Precise exome analysis of blastocyst biopsy scale samples using Primary Template directed Amplification

Precise exome analysis of blastocyst biopsy scale samples using Primary Template directed Amplification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Precise exome analysis of blastocyst biopsy scale samples using Primary Template directed Amplification Alina Samitova, Vera Belova, Iuliia Vasiliadis, Zhanna Repinskaia, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6745778/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2026 Read the published version in BMC Genomics → Version 1 posted 11 You are reading this latest preprint version Abstract This study evaluates primary template-directed amplification (PTA) for whole exome sequencing (WES) of small fibroblast cell groups, which mimics the limited cell quantities typical of trophectoderm embryo biopsies. PTA’s consistent amplification reduces allelic dropout (ADO) and improvesuniform coverage, overcoming challenges associated with conventional methods such as multiple displacement amplification (MDA). Using fibroblast samples alongside well-characterized genomic references (E701, NA12878), we benchmarked PTA-WES, achieving 97.5% target region coverage at 10x, meeting American College of Medical Genetics and Genomics (ACMG) standards. The completed filtering and variant calling provide a foundation for further optimization and analysis aimed at evaluating the reliability of PTA for routine clinical use. Preliminary results from embryo biopsies sequenced with PTA-WES revealed a median coverage of 102x, significantly improving upon the variability and coverage gaps observed with MDA-WES. These findings support the potential of PTA to increase the clinical applicability of WES for preimplantation genetic testing for monogenic disorders (PGT-M), expanding its ability to detect inherited and de novo mutations in embryos. PTA Exome sequencing Preimplantation genetic testing WGA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Background Preimplantation genetic testing (PGT) is a crucial tool in assisted reproductive technologies, allowing for the selection of embryos free from genetic abnormalities before implantation. Previously, many methods were unavailable for the analysis of trophectoderm biopsies from embryos. However, with improvements in whole genome amplification (WGA) protocols, it has become possible to obtain sufficient starting material from as little as 6–7 picograms of DNA from a single cell, overcoming the limitations of existing methods and expanding the potential for embryo genome analysis ( 1 – 5 ). Among current techniques, multiple displacement amplification (MDA) and Multiple annealing and looping-based amplification cycles (MALBAC) have shown significant promise, particularly in PGT-A (testing for aneuploidies), owing to their ability to amplify large quantities of DNA from minimum samples ( 6 – 8 ). Nevertheless whole-exome sequencing (WES), a powerful tool for detecting a wide range of genetic variants associated with inherited disorders, remains largely unavailable in PGT. Despite its advantages, uneven amplification of DNA by MDA leads to coverage gaps, limiting the effectiveness of WES for preimplantation genetic testing for monogenic disorders (PGT-M) ( 9 – 12 ). Currently, PGT-M involves the collection of maternal, paternal, and control samples, which are analyzed alongside amplified genomic DNA from trophectoderm biopsies to determine the mutation status of each embryo, via methods such as PCR with NGS or Sanger sequencing, karyomapping,and haplarithmisis. ( 12 – 14 ). Several studies highlight the critical role of PGT-M in the genetic analysis of embryos. For example, one study specifically highlighted the use of PGT-M to prevent the transmission of genetic conditions such as Marfan syndrome through preimplantation genetic testing, where a PGT-M protocol was developed and tested via multiplex fluorescent PCR and mini-sequencing ( 15 ). There is also a known case of PGT for Meckel syndrome, where the WGA products of each embryo were subjected to Sanger sequencing for direct identification of variant sites, and haplotyping analysis was conducted via SNP markers ( 16 ). Although these approaches are effective for detecting target variants, they do not provide the same resolution as whole-exome sequencing and do not allow for reliable identification of de novo variants. Some studies have provided data on the WGS and WES of single cells ( 17 , 18 ). In addition, one study highlighted that exome sequencing can reveal clinically significant information about preimplantation embryos that may not be detectable in parental genomes ( 19 ). However, limitations in current WGA techniques such as allele drop-out (ADO), locus drop-out (LDO), chimeric DNA molecules, base replication errors and unevenness in amplification often prevent these techniques from achieving the high coverage required by the standard of the American College of Medical Genetics and Genomics (ACMG) for accurate variant detection ( 20 ). It is also necessary to conduct not only mutation site detection but also SNP linkage analysis to ensure accuracy. Therefore, an improved ability to sequence high-quality exomes directly from trophectoderm biopsies could significantly increase the speed and accessibility of PGT-M, providing a broader range of genetic insights. A potential solution to this challenge is the introduction of primary template-directed amplification (PTA), a novel method that offers more uniform WGA from small amounts of material, including preimplantation biopsy samples. PTA more evenly amplifies both alleles in the same cell, resulting in significantly diminished allelic dropout (ADO) and skewing. While PTA offers distinct advantages over other methods, it can also lead to specific artifacts due to the amplification process ( 21 – 23 ). Therefore, it is essential consider these artifacts when interpreting the results. Recent research has led to the development of programs such as SCAN2 and PTATO with the objective of minimizing the number of false positives in PTA data analysis ( 24 , 25 ). These programs filter the artifact variants both with machine learning algorithms and with vcf thresholds outside the usage of models. However, despite consulting with the developers and receiving their guidance and support, we were unable to successfully integrate the PTATO and SCAN2 programs into our data analysis workflow. In this study, we utilized fibroblast cultures with the well-characterized in-lab reference genome E701, genomic DNA from the E701 sample, and the Platinum Genome DNA sample NA12878 ( 26 , 27 ). Additionally, we developed an artifact-filtering approach tailored to our specific needs. This method leverages variant-calling results from whole-exome sequencing (WES) and whole-genome sequencing (WGS) data of the well-characterized reference genome sample E701, along with four PTA-WES samples. This research aims to assess the applicability of PTA for expanding the capabilities of PGT, making WES a feasible and reliable option in clinical settings. Results 1. PTA-WGA of Genomic DNA and DNA from Fibroblast Cells To assess the quality of the material obtained after WGA via the PTA method, we utilized both genomic DNA and fibroblast cells. Fibroblast cells were grouped by cell count ( 4 , 10 , 16 , and 25 ) to model the typical cell quantities obtained from blastocyst trophectoderm biopsies. The PTA-WGA was successful in all tested samples with no observed outliers. On the basis of electrophoresis and concentration measurements all the samples presented similar characteristics. On average, we obtained ~ 1600 ng of DNA per sample after PTA-WGA and further purification using 2x Kapa Pure Beads (Roche). 2. Raw sequencing data and Pooling balance We obtained between 190 and 249 million (M) reads per 16 samples (Fig. 1 ). As shown in the graph, the amount of data for samples with a low number of cells was comparable to that of samples from other groups, indicating well-balanced pooling. Precapture pooling can be challenging when different sample types (e.g., genomic DNA or FFPE DNA) are used, as variations in initial DNA quality can lead to inconsistent data yields postenrichment. Therefore, this initial test of pooling different cell groups within a single pool was successful in achieving comparable data output across samples. 3. Exome enrichment quality after PTA-WGA for fibroblast and genomic DNA To assess exome sequencing quality after PTA-WGA, we grouped the samples as follows: samples with 4, 10, 16 and 25 fibroblast, and genomic DNA (E701 and NA12878) were originally taken. Coverage statistics were calculated via Picard and metrics were averaged across the sample groups. The results of Picard key metrics for all the samples are presented in Additional file 1: Table 1. The percentages of on-target reads, off-target reads and duplicates for each sample group were calculated (Fig. 2 A). The distribution of these metrics did not reveal any particular dependence between groups of samples. However, it should be noted that under practical conditions it is not always possible to obtain ~ 200 M reads per sample. We therefore downsampled each sample to 100 M reads. This process allows us to simulate conditions closer to basic exome data yield for clinical purposes. In this way, we can better estimate sequencing metrics for samples with less data. The graph (Fig. 2 B) shows that the use of 100 M reads per sample increased the average on-target percentage by 0.48% compared with the data shown in the graph (Fig. 2 A) for samples with 200 million reads. This increase may be due to the reduction in duplicates observed with fewer reads. The gDNA group included the laboratory standard sample E701 and the NA12878 reference sample, both of which were subjected to PTA at amounts of 64 pg and 256 pg, corresponding to approximately 10 and 40 genome equivalents, respectively. The f-4 group consisted of three replicate fibroblast samples, each containing four cells used for PTA. Similarly, the f-10 group consisted of three replicate fibroblast samples of ten cells each, whereas the f-16 group consisted of three replicate samples of sixteen cells each. The f-25 group consisted of three replicate fibroblast samples of twenty-five cells each for WGA. The mean target coverage for all samples was 247x, with a median target coverage of 226x (Fig. 3 ). Analysis of these coverage metrics revealed no observable link between the sequencing results and the type of starting material used. Furthermore, no dependency was noted between groups stratified by the number of cells used for PTA, indicating that variations in cell number in this experiment did not affect the consistency of coverage across samples. The mean breadth, defined as the percentage of target regions covered at least x times per sample, for all samples in the pool was 97.5% (± 0.26%SD) at 10x coverage depth, 96.25% (± 0.59%SD) at 20x and 94.84% (± 0.90%SD) at 30x coverage depth. These parameters are characterized by a low standard deviation, indicating high homogeneity of the data (Fig. 4 ). In this study, we observed an average of 1.28% (± 0.03%SD) regions with zero coverage, which is fully consistent with the exome data typically obtained for samples isolated from blood ( 28 ). 