank entry (MN995838.1) using minimap2 v2.24 \u0026ndash; [37]. A consensus genome was generated using Samtools and Medaka [38]. All positions with a depth below 3X were masked.\u003c/p\u003e\n\u003ch5\u003e1.2.1.5. Experiment C) Low pass Illumina Nextera XT run\u003c/h5\u003e\n\u003cp\u003eIn this experiment we attempted to recover the whole genome sequence using low-pass Illumina Nextera based paired end sequencing and subsequent mapping to the reference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs a pre-treatment, one subsample of the homogenate was pre-treated with Baseline Zero DNAse for 20 minutes. Subsequently the DNA was extracted from the treated subsample of the homogenate using the Machery Nagel (MN) NA tissue kit. The concentration of the sample was assessed using the Qubit 4.0 HS dsDNA kit. C\u003csub\u003eT\u003c/sub\u003e values were determined using the method developed by [33], which targets the D5R gene.\u003c/p\u003e\n\u003cp\u003eLibrary preparation was done using the Nextera XT kit (Illumina) according to the manufacturer\u0026rsquo;s instructions and whole genome sequencing was done using the Illumina MiSeq instrument. Paired-end reads of 250bp length were generated (MiSeq V3 chemistry, i.c. Miseq v3 600c reagent cassette). Resulting data was de-multiplexed and adapters and Illumina indices were removed before continuing with downstream analyses.\u003c/p\u003e\n\u003cp\u003eRead mapping was performed using the LSDV GenBank reference sequence NC_003027.1 complete genome using the BWA mapper [39-40]. Because the viral read yield was low (see the results section) no quality filtering on the reads was applied. All reads from the raw data were included in the assembly. Samtools [41] was used to create a consensus and calculate read depth and genome coverage The resulting assembly was evaluated in the program \u0026lsquo;Tablet\u0026rsquo; [42] and a majority rule was applied when assessing variable indel positions. A threshold of 10% read variation per base position was used to call ambiguous (if present), non indel, sites (i.e., if \u0026gt;10% of the mapped reads show a consistent variant, the base was called as being ambiguous). The consensus was manually assessed and curated to verify polymorphisms and indel sites.\u003c/p\u003e\n\u003ch5\u003e1.2.1.6. Experiment D) low pass Illumina TruSeq DNA Nano run\u003c/h5\u003e\n\u003cp\u003eIn this experiment we attempted to recover the whole genome sequence using low-pass Illumina based paired end sequencing using the TruSeq approach with subsequent mapping to the reference.\u003c/p\u003e\n\u003cp\u003eAs a pre-treatment the homogenate was pre-treated with Baseline Zero DNAse for 20 minutes. Subsequently the DNA was extracted from one subsample of the homogenate using the Machery Nagel (MN) NA tissue kit. The concentration of the sample was assessed using the Qubit 4.0 HS dsDNA kit. C\u003csub\u003eT\u003c/sub\u003e values were determined using the method developed by [33], which targets the D5R gene.\u003c/p\u003e\n\u003cp\u003eLibrary preparation was done using the TruSeq DNA Nano sequencing kit (Illumina) in combination with the IDT for Illumina \u0026ndash; TruSeq DNA UD Indexes v2 (Illumina) according to the manufacturer\u0026rsquo;s instructions following the protocol for mechanical fragmentation into 350bp fragments (100ng input). Paired-end reads of 150bp length were generated on an iSeq Sequencer (iSeq100 v2 chemistry, i.c. iSeq100 i1 Reagent v2 (300-cycle) reagent cassette.).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResulting data was de-multiplexed and adapters and Illumina indices were removed before continuing with downstream analyses. Read mapping was performed using the LSDV GenBank reference sequence NC_003027.1 complete genome using the BWA mapper [39-40]. Because the viral read yield was low (see the results section) no quality filtering on the reads was applied. All reads from the raw data were included in the assembly. A first draft consensus sequence was generated using Samtools. Read depth and coverage was also evaluated using Samtools [41]. The total assembly was evaluated in the program \u0026lsquo;Tablet\u0026rsquo; [42] and a majority rule was applied when assessing variable indel positions. A threshold of 10% read variation per base position was used to call ambiguous (if present), non indel, sites (i.e., if \u0026gt;10% of the mapped reads show a consistent variant, the base was called as being ambiguous). The consensus was manually assessed and curated to verify polymorphisms and indel sites.\u003c/p\u003e\n\u003ch5\u003e1.2.1.7. Experiment E) Illumina Nextera XT run with hybrid probe capture\u003c/h5\u003e\n\u003cp\u003eIn this experiment we attempted hybrid probe capture of the LSDV genome and subsequent paired end sequencing using the Nextera/TruSeq based Illumina chemistry and reference based read mapping.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDNA was extracted using the MagMAX Viral RNA Isolation kit (ThermoFisher) on an automated MagMAX Express 24 sample processer. C\u003csub\u003eT\u003c/sub\u003e values were determined using the capripoxvirus real-time PCR developed by [39], which targets ORF074 of the viral genome. The DNA concentration was determined using the Qubit dsDNA HS Assay Kit (Thermofisher Scientific).