Mitochondrial Genome Assembly of the Peruvian Paso Horse Through PacBio Long-Read Sequencing

Mitochondrial Genome Assembly of the Peruvian Paso Horse Through PacBio Long-Read Sequencing | 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 Article Mitochondrial Genome Assembly of the Peruvian Paso Horse Through PacBio Long-Read Sequencing Carla Saldaña, Santiago Justo, Luis Murga, Héctor Vásquez, Jorge Maicelo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6515928/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract The Peruvian Paso Horse (PPH) is a unique breed recognized for its distinctive gait and cultural significance. Despite its importance, its mitochondrial genome (mitogenome) remains largely uncharacterized. This study presents the first complete mitogenome of the PPH, which was assembled using PacBio long-read sequencing. The resulting high-quality circular mitogenome (16,617 bp) contains 13 protein-coding genes (PCGs), 22 tRNAs, 2 rRNAs, and a control region (D-loop). The Nucleotide composition was 24.44% A, 25.13% T, 25.62% C, and 24.81% G, consistent with other equine mitogenomes. Codon usage analysis revealed a bias toward CUA(Leu), AUC(Ile), and AUA(Ile), suggesting optimization for mitochondrial translation. The tRNA genes exhibited a typical cloverleaf secondary structure, except for tRNA Ser . The D-loop (1,152 bp) was positioned between tRNA Pro and tRNA Phe , serving as the primary site for mitochondrial replication and transcription. Phylogenetic analysis placed the PPH within a European breed clade, closely related to Westphalian and Maremmano and two individuals from Germany and Serbia. with potential connections to Asian lineages. These findings highlight the effectiveness of long-read sequencing for resolving complex mitochondrial regions, providing a valuable resource for equine genetics, breed conservation, and evolutionary studies. Biological sciences/Biotechnology/Animal biotechnology Biological sciences/Biotechnology/Genomics mitogenome NGS bioinformatics phylogenomic equine Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Since horses were first brought to America by Spanish settlers, many local breeds have taken root in the New World. These breeds are the result of the mix of different horses that came from Europe. Over 400 years of natural selection, the creole horse in various parts of South America has adapted to its surroundings [ 1 ]. Breeders have focused on improving traits like looks, stamina, strength, and gait in local horses. The Peruvian Paso Horse (PPH) is a breed native to Peru [ 2 ] recognized as part of Peru's cultural heritage [ 3 ]. The PPH is renowned for its distinctive gait, known as the "paso llano." This natural four-beat lateral movement allows each hoof to touch the ground independently and at regular intervals, providing an exceptionally smooth ride without the vertical movement typical of other gaits. Additionally, the Peruvian Paso Horse exhibits a unique forelimb motion called "termino," characterized by an elegant lateral rotation of the front legs [ 4 ]. Previous studies have looked at its reproduction [ 5 , 6 ] and heritability [ 7 , 8 , 9 ]. However, research on the Peruvian Paso horse’s genomics is still scarce. Genetically characterizing populations and understanding the structure of the horse genome is a valuable tool for conserving breeds, as well as for guiding future reproductive strategies and management plans [ 10 ]. Mitochondrial genomes (mitogenomes) of animals are generally 14–20 kb long circular molecules and it´s higher mutation rate compared to nuclear DNA, combined with the varying degrees of conservation across its regions, enables the identification of different levels of phylogenetic relationships among species [ 11 ]. In horses mtDNA is routinely used at both the population and species levels in studies of phylogeography [ 12 , 13 ], phylogenetics [ 14 , 15 , 16 , 17 , 18 ] and paleogenomics [ 19 ]. However, mtDNA genome includes specific complex regions, particularly repetitive sequences and segmental duplications, sometimes located within the control region (CR), which have traditionally posed difficulties in resolution. In theory, when repeat sequences exceed the length of sequencing reads, assembly is restricted by the limits of these repetitive elements [ 20 , 21 ]. Next-generation sequencing (NGS) approaches such as sequencing by synthesis (e.g., Illumina sequencers, San Diego, CA, USA) and semiconductor sequencing (e.g., Ion Torrent systems from Thermo Fisher Scientific) have been widely used for omic studies. However, they are constrained by the limited length of the DNA fragments they can process, which complicates the accurate assembly of sequencing reads. New whole-genome sequencing (WGS) strategies based on single-molecule, long-read sequencing technologies have successfully improved the quality of genome assemblies, particularly in repetitive regions [ 22 , 23 ]. With the ability to generate reads of at least 10–20 kbp in length, single-molecule DNA sequencing has the potential to capture entire mtDNA genomes in a single read. In this study, we present, the first mitochondrial genome of the Peruvian Paso Horse, sequenced using PacBio long-read technology. Our study contributes to a deeper understanding of the maternal lineage of the Peruvian Paso Horse, providing valuable insights for phylogenetic analyses, breed conservation, future genomic research and conservation programs. Results 2.1. Genome organization The mitochondrial genome was sequenced using PacBio HiFi technology yielding a total of 14,549,851 HiFi reads and 239.18 Gbp of data, with an average read length of ~ 16.4 kb and a median quality score of Q35. The average coverage was approximately 14.4 million, enabling highly accurate de novo assembly and variant analysis. The assembled mtDNA has a length of 16,617 bp. This genome comprises 13 protein-coding genes, 22 tRNA genes, and 2 rRNA genes and one control region (D-loop) (Table 1 , Fig. 1 ). The heavy (H) strand harbored the majority of genes, (12 protein-coding genes and 14 tRNA), while the light (L) strand housed ND6 and eight tRNAs (Gln, Ala, Asn, Cys, Tyr, Ser, Glu, and Pro). The elemental composition of this genome was distributed as follows: 24.44% Adenine (A), 25.13% Thymine (T), 25.62% Cytosine (C), and 24.81% Guanine (G). The complete mitochondrial genome sequence has been deposited in the GenBank database under accession number PQ663616. The corresponding BioProject and BioSample identifiers are PRJNA1149496, SAMN43249583 respectively. Table 1 Gene organization of the mitochondrial genome of Peruvian Paso Horse. Gene Nucleotide Positions Size Strand* Codon tRN Phe 1–70 70 H TTC rrnS 71-1046 976 H tRNA Val 1047–1113 67 H GTA rrnL 1114–2693 1580 H tRNA Leu2 2694–2768 75 H TTA ND1 2771–3727 957 H tRNA Ile 3727–3795 69 H ATC tRNA Gln 3793–3865 73 L CAA tRNA Met 3868–3936 69 H ATG ND2 3937–4977 1042 H tRNA Trp 4976–5045 70 H TGG tRNA Ala 5051–5119 69 L GCC tRNA Asn 5132–5187 56 L AAC Rep_origin 5188–5225 38 H tRNA Cys 5226–5291 66 L TGC tRNA Tyr 5292–5358 67 L TAC COX1 5360–6904 1545 H tRNA Ser 6902–6970 69 L TCA tRNA Asp 6979–7045 67 H GAC COX2 7046–7729 684 H tRNA Lys 7733–7800 68 H AAA ATP8 7802–8005 204 H ATP6 7963–8643 681 H COX3 8643–9425 783 H tRNA Gly 9427–9495 69 H GGA ND3 9496–9852 357 H tRNA Arg 9843–9911 69 H CGA ND4L 9913–10209 297 H ND4 10203–11579 1377 H tRNA His 11581–11649 69 H CAC tRNA Ser2 11650–11709 60 H AGC tRNA Leu 11711–11780 70 H CTA ND5 11787–13601 1815 H ND6 13585–14112 528 L tRNA Glu 14113–14181 69 L GAA CytB 14186–15325 1140 H tRNA Thr 15326–15398 73 H ACA tRNA Pro 15400–15465 66 L CCA D-loop 15466–16617 1152 H 2.2. Protein Coding Genes (PCGs) and Codon Usage Ten of the protein-coding genes ( ND1, COX1, COX2, ATP8, ATP6, COX3, ND4, ND4L, ND5, ND6 , and CYTB ) use ATG as the start codon, while two genes ( ND2 and ND3 ) initiate with ATA. Regarding stop codons, four genes ( ND1, ND2, ATP8 and ND3 ) have the complete TAG stop codon, while COX1, COX2, ATP6, ND4L, ND5 and ND6 terminates with TAA and CYTB with AGA. Additionally, ND4 exhibit an incomplete TA(A) stop codon, whereas COX3 has a truncated T(AA) stop codon. PCG also exhibit an AT bias, with AT content ranging from 54.6% in CytB to 64.2% in ATP8 . Additionally, the length of the PCGs varies significantly, from 204 bp in ATP8 to 1815bp in NAD5 (Table 2 ). The amino acid (AA) codon usages were assessed by calculating relative synonymous codon usage (RSCU) values in 13 PCGs. A total of 3645 codons were encoded by 13 PCGs, and the most frequently used codons were CUA (7.81%), AUC (5.6%), and AUA (5.13%) (Fig. 2 A, Supplementary Table S1 ). The analysis of the AA components and their