4. ACMG gene coverage analysis We calculated the depth of coverage for 81 genes recommended by the ACMG for the identification of pathogenic variants in clinical reporting (ACMG SF v3.2) ( 29 ). The average percentage of target regions that covered at least 10x was greater than 98.4%. Further examination of the coverage distribution confirmed that the vast majority of target regions achieved uniform coverage in all samples (Additional file 2: Table 2). Among the 81 clinically important genes analyzed, 49 were fully covered at 100% across all the samples. A further 16 genes achieved average coverage of 95–99% in all the samples. However, coverage issues were observed for 6 genes, with an average coverage of approximately 80%. (Fig. 5 ). 5. Variant Filtering Criteria Variant calling results in VCF format were obtained for WES E701 and WGS E701, as well as for the four PTA-WES samples. After filtering by read depth and difficult-to-sequence regions, the WES E701 and WGS E701 variants intersected, resulting in three sets (Fig. 6 ). Variant parameter distributions In the next stage, we focused on variants unique to either WES E701 or WGS E701 (which were absent from their intersection). Such variants are likely false positives, and our goal was to identify their common characteristics that might explain the reasons for their inclusion. To achieve this, we plotted the variant allele frequency (VAF) and quality (QUAL) distributions for two unique variant sets (WES and WGS) and compared them with the corresponding distributions of common WES/WGS variants. The analysis of VAF distributions revealed that the majority of common WES/WGS variants presented VAF values in the range of 0.4 to 0.6 (heterozygotes) or equal to 1 (homozygotes) (Fig. 7 A and 7 B). In contrast, over 37% of the unique WES and WGS variants had VAF values less than 0.4. As shown in Fig. 7 C and 7 D, a significant proportion of common WES/WGS variants were characterized by QUAL values between 50 and 75, whereas more than 79% of the unique WES and WGS variants presented QUAL values less than 50. This may indicate the low confidence of such variants. On the basis of the observed differences in VAF and QUAL distributions, we aimed to establish threshold values to distinguish unique WES and WGS variants from common WES/WGS variants. To achieve this, we calculated the 5th percentile of VAF and QUAL distributions for common WES/WGS variants. The values obtained were as follows: VAF: 0.371429 (WES) and 0.380952 (WGS), QUAL: 30.4 (WES) and 32.8 (WGS). On the basis of these data, we propose the use of thresholds of VAF < 0.37 and QUAL < 30 for variants filtering. We suggest that applying these thresholds in sample processing ensures minimal loss of true positive variants while effectively excluding false positives. Variant type distributions The subsequent stage of the analysis involved a pairwise intersection of variants identified in four PTA-WES samples with common WES/WGS variants. Unique PTA-WES variants constituted approximately 5–6% of total variants identified in each sample (Fig. 8 , Additional file 3: Fig. 9). For unique PTA-WES and common WES/WGS variants, we plotted the distributions of mutation types, expressed as percentages, to assess the relative frequency of each mutation type (Fig. 10 A and 10 B, Additional file 3: Fig. 11). Among the common WES/WGS variants, the most prevalent mutations were G->A (16.53%), C->T (16.20%), A->G (14.89%) and T->C (14.84%) transitions. The proportion of indels was 11.94%. In contrast, unique PTA-WES variants were characterized by a high proportion of indels (> 45%), as well as high frequency of C->T and G->A transitions compared with other mutation types. A comparison of the variant type distributions across the samples revealed notable differences in the overall data structure. Specifically, unique PTA-WES variants presented a greater proportion of indels, as well as C->T and G->A mutations. Furthermore, these mutation types were integrated into the variant filtering process. Indel length distributions Given the high proportion of indels among the unique PTA-WES variants, our objective was to identify any patterns that can be considered typical for these variants. To achieve this, we plotted the indel length distributions for two variant sets: common WES/WGS and unique PTA-WES. Indel length was defined as the difference between the lengths of the reference and alternative alleles. The analysis revealed that single-nucleotide insertions and deletions were the most prevalent among the common WES/WGS variants. In contrast, single-nucleotide insertions constituted the majority of unique PTA-WES variants (Fig. 12 A and 12 B, Additional file 3: Fig. 13). The observed differences in indel length distributions suggest that single-nucleotide insertions may significantly contribute to the total number of artifacts observed. Therefore, on the basis of the results obtained, we propose the following filters to exclude low-quality and potentially artifactual variants: VAF < 0.37, QUAL T or G->A or 1-bp insertion. 6. Applying filters to PTA-WES samples We applied the aforementioned filters to 16 PTA-WES samples (Table 3 ). The highest proportions of filtered variants were observed in samples e701-64 (3.86%) and na12828-64 (4.12%). For the other samples, the proportion of filtered variants was approximately 2–3% of the total identified variants. Table 3 Results of PTA-WES variants filtering Sample Total variants (count) Filtered variants (count) Filtered variants (%) f-4-1 73967 2078 2.81 f-4-2 74427 1775 2.38 f-4-3 74217 1786 2.41 f-10-1 74051 1606 2.17 f-10-2 74021 1845 2.49 f-10-3 74191 1628 2.19 f-16-1 74024 1514 2.05 f-16-2 73804 1587 2.15 f-16-3 74072 1591 2.15 f-24-1 73540 1588 2.16 f-24-2 73785 1452 1.97 f-24-3 73993 1442 1.95 na12828-64 72571 2992 4.12 na12828-256 74252 1993 2.68 e701-64 72513 2802 3.86 e701-256 73842 1895 2.57 To evaluate the effectiveness of filtering, we intersected filtered PTA-WES variants with common WES/WGS and compared the number of variants not falling into the intersection before and after filtering. The proportion of PTA-WES variants outside the intersection decreased by approximately 1.5% (Fig. 14 , Additional file 3: Figure S15). We further analyzed the distributions of variant types and indel lengths for unique PTA-WES variants after filtering. The results revealed a decrease in the relative proportion of indels among all mutation types and a significant decrease in the histogram peak corresponding to single-nucleotide insertions (Fig. 10 C, 12 C). These findings indicate that the proposed filtering thresholds are expected to effectively eliminate low-quality and artifactual variants from the PTA-WES samples, thereby substantially enhancing the accuracy and robustness of the analysis. 7. PTA-WES vs MDA-WES for blastocyst trophectoderm embryo biopsies Here we also present our preliminary results from an experiment in which trophectoderm biopsies from blastocyst trophectoderms were subjected to the PTA-WGA method. Seven different exomes from embryo biopsies samples were sequenced, resulting in 70–120 M reads per sample with mean coverage of 136x and a median coverage of 102x with 85.71% on-target reads. Coverage statistics across samples indicated that the percentage of target regions covered 10x ranged from 86.39–96.58%, with an average of 92.29% (± 4.25%SD); for coverage at 20x it ranged from 77.73–94.75%, with an average of 86.93% (SD = 6.86%). The proportion of target regions with zero coverage remained low (1.27–2.23%, SD = 0.33%) (Additional file 4: Table S4 ). Additionally, in our previous experiments with six WESs from blastocyst trophectoderm biopsies via WGA by MDA we encountered the problems of a high proportion of uncovered regions and greater coverage unevenness. With a similar range of data yield per exome (70–160 M reads) the mean target coverage was 124x (± 40.56%SD) but the median was only 6x (± 9.97%SD) suggesting that many regions may not consistently reach adequate coverage in all samples. Percentage of regions covered 10x did not reach 95% and ranged only from 20.79–63.30% (SD = 16%) and the percentage of regions with zero coverage ranged from 11.78–55.13% (SD = 18.5%) demonstrating the inability of MDA-WES to achieve consistent target coverage. Discussion In this study, we evaluated the quality of WES on small fibroblast groups that underwent PTA-based WGA simulating the scale of trophectoderm embryo biopsies. Additionally, we propose a custom artifact-filtering approach tailored to address the specific challenges associated with the PTA method. To ensure a robust comparison, we included in-lab reference genomic the DNA E701 and DNA of NA12878 Platinum genome sample. The results demonstrate that PTA-based amplification generates sufficient amplified DNA material for library preparation and further exome enrichment regardless of the initial amount of fibroblast cells. After the PTA step, the amplified products