Draft genome and SSR data mining of Typhonium flagelliforme, an anti-cancer medicinal plant

Draft genome and SSR data mining of Typhonium flagelliforme, an anti-cancer medicinal plant | 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 Short Report Draft genome and SSR data mining of Typhonium flagelliforme, an anti-cancer medicinal plant Devit Purwoko, Siti Zulaeha, Gemilang Rahmadara, Suparjo Suparjo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7296811/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Dec, 2025 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 10 You are reading this latest preprint version Abstract Typhonium flagelliforme , a medicinal plant endemic to Indonesia and belonging to the Araceae family, has garnered significant attention due to its potential anticancer properties. Given its therapeutic relevance, this species represents a promising genetic resource for future plant breeding initiatives. In the present study, whole genome sequencing (WGS) of T. flagelliforme was performed using the Illumina NextSeq 2000 platform. Sequencing was conducted with a paired-end 150 bp (PE150) approach, yielding approximately 112 GB of raw data. The estimated genome size was 714.70 Mb, with an assembly contig N50 of 3,971 bp and a BUSCO completeness score of 76.08%. Also, we identified 64.41% repetitive DNA from the genome assembly, in which retroelements occupied 21.40% of the total genome. This first T. flagelliforme genome is expected to contribute to a better understanding of its genetics for molecular breeding programs, development of medicinal plant-based biotechnology, and sustainable conservation of rodent tubber germplasm. Assembly Genomics Illumina Microsatellite Rodent tubber SSR data mining Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Indonesia is a megabiodiversity country rich in medicinal plants with great potential to be developed as raw materials for the pharmaceutical industry, phytotherapy, and biotechnology. One of the herbal plants that has long been known to have medicinal properties is the rodent tubber ( Typhonium flagelliforme Lodd.), a member of the Araceae family. Various studies have shown that the rodent tubber has the potential as an anticancer, antioxidant, antimicrobial, and anti-inflammatory agent, as well as other important pharmacological activities (Mohan et al. 2011 ; Farida et al. 2014 ; Mirgane et al. 2020; Septaningsih et al. 2021 ). Although the rodent tubber has high bioactive potential, the development of its cultivation and widespread utilization still faces various challenges. One of the main obstacles is the limited genetic information of this plant, which hinders the breeding of superior varieties and the development of derivative products. Previous studies have mostly been in the form of secondary metabolite bioprospecting or biological activity tests, while genomic data for this plant is still very minimal or even not yet comprehensively available. Next-generation sequencing (NGS) technologies have revolutionized genomics by enabling rapid generation of high-throughput sequencing data (Satam et al. 2023 ; Panahi et al. 2024 ; Patwekar et al. 2025 ). With the advancement of molecular technology, especially Next Generation Sequencing (NGS), more sophisticated and efficient approaches are now available to explore the genetic information of non-model organisms. One of the latest technologies, Whole Genome Sequencing (WGS), allows comprehensive mapping of the entire genome of a species, including previously inaccessible genetic regions such as introns, intergenic regions, and regulatory elements. Sequencing and assembling the genomes of large eukaryotic organisms is still a challenge (Collins 2018 ; Liao et al. 2019 ; Blaxter et al. 2022 ). Genome assembly is a foundational task in genomics, reliant on computational assemblers that vary in algorithmic design and performance under different conditions (Alhakami et al. 2017 ; Mochizuki et al. 2023 ). Optimizing assembler selection remains critical for accurate genome reconstruction. The process of assembling the genome of the rodent tubber was carried out de novo considering the lack of complete information on the genome structure, such as the length, location, and DNA composition of this species. Using NGS, Yin et al. ( 2021 ) assembled the complete genome of Colocasia esculenta followed by Pan et al. ( 2024 ) in revealing InDel-SSR associated with genes which regulate leaf development. Several members of the Araceae family have had their complete genomes successfully sequenced in recent years. Notable examples include Metroxylon sagu (Purwoko et al. 2019 ), Amorphophallus titanum (Frisse et al. 2022 ), Amorphophallus konjac (Li et al. 2023 ), and Amorphophallus albus (Duan el al. 2025), which provide valuable genomic resources for comparative and evolutionary studies within the family. Despite growing genomic resources in Araceae, rodent tuber remains underexplored. Here, we report the first genome sequence of Typhonium flagelliforme , a medicinal plant endemic to Indonesia and belonging to the Araceae family, has garnered significant attention due to its potential anticancer properties. The application of WGS to the rodent tubber is expected to provide fundamental information on genome structure, gene annotation, biosynthesis of secondary metabolite compounds, and identification of key genes involved in plant defense mechanisms and production of bioactive compounds. These data are important as a basis for molecular breeding programs, development of medicinal plant-based biotechnology, and sustainable conservation of rodent tubber germplasm. Materials and methods Sample collection and DNA extraction We collected leaves of Typhonium flagelliforme from Laboratory for Biotechnology BRIN. Science and Technology Area of BJ Habibie, Serpong, South Tangerang, Banten, Indonesia. Genomic DNA (gDNA) was extracted from whole leaves using Genomic DNA extraction using Quick-DNA Magbead Plus Kit (Zymoresearch, D4082) following the manufacturer’s protocol. Initial quantification and purity were performed using Nanodrop 2000 (Thermo Scientific), DNA visualization using 1% TBE agarose gel electrophoresis, quantification accuracy using Qubit dsDNA HS Assay Kits (Thermo Scientific) using Qubit™ Flex Fluorometer, DNA integrity quality check using 4150 TapeStation (Agilent). DNA sequencing and data quality control Total extracted gDNA was used as input for library preparation. gDNA was fragmented using an enzymatic method to match the expected insert size. The fragmented DNA was ligated with Illumina-compatible adapters (forward adapter: AGATCGGAAGAGCACACGTCTGAACTCCAGTCA, reverse adapter: AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT) with a unique index for each sample. Paired-end libraries (PE) generated from gDNA were then quality checked (QC) using the xGen DNA Library Prep EZ UNI Kit (IDT, 10009822) according to manufacturer's specifications and validated using a 4150 TapeStation (Agilent) and Qubit™ Flex Fluorometer. Libraries that passed QC were sequenced using Illumina NextSeq 2000 for a read length of 300 cycles (PE150) at PT. Genetika Science Indonesia. Raw reads obtained in fastq format were further subjected to quality control (QC) steps to ensure data quality before further analysis. QC was performed through initial quality checks using FastQC (version 0.12.1) to evaluate parameters of quality distribution per base, quality distribution per read, GC content, adapter contamination and overrepresented sequences. The resulting reads were cleaned (trimmed) using fastp software for advanced quality control, adapter trimming, quality filtering and per-reads quality trimming (Chen et al 2018 ). The quality of the reads was re-evaluated with fastQC and summarized using multiQC. Genome Assembly The resulting high-quality reads were subsequently assembled with SPAdes v3.15.5 (Prjibelski et al. 2020 ). Data transformation was performed using samtools v1.19.2 (Danecek et al. 2021 ). Mapping was performed using bwa (-mem) v0.7.17-r1188 (Li, 2013 ). The quality of the assembled sequences was determined using Quast v5.0.2 (Gurevich et al. 2013 ) and Qualimap v2.3 (Okonechnikov et al. 2016 ). The quality and accuracy of the genome assembly were evaluated through two complementary approaches. First, the filtered paired-end Illumina reads were realigned to the assembled contigs using Bowtie2 v2.4.2 (Langmead and Salzberg, 2012 ), and alignment statistics were analyzed with SamTools v1.7 (Li et al. 2009 ) to identify potential assembly errors. Second, assembly completeness and gene content coverage were assessed using the BUSCO pipeline (Simão et al. 2015 ) by comparing against a lineage-specific dataset, enabling evaluation of conserved single-copy ortholog representation within the assembled genome. To assess the contiguity of the genome assembly, the N50 value was calculated, representing the minimum contig or scaffold length needed to cover 50% of the total genome. Assembly quality metrics, including quality value (QV), k-mer error rate, and k-mer completeness, were determined using Merqury v1.3 (Rhie et al. 2020 ) with a k-mer size of 21. Genome annotation and SSR data mining Gene prediction was carried out using the BRAKER pipeline (Hoff et al. 2016 ), which integrates evidence-based and ab initio approaches for accurate gene model identification. Functional annotation of predicted proteins was performed through BLASTp searches against the UniRef90 (Suzek et al. 2015 ) and CAZy (Cantarel et al. 2009 ) databases to identify homologous sequences and assign putative functions, including carbohydrate-active enzymes. Genomic feature visualization was generated using Circos (Krzywinski et al. 2009 ), providing a circular representation of genome structure and annotation data. To verify taxonomic assignment, BLASTn was used to align genome scaffolds against the NCBI non-redundant nucleotide (nt) database. Finally, genome completeness was assessed using BUSCO (Benchmarking Universal Single-Copy Orthologs) (Simão et al. 2015 ), allowing evaluation of the representation of conserved orthologous genes. Repetitive elements within the Typhonium flagelliforme genome were identified through a combination of de novo and homology-based approaches. For the de novo prediction, RepeatModeler v2.0.5 (Flynn et al. 2020 ) was employed with default parameters to construct a species-specific repeat library. This custom library was then used in RepeatMasker v4.1.5 (Tempel, 2012 ) for repeat annotation. In parallel, homology-based detection was conducted using RepeatMasker, supported by the Repbase v4.0.7 database (Bao et al. 2015 ) and RMBlast v2.2.27 (Korf, 2004 ) as the search engine. The outputs from both approaches were subsequently merged, and a comprehensive summary of repetitive elements was generated using RepeatMasker. Microsatellite sequences (SSRs) were detected across the genome using the MISA tool ( http://pgrc.ipk-gatersleben.de/misa/ ) as described by Beier et al. ( 2017 ). The search