\u003c/p\u003e\n\u003cp\u003eA probe hybridization procedure for LSDV was developed using the MyBaits Target Capture Kit (Arbor Biosciences), which uses complementary oligonucleotides for selectively binding target DNA in favour of host or environmental DNA. The oligonucleotide hybrid capture probes for enrichment were designed using representative LSDV genomes from GenBank. The final design consisted of 18,943 probes of 90 bp each with 3x depth over the LSDV genomes. Sequencing libraries for the samples were prepared using the Nextera XT DNA Library Prep Kit (Illumina) and enriched for LSDV fragments using the probes as per the manufacturer\u0026rsquo;s recommendations (Arbor Biosciences). The libraries were reamplified and sequenced using a P1 300-cycle cartridge on a NextSeq2000 instrument (Illumina). The raw reads were cleaned using Trimmomatic v.0.3911 and mapped to the Neethling Warmbaths LSDV genome (GenBank Acc. AF409137.1) using Bowtie v.2.4.412 [43]. The mapping was manually examined and edited with Geneious Prime v.2023.1.2.\u003c/p\u003e\n\u003ch5\u003e1.2.1.8. Experiment F) Illumina Nextera run high output\u003c/h5\u003e\n\u003cp\u003eIn this experiment we attempted to recover the whole genome sequence using deep Illumina Nextera based, paired end sequencing and subsequent mapping to the reference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDNA was extracted from one subsample of the homogenate using the MagMAX CORE Nucleic Acid Purification Kit (Applied Biosystems), this was then treated with BaselineZero DNAse for fifteen minutes. The concentration of the sample was assessed using Qubit 4.0 HS dsDNA kit. C\u003csub\u003eT\u003c/sub\u003e values were determined using an in house developed method, which targets the P32 gene.\u003c/p\u003e\n\u003cp\u003eA total of 1ng of DNA was used as input of the Nextera XT DNA library preparation kit. Sequencing was performed using the P1 (300 cycles /100M reads) sequencing cartridge for the Illumina NextSeq 2000 sequencing platform. Initial quality control (QC) of Illumina paired-end reads was performed using FastQC, followed by trimming of low-quality bases and adapter sequences using fastp. An iterative mapping and assembly approach was then applied. Reads were first filtered by mapping to the reference genome sequence OM033705 using BBMap v39.06 (https://sourceforge.net/projects/bbmap/, 2024). The filtered reads were then de novo assembled using SPAdes, and assembly quality was evaluated with QUAST. The resulting contigs were queried against the NCBI virus database using BLASTn [44-45] to identify the closest reference sequence. The best matching reference (KY829023.3) was selected for subsequent read alignment using BWA and the resulting Alignment quality was first assessed with Qualimap, after which duplicate reads were removed using samtools markdup. The deduplicated reads were then realigned to the reference using BWA for improved accuracy, followed by a second round of alignment quality assessment with Qualimap. Variants were called with bcftools, mpileup and bcftools call, and consensus sequences were generated accordingly. All alignments, variants, and consensus sequences were visually inspected using the IGV [32].\u003c/p\u003e\n\u003ch5\u003e1.2.1.9. Experiment G) Illumina DNA-prep run\u0026nbsp;\u003c/h5\u003e\n\u003cp\u003eIn this experiment we attempted to recover the whole genome sequence using low-pass Illumina based paired end sequencing using the DNA prep approach with subsequent mapping to the reference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne subsample of the homogenate was pre-treated with Baseline Zero DNAse for 20 minutes. Subsequently the DNA was extracted from the treated subsample of the homogenate using the Machery Nagel (MN) NA tissue kit. The concentration of the sample was assessed using the Qubit 4.0 HS dsDNA kit. C\u003csub\u003eT\u003c/sub\u003e values were determined using the method developed by [33], which targets the D5R gene.\u003c/p\u003e\n\u003cp\u003eLibrary preparation was done using the Illumina DNA Prep Library preparation kit (Illumina) according to the manufacturer\u0026rsquo;s instructions. Paired-end reads of 150bp length \u0026nbsp;were generated on an iSeq\u0026trade; Sequencer (iSeq100 v2 chemistry, i.c. iSeq100 i1 Reagent v2 (300-cycle)reagent cassette ).\u003c/p\u003e\n\u003cp\u003eResulting data was de-multiplexed and adapters and Illumina indices were removed before continuing with downstream analyses. Read mapping was performed using the LSDV GenBank reference sequence NC_003027.1 complete genome using the BWA mapper [39-40]. Because the viral read yield was low (see the results section) no quality filtering on the reads was applied. All reads from the raw data were included in the assembly. A first draft consensus sequence was generated using Samtools. Read depth and coverage was also evaluated using Samtools [41]. The total assembly was evaluated in the program \u0026lsquo;Tablet\u0026rsquo; [42] and a majority rule was applied when assessing variable indel positions. A threshold of 10% read variation per base position was used to call ambiguous (if present), non indel, sites (i.e., if \u0026gt;10% of the mapped reads show a consistent variant, the base was called as being ambiguous). The consensus was manually assessed and curated to verify polymorphisms and indel sites.