display a wide range (of ~ 250 to > 1,500 bp) of fragment sizes, as confirmed by gel electrophoresis results, suggesting that library preparation could bypass the fragmentation step. However, we chose to retain this step in the current study to avoid any potential loss of unique fragments. Fibroblast DNA library samples containing varying initial cell counts (4, 10, 16 and 25 cells) in this study were precapture and multiplexed without compromising the final data output. Low cell-count samples produced data yields similar to those of larger groups highlighting that PTA-WGA might support high-throughput sequencing in diverse cell input conditions. However we want to mention that in our previous experiments with trophectoderm embryo biopsies samples (data not shown), we encountered greater variability (5x) in the amount of data obtained between samples within the same pool, despite identical preparation conditions. Most likely, for embryo biopsy samples there are other factors that may influence precapture pooling balance, such as the quality of the embryo itself, and its DNA integrity. Further research into embryo-specific characteristics that impact this stage of exome enrichment is needed. The sequencing analysis results demonstrated that PTA enables consistent and efficient amplification across fibroblast single-cell genomes. Samples amplified by PTA achieved a mean percentage of target regions of 97.5% at 10x depth meeting the ACMG recommendations of 95% at 10x depth. The mean and median target coverage values were comparable to GIAB and E701 DNA exomes after PTA. Additionally, we observed that PTA-amplified samples contained only approximately 1.28% uncovered regions, aligning with the expected coverage gaps often encountered in exome sequencing owing to low mappability genomic regions ( 28 , 30 ). The primary quality metrics of PTA-amplified fibroblast samples at the embryo biopsy scale meet established ACMG standards, showing their potential for clinical genetic analysis. Therefore we present coverage breadth results for the 81 genes recommended by the ACMG for pathogenic variant identification. The average coverage for more than 65 genes exceeded 95% at a depth of > 10x, demonstrating the effectiveness of the PTA amplification method. The artifact-filtering approach we proposed for whole-genome amplification (WGA) was successfully applied to all samples processed via PTA. The filtering criteria were carefully adjusted to exclude false-positive variants from the data analysis while ensuring the retention of clinically significant variants. These results indicate that PTA-based WGA meets initial requirements for further accurate variant detection for screening for monogenic disorders. Our study is the first to achieve successful WES from trophectoderm human embryo biopsies. The comparative analysis of PTA-WES and MDA-WES on trophectoderm biopsies highlights significant advancements and limitations in PGT-M. The PTA-WGA approach for embryos demonstrated superior performance providing more uniform amplification and thus higher median exome coverage (102x), low zero-coverage regions (mean 1.51%), and stable target region coverage at 10x and 20x depths (mean 92.29% and 86.93%, respectively). At this stage, we hypothesize that increasing the read output per sample could further improve coverage for WES in embryo biopsies, as several samples already exceeded 95% of target regions at 10x depth. These findings underscore the potential of PTA-based WGA to support robust WES in PGT-M, facilitating the detection of genetic variants. Our future work will include variant calling analyses to enhance our evaluation of PTA-WES. In contrast, the MDA-WGA method produced less consistent results, with substantial coverage gaps and a higher percentage of zero-coverage regions (up to 55.13%), underscoring the challenges associated with its use in PGT-M. In our study PTA by BioSkryb Genomics is a promising WGA tool for exome sequencing in PGT-M. PTA’s improved performance could expand the utility of WES in clinical applications by allowing for the detection of both inherited and de novo variants. Detecting de novo pathogenic variants is particularly valuable in PGT-M, as these variants may not be present in parental genomes and may be associated with severe developmental disorders. Despite these advancements, there are still challenges in integrating WES with the PTA-WGA into standard PGT-M workflows. Variability in starting material quality and sample preparation can affect data output. Future research should continue to evaluate the long-term reliability of PTA’s in clinical applications and explore further optimization in both wet lab and computational methods. Conclusions Our findings underscore the potential of the PTA-WGA to provide more uniform amplification, improved coverage uniformity, and reduced zero-coverage regions compared with alternative methods such as the MDA-WGA. These advancements are crucial for detecting both inherited and de novo pathogenic variants, which are vital for addressing genetic disorders. The application of a tailored artifact-filtering approach further improved data accuracy, demonstrating the adaptability of the PTA-WGA for diverse clinical genetic analyses. However, the study also highlights challenges, including variability in data output influenced by sample preparation and starting material quality, particularly in trophectoderm embryo biopsies. Future work should focus on optimizing both laboratory protocols and computational methods to further improve PTA's reliability and expand its integration into workflows requiring DNA sequencing from minimal starting material. Overall, PTA by BioSkryb Genomics represents a promising WGA tool for advancing the application of WES in clinical genetics. Materials and methods Sample collection Human fibroblasts from a reference sample E701 from our laboratory were used for this study. They were carefully thawed from cryostorage and cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS), penicillin-streptomycin (50 U/ml) and L-glutamine (2 mM) to promote cell growth and maintain optimal conditions for cell proliferation. Once the number of cells was reached, the fibroblasts were detached from the culture flask surface via 0.25% trypsin-EDTA solution according to a standard trypsinisation protocol to ensure cell viability and maintain consistent cell morphology for downstream applications. The fibroblasts were divided into groups of 4, 10, 16, and 25 cells, which were then placed in 0.2 ml tubes. Each group was prepared in triplicate. In addition, reference genomic DNA NA12878 and our in-lab reference DNA sample E701 was used at concentrations equivalent to 10 and 40 genomes. This study also presents WES results from trophectoderm embryo biopsies. All embryo samples were donated for research purposes and provided by the V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology, and Perinatology under the category of not suitable for implantation. The embryos were created by via intracytoplasmic sperm injection (ICSI). Embryos on days 5–6 were biopsied according to standard operating procedures (SOP) in Kulakov Center. Each biopsy sample was collected in a 200 µL PCR tube containing 3 µL of cell buffer. WGA PTA was performed according to the manufacturer’s instructions (BioSkryb Genomics). After PTA, the DNA was purified via 2X Kapa Pure Beads (Roche). The DNA yield was quantified with the Qubit dsDNA HS Assay system (Life Technologies) and its quality was assessed by 1.5% agarose gel electrophoresis. MDA was measured using QIAGEN REPLI-g kits according to the manufacturer's instructions (Qiagen). Sample Preparation and Exome Sequencing For each library, 500 ng of PTA product or genomic DNA (for NA12878 and E701) was sheared via a Covaris LE220 according to the manufacturer’s protocol, followed by size selection with Kapa Pure Beads (Roche) to achieve a fragment distribution peak at ~ 250 bp. DNA libraries were prepared using the MGI Universal DNA Library Prep Set, followed by final amplification with 8 PCR cycles. The concentration of the prepared libraries was measured using the Qubit Flex system (ThermoFisher) and the dsDNA HS Assay Kit. Quality control of the DNA libraries was performed via high-sensitivity analysis on a Bioanalyzer 2100 system (Agilent Technologies). Then, we pooled 900 ng of each of the 16 libraries into a single pool which was concentrated using a SpeedVac concentrator (ThermoFisher) at 60°C. Exome enrichment of the pool with the Agilent SureSelect Human All Exon V8 probes was performed according to the RSMU_exome protocol ( 31 ). The pool was then circularized, downloaded into 4 flow cell lanes and sequenced paired-end mode on the DNBSEQ-G400 platform, using the DNBSEQ-G400RS high-throughput sequencing set PE100 kit, following the manufacturer's instructions (MGI Tech). Genomic Data Analyses The quality of the obtained fastq files was analyzed using FastQC v0.11.9 ( 32 ). On the basis of the quality metrics, the fastq files were trimmed via BBDuk by BBMap v38.96 ( 33 ). The reads were aligned to the indexed reference genome GRCh38.p14 using bwa-mem2 v2.2.1 ( 34 ). SAM files were converted into BAM files and sorted using SAMtools v1.9 to check the percentage of the aligned­ reads ( 35 ). On the basis of the obtained BAM files, the quality metrics of exome enrichment and sequencing were calculated using Picard v2.22.4, and the number of duplicates was calculated using Picard MarkDuplicates v2.22.4 ( 36 ). Variant Calling and Filtering Variant calling was performed using DeepVariant v1.5.0 ( 37 ). The multiallelic variants in VCF files were decomposed into biallelic variants using vt decompose v0.5772 and then normalized using vt normalize v0.5772 ( 38 ). A depth coverage filter (DP > = 3) and FILTER = PASS was applied to the variants obtained. The variants were subsequently filtered by the target exome panel (Agilent SureSelect v8). Additionally, difficult-to-sequence regions in exome data, which are affected mainly by low-mappability regions, such as pseudogenes, tandem repeats, homopolymers, and other low-complexity regions (1.19 Mb in sum), were excluded ( 30 ). Declarations Ethics declarations The appropriate institutional review board approval for this study was obtained from the Ethics Committee at the Pirogov Russian National Research Medical University.Consent for publication Availability of data and materials Exome sequences (24 fastq pairs) from the E701 reference DNA were deposited into the NCBI open-access sequence read archive (SRA) in fastq.gz format under BioProject ID PRJNA1137605. Additionally, whole genome sequencing data have been deposited under BioProject ID PRJNA1083205. The exome sequences of the samples used in this study are available upon request. Competing interests The authors declare no competing interests Funding The study was been carried out within the framework of state assignment № 124020400004-9 on the topic Development of a virally delivered gene therapy drug for the treatment of Crigler-Najjar syndrome types I and II. Authors' contributions AS: designed, performed research and analyzed data; writing - original draft; writing - review & editing; VB: designed, performed research and analyzed data; supervision; writing - review & editing; IV: software and visualization; ZR: software and visualization; TG: methodology; provided experimental samples; ER: methodology; provided experimental samples; MP: provided experimental samples; EG: provided experimental samples; TN: provided experimental samples; DR: funding acquisition; DK: designed research; resources and funding acquisition; conceptualization; project administration. References Tšuiko, O., Gallardo, E. F., Voet, T., & Vermeesch, J. R. (2020). 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Scientific Reports, 12(1), 609. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data; Babraham Institute: Cambridge, UK, 2017. Bushnell, B. BBMap: a fast, accurate, splice-aware aligner. 2014. Available online: https://github.com/BioInfoTools/BBMap (accessed on 20 February 2023). Li, H.; Durbin, H. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 2009, 25, 1754–1760. Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R.; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map Format and SAMtools. Bioinformatics 2009, 25, 2078–2079. Broad Institute. Picard Toolkit. 2014. Available online: https://broadinstitute.github.io/picard/ Poplin, R., Chang, P. C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., ... & DePristo, M. A. (2018). A universal SNP and small-indel variant caller using deep neural networks. Nature biotechnology, 36(10), 983-987. Tan, A., Abecasis, G. R., & Kang, H. M. (2015). Unified representation of genetic variants. Bioinformatics, 31(13), 2202-2204. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additional file 1: Table 1. Summary of key metrics for exome sequencing. Additionalfile2.xlsx Additional file 2: Table 2. ACMG gene coverage metrics. Additionalfile3.docx Additional file 3: Fig. 9. Venn diagram showing the intersection of PTA-WES and common WES/WGS variants. Fig. 11. Distribution of variant types among unique PTA-WES variants. Fig. 13. Indel length distributions among unique PTA-WES variants. Indels with a length greater than 50 were excluded. Fig. S15. Venn diagram showing the intersection of filtered PTA-WES and common WES/WGS variants. Additionalfile4.xlsx Additional file 4: Table S4. Exome sequencing metrics for embryo biopsies subjected to WGA using PTA and MDA. Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2026 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 07 Jul, 2025 Reviews received at journal 23 Jun, 2025 Reviews received at journal 11 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers agreed at journal 07 Jun, 2025 Reviewers invited by journal 05 Jun, 2025 Editor invited by journal 05 Jun, 2025 Editor assigned by journal 01 Jun, 2025 Submission checks completed at journal 01 Jun, 2025 First submitted to journal 25 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6745778","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467920561,"identity":"08690d32-e668-41e4-8b2f-523b34a6a5a0","order_by":0,"name":"Alina 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22:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6745778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6745778/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-12511-y","type":"published","date":"2026-03-05T15:59:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84228100,"identity":"1e67b7ee-e12e-4533-8350-4eba0bc4f07b","added_by":"auto","created_at":"2025-06-09 13:21:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65415,"visible":true,"origin":"","legend":"\u003cp\u003eThe barplot shows the number of reads per sample\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/2949c7447531c220270fd3a4.png"},{"id":84228101,"identity":"1f01c425-968e-4a23-952f-d1fb99945a95","added_by":"auto","created_at":"2025-06-09 13:21:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128661,"visible":true,"origin":"","legend":"\u003cp\u003eBarplots showing the average on-targets, duplicates, and off-targets reads in each group for: A) raw data, and B) downsampled data.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/1fd01ebebd789a83e780a529.png"},{"id":84228099,"identity":"2335ae5b-f4c7-4816-ad01-fe1c3797577f","added_by":"auto","created_at":"2025-06-09 13:21:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24646,"visible":true,"origin":"","legend":"\u003cp\u003eAverage of the mean and median coverage target coverage of the samples that were analyzed without downsampling.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/4253bbb72f321c375d74c923.png"},{"id":84228108,"identity":"6aee94a6-9c4d-495f-889a-6180048d772f","added_by":"auto","created_at":"2025-06-09 13:21:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69464,"visible":true,"origin":"","legend":"\u003cp\u003eThe average percentage of regions with 10x, 20x, 30x, 40x, 50x and 100x coverage depths for different groups of samples.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/aa9bb447e255fc780fc3c509.png"},{"id":84228851,"identity":"1a2ed6ae-2daf-4451-abc4-af77d74ec102","added_by":"auto","created_at":"2025-06-09 13:29:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":193979,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of the breadth of coverage with per-site read depth ≥10x \u0026nbsp;is shown for each of the 81 genes recommended by the American College of \u0026nbsp;Medical Genetics and Genomics (ACMG) among the samples.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/b233369f9b8753286264f8e7.png"},{"id":84228856,"identity":"deaeb6b5-0365-41dd-9ed6-2846fca44b91","added_by":"auto","created_at":"2025-06-09 13:29:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":29767,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram showing the intersection of the WES E701 and WGS E701 variants. Three datasets are depicted: variants common to WES E701 and WGS E701 (70,953), variants unique to WES E701 (1,574), and variants unique to WGS E701 (2,911).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/28553412313d627bd52965e4.png"},{"id":84228113,"identity":"0b0fded7-32a8-475e-b610-717400c46447","added_by":"auto","created_at":"2025-06-09 13:21:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":63686,"visible":true,"origin":"","legend":"\u003cp\u003eHistograms of distributions: A) VAF for WES E701 variants, B) VAF for WGS E701 variants, C) QUAL for WES E701 variants, and D) QUAL for WGS E701 variants.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/6b189890fd63904b407e0b44.png"},{"id":84228125,"identity":"77956d5d-3224-419f-ba8a-df5eba318937","added_by":"auto","created_at":"2025-06-09 13:21:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":31571,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram showing the intersection of PTA-WES and common WES/WGS variants.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/7d1a7ac509d7513ba8f4188b.png"},{"id":84228117,"identity":"886969d0-f2e2-4e11-a875-3602b19124ca","added_by":"auto","created_at":"2025-06-09 13:21:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":116552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 10\u003c/strong\u003e. Distributions of variant types among A) common WES/WGS variants, B) unique PTA-WES variants, and C) unique PTA-WES variants after filtering.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/4a1e928c3abd84fd42942979.png"},{"id":84228858,"identity":"af13252a-705d-4dbe-8f2c-f8e755802b1c","added_by":"auto","created_at":"2025-06-09 13:29:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":55060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 12.\u003c/strong\u003e Indel length distributions among A) common WES/WGS variants, B) unique PTA-WES variants, and C) unique PTA-WES variants after filtering. Indels with a length greater than 50 were excluded.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/81c06ec0f75c59acf410056c.png"},{"id":84228120,"identity":"bbdf5bd8-f42b-4dd6-bdd4-3d5f2ca3a7e3","added_by":"auto","created_at":"2025-06-09 13:21:25","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":32364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 14.\u003c/strong\u003e Venn diagram showing the intersection of filtered PTA-WES and common WES/WGS variants.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/9f22670e37c4f31bfd59fddc.png"},{"id":104250930,"identity":"d260e9ca-23c6-438c-b419-371c79a71964","added_by":"auto","created_at":"2026-03-09 16:11:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1536875,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/af854ffa-bf5a-455e-bc03-bc18b3452acd.pdf"},{"id":84228107,"identity":"f22600c2-7ca1-4415-8532-b18ab326b709","added_by":"auto","created_at":"2025-06-09 13:21:24","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27340,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Table 1. Summary of key metrics for exome sequencing.\u003c/p\u003e","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/c2745b414d7adef688677491.xlsx"},{"id":84228103,"identity":"ea1996bd-1cd0-4746-b388-8056d37bfa5a","added_by":"auto","created_at":"2025-06-09 13:21:24","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16822,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Table 2. ACMG gene coverage metrics.