codon usage revealed that the codons encoding Leu(CUA) represented over 100 codons per thousand (CDpT), AUA (isoleucine), AUC (isoleucine), AUA (isoleucine), and UCC (Phenilalanine) were the most frequently used codons (Fig. 2 B). Table 2 Features of the protein-coding genes identified in the mitochondrial genome of Peruvian Paso Horse. Gene Gene Length (bp) A + T Content (%) Start/Stop Cod on Protein Length (aa) ND1 957 56.6 ATG/TAG 318 ND2 1041 61.5 ATA/TAG 346 COX1 1545 56.3 ATG/TAA 514 COX2 684 57.7 ATG/TAA 227 ATP8 204 64.2 ATG/TAG 67 ATP6 681 56.7 ATG/TAA 226 COX3 783 54.7 ATG/T-- 261 ND3 357 58.5 ATA/TAG 118 ND4L 297 59.9 ATG/TAA 98 ND4 1377 58.3 ATG/TA- 459 ND5 1815 57.2 ATG/TAA 604 ND6 528 60.4 ATG/TAA 175 CytB 1140 54.6 ATG/AGA 379 2.3. rRNA, tRNA, and Non-Coding Genomic Regions The two rRNA genes (12S and 16S) had a combined length of 2,556 bp and were flanked by tRNA Phe and tRNA Leu2 (Table 1 , Fig. 1 ). A total of 22 tRNA genes were identified, spanning 1,500 bp, with lengths ranging from 56 bp (tRNA Asn ) to 75 bp (tRNA Leu2 ) (Table 1 ). The heavy (H) strand encoded 14 tRNA genes, while the light (L) strand encoded eight. All tRNA genes exhibited a typical cloverleaf secondary structure, except for tRNA Ser (Fig. 3 ). Non-coding regions included the origin of replication, intergenic spacers, and the control region. The origin of replication was 38 bp long and located between tRNA Asn and tRNA Cys. Additionally, the control region (D-loop) measured 1152 bp and was flanked by tRNA Pro and tRNA Phe (Table 1 , Fig. 1 ). 2.3. Phylogenetic Analysis Our phylogenetic trees resolved two main clades (C1 and C2) and a grade topology. Clade 2 (with > 70% support). Figure 4 shows the ML tree grouping the horses based on their regions of origin. This tree shows that the PPH is most related to south and central European representatives. Breeds included in the well-supported clade of PPH are Maremmano, Westphalian, Holsteiner, Shagya - Arab and two individuals from Germany and Serbia. Moreover, the clade also comprises individuals from the Thoroughbred breed originating in Asia and Oceania, along with Kinsky horses and other representatives from Central Europe (Fig. 4 ). On the other hand, no clear pattern was observed linking horse evolutionary relationships to either continent of origin (Supplementary Fig. S1 ). Discussions The Peruvian Paso Horse is a breed of significant cultural and genetic value, renowned for its distinctive gait and well-defined lineage. The assembly and annotation of the Peruvian Paso Horse (PPH) mitochondrial genome using PacBio Long-Read technology yielded a high-quality circular contig of 16,617 bp, whose organization aligns with the classical structure reported in other equine mitogenome studies [ 24 ]. This genetic arrangement, with the majority of genes located on the heavy (H) strand and a smaller number on the light (L) strand, reflects the conserved pattern in Equus caballus mitogenomes and suggests stability in genomic organization throughout evolution [ 25 , 26 ].The nearly balanced nucleotide composition—24.44% Adenine, 25.13% Thymine, 25.62% Cytosine, and 24.81% Guanine—indicates a stable profile similar to that observed in other studies of equine mitochondria, which may be associated with a low mutation rate and efficient maintenance of mitochondrial genome integrity [ 27 ]. The correct assembly circularization and annotation of the mitogenome, demonstrating its robustness and efficiency for obtaining complete mitochondrial sequences from long-read data. This approach offers a significant advantage over short-read methods, particularly for resolving complex regions such as the D-loop and repetitive sequences [ 21 ]. The analysis of protein-coding genes (PCGs) and codon usage in the mitochondrial genome of the Peruvian Paso Horse (CPP) reveals implications about evolution and translational efficiency in equine mitochondria. Codon usage bias (CUB) is influenced by multiple factors such as mutation pressure, natural selection, GC content, nucleotide skewness, gene expression levels, protein secondary structure, biochemical properties, transcription, and open reading frame length [ 28 ]. However, the main drivers of CUB are natural selection and mutation pressure acting on the background nucleotide composition [ 29 ]. Consequently, certain synonymous codons are