\u003c/p\u003e\n\u003ch5\u003e1.2.1.10. \u0026nbsp;Detailed methodology of sequencing methods\u003c/h5\u003e\n\u003cp\u003eSeven different sequencing experiments (A to G) were performed to determine the WGS of the LSDV strain present in the sample. Detailed protocols for all of the sequencing methods can be found in supplementary data (OSM1). An overview of the key steps and kits used in each of the sequencing protocols can be found in Table 1 and Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e DNA extraction methods and evaluations as well as yield and C\u003csub\u003eT\u003c/sub\u003e values for each extract and subsequent NGS sequencing yield for each resulting NGS dataset.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"676\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDNA extraction method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePuregene tissue core kit (Qiagen)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMachery Nagel DNA tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMachery Nagel DNA tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMachery Nagel DNA tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMagMAX Viral RNA Isolation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMagMAX CORE Nucleic Acid Purification\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMachery Nagel DNA tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDNAse yes/no\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003econcentration assesment method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eScreenTape on Tapestation 2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQubit 4.0 HS dsDNA kit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQubit 4.0 HS dsDNA kit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQubit 4.0 HS dsDNA kit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQubit 4.0 HS dsDNA kit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQubit 4.0 HS dsDNA kit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQubit 4.0 HS dsDNA kit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAssement of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eC\u003csub\u003eT\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[*]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[52]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEnrichment step\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ehybrid probe capture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLibrary prep kit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSMRTbell prep kit 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003erapid library preparation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNextera XT kit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTruSeq Nano\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNextera XT kit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNextera XT kit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDNA prep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSequencing platform\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePacific Bioscience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eR9 MinION GridION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMiSeq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eiSeq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNextSeq2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNextSeq2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eiSeq\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBio-informatic analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDe novo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ereference based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ereference based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ereference based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ereference based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ereference based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ereference based\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e[*] unpublished method, targeting the P32 gene.\u003c/p\u003e\n\u003ch4\u003e1.2.2. \u0026nbsp;Genome re-mapping of datasets B-G\u003c/h4\u003e\n\u003cp\u003eDatasets obtained by methods B-G were re-mapped against the dataset obtained by method A to remove any potential effect of different assembly and mapping methods. For datasets C-G the BWA mapper was used. For dataset B\u0026nbsp;minimap2 v2.24 was used and no minimum value was set for read depth used to report the consensus. All assemblies were manually evaluated in Tablet. The visualization of the read depth was done via Coverage and Depth (or \u0026ldquo;CoDe\u0026rdquo;) plots prepared in R v4.2.0. [24] using ggplot2 v3.5.1 [46].\u003c/p\u003e\n\u003ch4\u003e1.2.3. Mapping evaluation and genome completeness\u003c/h4\u003e\n\u003cp\u003eWe evaluated the final re-mapped results and compared these with regards to minimum, maximum and average read depth. We also evaluated to which extent each assembly contained regions of low (\u0026lt;10x), very low (\u0026lt;5x) or no (0) coverage. This allowed us to detect regions of low or no coverage and ignore the effect of the zero reads mapping on the flanking regions and asses the result in more detail. We then determined the standard deviation (sd) of the read depth as a reflection of the differences of depth across each genome position.