\u003c/p\u003e","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/07113023a99a5ae938527c84.xlsx"},{"id":84228852,"identity":"40c3130a-4860-4e65-bd3a-3f2acc2a57d9","added_by":"auto","created_at":"2025-06-09 13:29:25","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1791171,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3: Fig. 9. Venn diagram showing the intersection of PTA-WES and common WES/WGS variants. Fig. 11. Distribution of variant types among unique PTA-WES variants. Fig. 13. Indel length distributions among unique PTA-WES variants. Indels with a length greater than 50 were excluded. Fig. S15. Venn diagram showing the intersection of filtered PTA-WES and common WES/WGS variants.\u003c/p\u003e","description":"","filename":"Additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/dfa89f593d1b09c6f7c8bc8c.docx"},{"id":84229366,"identity":"ac320dbb-34db-4d26-97a9-cd6e8f347ec4","added_by":"auto","created_at":"2025-06-09 13:37:25","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17558,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 4: Table S4. Exome sequencing metrics for embryo biopsies subjected to WGA using PTA and MDA.\u003c/p\u003e","description":"","filename":"Additionalfile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6745778/v1/bb5a5fc814fb7c6d2ea06370.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Precise exome analysis of blastocyst biopsy scale samples using Primary Template directed Amplification","fulltext":[{"header":"Background","content":"\u003cp\u003ePreimplantation genetic testing (PGT) is a crucial tool in assisted reproductive technologies, allowing for the selection of embryos free from genetic abnormalities before implantation. Previously, many methods were unavailable for the analysis of trophectoderm biopsies from embryos. However, with improvements in whole genome amplification (WGA) protocols, it has become possible to obtain sufficient starting material from as little as 6\u0026ndash;7 picograms of DNA from a single cell, overcoming the limitations of existing methods and expanding the potential for embryo genome analysis (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Among current techniques, multiple displacement amplification (MDA) and Multiple annealing and looping-based amplification cycles (MALBAC) have shown significant promise, particularly in PGT-A (testing for aneuploidies), owing to their ability to amplify large quantities of DNA from minimum samples (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Nevertheless whole-exome sequencing (WES), a powerful tool for detecting a wide range of genetic variants associated with inherited disorders, remains largely unavailable in PGT. Despite its advantages, uneven amplification of DNA by MDA leads to coverage gaps, limiting the effectiveness of WES for preimplantation genetic testing for monogenic disorders (PGT-M) (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, PGT-M involves the collection of maternal, paternal, and control samples, which are analyzed alongside amplified genomic DNA from trophectoderm biopsies to determine the mutation status of each embryo, via methods such as PCR with NGS or Sanger sequencing, karyomapping,and haplarithmisis. (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Several studies highlight the critical role of PGT-M in the genetic analysis of embryos. For example, one study specifically highlighted the use of PGT-M to prevent the transmission of genetic conditions such as Marfan syndrome through preimplantation genetic testing, where a PGT-M protocol was developed and tested via multiplex fluorescent PCR and mini-sequencing (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). There is also a known case of PGT for Meckel syndrome, where the WGA products of each embryo were subjected to Sanger sequencing for direct identification of variant sites, and haplotyping analysis was conducted via SNP markers (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Although these approaches are effective for detecting target variants, they do not provide the same resolution as whole-exome sequencing and do not allow for reliable identification of \u003cem\u003ede novo\u003c/em\u003e variants. Some studies have provided data on the WGS and WES of single cells (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In addition, one study highlighted that exome sequencing can reveal clinically significant information about preimplantation embryos that may not be detectable in parental genomes (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, limitations in current WGA techniques such as allele drop-out (ADO), locus drop-out (LDO), chimeric DNA molecules, base replication errors and unevenness in amplification often prevent these techniques from achieving the high coverage required by the standard of the American College of Medical Genetics and Genomics (ACMG) for accurate variant detection (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). It is also necessary to conduct not only mutation site detection but also SNP linkage analysis to ensure accuracy. Therefore, an improved ability to sequence high-quality exomes directly from trophectoderm biopsies could significantly increase the speed and accessibility of PGT-M, providing a broader range of genetic insights.\u003c/p\u003e \u003cp\u003eA potential solution to this challenge is the introduction of primary template-directed amplification (PTA), a novel method that offers more uniform WGA from small amounts of material, including preimplantation biopsy samples. PTA more evenly amplifies both alleles in the same cell, resulting in significantly diminished allelic dropout (ADO) and skewing. While PTA offers distinct advantages over other methods, it can also lead to specific artifacts due to the amplification process (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Therefore, it is essential consider these artifacts when interpreting the results. Recent research has led to the development of programs such as SCAN2 and PTATO with the objective of minimizing the number of false positives in PTA data analysis (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These programs filter the artifact variants both with machine learning algorithms and with vcf thresholds outside the usage of models. However, despite consulting with the developers and receiving their guidance and support, we were unable to successfully integrate the PTATO and SCAN2 programs into our data analysis workflow.\u003c/p\u003e \u003cp\u003eIn this study, we utilized fibroblast cultures with the well-characterized in-lab reference genome E701, genomic DNA from the E701 sample, and the Platinum Genome DNA sample NA12878 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Additionally, we developed an artifact-filtering approach tailored to our specific needs. This method leverages variant-calling results from whole-exome sequencing (WES) and whole-genome sequencing (WGS) data of the well-characterized reference genome sample E701, along with four PTA-WES samples. This research aims to assess the applicability of PTA for expanding the capabilities of PGT, making WES a feasible and reliable option in clinical settings.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1. PTA-WGA of Genomic DNA and DNA from Fibroblast Cells\u003c/h2\u003e \u003cp\u003eTo assess the quality of the material obtained after WGA via the PTA method, we utilized both genomic DNA and fibroblast cells. Fibroblast cells were grouped by cell count (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, and \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) to model the typical cell quantities obtained from blastocyst trophectoderm biopsies. The PTA-WGA was successful in all tested samples with no observed outliers. On the basis of electrophoresis and concentration measurements all the samples presented similar characteristics. On average, we obtained\u0026thinsp;~\u0026thinsp;1600 ng of DNA per sample after PTA-WGA and further purification using 2x Kapa Pure Beads (Roche).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. Raw sequencing data and Pooling balance\u003c/h3\u003e\n\u003cp\u003eWe obtained between 190 and 249\u0026nbsp;million (M) reads per 16 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As shown in the graph, the amount of data for samples with a low number of cells was comparable to that of samples from other groups, indicating well-balanced pooling. Precapture pooling can be challenging when different sample types (e.g., genomic DNA or FFPE DNA) are used, as variations in initial DNA quality can lead to inconsistent data yields postenrichment. Therefore, this initial test of pooling different cell groups within a single pool was successful in achieving comparable data output across samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3. Exome enrichment quality after PTA-WGA for fibroblast and genomic DNA\u003c/h3\u003e\n\u003cp\u003eTo assess exome sequencing quality after PTA-WGA, we grouped the samples as follows: samples with 4, 10, 16 and 25 fibroblast, and genomic DNA (E701 and NA12878) were originally taken. Coverage statistics were calculated via Picard and metrics were averaged across the sample groups. The results of Picard key metrics for all the samples are presented in Additional file 1: Table\u0026nbsp;1. The percentages of on-target reads, off-target reads and duplicates for each sample group were calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The distribution of these metrics did not reveal any particular dependence between groups of samples. However, it should be noted that under practical conditions it is not always possible to obtain\u0026thinsp;~\u0026thinsp;200 M reads per sample. We therefore downsampled each sample to 100 M reads. This process allows us to simulate conditions closer to basic exome data yield for clinical purposes. In this way, we can better estimate sequencing metrics for samples with less data. The graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) shows that the use of 100 M reads per sample increased the average on-target percentage by 0.48% compared with the data shown in the graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) for samples with 200\u0026nbsp;million reads. This increase may be due to the reduction in duplicates observed with fewer reads.