favored over others, which in turn can promote adaptive evolution [ 30 ]. The start codon ATG and stop codon TAA were the most abundant in the PPH mitocondrial genome, like in previous studies [ 31 , 32 , 33 ]. ND4 exhibits an incomplete stop codon (TA[A]), and COX3 shows a truncated codon (T[AA]). This variability in start and stop codons is consistent with other other vertebrates [ 31 , 34 ] and equines mitogenomes may be related to post-transcriptional mechanisms that complete incomplete codons during mRNA maturation [ 35 , 36 ]. These incomplete stop codons are completed through polyadenylation at the 3' end of the mRNA, thereby converting them into functional stop codons [ 37 , 38 ]. The analysis of codon usage, based on relative synonymous codon usage (RSCU) values, revealed that the most frequent being CUA (7.81%), AUC (5.6%), and AUA (5.13%). In particular, the high frequency of the CUA codon for leucine, along with the elevated utilization of codons for isoleucine (AUC and AUA), suggests that there is an optimization in codon usage that could reflect the relative abundance of the corresponding tRNAs and an adaptation to maximize the efficiency of mitochondrial protein synthesis. This pattern of codon usage bias is fundamental for understanding the evolution of the translational machinery in mitochondria [ 39 , 40 ]. The ribosomal RNA genes (12S and 16S) in the PPH mitochondrial are flanked by tRNA Phe and tRNA Leu2 . This organization is consistent with the mitochondrial genomes of other mammals, where rRNA genes are highly conserved and play a crucial role in mitochondrial protein synthesis as integral components of the small (12S) and large (16S) ribosomal subunits [ 41 ]. This conserved structure is essential for maintaining translational efficiency in mitochondria, as previously reported in mammalian species such as Rattus norvegicus [ 42 ]. Most tRNAs exhibit a typical cloverleaf secondary structure, except for tRNA Ser , which lacks the D-arm, a common feature in mammalian mitogenomes [ 43 ]. This variation in secondary structure may influence the efficiency of aminoacylation and translation [ 44 ]. The non-coding regions include the origin of replication, intergenic spacers, and the control region (D-loop). Overall, the organization and characteristics of the non-coding regions in the PPH mitochondrial genome exhibit a high degree of conservation with other mammalian mitogenomes, indicating strong evolutionary constraints to maintain essential functions in gene expression and mitochondrial DNA replication [ 41 ]. This structural conservation highlights the importance of mitochondrial genome stability in equine evolution and suggests potential applications in phylogenetics and breed conservation. The phylogenetic analysis based on maximum likelihood identified three principal clades that provide valuable insights into the maternal lineage of the PPH and its evolutionary relationships with other equine breeds. Traditionally, it has been held —based on historical documents— that the Peruvian Paso Horse descends from horses introduced to Peru by the Spanish in the 16th century, specifically from Andalusian and Barb breeds. The PPH may have evolved under particular Peruvian ecological conditions and management practices, giving rise to the current breed [ 45 ]. In our phylogenomic tree, the PPH was placed within cluster 2, displaying a close genetic relationship with individuals from Southern European breed (Maremmano) and five Central European breeds: Westphalian, German, Serbian, Holsteiner, and Shagya-Arab. These findings suggest a shared genetic ancestry, likely of Mediterranean origin, which aligns with historical records concerning the development of the breed [ 46 ]. Moreover, this clade also included individuals from Asia and other representatives from Central Europe. This finding is consistent with previous phylogenetic studies that have detected genetic signals from ancient Asian horse lineages in various modern breeds [ 15 , 16 ]. The presence of such Asian mitochondrial haplotypes may be attributed to historical horse trade routes or introgression events that occurred before or during early colonial expansion [ 17 ]. Interestingly, the accessions reported for the Andalusian breed, (JN398430 and JN398443.1) was more closely related to individuals from Asian (Chinese and