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo determine the completeness of our denovo reconstructed genome we evaluated the length of the terminal repeat region (TR-region). Dotplots generated by Mafft [47] and an alignment with the LSDV reference (GenBank acc. nr. NC_003027.1) were used to determine the length of the TR. Our genome reconstructions were considered complete when the length of the TR-regions reached 99%, were as long as, or longer than what is reported for the reference sequence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe consensus of experiment A was submitted to GenBank with accession number: ######.\u003c/p\u003e"},{"header":"Results","content":"\u003ch4\u003e1.3.1. DNA extraction, viral load and sequencing output\u003c/h4\u003e\n\u003cp\u003eAn overview of the DNA extraction methods, concentrations and\u0026nbsp;C\u003csub\u003eT\u003c/sub\u003e measurements, enrichment methods, sequencing methods as well as results of each separate WGS reconstruction as described in the paragraphs below can be found in Table 2.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e DNA concentration and C\u003csub\u003eT\u003c/sub\u003e values for each extract and subsequent NGS sequencing yield for each resulting NGS dataset.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eConcentration (ng/\u0026micro;l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eC\u003csub\u003eT\u003c/sub\u003e virus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e20.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e20.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e20.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e18.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e20.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eC\u003csub\u003eT\u003c/sub\u003e host\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e29.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e29.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e29.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eC\u003csub\u003eT\u003c/sub\u003e ratios (target/non target)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ecomplete genome reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eN*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003egenome coverage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eoutput file (10\u003csup\u003e6\u003c/sup\u003e reads)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e30.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003egenome length recovered after re-mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e150896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e150896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e150855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e150886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e150680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e150728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e150891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e% of LSDV associated reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ecount of LSDV associated reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e9937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e130410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e269965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3120596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e176980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e271316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*\u0026nbsp;This sequence was not complete because the regular genomic regions were gapped. The terminal repeats were near complete as was the case for the other reconstructions.\u003c/p\u003e\n\u003cp\u003eDifferent extraction kits were used, but the DNA concentration recovered was generally low. For the DNase treated samples the concentrations ranged from 0.20 to 0.5 ng/\u0026micro;l. for the untreated samples this ranged from 3.5-6.71 ng/\u0026micro;l. Obtained C\u003csub\u003eT\u003c/sub\u003e values for the viral target varied in the DNase treated samples were between 20.44 (dataset B \u0026amp; C) and 25 (dataset F). For the untreated samples the range was between 18.35 and 20.96. Experiments B and C, D and G also determined the C\u003csub\u003eT\u003c/sub\u003e values of a host gene, making that the ratio of host/virus could be determined. This ratio was 0.77 in experiment B and 0.69 in experiments C, D and G (as these datasets were all derived from the same sample (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe resulting read libraries varied in size.\u0026nbsp;The smallest read-library was obtained for experiment A with 3.6 * 10\u003csup\u003e6\u003c/sup\u003e reads and the largest read-library was obtained for experiment E with ~30 * 10\u003csup\u003e6\u003c/sup\u003e reads. The total read library sizes are listed in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003e1.3.2. Target read yield and mapping results\u003c/h4\u003e\n\u003cp\u003eDepending on the mapping approach and reference genome used, some of the obtained consensus genomes contained gaps when compared to the sequence obtained by method A. To be able to compare the results obtained by the different methods and by the different participating laboratories\u0026nbsp;we remapped the datasets against the sequence obtained via method A.