parameters were configured to identify repeat motifs with a minimum threshold of 12 repeat units for mononucleotides, 6 for dinucleotides, 5 for trinucleotides, and 4 repeat units for tetra-, penta-, and hexanucleotide motifs. These criteria ensure comprehensive detection of both common and rare microsatellite motifs, contributing to the effective characterization of SSR distribution within the genome. Results Sequence Data Quality and Filtering Outcomes A total of 400,390,368 raw reads were generated prior to quality filtering, with an average read length of 151 bp, yielding approximately 60.46 billion nucleotide bases (Table 1 ). Following the filtering process, 375,783,156 high-quality reads were retained, representing 93.86% of the initial dataset. The average read length slightly decreased to 144 bp, resulting in a total of 54.07 billion high-quality bases. Significant improvements were observed in the quality parameters. The proportion of Q20 bases (indicating an error probability of ≤ 1%) increased from 95.39–97.19%, while Q30 bases (indicating an error probability of ≤ 0.1%) improved from 90.25–92.69%. These metrics indicate that the quality control step effectively enhanced the proportion of high-confidence sequences within the dataset. A slight decrease in GC content was noted, declining from 42.90% in the raw dataset to 42.54% after filtering. This change is likely attributed to the removal of reads originating from genomic regions with extreme GC content. Nevertheless, the reduction is marginal and does not suggest any significant bias or distortion in the overall genomic composition. Table 1 Short read sequencing statistical data of T. flagelliforme Before filtering After filtering Total reads 400,390,368 375,783,156 Mean length (bp) 151 144 Total bases 60,458,946,000 54,074,738,000 Q20 bases 57,672,652,000 (95.39%) 52,553,582,000 (97.19%) Q30 bases 54,562,643,000 (90.25%) 50,123,213,000 (92.69%) GC content 42.90% 42.54% Genome Assembly Summary The genome assembly of T. flagelliforme yielded a total of 235,460 contigs, with an aggregate length of 714,696,677 bp (Table 2 ). Notably, all contigs measured ≥ 1,000 bp, as indicated by the identical values for total contigs and contigs ≥ 1,000 bp, both amounting to 235,460. The longest contig reached 218,692 bp, demonstrating the assembler's capacity to generate long, continuous sequences. The N50 value was calculated at 3,971 bp, with an L50 of 46,390 contigs, indicating that 50% of the assembly is represented by contigs of at least this length. In addition, N90 and L90 values were 1,355 bp and 173,985 contigs, respectively. The area under the N-curve (auN) was 7,583.7, providing a robust summary of contig length distribution. The total sequence length was 714,696,913 bp (≥ 0 bp), closely matching the length of contigs ≥ 1,000 bp, suggesting only a negligible number of ultra-short sequences. The GC content of the assembled genome was determined to be 41.03%, consistent with typical eukaryotic genome composition. Table 2 BUSCO assessment of T. flagelliforme Parameter Number Assembly Statistics # contigs 235,460 # contigs ( > = 0 bp) 235,461 # contigs ( > = 1000 bp) 235,460 Largest contig 218,692 Total length 714,696,677 Total length ( > = 0 bp) 714,696,913 Total length ( > = 1000 bp) 714,696,677 N50 3,971 N90 1,355 auN 7,583.70 L50 46,390 L90 173,985 GC (%) 41.03 Genome Completeness Complete BUSCOs (C) 194 (76.08%) Complete and single-copy BUSCOs (S) 154 (60.39%) Complete and duplicated BUSCOs (D) 40 (15.69%) Fragmented BUSCOs (F) 45 (17.65%) Missing BUSCOs (M) 16 (6.27%) Total BUSCO groups searched 255 Merqury Quality Value (QV) 38.17 k-mer error rate 1.55449e-04 k-mer completeness (%) 60.731 The completeness of the assembled genome was evaluated using the Benchmarking Universal Single-Copy Orthologs (BUSCO) approach (Simão et al. 2015 ), which provides a quantitative measure of gene space completeness based on evolutionarily conserved orthologous genes. A total of 255 BUSCO groups were searched using a plant-specific lineage dataset (Table 2 ). The results revealed that 194 BUSCOs (76.08%) were identified as complete, indicating the presence of most essential gene components within the assembly. Among these, 154 BUSCOs (60.39%) were detected as complete and single-copy, while 40 BUSCOs (15.69%) were categorized as complete and duplicated, suggesting the existence of some gene duplications, which is a common feature in plant genomes due to polyploidy or segmental duplications. In addition, 45 BUSCOs (17.65%) were classified as fragmented, reflecting the presence of partial gene sequences that may be the result of fragmented assembly or incomplete gene prediction. Only 16 BUSCOs (6.27%) were deemed missing, indicating a small fraction of conserved genes not captured in the current assembly. Quality value (QV) and k-mer completeness evaluated by Merqury, showed a QV of 38.17 and completeness of 60.73%. These results collectively demonstrate that the genome assembly exhibits a high level of completeness, with over three-quarters of essential gene content represented, thereby providing a solid foundation for downstream gene prediction and functional annotation. Figure 1 shows the k-mer multiplicity spectrum generated using Merqury to assess the quality and completeness of the T. flagelliforme genome assembly based on Illumina short-read data. The x-axis represents the k-mer multiplicity (how many times a k-mer appears in the read dataset), and the y-axis shows the total count of k-mers at each multiplicity level. The gray-shaded region on the left corresponds to unique k-mers found only in the raw reads but not present in the assembly, commonly attributed to sequencing errors. The red-shaded region represents k-mers occurring once in the assembly, while blue, green, purple, and orange areas indicate k-mers appearing 2, 3, 4, and more than 4 times, respectively. The spectrum exhibits a unimodal peak, characteristic of a diploid genome, with most valid k-mers peaking at a multiplicity around 6–7. This indicates a good representation of homozygous regions, with a tail suggesting the presence of repetitive or multi-copy regions. The relatively small gray area indicates a low proportion of missing k-mers, reflecting a high level of assembly completeness and low sequencing error. Additionally, the high overlap between k-mers in the reads and assembly supports strong base-level accuracy (QV) of the assembled genome. Genome annotation and SSR data mining The genome-wide analysis revealed that interspersed repeats occupy approximately 487,814,671 base pairs, accounting for 64.41% of the entire genome sequence (Table 3 ). Among these, retroelements were the most prevalent class, comprising 227,861 elements and covering 162,055,080 bp, which corresponds to 21.4% of the genome. Within retroelements, long terminal repeat (LTR) elements dominated, with 174,741 instances occupying 135,777,481 bp (17.93%). Among the LTRs, the Gypsy/DIRS1 superfamily was most abundant, contributing 10.74% of the genome (93,400 elements), followed by Ty1/Copia elements (79,920 elements; 7.08%). Retroviral elements were detected in relatively low numbers (964 elements, 0.05%). Long interspersed nuclear elements (LINEs) were the second most abundant retroelement group, comprising 52,394 elements and spanning 26,108,712 bp (3.45%). The most dominant LINE family was L1/CIN4, with 41,167 elements (2.87%), while RTE/Bov-B elements contributed 0.58%. No elements were detected from the CRE/SLACS, L2/CR1/Rex, R1/LOA/Jockey, or R2/R4/NeSL LINE subgroups. Short interspersed nuclear elements (SINEs) were rare, consisting of only 726 elements (0.02%), while Penelope elements were minimally present with 180 instances (0.01%). DNA transposons were also present but in smaller proportions than retroelements, with 33,428 elements occupying 21,669,896 bp (2.86%). The hobo-Activator and MULE-MuDR families were the most represented, contributing 0.80% and 1.43% of the genome, respectively. Tourist/Harbinger elements accounted for 0.14%, whereas other families such as En-Spm, PiggyBac, and Transib-related elements were absent. Rolling-circle elements were identified in 559 copies, occupying 469,727 bp (0.06%). A substantial fraction of the genome was composed of unclassified elements, totaling 944,678 sequences and occupying 304,049,562 base pairs, which corresponds to 40.15% of the entire assembly. This high proportion likely reflects the presence of highly diverse or lineage-specific repetitive elements that are not yet represented in existing repeat annotation databases. Additionally, simple sequence repeats (SSRs) and low complexity regions were identified. Simple repeats accounted for 0.90% of the genome (159,299 elements), while low complexity sequences comprised 0.16%. Minor fractions of the genome were occupied by small RNAs (0.01%) and satellite sequences (0.04%). Table 3 De novo identification of sequence repeats in the genome of T. flagelliforme number of elements* length occupied (bp) percentage of sequence (%) Retroelements 227861 162055080 21.4 SINEs : 726 168887 0.02 Penelope 180 40133 0.01 LINEs : 52394 26108712 3.45 CRE/SLACS 0 0 0 L2/CR1/Rex 0 0 0 R1/LOA/Jockey 0 0 0 R2/R4/NeSL 0 0 0 RTE/Bov-B 11227 4357489 0.58 L1/CIN4 41167 21751223 2.87 LTR elements : 174741 135777481 17.93 BEL/Pao 0 0 0 Ty1/Copia 79920 53654757 7.08 Gypsy/DIRS1 93400 81326359 10.74 Retroviral 964 415639 0.05 DNA transposons 33428 21669896 2.86 hobo-Activator 12410 6065926 0.8 Tc1-IS630-Pogo 1198 366356 0.05 En-Spm 0 0 0 MULE-MuDR 10303 10832871 1.43 PiggyBac 0 0 0 Tourist/Harbinger 1600 1070282 0.14 Other (Mirage, P-element, Transib) 0 0 0 Rolling-circles 559 469727 0.06 Unclassified : 944678 304049562 40.15 Total interspersed repeats : 487814671 64.41 Small RNA : 359 40911 0.01 Satellites : 1759 302417 0.04 Simple repeats : 159299 6822633 0.9 Low complexity : 22316 1202268 0.16 A total of 97,631 genomic simple sequence repeats (gSSRs) were identified from the analyzed genome assembly. Among these, dinucleotide repeats were the most abundant type, accounting for 60.51% (59,076 SSRs) of the total SSRs detected. This was followed by mononucleotide repeats, which constituted 28.83% (28,146 SSRs). Trinucleotide motifs were present at a moderate level with 8,876 SSRs (9.09%), while tetranucleotide, pentanucleotide, and hexanucleotide motifs were observed at much lower frequencies, comprising 1.16% (1,129 SSRs), 0.24% (233 SSRs), and 0.18% (171 SSRs), respectively (Fig. 2 ). Dinucleotide repeats emerged as the most prevalent class of simple sequence repeats (SSRs) in the genome assembly, with a total of 59,076 motifs identified. Among these, the AG/CT motif was the most frequent, comprising 23,173 occurrences, followed by the AT/AT motif with 20,934 instances (Fig. 3 ). The AC/GT motif was also relatively common, with 14,844 repeats identified. In contrast, the CG/CG motif was extremely rare, detected only 125 times, accounting for less than 0.25% of all dinucleotide SSRs. The analysis revealed 8,876 trinucleotide