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe gDNA group included the laboratory standard sample E701 and the NA12878 reference sample, both of which were subjected to PTA at amounts of 64 pg and 256 pg, corresponding to approximately 10 and 40 genome equivalents, respectively. The f-4 group consisted of three replicate fibroblast samples, each containing four cells used for PTA. Similarly, the f-10 group consisted of three replicate fibroblast samples of ten cells each, whereas the f-16 group consisted of three replicate samples of sixteen cells each. The f-25 group consisted of three replicate fibroblast samples of twenty-five cells each for WGA.\u003c/p\u003e \u003cp\u003eThe mean target coverage for all samples was 247x, with a median target coverage of 226x (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of these coverage metrics revealed no observable link between the sequencing results and the type of starting material used. Furthermore, no dependency was noted between groups stratified by the number of cells used for PTA, indicating that variations in cell number in this experiment did not affect the consistency of coverage across samples.\u003c/p\u003e \u003cp\u003eThe mean breadth, defined as the percentage of target regions covered at least x times per sample, for all samples in the pool was 97.5% (\u0026plusmn;\u0026thinsp;0.26%SD) at 10x coverage depth, 96.25% (\u0026plusmn;\u0026thinsp;0.59%SD) at 20x and 94.84% (\u0026plusmn;\u0026thinsp;0.90%SD) at 30x coverage depth. These parameters are characterized by a low standard deviation, indicating high homogeneity of the data (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, we observed an average of 1.28% (\u0026plusmn;\u0026thinsp;0.03%SD) regions with zero coverage, which is fully consistent with the exome data typically obtained for samples isolated from blood (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e4. ACMG gene coverage analysis\u003c/h3\u003e\n\u003cp\u003eWe calculated the depth of coverage for 81 genes recommended by the ACMG for the identification of pathogenic variants in clinical reporting (ACMG SF v3.2) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The average percentage of target regions that covered at least 10x was greater than 98.4%. Further examination of the coverage distribution confirmed that the vast majority of target regions achieved uniform coverage in all samples (Additional file 2: Table\u0026nbsp;2). Among the 81 clinically important genes analyzed, 49 were fully covered at 100% across all the samples. A further 16 genes achieved average coverage of 95\u0026ndash;99% in all the samples. However, coverage issues were observed for 6 genes, with an average coverage of approximately 80%. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e5. Variant Filtering Criteria\u003c/h3\u003e\n\u003cp\u003eVariant calling results in VCF format were obtained for WES E701 and WGS E701, as well as for the four PTA-WES samples. After filtering by read depth and difficult-to-sequence regions, the WES E701 and WGS E701 variants intersected, resulting in three sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariant parameter distributions\u003c/h2\u003e \u003cp\u003eIn the next stage, we focused on variants unique to either WES E701 or WGS E701 (which were absent from their intersection). Such variants are likely false positives, and our goal was to identify their common characteristics that might explain the reasons for their inclusion.\u003c/p\u003e \u003cp\u003eTo achieve this, we plotted the variant allele frequency (VAF) and quality (QUAL) distributions for two unique variant sets (WES and WGS) and compared them with the corresponding distributions of common WES/WGS variants.\u003c/p\u003e \u003cp\u003eThe analysis of VAF distributions revealed that the majority of common WES/WGS variants presented VAF values in the range of 0.4 to 0.6 (heterozygotes) or equal to 1 (homozygotes) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). In contrast, over 37% of the unique WES and WGS variants had VAF values less than 0.4. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, a significant proportion of common WES/WGS variants were characterized by QUAL values between 50 and 75, whereas more than 79% of the unique WES and WGS variants presented QUAL values less than 50. This may indicate the low confidence of such variants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the basis of the observed differences in VAF and QUAL distributions, we aimed to establish threshold values to distinguish unique WES and WGS variants from common WES/WGS variants. To achieve this, we calculated the 5th percentile of VAF and QUAL distributions for common WES/WGS variants. The values obtained were as follows: VAF: 0.371429 (WES) and 0.380952 (WGS), QUAL: 30.4 (WES) and 32.8 (WGS). On the basis of these data, we propose the use of thresholds of VAF\u0026thinsp;\u0026lt;\u0026thinsp;0.37 and QUAL\u0026thinsp;\u0026lt;\u0026thinsp;30 for variants filtering. We suggest that applying these thresholds in sample processing ensures minimal loss of true positive variants while effectively excluding false positives.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariant type distributions\u003c/h3\u003e\n\u003cp\u003eThe subsequent stage of the analysis involved a pairwise intersection of variants identified in four PTA-WES samples with common WES/WGS variants. Unique PTA-WES variants constituted approximately 5\u0026ndash;6% of total variants identified in each sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Additional file 3: Fig.\u0026nbsp;9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor unique PTA-WES and common WES/WGS variants, we plotted the distributions of mutation types, expressed as percentages, to assess the relative frequency of each mutation type (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eA and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eB, Additional file 3: Fig.\u0026nbsp;11). Among the common WES/WGS variants, the most prevalent mutations were G-\u0026gt;A (16.53%), C-\u0026gt;T (16.20%), A-\u0026gt;G (14.89%) and T-\u0026gt;C (14.84%) transitions. The proportion of indels was 11.94%. In contrast, unique PTA-WES variants were characterized by a high proportion of indels (\u0026gt;\u0026thinsp;45%), as well as high frequency of C-\u0026gt;T and G-\u0026gt;A transitions compared with other mutation types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA comparison of the variant type distributions across the samples revealed notable differences in the overall data structure. Specifically, unique PTA-WES variants presented a greater proportion of indels, as well as C-\u0026gt;T and G-\u0026gt;A mutations. Furthermore, these mutation types were integrated into the variant filtering process.\u003c/p\u003e\n\u003ch3\u003eIndel length distributions\u003c/h3\u003e\n\u003cp\u003eGiven the high proportion of indels among the unique PTA-WES variants, our objective was to identify any patterns that can be considered typical for these variants. To achieve this, we plotted the indel length distributions for two variant sets: common WES/WGS and unique PTA-WES. Indel length was defined as the difference between the lengths of the reference and alternative alleles. The analysis revealed that single-nucleotide insertions and deletions were the most prevalent among the common WES/WGS variants. In contrast, single-nucleotide insertions constituted the majority of unique PTA-WES variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e12\u003c/span\u003eA and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e12\u003c/span\u003eB, Additional file 3: Fig.\u0026nbsp;13). The observed differences in indel length distributions suggest that single-nucleotide insertions may significantly contribute to the total number of artifacts observed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTherefore, on the basis of the results obtained, we propose the following filters to exclude low-quality and potentially artifactual variants: VAF\u0026thinsp;\u0026lt;\u0026thinsp;0.37, QUAL\u0026thinsp;\u0026lt;\u0026thinsp;30, C-\u0026gt;T or G-\u0026gt;A or 1-bp insertion.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e6. Applying filters to PTA-WES samples\u003c/h2\u003e \u003cp\u003eWe applied the aforementioned filters to 16 PTA-WES samples (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest proportions of filtered variants were observed in samples e701-64 (3.86%) and na12828-64 (4.12%). For the other samples, the proportion of filtered variants was approximately 2\u0026ndash;3% of the total identified variants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of PTA-WES variants filtering\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal variants\u003c/p\u003e \u003cp\u003e(count)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFiltered variants (count)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFiltered variants\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-4-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-4-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-4-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-10-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-10-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-10-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-16-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-16-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-16-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-24-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-24-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef-24-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ena12828-64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ena12828-256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ee701-64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ee701-256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the effectiveness of filtering, we intersected filtered PTA-WES variants with common WES/WGS and compared the number of variants not falling into the intersection before and after filtering. The proportion of PTA-WES variants outside the intersection decreased by approximately 1.5% (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e14\u003c/span\u003e, Additional file 3: Figure S15).