Mongolian), Central European (Polish, Maremmano, and Hungarian Coldblood), Middle Eastern (Iranian), and Northern Europe breeds (Exmoor Pony) rather than to the PPH, suggesting a more complex pattern of ancestry and gene flow than previously assumed. The presence of European equine mitochondrial haplotypes in South American breeds has been reported in previous studies [ 12 , 17 ], further supporting this genetic connection. Similarly, results consistent with our findings and supporting the same dominant topology can be obtained using both control region (D-loop) sequences and complete mitochondrial genome data [ 43 , 12 , 17 ]. Furthermore, no clear pattern was observed linking horse evolutionary relationships to either breed classification or geographic origin. The application of genomic tools has profoundly reshaped our understanding of domestication, ancestry, and the diversification of domesticated species, spanning both crops and animals. High-throughput sequencing technologies and advanced phylogenomic methods have clarified the complex origins of staple crops [ 47 , 48 , 49 ]. In this context, our mitogenomic analysis of the Peruvian Paso Horse (PPH) contributes to a broader understanding of breed origin, lineage diversification, and conservation priorities. The integration of long-read sequencing and mitochondrial phylogenetics provides a high-resolution view into the maternal ancestry of this culturally iconic breed, echoing the advances achieved in crop evolution studies. The complete mitochondrial genome generated here represents a valuable genomic resource for future research in equine genetics, the conservation of native breeds, and mitochondrial evolutionary studies. Materials and Methods Sample collected and Genomic DNA Sequencing A representative individual of the PPH breed names “Amunet” (Register Number YN-19315 in the National Association of Peruvian Paso Horse Breeders, born in 2013) residing at the Experimental Station of Luya (Amazonas, Peru) of UNTRM, was selected for sampling. Blood was collected using a vacutainer containing EDTA as an anticoagulant and was immediately transport to the laboratory. The experimental procedure was approved by the Institutional Research Ethics Committee of UNTRM in concordance with Peruvian National Law No. 30407: “Animal Protection and Welfare”. High molecular weight DNA extraction, PacBio HiFi Library and SMRTcell PacBio sequencing was prepared in Biotechnology Center of the University of Wisconsin-Madison, USA. Briefly, Nanobind CBB DNA Kit (PacBio) was used to extract high molecular weight DNA. The quality of the extracted DNA was measured on a NanoDrop™ One instrument (ThermoFisher Scientific). Quantification of the extracted DNA was measured using the Qubit™ dsDNA High Sensitivity kit (ThermoFisher Scientific). A Pacific Biosciences HiFi library was prepared according to PN 102-166-600 Version 04 (Pacific Biosciences). Library quality was assessed using the Agilent FemtoPulse System. Library was quantified using the Qubit™ dsDNA High Sensitivity kit. The library was sequenced on a PacBio Revio at GTAC@MGI at Washington University in St Louis. Assembly and annotation The PacBio subreads for the mitochondrial genome were filtered out with pbcommand v2.4.4 using de argument pbccs. MitoHiFi v3.2.3 program [ 50 ] was used for assembly, circularization and annotation. As a reference genome we used EquCab3 (NCBI: NC_001640.1) of Equus caballus [ 24 ]. Graphical map of the mitogenome was generated by OGDRAW webserver ( https://chlorobox.mpimp-golm.mpg.de/OGDraw.html , accessed 9 December 2024). Codon Usage and tRNA Analysis A codon usage bias analysis was done using python script from https://github.com/rhondene/Codon-Usage-in To predict the secondary structure of each tRNA, we used the tRNAscan-SE website server ( http://lowelab.ucsc.edu/tRNAscan-SE/ ) Phylogenetic Analysis. Phylogenetic analyses were conducted using all available mitochondrial genomes (n = 682) assigned to taxid 9796, with lengths ranging from 16,000 to 18,000 bp, retrieved from the NCBI database (Supplementary Table S2). As an outgroup, we included Equus zebra, a species of the genus Equus. Genome alignments were performed with MAFFT v7.205b [ 51 ] and a maximum likelihood (ML) tree was inferred under the GTR + GAMMA model of nucleotide substitution. The best-scoring ML tree was