SSRs within the genome assembly. Of these, the AAG/CTT motif was the most dominant, occurring 3,121 times and representing a notable share of the total trinucleotide SSRs (Fig. 4 ). This was followed by CCG/CGG with 1,074 repeats, AAT/ATT with 1,127 repeats, and ACT/ATG with 619 repeats. Other relatively frequent motifs included AGG/CCT (682), AGT/ATC (678), AGC/CGT (399), ACG/CTG (459), AAC/GTT (477), and ACC/GGT (240). These data reflect a moderate level of diversity among trinucleotide SSRs, with a notable bias toward A/T-rich motifs. Discussion The draft genome assembly of Typhonium flagelliforme presented in this study provides a foundational resource for future genetic and genomic research in this underexplored medicinal plant species. The assembly quality, supported by multiple evaluation metrics such as N50, BUSCO completeness, and k-mer spectrum analysis, reflects a high level of sequence integrity and representation. The predominance of long contigs and the low proportion of missing k-mers, as revealed by Merqury analysis, indicate minimal sequencing errors and strong coverage across the genome. The results reflect a high-quality preliminary assembly, with the majority of contigs exceeding 1,000 base pairs an essential benchmark, as shorter contigs are typically less informative and may impede downstream genomic analyses (Seitz and Nieselt 2017 ). The presence of a maximum contig length exceeding 218 kb suggests successful recovery of extended genomic regions that may encompass complete gene structures, including regulatory elements and intergenic sequences (Gurevich et al. 2013 ). Although chromosome-level contiguity has not yet been achieved, this outcome is notable for a de novo assembly of a plant genome, which often presents challenges due to its complexity and repeat content. The moderate N50 and high L50 values reflect a degree of fragmentation in the assembly, indicating that future efforts could aim to improve contiguity through scaffolding technologies or long-read sequencing. The auN metric, which integrates contig lengths more comprehensively than N50, further supports the observation of moderate contiguity while providing a robust measure for comparing assembly quality across tools and datasets (Bradnam et al. 2013 ). The GC content of 41.03% falls within the normal range for plant genomes and suggests an absence of major biases or contamination. GC content remains a key factor in assessing sequencing and assembly performance, given its influence on DNA stability, sequencing efficiency, and potential representation biases (Benjamini and Speed 2012 ). Any significant deviation from expected GC content might indicate technical artifacts or foreign sequence contamination, which was not evident in this dataset. In this study, a whole-genome shotgun sequencing strategy was employed for T. flagelliforme , utilizing exclusively paired-end read libraries for de novo assembly. This approach yielded scaffold N50 values in the moderate range, a result largely attributable to the absence of mate-pair libraries, which are known to enhance scaffold length and assembly contiguity. Paired-end libraries alone are often insufficient to resolve complex repetitive regions in plant genomes, which typically have a high proportion of repetitive sequences (Liao et al. 2023 ). Previous studies have demonstrated that the incorporation of mate-pair libraries can significantly improve assembly metrics, with N50 values increasing by one to two orders of magnitude (Belova et al. 2013 ). Despite this limitation, the scaffold N50 in our T. flagelliforme assembly exceeds 20 kb, comparable to those reported for Cannabis sativa (Wei et al. 2024 ) and Tapiscia sinensis (Zhao et al. 2020 ). Moreover, the scaffold N50 achieved here is consistent with those obtained in other plant genomes assembled without mate-pair data (Belova et al. 2013 ), suggesting that a substantial portion of the non-repetitive genomic regions has been successfully captured. To improve assembly quality in applications such as comparative genomics, whole genome duplication analysis, or evolutionary studies in rodent tubers some future efforts may benefit from the integration of additional long-range information, such as mate-pair libraries, optical or physical maps, or cytogenetic data. Nevertheless, the current assembly provides a valuable resource for characterizing key genomic features of T. flagelliforme , including its repeat landscape and gene content. The k-mer spectrum analysis using Merqury provides critical insights into the quality and completeness of the T. flagelliforme genome assembly. The unimodal peak observed at a k-mer multiplicity of approximately 6–7 is characteristic of a diploid genome and indicates a high representation of homozygous regions. Such patterns are typical of well-assembled diploid plant genomes and support the structural integrity of the current assembly (Rhie et al. 2020 ; Zimin et al. 2017 ). A minimal, gray-shaded region, representing k-mers present in the raw reads but absent in the assembly, suggests a low sequencing error rate and high completeness of the assembly. This is critical, as an overrepresentation of such "error" k-mers would indicate substantial sequence loss or misassembly (Vurture et al. 2017 ). The high degree of overlap between k-mers in the raw reads and those in the assembly confirms successful incorporation of most of the genomic information and supports a high base-level accuracy (QV) (Rhie et al. 2020 ; Jayakumar and Sakakibara, 2022 ). The small fraction of high-frequency k-mers (multiplicity > 4), typically corresponding to repetitive or multi-copy elements, suggests that repetitive regions were adequately resolved. Though short-read data can pose limitations in assembling highly repetitive regions (Michael and VanBuren, 2020 ), the smooth tail and narrow error peak observed here indicate that the assembler managed such sequences effectively. Merqury’s k-mer-based approach is particularly advantageous for non-model plant species like T. flagelliforme , where reference genomes are not available. It allows robust, reference-free assessment of genome assembly metrics including completeness, base accuracy, and phasing quality (Rhie et al. 2020 ; Lee et al. 2023 ). This analysis confirms that the assembly is of sufficient quality to serve as a foundation for downstream applications such as gene annotation, SSR marker mining, and comparative genomics across Araceae. The high proportion of interspersed repeats (64.41%) in this genome is characteristic of many plant genomes, especially those with large genome sizes and complex evolutionary histories. The predominance of retrotransposons, particularly the Gypsy and Copia LTR families, is a common feature in higher plants and has been linked to genome expansion and adaptation (Ou et al. 2019 ; Su et al. 2021 ). Gypsy elements, often found near centromeric regions, have been shown to play a role in chromatin structure and genome stability, whereas Copia elements are more frequently associated with euchromatic and gene-rich regions. The considerable presence of LINEs, especially L1/CIN4 elements, supports findings from other angiosperms, where these elements contribute to structural variations and influence gene expression through insertional mutagenesis (Makarevitch et al. 2021 ). The near absence of certain LINE families (e.g. L2/CR1/Rex and R1/LOA/Jockey) may reflect lineage-specific loss or silencing mechanisms in this species. The identification of DNA transposons, particularly from the hobo-Activator and MULE-MuDR families, is consistent with their widespread occurrence in plant genomes, where they are often implicated in gene duplication and regulatory evolution (Chuong et al. 2023 ). The large fraction of unclassified repeats (40.15%) highlights the potential presence of novel or species-specific transposable elements that are not captured in current reference databases, underscoring the need for continued annotation improvement and repeat library curation (Navarro-Muñoz et al. 2020 ). The microsatellite density in the T. flagelliforme genome, calculated at 345,202 SSR/Mb, offers a useful metric for assessing the abundance of these repetitive elements within the genome. This frequency is particularly informative when compared across species, as variation in SSR density may reflect underlying differences in genome organization, mutation rates, or levels of genetic diversity among Typhonium species (Srivastava et al. 2019 ; Fischer et al. 2017 ). The comparatively lower number of SSRs observed in T. flagelliforme may be attributed to species-specific evolutionary processes, such as selective constraints or the effects of genetic drift, which could have contributed to the gradual loss or suppression of these repetitive elements over time (Bagshaw, 2017 ). Understanding the evolutionary forces and genomic contexts that shape microsatellite abundance will be important for elucidating the genetic diversity, adaptability, and evolutionary history of T. flagelliforme . Further comparative studies across Araceae genomes may help clarify these patterns. The predominance of dinucleotide repeats in the T. flagelliforme genome is consistent with findings in other plant species, where AT- and AG-rich motifs frequently occur in intergenic and intronic regions (Chen et al. 2021 ; Singh et al. 2023 ). Their abundance is likely driven by replication slippage and relaxed selective pressures in non-coding regions. Among these, the AG/CT and AT/AT motifs were the most common, reinforcing their potential as informative and polymorphic molecular markers (Yadav et al. 2022 ). Although mononucleotide repeats are also abundant, particularly poly-A/T stretches, they are often excluded from marker development due to sequencing errors and homopolymer-related artifacts (Kumar et al. 2020 ). In contrast, trinucleotide SSRs, which represented 9.09% of total SSRs, are more stable and frequently located within coding regions. Their preservation of the reading frame makes them suitable for gene-associated marker development (Yadav et al. 2022 ; Chen et al. 2021 ). Among trinucleotide motifs, AAG/CTT was the most frequent—consistent with its common presence in untranslated and regulatory regions of plant genomes (Kumar et al. 2020 ). The high occurrence of CCG/CGG motifs suggests possible roles in gene regulation due to their GC-rich content and localization in coding sequences (Ali et al. 2023 ). In contrast, motifs such as AAT/ATT and ACT/ATG, while less frequent, are valued for their high mutation rates, making them useful for genetic diversity studies (Singh et al. 2023 ). The lower frequency of tetra-, penta-, and hexanucleotide repeats aligns with previous SSR profiling studies in plants, which report that longer motif units are less common and likely subject to stronger purifying selection (Basak et al. 2019 ; Ali et al. 2023 ). The motif-specific distribution observed in this study supports the utility of di- and trinucleotide SSRs for marker-assisted