\u003c/p\u003e\n\u003cp\u003eThe yield of target (viral) reads per dataset differed (Table 3). The lowest percentage of target reads was recovered for both LRS datasets with PacBio and ONP yielding 0.016% (method A) and 0.15% (method B) of viral reads, respectively. The difference in target read output between the same sample processed with the Nextera (method C) and DNA-prep (method G) kits vs. the TruSeq kit (method D) is twofold as they yielded 3.6% (C, G) vs 7.2% (D) target reads, respectively. Method F, representing the deep sequencing effort using the Nextera kit, yielded 0.3% of viral reads. Dataset E, combining TruSeq library preparation with target enrichment via hybrid capture probes, yielded 34.1% of viral reads.\u003c/p\u003e\n\u003cp\u003eThe recovered LSDV sequence contained an AT-percentage of 74.12% and a GC content of 25.88%. There were no ambiguous base positions in any assembly except in the assembly for dataset G which contains two ambiguous positions and one deletion when compared to the sequence from experiment A. The terminal repeat was 2471bp in length and was present on both the 5\u0026rsquo; and 3\u0026rsquo; side of the reconstructed WGS from dataset A (Figure 2 \u0026amp; OSM2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll datasets, except G missed 5bp of the terminal repeats compared to the sequence obtained by the PACBIO LRS in method A. Method C which used the Nextera kit even missed 36 bp at the 3\u0026rsquo; end of the genome. Method C was also the only which resulted in a dataset with five unmapped positions in the core part of the genome and furthermore had a large number of positions (1367) only covered by less than five reads.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003e1.3.3. Read distribution and read depth\u003c/h4\u003e\n\u003cp\u003eTable 3 provides an overview of the obtained read depth and the read distribution over the genome is visualized in the CoDe plots in Figure 3. The results show that datasets C-G all had an average read depth of \u0026gt;100 reads per position. Dataset E showed the highest average read depth of 2559 reads per position and dataset B the lowest with 48 reads per position. The variation of the read depth over the genome however strongly differs between the methods. The read distribution for datasets generated with the transposon-based kits (B, C and F) are highly unequal (Figures 3 and 4). The sd\u0026rsquo;s for these sets are \u0026gt;100% of the average (Table 3). The distribution of reads for datasets A, D and G is more homogenous and the corresponding sd\u0026rsquo;s of distribution are lower. For these three datasets there is also an increased read mapping at the flanks of the genome. Dataset E also has a high sd (1659, 56.1% of the average). Dataset also has a transposon component involved but it is treated differently during sample preparation (see the discussion).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eMapping details for each experiment.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eaverage read depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003estandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e13.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e50.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e325.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1659.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e155.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003estandard deviation as % of the average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e106.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e121.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e56.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e115.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003emax. depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003emin. depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5\u0026apos; missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3\u0026apos; missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003enumber of positions at zero\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003enumber of positions at \u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003enumber of positions at \u0026lt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e6092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e6087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003emin. depth excluding the first and last 5 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe use of whole genome sequences in virus studies is of increasing importance as it provides insights into the changes of the whole virus genome, not just a separate gene. Especially with relatively slow evolving viruses such as the poxviruses [48] there is need to obtain more comprehensive overview of the virus under study. But poxviruses show skewed AT-GC ratios [19] which may impact sequencing success and therefore the reliability of the WGS reconstruction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe first step of each sequencing project is the extraction of the DNA from any given sample. While this is