selected, and node support was assessed using 1,000 nonparametric bootstrap replicates implemented in RAxML v8.2.11 [ 52 ]. Additionally, genetic distances were estimated based on Prevosti’s coefficient [ 53 ] and a neighbor-joining dendrogram was generated using the poppr v1.1.4 R package [ 54 ]. The grouping factor of the 682 individuals was: breed, region and continent. This information was obtained from NCBI and/or publications where those individuals were reported. Declarations Data availability The DNA sequence generated and analyzed during the current study are available in the NCBI repository under BioProject PRJNA1149496 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1149496). Other data from the study are available from the corresponding authors. Acknowledgements We thank Elías Carranza and Maribel Vásquez for their valuable assistance in the care of the mare Amunet and for their support during the blood sample collection. We also thank Ángel Pilco and Mario Ernesto for their help with data submission. Our gratitude extends to the National Association of Peruvian Paso Horse Breeders (ANCPCPP) for their continued support and collaboration. In addition, we are grateful to Stefany Lobato and Adriana Díaz for their assistance in editing the figures included in this manuscript, and to Maili Muñoz for her support with the logistical activities of the project. Also, the authors acknowledge the High-Performance Computational Cluster of the National University Toribio Rodríguez de Mendoza of Amazonas (UNTRM–DATA SCIENCE) and the Bioinformatics High-Performance Computing Server of the Universidad Nacional Agraria La Molina for providing computational resources for data analysis. The authors thank the University of Wisconsin – Madison Biotechnology Center’s DNA Sequencing Facility (Research Resource Identifier – RRID:SCR_017759) for extracting HMW DNA and generating PacBio libraries. Finally, we thank the Genome Technology Access Center at the McDonnell Genome Institute at Washington University School of Medicine (Research Resource Identifier - RRID:SCR_001030) for help with genomics services. The Center is partially supported by NCI Cancer Center Support Grant #P30 CA91842 to the Siteman Cancer Center from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. This publication is solely the responsibility of the authors and does not necessarily represent the official view of NCR R or NIH.” Author contributions C.L.S.: designed the study, sample collection, sample processing, analyzed the data, writing-original draft, writing-review & editing, S.J.: designed the study, analyzed the data, writing-original draft, writing-review & editing, L.M.: Sample collection, sample processing, supervision, writing-original draft, Writing-review & editing, H.V.V: supervision, writing-original draft, writing-review & editing, J.L.M: supervision, funding acquisition, writing-original draft, writing-review & editing, C.I.A.: designed the study, sample processing, analyzed the data, supervision, writing-original draft, writing-review & editing, W.B: designed the study, sample collection, analyzed the data, writing-original draft, writing-review & editing. All authors discussed the methodologies, results, and read and approved the manuscript. Funding We thank the proyect “Creación del servicio de investigación y enseñanza en equinos en la Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas” (Grant ID: CUI N° 2314510) of the Peruvian Goverment and "Tesis de Pregrado y Postgrado en Ciencia, Tecnologia e Innovacion Tecnologica" (Grant ID: PE501085176-2023) of the Programa Nacional de Investigacion Científica y Estudios Avanzados, PROCIENCIA. References Kelly, L., Postiglioni, A., De Andrés, D. F., Vega-Plá, J. L., Gagliardi, R., Biagetti, R., & Franco, J. Genetic characterisation of the Uruguaayan Creole horse and analysis of relationships among horse breeds. Res. Vet. Sci. 72(1), 69–73. (2002). Gonzales R., Li R., Kemper G., Del Carpio C., Ruiz E. An algorithm for estimating the variation of the joint angles of the limbs of Peruvian Paso horse. Proc. IEEE 25th Int. Conf. Electron. Electr. Eng. Comput. (INTERCON) (2018). Ministerio de Comercio Exterior y Turismo del Perú (MINCETUR). 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Bratic A., Clemente P., Calvo-Garrido J., Maffezzini C., Felser A., Wibom R. et al. Mitochondrial polyadenylation is a one-step process required for mRNA integrity and tRNA maturation. PLoS Genet. 