selection, genetic mapping, and diversity analysis in T. flagelliforme and related species. Conclusion This study presents a comprehensive draft genome assembly of Typhonium flagelliforme , generated through a whole-genome shotgun approach using paired-end reads and SPAdes assembler. The assembly yielded a total length of ~ 714.7 Mb with moderate contiguity (N50 = 3.9 kb), and a GC content of 41.03%, consistent with other eukaryotic plant genomes. Despite the absence of mate-pair libraries, the assembly successfully captured large portions of non-repetitive regions and provides a valuable foundation for further genomic investigation. Repeat analysis revealed that interspersed repeats accounted for 64.41% of the genome, with LTR retrotransposons, especially Gypsy and Copia elements, dominating the repetitive landscape. A substantial number of unclassified elements also suggest the presence of lineage-specific or novel transposable elements. The genome exhibited a microsatellite density of 345,202 SSRs/Mb, with dinucleotide repeats, notably AG/CT and AT/AT motifs, representing the most abundant SSR categories. Trinucleotide SSRs, especially AAG/CTT and CCG/CGG, were also prominent and offer strong potential for gene-associated marker development. These findings suggest that the assembly sufficiently captures both the genic and intergenic regions, enabling downstream analyses such as gene prediction, marker development, and comparative genomics. Given the limited genomic resources currently available for the genus Typhonium , this draft genome provides a valuable platform for understanding species-specific traits and for advancing conservation and breeding efforts. Declarations Conflict of interest All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Funding This research was supported by Join Collaboration Inhouse Biological and Environmental Research Organization BRIN Program with number B-1797/III.5/PR.03.06/6/2024 and B1721/III.5/PR.03.06/7/2025 and RIIM Collaborative Platform Biology Biomolecular Structure Biodiversity Batch I number B-2657/III.5/FR.06.00/6/2024. The authors would like to thank the two anonymous reviewers for their insightful feedback on an earlier draft of this paper. Author Contribution Conceptualization, DP, So and TT; methodology, DP and So; software, DP; validation, DP, TT; formal analysis, DP and SZ; investigation, DP; resources, Su and GR; data curation, DP, and SZ; writing—original draft preparation, DP, TT, and So; writing—review and editing, DP, So, TT and SJS; visualization, DP, and SJS; supervision, So, TT, AK and WBS; project administration, DP; funding acquisition, DP. All authors have read and agreed to the published version of the manuscript. Acknowledgement Thanks to Applied Botany Research Center and Talent Management BRIN for funding DBR research, laboratories and scholarships. Data Availability The whole genome sequencing data was deposited in the Short Read Archive (SRA) database under accession number PRJNA1141733. And will be Release on 2026-09-01 or if the paper has been accepted. References Alhakami H, Mirebrahim H, Lonardi S (2017) A comparative evaluation of genome assembly reconciliation tools. 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Genome Research 27(5):787–792. https://doi.org/10.1101/gr.213405.116 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Dec, 2025 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted Editorial decision: Revision requested 17 Aug, 2025 Reviews received at journal 17 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Editor assigned by journal 07 Aug, 2025 Submission checks completed at journal 07 Aug, 2025 First submitted to journal 05 Aug, 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. 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(A) Copy number spectrum plot. (B) Assembly spectrum plot for evaluating K-mer completeness.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7296811/v1/628fbc4978727a8296bd7b51.png"},{"id":88912848,"identity":"1fc37957-bc63-480b-8ca6-78d46b18cfb0","added_by":"auto","created_at":"2025-08-12 15:44:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137064,"visible":true,"origin":"","legend":"\u003cp\u003eSSR distribution of \u003cem\u003eT. flagelliforme\u003c/em\u003e genome\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7296811/v1/92429870d49abb4f46e137e9.png"},{"id":88912033,"identity":"665af679-075d-4528-9711-af3ab0670c10","added_by":"auto","created_at":"2025-08-12 15:36:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93986,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of different motifs in dinucleotide repeats in \u003cem\u003eT. flagelliforme\u003c/em\u003e genome\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7296811/v1/78545609b924c98d4275eca8.png"},{"id":88912026,"identity":"37696e2a-a392-4baf-961d-1554adaa3208","added_by":"auto","created_at":"2025-08-12 15:36:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":140218,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of different motifs in trinucleotide repeats in \u003cem\u003eT. flagelliforme\u003c/em\u003e genome\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7296811/v1/620ae2ec7bc0b2ab6380fda5.png"},{"id":98814009,"identity":"c045270d-ad9b-45ae-81dd-24ceab0c0388","added_by":"auto","created_at":"2025-12-22 16:09:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1321189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7296811/v1/87ceaa28-0318-49cd-8674-7b7015598598.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Draft genome and SSR data mining of Typhonium flagelliforme, an anti-cancer medicinal plant","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIndonesia is a megabiodiversity country rich in medicinal plants with great potential to be developed as raw materials for the pharmaceutical industry, phytotherapy, and biotechnology. One of the herbal plants that has long been known to have medicinal properties is the rodent tubber (\u003cem\u003eTyphonium flagelliforme\u003c/em\u003e Lodd.), a member of the Araceae family. Various studies have shown that the rodent tubber has the potential as an anticancer, antioxidant, antimicrobial, and anti-inflammatory agent, as well as other important pharmacological activities (Mohan et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Farida et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mirgane et al. 2020; Septaningsih et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough the rodent tubber has high bioactive potential, the development of its cultivation and widespread utilization still faces various challenges. One of the main obstacles is the limited genetic information of this plant, which hinders the breeding of superior varieties and the development of derivative products. Previous studies have mostly been in the form of secondary metabolite bioprospecting or biological activity tests, while genomic data for this plant is still very minimal or even not yet comprehensively available.\u003c/p\u003e\u003cp\u003eNext-generation sequencing (NGS) technologies have revolutionized genomics by enabling rapid generation of high-throughput sequencing data (Satam et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Panahi et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Patwekar et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). With the advancement of molecular technology, especially Next Generation Sequencing (NGS), more sophisticated and efficient approaches are now available to explore the genetic information of non-model organisms. One of the latest technologies, Whole Genome Sequencing (WGS), allows comprehensive mapping of the entire genome of a species, including previously inaccessible genetic regions such as introns, intergenic regions, and regulatory elements. Sequencing and assembling the genomes of large eukaryotic organisms is still a challenge (Collins \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liao et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Blaxter et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Genome assembly is a foundational task in genomics, reliant on computational assemblers that vary in algorithmic design and performance under different conditions (Alhakami et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mochizuki et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Optimizing assembler selection remains critical for accurate genome reconstruction. The process of assembling the genome of the rodent tubber was carried out de novo considering the lack of complete information on the genome structure, such as the length, location, and DNA composition of this species.\u003c/p\u003e\u003cp\u003eUsing NGS, Yin et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) assembled the complete genome of \u003cem\u003eColocasia esculenta\u003c/em\u003e followed by Pan et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in revealing InDel-SSR associated with genes which regulate leaf development. Several members of the Araceae family have had their complete genomes successfully sequenced in recent years. Notable examples include \u003cem\u003eMetroxylon sagu\u003c/em\u003e (Purwoko et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u003cem\u003eAmorphophallus titanum\u003c/em\u003e (Frisse et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u003cem\u003eAmorphophallus konjac\u003c/em\u003e (Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and \u003cem\u003eAmorphophallus albus\u003c/em\u003e (Duan el al. 2025), which provide valuable genomic resources for comparative and evolutionary studies within the family. Despite growing genomic resources in Araceae, rodent tuber remains underexplored. Here, we report the first genome sequence of \u003cem\u003eTyphonium flagelliforme\u003c/em\u003e, a medicinal plant endemic to Indonesia and belonging to the Araceae family, has garnered significant attention due to its potential anticancer properties. The application of WGS to the rodent tubber is expected to provide fundamental information on genome structure, gene annotation, biosynthesis of secondary metabolite compounds, and identification of key genes involved in plant defense mechanisms and production of bioactive compounds. These data are important as a basis for molecular breeding programs, development of medicinal plant-based biotechnology, and sustainable conservation of rodent tubber germplasm.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eSample collection and DNA extraction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe collected leaves of \u003cem\u003eTyphonium flagelliforme\u003c/em\u003e from Laboratory for Biotechnology BRIN. Science and Technology Area of BJ Habibie, Serpong, South Tangerang, Banten, Indonesia. Genomic DNA (gDNA) was extracted from whole leaves using Genomic DNA extraction using Quick-DNA Magbead Plus Kit (Zymoresearch, D4082) following the manufacturer\u0026rsquo;s protocol. Initial quantification and purity were performed using Nanodrop 2000 (Thermo Scientific), DNA visualization using 1% TBE agarose gel electrophoresis, quantification accuracy using Qubit dsDNA HS Assay Kits (Thermo Scientific) using Qubit\u0026trade; Flex Fluorometer, DNA integrity quality check using 4150 TapeStation (Agilent).