critical there appears to be not much difference between the methods used in this study. All commercially available kits will yield approximately the same amount of DNA. The application of DNase to increase the virus to host ratio is based on the idea that the encapsulated virus (prior to extraction) may be more resistant to the treatment than the background DNA and also that there is much more background DNA than the target DNA which will thus be, relatively speaking, less affected. While this may be the case, given the slight improvement in virus to host C\u003csub\u003eT\u003c/sub\u003e ratios observed (0.77 to 0.69), there is a considerable loss of both target and non-target DNA what may impair downstream applications such as hybrid probe capture of perhaps even LRS methods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results show that truly random DNA fragmentation using sonication as applied in the TruSeq kit results in a more even distribution of reads over the genome when compared with the non-random tagmentation as employed in the Nextera Kit. Tagmentation is achieved by the use of a transposon. We hypothesize that the transposon used to fragment the DNA has a preference for GC-rich regions where it will attach and subsequently catalyse the fragmentation. These regions are therefore more represented in the resulting sequence read libraries. This is not a problem if AT/GC content is roughly equal within a target genome (although GC-poor regions may still be missed there), but it is not efficient in the case of \u003cem\u003eCapripox\u003c/em\u003e genomes. When leaving out the transposon-based steps, both depth (yield) and breadth (distribution across the genome) of reads were increased. This can be seen in the case of datasets C, D and G where the yield of virus associated reads vs. host reads is almost doubled when using the TruSeq kit vs. the Nextera and DNA-prep kits (in terms of read count and percentage). This further underscores the bias introduced by the transposon-based methods and confirms what was found by [20-21]. While [49] report a minor negative AT-bias when using the Nextera kit, our results suggest a more profound effect. In this study we also tested the DNA-prep Illumina kit (experiment G), which claims to avoid the negative AT-bias by binding the transposon to beads forcing a maximum of DNA to bind to the beads irrespective of the AT/GC-ratio of the DNA [50]. Our results show that this is indeed the case, but the yield of target reads was lower than with the TruSeq based experiment (Experiment D) (Table 1, Figures 3 \u0026amp; 4). The use of hybrid capture probes proved to be very effective. The amount of target reads recovered increased more than 4.5 times, to \u0026gt;30% of the read library, when compared to the next best result of obtained with the TruSeq experiment (D). In the case of important samples or samples with low virus yield this approach may be useful and effective.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother way to overcome the low virus yield would be to use a PCR based approach. The long-range PCR protocol developed by [22] overcomes this partially. This is a very robust protocol resulting in a large amount of viral material. The downside of this procedure is that it is also time consuming and requires experienced personnel to perform. Further this protocol does not allow to reliably detect sequence variants that may occur in a virus population, which is especially of concern when evaluating vaccine sequences of live attenuated vaccines.\u003c/p\u003e\n\u003cp\u003eThe data processing and genome assembly is relatively straightforward, but in the case of LRS data requires powerful computing units and a good understanding of the various tools needed. The main pitfall when using the SRS data is the correct selection and use of the mapping reference sequence and proper inspection of the resulting assembly. Use of a reference that is too variable, especially when large indels are present, may influence the result. In the case of this study, we encountered this in the case of the hybrid probe capture procedure and the Nextera deep sequencing method whereby the initially reported WGS differed from the results of the other experiments (see OSM2). Remapping revealed that this was caused by the use of a different, less related reference. Another known problem is caused by long repeat regions (\u0026gt;1000bp) in a genome. These cannot be covered by single reads from SRS methods and mapping will be less reliable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLong read sequencing methods hold great promise for virus studies [51] but have not been done routinely in poxviruses (e.g., 52-53]. Perhaps the fact that LRS methods might also have a bias in AT-rich genomes [54] may have discouraged people from using them. However, LRS methods avoid problems that arise from the presence of repeats in a genome and the longer reads that are generated may traverse otherwise hard to assemble parts of the genome. Further, the length of the reads also makes these methods more suitable for denovo genome reconstruction. Our results show that, while suited for denovo sequencing, LRS methods result in low virus associated read recovery. That being said, the length of the reads more than compensates for this in our