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further analyzed the distributions of variant types and indel lengths for unique PTA-WES variants after filtering. The results revealed a decrease in the relative proportion of indels among all mutation types and a significant decrease in the histogram peak corresponding to single-nucleotide insertions (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eC, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e12\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThese findings indicate that the proposed filtering thresholds are expected to effectively eliminate low-quality and artifactual variants from the PTA-WES samples, thereby substantially enhancing the accuracy and robustness of the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e7. PTA-WES vs MDA-WES for blastocyst trophectoderm embryo biopsies\u003c/h2\u003e \u003cp\u003eHere we also present our preliminary results from an experiment in which trophectoderm biopsies from blastocyst trophectoderms were subjected to the PTA-WGA method. Seven different exomes from embryo biopsies samples were sequenced, resulting in 70\u0026ndash;120 M reads per sample with mean coverage of 136x and a median coverage of 102x with 85.71% on-target reads. Coverage statistics across samples indicated that the percentage of target regions covered 10x ranged from 86.39\u0026ndash;96.58%, with an average of 92.29% (\u0026plusmn;\u0026thinsp;4.25%SD); for coverage at 20x it ranged from 77.73\u0026ndash;94.75%, with an average of 86.93% (SD\u0026thinsp;=\u0026thinsp;6.86%). The proportion of target regions with zero coverage remained low (1.27\u0026ndash;2.23%, SD\u0026thinsp;=\u0026thinsp;0.33%) (Additional file 4: Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, in our previous experiments with six WESs from blastocyst trophectoderm biopsies via WGA by MDA we encountered the problems of a high proportion of uncovered regions and greater coverage unevenness. With a similar range of data yield per exome (70\u0026ndash;160 M reads) the mean target coverage was 124x (\u0026plusmn;\u0026thinsp;40.56%SD) but the median was only 6x (\u0026plusmn;\u0026thinsp;9.97%SD) suggesting that many regions may not consistently reach adequate coverage in all samples. Percentage of regions covered 10x did not reach 95% and ranged only from 20.79\u0026ndash;63.30% (SD\u0026thinsp;=\u0026thinsp;16%) and the percentage of regions with zero coverage ranged from 11.78\u0026ndash;55.13% (SD\u0026thinsp;=\u0026thinsp;18.5%) demonstrating the inability of MDA-WES to achieve consistent target coverage.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we evaluated the quality of WES on small fibroblast groups that underwent PTA-based WGA simulating the scale of trophectoderm embryo biopsies. Additionally, we propose a custom artifact-filtering approach tailored to address the specific challenges associated with the PTA method. To ensure a robust comparison, we included in-lab reference genomic the DNA E701 and DNA of NA12878 Platinum genome sample.\u003c/p\u003e \u003cp\u003eThe results demonstrate that PTA-based amplification generates sufficient amplified DNA material for library preparation and further exome enrichment regardless of the initial amount of fibroblast cells. After the PTA step, the amplified products display a wide range (of ~\u0026thinsp;250 to \u0026gt;\u0026thinsp;1,500 bp) of fragment sizes, as confirmed by gel electrophoresis results, suggesting that library preparation could bypass the fragmentation step. However, we chose to retain this step in the current study to avoid any potential loss of unique fragments.\u003c/p\u003e \u003cp\u003eFibroblast DNA library samples containing varying initial cell counts (4, 10, 16 and 25 cells) in this study were precapture and multiplexed without compromising the final data output. Low cell-count samples produced data yields similar to those of larger groups highlighting that PTA-WGA might support high-throughput sequencing in diverse cell input conditions. However we want to mention that in our previous experiments with trophectoderm embryo biopsies samples (data not shown), we encountered greater variability (5x) in the amount of data obtained between samples within the same pool, despite identical preparation conditions. Most likely, for embryo biopsy samples there are other factors that may influence precapture pooling balance, such as the quality of the embryo itself, and its DNA integrity. Further research into embryo-specific characteristics that impact this stage of exome enrichment is needed.\u003c/p\u003e \u003cp\u003eThe sequencing analysis results demonstrated that PTA enables consistent and efficient amplification across fibroblast single-cell genomes. Samples amplified by PTA achieved a mean percentage of target regions of 97.5% at 10x depth meeting the ACMG recommendations of 95% at 10x depth. The mean and median target coverage values were comparable to GIAB and E701 DNA exomes after PTA. Additionally, we observed that PTA-amplified samples contained only approximately 1.28% uncovered regions, aligning with the expected coverage gaps often encountered in exome sequencing owing to low mappability genomic regions (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe primary quality metrics of PTA-amplified fibroblast samples at the embryo biopsy scale meet established ACMG standards, showing their potential for clinical genetic analysis. Therefore we present coverage breadth results for the 81 genes recommended by the ACMG for pathogenic variant identification. The average coverage for more than 65 genes exceeded 95% at a depth of \u0026gt;\u0026thinsp;10x, demonstrating the effectiveness of the PTA amplification method. The artifact-filtering approach we proposed for whole-genome amplification (WGA) was successfully applied to all samples processed via PTA. The filtering criteria were carefully adjusted to exclude false-positive variants from the data analysis while ensuring the retention of clinically significant variants. These results indicate that PTA-based WGA meets initial requirements for further accurate variant detection for screening for monogenic disorders.\u003c/p\u003e \u003cp\u003eOur study is the first to achieve successful WES from trophectoderm human embryo biopsies. The comparative analysis of PTA-WES and MDA-WES on trophectoderm biopsies highlights significant advancements and limitations in PGT-M. The PTA-WGA approach for embryos demonstrated superior performance providing more uniform amplification and thus higher median exome coverage (102x), low zero-coverage regions (mean 1.51%), and stable target region coverage at 10x and 20x depths (mean 92.29% and 86.93%, respectively). At this stage, we hypothesize that increasing the read output per sample could further improve coverage for WES in embryo biopsies, as several samples already exceeded 95% of target regions at 10x depth. These findings underscore the potential of PTA-based WGA to support robust WES in PGT-M, facilitating the detection of genetic variants. Our future work will include variant calling analyses to enhance our evaluation of PTA-WES. In contrast, the MDA-WGA method produced less consistent results, with substantial coverage gaps and a higher percentage of zero-coverage regions (up to 55.13%), underscoring the challenges associated with its use in PGT-M.\u003c/p\u003e \u003cp\u003eIn our study PTA by BioSkryb Genomics is a promising WGA tool for exome sequencing in PGT-M. PTA\u0026rsquo;s improved performance could expand the utility of WES in clinical applications by allowing for the detection of both inherited and de novo variants. Detecting de novo pathogenic variants is particularly valuable in PGT-M, as these variants may not be present in parental genomes and may be associated with severe developmental disorders. Despite these advancements, there are still challenges in integrating WES with the PTA-WGA into standard PGT-M workflows. Variability in starting material quality and sample preparation can affect data output. Future research should continue to evaluate the long-term reliability of PTA\u0026rsquo;s in clinical applications and explore further optimization in both wet lab and computational methods.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings underscore the potential of the PTA-WGA to provide more uniform amplification, improved coverage uniformity, and reduced zero-coverage regions compared with alternative methods such as the MDA-WGA. These advancements are crucial for detecting both inherited and de novo pathogenic variants, which are vital for addressing genetic disorders. The application of a tailored artifact-filtering approach further improved data accuracy, demonstrating the adaptability of the PTA-WGA for diverse clinical genetic analyses.\u003c/p\u003e \u003cp\u003eHowever, the study also highlights challenges, including variability in data output influenced by sample preparation and starting material quality, particularly in trophectoderm embryo biopsies. Future work should focus on optimizing both laboratory protocols and computational methods to further improve PTA's reliability and expand its integration into workflows requiring DNA sequencing from minimal starting material. Overall, PTA by BioSkryb Genomics represents a promising WGA tool for advancing the application of WES in clinical genetics.