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDNA sequencing and data quality control\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTotal extracted gDNA was used as input for library preparation. gDNA was fragmented using an enzymatic method to match the expected insert size. The fragmented DNA was ligated with Illumina-compatible adapters (forward adapter: AGATCGGAAGAGCACACGTCTGAACTCCAGTCA, reverse adapter: AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT) with a unique index for each sample. Paired-end libraries (PE) generated from gDNA were then quality checked (QC) using the xGen DNA Library Prep EZ UNI Kit (IDT, 10009822) according to manufacturer's specifications and validated using a 4150 TapeStation (Agilent) and Qubit\u0026trade; Flex Fluorometer. Libraries that passed QC were sequenced using Illumina NextSeq 2000 for a read length of 300 cycles (PE150) at PT. Genetika Science Indonesia.\u003c/p\u003e\u003cp\u003eRaw reads obtained in fastq format were further subjected to quality control (QC) steps to ensure data quality before further analysis. QC was performed through initial quality checks using FastQC (version 0.12.1) to evaluate parameters of quality distribution per base, quality distribution per read, GC content, adapter contamination and overrepresented sequences. The resulting reads were cleaned (trimmed) using fastp software for advanced quality control, adapter trimming, quality filtering and per-reads quality trimming (Chen et al \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The quality of the reads was re-evaluated with fastQC and summarized using multiQC.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenome Assembly\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe resulting high-quality reads were subsequently assembled with SPAdes v3.15.5 (Prjibelski et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Data transformation was performed using samtools v1.19.2 (Danecek et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Mapping was performed using bwa (-mem) v0.7.17-r1188 (Li, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The quality of the assembled sequences was determined using Quast v5.0.2 (Gurevich et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Qualimap v2.3 (Okonechnikov et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The quality and accuracy of the genome assembly were evaluated through two complementary approaches. First, the filtered paired-end Illumina reads were realigned to the assembled contigs using Bowtie2 v2.4.2 (Langmead and Salzberg, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and alignment statistics were analyzed with SamTools v1.7 (Li et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) to identify potential assembly errors. Second, assembly completeness and gene content coverage were assessed using the BUSCO pipeline (Sim\u0026atilde;o et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) by comparing against a lineage-specific dataset, enabling evaluation of conserved single-copy ortholog representation within the assembled genome. To assess the contiguity of the genome assembly, the N50 value was calculated, representing the minimum contig or scaffold length needed to cover 50% of the total genome. Assembly quality metrics, including quality value (QV), k-mer error rate, and k-mer completeness, were determined using Merqury v1.3 (Rhie et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with a k-mer size of 21.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenome annotation and SSR data mining\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGene prediction was carried out using the BRAKER pipeline (Hoff et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which integrates evidence-based and ab initio approaches for accurate gene model identification. Functional annotation of predicted proteins was performed through BLASTp searches against the UniRef90 (Suzek et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and CAZy (Cantarel et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) databases to identify homologous sequences and assign putative functions, including carbohydrate-active enzymes. Genomic feature visualization was generated using Circos (Krzywinski et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), providing a circular representation of genome structure and annotation data. To verify taxonomic assignment, BLASTn was used to align genome scaffolds against the NCBI non-redundant nucleotide (nt) database. Finally, genome completeness was assessed using BUSCO (Benchmarking Universal Single-Copy Orthologs) (Sim\u0026atilde;o et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), allowing evaluation of the representation of conserved orthologous genes.\u003c/p\u003e\u003cp\u003eRepetitive elements within the \u003cem\u003eTyphonium flagelliforme\u003c/em\u003e genome were identified through a combination of de novo and homology-based approaches. For the de novo prediction, RepeatModeler v2.0.5 (Flynn et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was employed with default parameters to construct a species-specific repeat library. This custom library was then used in RepeatMasker v4.1.5 (Tempel, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for repeat annotation. In parallel, homology-based detection was conducted using RepeatMasker, supported by the Repbase v4.0.7 database (Bao et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and RMBlast v2.2.27 (Korf, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) as the search engine. The outputs from both approaches were subsequently merged, and a comprehensive summary of repetitive elements was generated using RepeatMasker. Microsatellite sequences (SSRs) were detected across the genome using the MISA tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pgrc.ipk-gatersleben.de/misa/\u003c/span\u003e\u003cspan address=\"http://pgrc.ipk-gatersleben.de/misa/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as described by Beier et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The search parameters were configured to identify repeat motifs with a minimum threshold of 12 repeat units for mononucleotides, 6 for dinucleotides, 5 for trinucleotides, and 4 repeat units for tetra-, penta-, and hexanucleotide motifs. These criteria ensure comprehensive detection of both common and rare microsatellite motifs, contributing to the effective characterization of SSR distribution within the genome.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSequence Data Quality and Filtering Outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 400,390,368 raw reads were generated prior to quality filtering, with an average read length of 151 bp, yielding approximately 60.46\u0026nbsp;billion nucleotide bases (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Following the filtering process, 375,783,156 high-quality reads were retained, representing 93.86% of the initial dataset. The average read length slightly decreased to 144 bp, resulting in a total of 54.07\u0026nbsp;billion high-quality bases. Significant improvements were observed in the quality parameters. The proportion of Q20 bases (indicating an error probability of \u0026le;\u0026thinsp;1%) increased from 95.39\u0026ndash;97.19%, while Q30 bases (indicating an error probability of \u0026le;\u0026thinsp;0.1%) improved from 90.25\u0026ndash;92.69%. These metrics indicate that the quality control step effectively enhanced the proportion of high-confidence sequences within the dataset. A slight decrease in GC content was noted, declining from 42.90% in the raw dataset to 42.54% after filtering. This change is likely attributed to the removal of reads originating from genomic regions with extreme GC content. Nevertheless, the reduction is marginal and does not suggest any significant bias or distortion in the overall genomic composition.\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 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eShort read sequencing statistical data of \u003cem\u003eT. flagelliforme\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBefore filtering\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAfter filtering\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal reads\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400,390,368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e375,783,156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean length (bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal bases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60,458,946,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54,074,738,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ20 bases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57,672,652,000 \u003cb\u003e(95.39%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52,553,582,000 \u003cb\u003e(97.19%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ30 bases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54,562,643,000 \u003cb\u003e(90.25%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50,123,213,000 \u003cb\u003e(92.69%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGC content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.54%\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\u003e\u003cb\u003eGenome Assembly Summary\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe genome assembly of \u003cem\u003eT. flagelliforme\u003c/em\u003e yielded a total of 235,460 contigs, with an aggregate length of 714,696,677 bp (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, all contigs measured\u0026thinsp;\u0026ge;\u0026thinsp;1,000 bp, as indicated by the identical values for total contigs and contigs\u0026thinsp;\u0026ge;\u0026thinsp;1,000 bp, both amounting to 235,460. The longest contig reached 218,692 bp, demonstrating the assembler's capacity to generate long, continuous sequences. The N50 value was calculated at 3,971 bp, with an L50 of 46,390 contigs, indicating that 50% of the assembly is represented by contigs of at least this length. In addition, N90 and L90 values were 1,355 bp and 173,985 contigs, respectively. The area under the N-curve (auN) was 7,583.7, providing a robust summary of contig length distribution. The total sequence length was 714,696,913 bp (\u0026ge;\u0026thinsp;0 bp), closely matching the length of contigs\u0026thinsp;\u0026ge;\u0026thinsp;1,000 bp, suggesting only a negligible number of ultra-short sequences. The GC content of the assembled genome was determined to be 41.03%, consistent with typical eukaryotic genome composition.