results with a comprehensive mean coverage of 58 (PacBio) and 40 (ONP) reads per position. This seems sufficient to us for a reliable recovery of the virus genome, provided the coverage of the genome is homogenous [55]. While the read distribution of the PacBio result is homogenous (dataset A, Figure 3A \u0026amp; 4A), this was less for the ONP LRS (dataset B, Figure 3B \u0026amp; 4B) where the results showed similarities with those observed in the Nextera based protocols (compare Figure 3B with Figure 3C). We attribute this to the fact that the ONP procedure also involves the use of a transposon during the tagmentation step [56]. This may indicate that the employed ONP approach also suffers from a sequencing bias in AT-rich genomes and it may therefore be less suited than the PacBio technology for sequencing genomes with skewed AT/GC ratios.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the occurrence of restriction enzyme sites, the TR regions of \u003cem\u003eCapripox\u003c/em\u003e were historically determined to be between 2.25 and 3.4 kb [57]. Research from [58] described the reference LSDV genome having a 2418bp inverted repeat. In the reconstruction of the LSDV genome in our sample, the TR region was ~2471 bp (Figure 2 and OSM2), which is 53bp longer than the reference. Our denovo reconstructed genome therefore probably represents the complete genome. While the TruSeq and PacBio read distributions are considered the most homogenous, an increased number of reads mapping at both flanks in the TR-region of the reconstructed genomes was observed (Figure 3A, D \u0026amp; G). This can be explained by unequal mapping of reads across the TR-region relative to the reference, which happens when multiple optimal mapping coordinates occur in a reference genome.\u003c/p\u003e\n\u003cp\u003eThe terminal repeats of LSDV are currently reported to be very variable (see the alignments produced by [14]), we are not sure whether this is really the case or if this is an artefact of low coverage of these regions in the studies publishing them or as a result of sequencing procedures. The CoDe plots used in this study are a simple to create and visually easy way to evaluate and present this. Finally, to asses genome completeness a dot plot can be reconstructed and the length of the TR-regions can be compared with the known literature and confirmed full length genome sequences present in public databases (i.e., GenBank).\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003e1.4.1. \u0026nbsp;Recommendations\u003c/h4\u003e\n\u003cp\u003eBased on the sequencing breadth and depth obtained for the different sequencing protocols tested we recommend to use the TruSeq kit for whole genome sequencing of \u003cem\u003eCapripox\u003c/em\u003e viruses in samples with sufficient viral load and high enough DNA concentration as the TruSeq kit does need more input material. When it is needed to sequence a sample with very low virus content the hybrid probe capture method will be valuable to use in combination with DNA prep-based sequencing as it allows for lower concentrations of start material. Both methods can be easily implemented by any laboratory already using Illumina sequencing. For mapping and assembling we recommend to use an iterative strategy with multiple reference genomes when SRS is used. As a mis mapping occurs easily, especially in the variable regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn order to be able to report reliable sequencing results, we recommend to strive for a read depth of 30, especially in the case of error prone methods such as ONP, as this will allow to safely capture both INDEL and nucleotide variation [59]. This furthermore strongly reduces the chance of miscalling bases due to base calling errors [55]. This is already true at 10 times coverage, but indel variation is often more difficult to capture. We also recommend to report information on read coverage and depth for each reconstructed genome so the readers can have an idea about the robustness of the reported sequence information. The latter is currently often lacking and might be the cause of difficulties during the alignment of the terminal repeat regions or regions with indels when such sequences are used as reference themselves.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e1.5. Author Contributions:\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization\u003c/strong\u003e: FCB NDR. \u003cstrong\u003emethodology\u003c/strong\u003e; FCB, NDR, SH. \u003cstrong\u003ewriting—original draft preparation\u003c/strong\u003e: FCB, NDR. \u003cstrong\u003ewriting—review and editing\u003c/strong\u003e. FCB, AH, WP, NDR, SH, SdK, , TRB, CB, CW, CEL. \u003cstrong\u003eNGS\u003c/strong\u003e: SH, SdK, PM MJW CB, CW, TBKS. \u003cstrong\u003eData analyses\u003c/strong\u003e: FCB, MJW, CB, TRB, TBKS, CEL, CW, CB. \u003cstrong\u003eVisualization:\u003c/strong\u003e FCB. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch3\u003e1.6. Funding:\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThis study was financed by a grant from the Bill and Melinda Gates foundation, grant nr:\u0026nbsp;\u0026nbsp;INV-055082 awarded Sciensano. PacBio sequencing was funded by the IAEA laboratory in Vienna.