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eHuman fibroblasts from a reference sample E701 from our laboratory were used for this study. They were carefully thawed from cryostorage and cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS), penicillin-streptomycin (50 U/ml) and L-glutamine (2 mM) to promote cell growth and maintain optimal conditions for cell proliferation. Once the number of cells was reached, the fibroblasts were detached from the culture flask surface via 0.25% trypsin-EDTA solution according to a standard trypsinisation protocol to ensure cell viability and maintain consistent cell morphology for downstream applications. The fibroblasts were divided into groups of 4, 10, 16, and 25 cells, which were then placed in 0.2 ml tubes. Each group was prepared in triplicate. In addition, reference genomic DNA NA12878 and our in-lab reference DNA sample E701 was used at concentrations equivalent to 10 and 40 genomes.\u003c/p\u003e \u003cp\u003eThis study also presents WES results from trophectoderm embryo biopsies. All embryo samples were donated for research purposes and provided by the V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology, and Perinatology under the category of not suitable for implantation. The embryos were created by via intracytoplasmic sperm injection (ICSI). Embryos on days 5\u0026ndash;6 were biopsied according to standard operating procedures (SOP) in Kulakov Center. Each biopsy sample was collected in a 200 \u0026micro;L PCR tube containing 3 \u0026micro;L of cell buffer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWGA\u003c/h2\u003e \u003cp\u003ePTA was performed according to the manufacturer\u0026rsquo;s instructions (BioSkryb Genomics). After PTA, the DNA was purified via 2X Kapa Pure Beads (Roche). The DNA yield was quantified with the Qubit dsDNA HS Assay system (Life Technologies) and its quality was assessed by 1.5% agarose gel electrophoresis. MDA was measured using QIAGEN REPLI-g kits according to the manufacturer's instructions (Qiagen).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSample Preparation and Exome Sequencing\u003c/h2\u003e \u003cp\u003eFor each library, 500 ng of PTA product or genomic DNA (for NA12878 and E701) was sheared via a Covaris LE220 according to the manufacturer\u0026rsquo;s protocol, followed by size selection with Kapa Pure Beads (Roche) to achieve a fragment distribution peak at ~\u0026thinsp;250 bp. DNA libraries were prepared using the MGI Universal DNA Library Prep Set, followed by final amplification with 8 PCR cycles. The concentration of the prepared libraries was measured using the Qubit Flex system (ThermoFisher) and the dsDNA HS Assay Kit. Quality control of the DNA libraries was performed via high-sensitivity analysis on a Bioanalyzer 2100 system (Agilent Technologies). Then, we pooled 900 ng of each of the 16 libraries into a single pool which was concentrated using a SpeedVac concentrator (ThermoFisher) at 60\u0026deg;C. Exome enrichment of the pool with the Agilent SureSelect Human All Exon V8 probes was performed according to the RSMU_exome protocol (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The pool was then circularized, downloaded into 4 flow cell lanes and sequenced paired-end mode on the DNBSEQ-G400 platform, using the DNBSEQ-G400RS high-throughput sequencing set PE100 kit, following the manufacturer's instructions (MGI Tech).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGenomic Data Analyses\u003c/h2\u003e \u003cp\u003eThe quality of the obtained fastq files was analyzed using FastQC v0.11.9 (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). On the basis of the quality metrics, the fastq files were trimmed via BBDuk by BBMap v38.96 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The reads were aligned to the indexed reference genome GRCh38.p14 using bwa-mem2 v2.2.1 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). SAM files were converted into BAM files and sorted using SAMtools v1.9 to check the percentage of the aligned\u0026shy; reads (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). On the basis of the obtained BAM files, the quality metrics of exome enrichment and sequencing were calculated using Picard v2.22.4, and the number of duplicates was calculated using Picard MarkDuplicates v2.22.4 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eVariant Calling and Filtering\u003c/h2\u003e \u003cp\u003eVariant calling was performed using DeepVariant v1.5.0 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The multiallelic variants in VCF files were decomposed into biallelic variants using vt decompose v0.5772 and then normalized using vt normalize v0.5772 (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). A depth coverage filter (DP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3) and FILTER\u0026thinsp;=\u0026thinsp;PASS was applied to the variants obtained. The variants were subsequently filtered by the target exome panel (Agilent SureSelect v8). Additionally, difficult-to-sequence regions in exome data, which are affected mainly by low-mappability regions, such as pseudogenes, tandem repeats, homopolymers, and other low-complexity regions (1.19 Mb in sum), were excluded (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe appropriate institutional review board approval for this study was obtained from the Ethics Committee at the Pirogov Russian National Research Medical University.Consent for publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExome sequences (24 fastq pairs) from the E701 reference DNA were deposited into the NCBI open-access sequence read archive (SRA) in fastq.gz format under BioProject ID PRJNA1137605. Additionally, whole genome sequencing data have been deposited under BioProject ID PRJNA1083205. The exome sequences of the samples used in this study are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was been carried out within the framework of state assignment № 124020400004-9 on the topic Development of a virally delivered gene therapy drug for the treatment of Crigler-Najjar syndrome types I and II.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAS: designed, performed research and analyzed data; writing - original draft; writing - review \u0026amp; editing; VB: designed, performed research and analyzed data; supervision; writing - review \u0026amp; editing; IV: software and visualization; ZR: software and visualization; TG: methodology; provided experimental samples; ER: methodology; provided experimental samples; MP: provided experimental samples; EG: provided experimental samples; TN: provided experimental samples; DR: funding acquisition; DK: designed research; resources and funding acquisition; conceptualization; project administration.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eT\u0026scaron;uiko, O., Gallardo, E. 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R., \u0026amp; Kang, H. M. (2015). Unified representation of genetic variants. Bioinformatics, 31(13), 2202-2204.\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":"PTA, Exome sequencing, Preimplantation genetic testing, WGA","lastPublishedDoi":"10.21203/rs.3.rs-6745778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6745778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluates primary template-directed amplification (PTA) for whole exome sequencing (WES) of small fibroblast cell groups, which mimics the limited cell quantities typical of trophectoderm embryo biopsies. PTA’s consistent amplification reduces allelic dropout (ADO) and improvesuniform coverage, overcoming challenges associated with conventional methods such as multiple displacement amplification (MDA). Using fibroblast samples alongside well-characterized genomic references (E701, NA12878), we benchmarked PTA-WES, achieving 97.5% target region coverage at 10x, meeting American College of Medical Genetics and Genomics (ACMG) standards. The completed filtering and variant calling provide a foundation for further optimization and analysis aimed at evaluating the reliability of PTA for routine clinical use. Preliminary results from embryo biopsies sequenced with PTA-WES revealed a median coverage of 102x, significantly improving upon the variability and coverage gaps observed with MDA-WES. These findings support the potential of PTA to increase the clinical applicability of WES for preimplantation genetic testing for monogenic disorders (PGT-M), expanding its ability to detect inherited and de novo mutations in embryos.\u003c/p\u003e","manuscriptTitle":"Precise exome analysis of blastocyst biopsy scale samples using Primary Template directed Amplification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 13:21:20","doi":"10.21203/rs.3.rs-6745778/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-07T09:39:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T13:13:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-11T15:40:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261834853836100696322574748635659586499","date":"2025-06-11T14:45:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12702792348839673145670474458319702324","date":"2025-06-10T15:59:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106803430549979036058897392130503960118","date":"2025-06-07T12:44:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T12:34:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-05T04:30:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-01T22:43:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-01T22:42:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-05-25T21:54:31+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":"2bad70bb-0138-4a63-8e21-3d9c9b478e62","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:07:34+00:00","versionOfRecord":{"articleIdentity":"rs-6745778","link":"https://doi.org/10.1186/s12864-025-12511-y","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2026-03-05 15:59:55","publishedOnDateReadable":"March 5th, 2026"},"versionCreatedAt":"2025-06-09 13:21:20","video":"","vorDoi":"10.1186/s12864-025-12511-y","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12511-y","workflowStages":[]},"version":"v1","identity":"rs-6745778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6745778","identity":"rs-6745778","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}