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBUSCO assessment of \u003cem\u003eT. flagelliforme\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAssembly Statistics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e# contigs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e235,460\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e# contigs (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0 bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e235,461\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e# contigs (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1000 bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e235,460\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLargest contig\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e218,692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e714,696,677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal length (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0 bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e714,696,913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal length (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1000 bp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e714,696,677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,971\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,355\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eauN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,583.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46,390\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173,985\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGenome Completeness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplete BUSCOs (C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194 (76.08%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplete and single-copy BUSCOs (S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154 (60.39%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplete and duplicated BUSCOs (D)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (15.69%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFragmented BUSCOs (F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (17.65%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing BUSCOs (M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (6.27%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal BUSCO groups searched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMerqury\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuality Value (QV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ek-mer error rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.55449e-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ek-mer completeness (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.731\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\u003eThe completeness of the assembled genome was evaluated using the Benchmarking Universal Single-Copy Orthologs (BUSCO) approach (Sim\u0026atilde;o et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which provides a quantitative measure of gene space completeness based on evolutionarily conserved orthologous genes. A total of 255 BUSCO groups were searched using a plant-specific lineage dataset (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results revealed that 194 BUSCOs (76.08%) were identified as complete, indicating the presence of most essential gene components within the assembly. Among these, 154 BUSCOs (60.39%) were detected as complete and single-copy, while 40 BUSCOs (15.69%) were categorized as complete and duplicated, suggesting the existence of some gene duplications, which is a common feature in plant genomes due to polyploidy or segmental duplications. In addition, 45 BUSCOs (17.65%) were classified as fragmented, reflecting the presence of partial gene sequences that may be the result of fragmented assembly or incomplete gene prediction. Only 16 BUSCOs (6.27%) were deemed missing, indicating a small fraction of conserved genes not captured in the current assembly. Quality value (QV) and k-mer completeness evaluated by Merqury, showed a QV of 38.17 and completeness of 60.73%. These results collectively demonstrate that the genome assembly exhibits a high level of completeness, with over three-quarters of essential gene content represented, thereby providing a solid foundation for downstream gene prediction and functional annotation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e shows the k-mer multiplicity spectrum generated using Merqury to assess the quality and completeness of the \u003cem\u003eT. flagelliforme\u003c/em\u003e genome assembly based on Illumina short-read data. The x-axis represents the k-mer multiplicity (how many times a k-mer appears in the read dataset), and the y-axis shows the total count of k-mers at each multiplicity level. The gray-shaded region on the left corresponds to unique k-mers found only in the raw reads but not present in the assembly, commonly attributed to sequencing errors. The red-shaded region represents k-mers occurring once in the assembly, while blue, green, purple, and orange areas indicate k-mers appearing 2, 3, 4, and more than 4 times, respectively. The spectrum exhibits a unimodal peak, characteristic of a diploid genome, with most valid k-mers peaking at a multiplicity around 6\u0026ndash;7. This indicates a good representation of homozygous regions, with a tail suggesting the presence of repetitive or multi-copy regions. The relatively small gray area indicates a low proportion of missing k-mers, reflecting a high level of assembly completeness and low sequencing error. Additionally, the high overlap between k-mers in the reads and assembly supports strong base-level accuracy (QV) of the assembled genome.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenome annotation and SSR data mining\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe genome-wide analysis revealed that interspersed repeats occupy approximately 487,814,671 base pairs, accounting for 64.41% of the entire genome sequence (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among these, retroelements were the most prevalent class, comprising 227,861 elements and covering 162,055,080 bp, which corresponds to 21.4% of the genome. Within retroelements, long terminal repeat (LTR) elements dominated, with 174,741 instances occupying 135,777,481 bp (17.93%). Among the LTRs, the Gypsy/DIRS1 superfamily was most abundant, contributing 10.74% of the genome (93,400 elements), followed by Ty1/Copia elements (79,920 elements; 7.08%). Retroviral elements were detected in relatively low numbers (964 elements, 0.05%). Long interspersed nuclear elements (LINEs) were the second most abundant retroelement group, comprising 52,394 elements and spanning 26,108,712 bp (3.45%). The most dominant LINE family was L1/CIN4, with 41,167 elements (2.87%), while RTE/Bov-B elements contributed 0.58%. No elements were detected from the CRE/SLACS, L2/CR1/Rex, R1/LOA/Jockey, or R2/R4/NeSL LINE subgroups. Short interspersed nuclear elements (SINEs) were rare, consisting of only 726 elements (0.02%), while Penelope elements were minimally present with 180 instances (0.01%). DNA transposons were also present but in smaller proportions than retroelements, with 33,428 elements occupying 21,669,896 bp (2.86%). The hobo-Activator and MULE-MuDR families were the most represented, contributing 0.80% and 1.43% of the genome, respectively. Tourist/Harbinger elements accounted for 0.14%, whereas other families such as En-Spm, PiggyBac, and Transib-related elements were absent. Rolling-circle elements were identified in 559 copies, occupying 469,727 bp (0.06%). A substantial fraction of the genome was composed of unclassified elements, totaling 944,678 sequences and occupying 304,049,562 base pairs, which corresponds to 40.15% of the entire assembly. This high proportion likely reflects the presence of highly diverse or lineage-specific repetitive elements that are not yet represented in existing repeat annotation databases. Additionally, simple sequence repeats (SSRs) and low complexity regions were identified. Simple repeats accounted for 0.90% of the genome (159,299 elements), while low complexity sequences comprised 0.16%. Minor fractions of the genome were occupied by small RNAs (0.01%) and satellite sequences (0.04%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDe novo identification of sequence repeats in the genome of \u003cem\u003eT. flagelliforme\u003c/em\u003e\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003enumber of elements*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003elength occupied (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epercentage of sequence (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRetroelements\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e227861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e162055080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSINEs\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e168887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePenelope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLINEs\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26108712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRE/SLACS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL2/CR1/Rex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR1/LOA/Jockey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR2/R4/NeSL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRTE/Bov-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4357489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL1/CIN4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21751223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLTR elements\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e174741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135777481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEL/Pao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTy1/Copia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53654757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGypsy/DIRS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81326359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetroviral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e415639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDNA transposons\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21669896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehobo-Activator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6065926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTc1-IS630-Pogo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e366356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEn-Spm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMULE-MuDR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10832871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePiggyBac\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTourist/Harbinger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1070282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther (Mirage, P-element, Transib)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRolling-circles\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e469727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnclassified\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e944678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e304049562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal interspersed repeats\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e487814671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmall RNA\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSatellites\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e302417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSimple repeats\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e159299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6822633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow complexity\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1202268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\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\u003eA total of 97,631 genomic simple sequence repeats (gSSRs) were identified from the analyzed genome assembly. Among these, dinucleotide repeats were the most abundant type, accounting for 60.51% (59,076 SSRs) of the total SSRs detected. This was followed by mononucleotide repeats, which constituted 28.83% (28,146 SSRs). Trinucleotide motifs were present at a moderate level with 8,876 SSRs (9.09%), while tetranucleotide, pentanucleotide, and hexanucleotide motifs were observed at much lower frequencies, comprising 1.16% (1,129 SSRs), 0.24% (233 SSRs), and 0.18% (171 SSRs), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDinucleotide repeats emerged as the most prevalent class of simple sequence repeats (SSRs) in the genome assembly, with a total of 59,076 motifs identified. Among these, the AG/CT motif was the most frequent, comprising 23,173 occurrences, followed by the AT/AT motif with 20,934 instances (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The AC/GT motif was also relatively common, with 14,844 repeats identified. In contrast, the CG/CG motif was extremely rare, detected only 125 times, accounting for less than 0.25% of all dinucleotide SSRs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analysis revealed 8,876 trinucleotide SSRs within the genome assembly. Of these, the AAG/CTT motif was the most dominant, occurring 3,121 times and representing a notable share of the total trinucleotide SSRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This was followed by CCG/CGG with 1,074 repeats, AAT/ATT with 1,127 repeats, and ACT/ATG with 619 repeats. Other relatively frequent motifs included AGG/CCT (682), AGT/ATC (678), AGC/CGT (399), ACG/CTG (459), AAC/GTT (477), and ACC/GGT (240). These data reflect a moderate level of diversity among trinucleotide SSRs, with a notable bias toward A/T-rich motifs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe draft genome assembly of \u003cem\u003eTyphonium flagelliforme\u003c/em\u003e presented in this study provides a foundational resource for future genetic and genomic research in this underexplored medicinal plant species. The assembly quality, supported by multiple evaluation metrics such as N50, BUSCO completeness, and k-mer spectrum analysis, reflects a high level of sequence integrity and representation. The predominance of long contigs and the low proportion of missing k-mers, as revealed by Merqury analysis, indicate minimal sequencing errors and strong coverage across the genome.\u003c/p\u003e\u003cp\u003eThe results reflect a high-quality preliminary assembly, with the majority of contigs exceeding 1,000 base pairs an essential benchmark, as shorter contigs are typically less informative and may impede downstream genomic analyses (Seitz and Nieselt \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The presence of a maximum contig length exceeding 218 kb suggests successful recovery of extended genomic regions that may encompass complete gene structures, including regulatory elements and intergenic sequences (Gurevich et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although chromosome-level contiguity has not yet been achieved, this outcome is notable for a de novo assembly of a plant genome, which often presents challenges due to its complexity and repeat content. The moderate N50 and high L50 values reflect a degree of fragmentation in the assembly, indicating that future efforts could aim to improve contiguity through scaffolding technologies or long-read sequencing. The auN metric, which integrates contig lengths more comprehensively than N50, further supports the observation of moderate contiguity while providing a robust measure for comparing assembly quality across tools and datasets (Bradnam et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The GC content of 41.03% falls within the normal range for plant genomes and suggests an absence of major biases or contamination. GC content remains a key factor in assessing sequencing and assembly performance, given its influence on DNA stability, sequencing efficiency, and potential representation biases (Benjamini and Speed \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Any significant deviation from expected GC content might indicate technical artifacts or foreign sequence contamination, which was not evident in this dataset.\u003c/p\u003e\u003cp\u003eIn this study, a whole-genome shotgun sequencing strategy was employed for \u003cem\u003eT. flagelliforme\u003c/em\u003e, utilizing exclusively paired-end read libraries for de novo assembly. This approach yielded scaffold N50 values in the moderate range, a result largely attributable to the absence of mate-pair libraries, which are known to enhance scaffold length and assembly contiguity. Paired-end libraries alone are often insufficient to resolve complex repetitive regions in plant genomes, which typically have a high proportion of repetitive sequences (Liao et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Previous studies have demonstrated that the incorporation of mate-pair libraries can significantly improve assembly metrics, with N50 values increasing by one to two orders of magnitude (Belova et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite this limitation, the scaffold N50 in our \u003cem\u003eT. flagelliforme\u003c/em\u003e assembly exceeds 20 kb, comparable to those reported for \u003cem\u003eCannabis sativa\u003c/em\u003e (Wei et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and \u003cem\u003eTapiscia sinensis\u003c/em\u003e (Zhao et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, the scaffold N50 achieved here is consistent with those obtained in other plant genomes assembled without mate-pair data (Belova et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), suggesting that a substantial portion of the non-repetitive genomic regions has been successfully captured. To improve assembly quality in applications such as comparative genomics, whole genome duplication analysis, or evolutionary studies in rodent tubers some future efforts may benefit from the integration of additional long-range information, such as mate-pair libraries, optical or physical maps, or cytogenetic data. Nevertheless, the current assembly provides a valuable resource for characterizing key genomic features of \u003cem\u003eT. flagelliforme\u003c/em\u003e, including its repeat landscape and gene content.\u003c/p\u003e\u003cp\u003eThe k-mer spectrum analysis using Merqury provides critical insights into the quality and completeness of the \u003cem\u003eT. flagelliforme\u003c/em\u003e genome assembly. The unimodal peak observed at a k-mer multiplicity of approximately 6\u0026ndash;7 is characteristic of a diploid genome and indicates a high representation of homozygous regions. Such patterns are typical of well-assembled diploid plant genomes and support the structural integrity of the current assembly (Rhie et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zimin et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A minimal, gray-shaded region, representing k-mers present in the raw reads but absent in the assembly, suggests a low sequencing error rate and high completeness of the assembly. This is critical, as an overrepresentation of such \"error\" k-mers would indicate substantial sequence loss or misassembly (Vurture et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The high degree of overlap between k-mers in the raw reads and those in the assembly confirms successful incorporation of most of the genomic information and supports a high base-level accuracy (QV) (Rhie et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jayakumar and Sakakibara, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The small fraction of high-frequency k-mers (multiplicity\u0026thinsp;\u0026gt;\u0026thinsp;4), typically corresponding to repetitive or multi-copy elements, suggests that repetitive regions were adequately resolved. Though short-read data can pose limitations in assembling highly repetitive regions (Michael and VanBuren, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the smooth tail and narrow error peak observed here indicate that the assembler managed such sequences effectively. Merqury\u0026rsquo;s k-mer-based approach is particularly advantageous for non-model plant species like \u003cem\u003eT. flagelliforme\u003c/em\u003e, where reference genomes are not available. It allows robust, reference-free assessment of genome assembly metrics including completeness, base accuracy, and phasing quality (Rhie et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This analysis confirms that the assembly is of sufficient quality to serve as a foundation for downstream applications such as gene annotation, SSR marker mining, and comparative genomics across Araceae.