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003e1.7. Data Availability Statement:\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eSequence alignments are available on request. The long read sequencing result has been deposited in GenBank with accession numbers PX492334. The sequence read archives are available on request.\u003c/p\u003e\n\u003ch3\u003e1.8. Informed consent statement:\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003e1.9. Acknowledgments:\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe authors are grateful to the laboratory teams at Sciensano, CSIRO, IAEA and Pirbright for their help with the molecular analyses as well as to the field workers for collecting the samples and the animals’ owners for generously sharing these. We are also grateful to the Bill \u0026amp; Melinda gates foundation for financial support.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e1.10. Conflicts of Interest:\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e McInnes CJ, Damon IK, Smith GL, McFadden G, Isaacs SN, Roper RL, Evans DH, Damaso CR, Carulei O, Wise LM, Lefkowitz EJ. ICTV Virus Taxonomy Profile: Poxviridae 2023. 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PMID: 34066658; PMCID: PMC8151199.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Capripox, lumpy skin disease virus, whole genome sequencing, AT-bias, direct sequencing","lastPublishedDoi":"10.21203/rs.3.rs-7868004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7868004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDirect whole genome sequencing of \u003cem\u003eCapripox\u003c/em\u003e virus genomes from diagnostic samples is not always straightforward. Low viral content in a sample, biased sequencing and subsequent assembly and mapping methods may all influence the outcome.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study we have tested and compared six next generation sequencing approaches on a homogenized skin sample from bull infected with LSDV. We compared enrichment vs. non-enriched strategies, different library preparation methods, short read Illumina sequencing with long read sequencing methods.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe found that methods that use an unbound transposon during tagmentation produced unbalanced results and lower target read yield versus methods that use other approaches to the tagmentation step. We further find that the use of hybrid capture probes increased the number of target reads. The result of subsequent mapping and assembly steps are influenced by the choice of reference when using reference-based assembly approaches.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhen, using a short read sequencing approach we advise to use a transposon free method or a method with bound transposons for DNA fragmentation. These methods outperform kits that employ free transposons for DNA fragmentation when targeting AT-rich genomes. When mapping the reads it is best to use a reference for assembly that is as closely related as possible to the sample under study. Mapping problems can be resolved by long read sequencing which we recommend for denovo whole genome sequencing. Pacific Bioscience based long read sequencing outperforms Oxford Nanopore sequencing because it is less error prone. The ONT approach used, displays the same bias as the from Illumina and is therefore less suitable when attempting (Capri)pox whole genome sequencing.\u003c/p\u003e","manuscriptTitle":"Comparison of whole genome sequencing approaches for Capripox viruses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 09:01:04","doi":"10.21203/rs.3.rs-7868004/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-03T04:52:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T14:16:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T01:59:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252670494392583316899976314130680344555","date":"2025-11-14T15:10:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202474796182381460081097249139828911428","date":"2025-11-11T23:21:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T05:43:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143937641075569583916364292915096521986","date":"2025-11-11T04:57:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190044883062985513300108401615560319969","date":"2025-11-07T11:05:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-07T11:00:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-07T10:55:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-07T09:04:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-07T08:47:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-11-07T08:38:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f160679-228d-47a2-a5aa-55e2aff8dcac","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:00:23+00:00","versionOfRecord":{"articleIdentity":"rs-7868004","link":"https://doi.org/10.1186/s12864-025-12463-3","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2026-01-08 15:57:12","publishedOnDateReadable":"January 8th, 2026"},"versionCreatedAt":"2025-11-19 09:01:04","video":"","vorDoi":"10.1186/s12864-025-12463-3","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12463-3","workflowStages":[]},"version":"v1","identity":"rs-7868004","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7868004","identity":"rs-7868004","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}