\u003c/p\u003e\u003cp\u003eThe high proportion of interspersed repeats (64.41%) in this genome is characteristic of many plant genomes, especially those with large genome sizes and complex evolutionary histories. The predominance of retrotransposons, particularly the Gypsy and Copia LTR families, is a common feature in higher plants and has been linked to genome expansion and adaptation (Ou et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Su et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Gypsy elements, often found near centromeric regions, have been shown to play a role in chromatin structure and genome stability, whereas Copia elements are more frequently associated with euchromatic and gene-rich regions. The considerable presence of LINEs, especially L1/CIN4 elements, supports findings from other angiosperms, where these elements contribute to structural variations and influence gene expression through insertional mutagenesis (Makarevitch et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The near absence of certain LINE families (e.g. L2/CR1/Rex and R1/LOA/Jockey) may reflect lineage-specific loss or silencing mechanisms in this species. The identification of DNA transposons, particularly from the hobo-Activator and MULE-MuDR families, is consistent with their widespread occurrence in plant genomes, where they are often implicated in gene duplication and regulatory evolution (Chuong et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The large fraction of unclassified repeats (40.15%) highlights the potential presence of novel or species-specific transposable elements that are not captured in current reference databases, underscoring the need for continued annotation improvement and repeat library curation (Navarro-Mu\u0026ntilde;oz et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe microsatellite density in the \u003cem\u003eT. flagelliforme\u003c/em\u003e genome, calculated at 345,202 SSR/Mb, offers a useful metric for assessing the abundance of these repetitive elements within the genome. This frequency is particularly informative when compared across species, as variation in SSR density may reflect underlying differences in genome organization, mutation rates, or levels of genetic diversity among Typhonium species (Srivastava et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fischer et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The comparatively lower number of SSRs observed in \u003cem\u003eT. flagelliforme\u003c/em\u003e may be attributed to species-specific evolutionary processes, such as selective constraints or the effects of genetic drift, which could have contributed to the gradual loss or suppression of these repetitive elements over time (Bagshaw, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Understanding the evolutionary forces and genomic contexts that shape microsatellite abundance will be important for elucidating the genetic diversity, adaptability, and evolutionary history of \u003cem\u003eT. flagelliforme\u003c/em\u003e. Further comparative studies across Araceae genomes may help clarify these patterns.\u003c/p\u003e\u003cp\u003eThe predominance of dinucleotide repeats in the \u003cem\u003eT. flagelliforme\u003c/em\u003e genome is consistent with findings in other plant species, where AT- and AG-rich motifs frequently occur in intergenic and intronic regions (Chen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Their abundance is likely driven by replication slippage and relaxed selective pressures in non-coding regions. Among these, the AG/CT and AT/AT motifs were the most common, reinforcing their potential as informative and polymorphic molecular markers (Yadav et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although mononucleotide repeats are also abundant, particularly poly-A/T stretches, they are often excluded from marker development due to sequencing errors and homopolymer-related artifacts (Kumar et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, trinucleotide SSRs, which represented 9.09% of total SSRs, are more stable and frequently located within coding regions. Their preservation of the reading frame makes them suitable for gene-associated marker development (Yadav et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among trinucleotide motifs, AAG/CTT was the most frequent\u0026mdash;consistent with its common presence in untranslated and regulatory regions of plant genomes (Kumar et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The high occurrence of CCG/CGG motifs suggests possible roles in gene regulation due to their GC-rich content and localization in coding sequences (Ali et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, motifs such as AAT/ATT and ACT/ATG, while less frequent, are valued for their high mutation rates, making them useful for genetic diversity studies (Singh et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The lower frequency of tetra-, penta-, and hexanucleotide repeats aligns with previous SSR profiling studies in plants, which report that longer motif units are less common and likely subject to stronger purifying selection (Basak et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ali et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The motif-specific distribution observed in this study supports the utility of di- and trinucleotide SSRs for marker-assisted selection, genetic mapping, and diversity analysis in \u003cem\u003eT. flagelliforme\u003c/em\u003e and related species.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents a comprehensive draft genome assembly of \u003cem\u003eTyphonium flagelliforme\u003c/em\u003e, generated through a whole-genome shotgun approach using paired-end reads and SPAdes assembler. The assembly yielded a total length of ~\u0026thinsp;714.7 Mb with moderate contiguity (N50\u0026thinsp;=\u0026thinsp;3.9 kb), and a GC content of 41.03%, consistent with other eukaryotic plant genomes. Despite the absence of mate-pair libraries, the assembly successfully captured large portions of non-repetitive regions and provides a valuable foundation for further genomic investigation. Repeat analysis revealed that interspersed repeats accounted for 64.41% of the genome, with LTR retrotransposons, especially Gypsy and Copia elements, dominating the repetitive landscape. A substantial number of unclassified elements also suggest the presence of lineage-specific or novel transposable elements. The genome exhibited a microsatellite density of 345,202 SSRs/Mb, with dinucleotide repeats, notably AG/CT and AT/AT motifs, representing the most abundant SSR categories. Trinucleotide SSRs, especially AAG/CTT and CCG/CGG, were also prominent and offer strong potential for gene-associated marker development. These findings suggest that the assembly sufficiently captures both the genic and intergenic regions, enabling downstream analyses such as gene prediction, marker development, and comparative genomics. Given the limited genomic resources currently available for the genus \u003cem\u003eTyphonium\u003c/em\u003e, this draft genome provides a valuable platform for understanding species-specific traits and for advancing conservation and breeding efforts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by Join Collaboration Inhouse Biological and Environmental Research Organization BRIN Program with number B-1797/III.5/PR.03.06/6/2024 and B1721/III.5/PR.03.06/7/2025 and RIIM Collaborative Platform Biology Biomolecular Structure Biodiversity Batch I number B-2657/III.5/FR.06.00/6/2024. The authors would like to thank the two anonymous reviewers for their insightful feedback on an earlier draft of this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, DP, So and TT; methodology, DP and So; software, DP; validation, DP, TT; formal analysis, DP and SZ; investigation, DP; resources, Su and GR; data curation, DP, and SZ; writing\u0026mdash;original draft preparation, DP, TT, and So; writing\u0026mdash;review and editing, DP, So, TT and SJS; visualization, DP, and SJS; supervision, So, TT, AK and WBS; project administration, DP; funding acquisition, DP. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThanks to Applied Botany Research Center and Talent Management BRIN for funding DBR research, laboratories and scholarships.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe whole genome sequencing data was deposited in the Short Read Archive (SRA) database under accession number PRJNA1141733. And will be Release on 2026-09-01 or if the paper has been accepted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlhakami H, Mirebrahim H, Lonardi S (2017) A comparative evaluation of genome assembly reconciliation tools. Genome Biol 18: 93. https://doi.org/10.1186/s13059-017-1213-3 \u003c/li\u003e\n\u003cli\u003eAli F, Hussain A, Khan MA et al (2019) Genome-wide SSR discovery and population structure analysis in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). 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Genome Research 27(5):787\u0026ndash;792. https://doi.org/10.1101/gr.213405.116\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":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Assembly, Genomics, Illumina, Microsatellite, Rodent tubber, SSR data mining","lastPublishedDoi":"10.21203/rs.3.rs-7296811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7296811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eTyphonium flagelliforme\u003c/em\u003e, a medicinal plant endemic to Indonesia and belonging to the Araceae family, has garnered significant attention due to its potential anticancer properties. Given its therapeutic relevance, this species represents a promising genetic resource for future plant breeding initiatives. In the present study, whole genome sequencing (WGS) of \u003cem\u003eT. flagelliforme\u003c/em\u003e was performed using the Illumina NextSeq 2000 platform. Sequencing was conducted with a paired-end 150 bp (PE150) approach, yielding approximately 112 GB of raw data. The estimated genome size was 714.70 Mb, with an assembly contig N50 of 3,971 bp and a BUSCO completeness score of 76.08%. Also, we identified 64.41% repetitive DNA from the genome assembly, in which retroelements occupied 21.40% of the total genome. This first \u003cem\u003eT. flagelliforme\u003c/em\u003e genome is expected to contribute to a better understanding of its genetics for molecular breeding programs, development of medicinal plant-based biotechnology, and sustainable conservation of rodent tubber germplasm.\u003c/p\u003e","manuscriptTitle":"Draft genome and SSR data mining of Typhonium flagelliforme, an anti-cancer medicinal plant","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 15:36:22","doi":"10.21203/rs.3.rs-7296811/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-18T02:51:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T02:49:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T07:58:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145548118633312903017554747972769601802","date":"2025-08-08T11:44:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196209227243635053738031758239567265664","date":"2025-08-08T06:33:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232557584346963648082413387279570092284","date":"2025-08-08T05:09:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T13:13:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-07T08:56:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T08:55:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2025-08-05T06:01:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"63157d18-8516-420c-a0b0-39b50f2eee5b","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:02:43+00:00","versionOfRecord":{"articleIdentity":"rs-7296811","link":"https://doi.org/10.1007/s10722-025-02661-z","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2025-12-15 15:58:16","publishedOnDateReadable":"December 15th, 2025"},"versionCreatedAt":"2025-08-12 15:36:22","video":"","vorDoi":"10.1007/s10722-025-02661-z","vorDoiUrl":"https://doi.org/10.1007/s10722-025-02661-z","workflowStages":[]},"version":"v1","identity":"rs-7296811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7296811","identity":"rs-7296811","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}