12(5), e1006028 (2016). Sharp P. M., Li W. H. An evolutionary perspective on synonymous codon usage in unicellular organisms. J. Mol. Evol. 24, 28–38 (1986). Plotkin J. B., Kudla G. Synonymous but not the same: the causes and consequences of codon bias. Nat. Rev. Genet. 12(1), 32–42 (2011). Liu X., Shan G. Mitochondria encoded non-coding RNAs in cell physiology. Front. Cell Dev. Biol. 9, 713729 (2021). Abhyankar A., Park H. B., Tonolo G., Luthman H. Comparative sequence analysis of the non-protein-coding mitochondrial DNA of inbred rat strains. PLoS One 4(12), e8148 (2009). Yoon S. H., Kim J., Shin D., Cho S., Kwak W., Lee H. K. et al. Complete mitochondrial genome sequences of Korean native horse from Jeju Island: uncovering the spatio-temporal dynamics. Mol. Biol. Rep. 44, 233–242 (2017). Taanman J. W. The mitochondrial genome: structure, transcription, translation and replication. Biochim. Biophys. Acta Bioenerg. 1410(2), 103–112 (1999). Ministerio de Desarrollo Agrario y Riego del Perú. Caballos de paso. https://www.midagri.gob.pe/portal/40-sector-agrario/situacion-de-las-actividades-de-crianza-y-producci/305-caballos-de-paso (s.f.). Giontella A., Cardinali I., Sarti F. M., Silvestrelli M., Lancioni H. Y-chromosome haplotype report among eight Italian horse breeds. Genes 14(8), 1602 (2023). Spooner D. M., McLean K., Ramsay G., Waugh R., Bryan G. J. A single domestication for potato based on multilocus amplified fragment length polymorphism genotyping. Proc. Natl. Acad. Sci. USA 102(41), 14694–14699 (2005). https://doi.org/10.1073/pnas.0507400102 Iorizzo M. et al. A high-quality carrot genome assembly provides new insights into carotenoid accumulation and asterid genome evolution. Nat. Genet. 48(6), 657–666 (2016). https://doi.org/10.1038/ng.3565 Hufford M. B. et al. Comparative population genomics of maize domestication and improvement. Nat. Genet. 44(7), 808–811 (2012). Uliano-Silva M., Ferreira J. G. R., Krasheninnikova K., Formenti G., Abueg L., Torrance J. et al. MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads. BMC Bioinformatics 24(1), 288 (2023). Katoh K., Standley D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30(4), 772–780 (2013). Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30(9), 1312–1313 (2014) Prevosti A., Ocana J., Alonso G. Distances between populations of Drosophila subobscura, based on chromosome arrangement frequencies. Theor. Appl. Genet. 45(6), 231–241 (1975). Kamvar Z. N., Tabima J. F., Grünwald N. J. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2 , e281 (2014). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6515928","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447609178,"identity":"b95920c3-1ef5-4e64-a604-f8cb17acec31","order_by":0,"name":"Carla Saldaña","email":"","orcid":"","institution":"Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)","correspondingAuthor":false,"prefix":"","firstName":"Carla","middleName":"","lastName":"Saldaña","suffix":""},{"id":447609179,"identity":"ae88e47f-98ff-44a2-8822-34cf673035ea","order_by":1,"name":"Santiago Justo","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Santiago","middleName":"","lastName":"Justo","suffix":""},{"id":447609180,"identity":"773da7dc-b08a-47c7-ab86-4789ebc173d4","order_by":2,"name":"Luis Murga","email":"","orcid":"","institution":"Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Murga","suffix":""},{"id":447609181,"identity":"21f53432-8fc3-4180-9cf1-3a3816f6e1b3","order_by":3,"name":"Héctor Vásquez","email":"","orcid":"","institution":"Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)","correspondingAuthor":false,"prefix":"","firstName":"Héctor","middleName":"","lastName":"Vásquez","suffix":""},{"id":447609182,"identity":"c1975e81-01c9-416d-b654-28591132bdc7","order_by":4,"name":"Jorge Maicelo","email":"","orcid":"","institution":"Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Maicelo","suffix":""},{"id":447609183,"identity":"898bd278-c230-4970-a557-6c40e860fb47","order_by":5,"name":"Carlos Arbizu","email":"","orcid":"","institution":"Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Arbizu","suffix":""},{"id":447609184,"identity":"afe2c2b9-f97c-4247-bee2-05f3e2574eff","order_by":6,"name":"William Bardales","email":"data:image/png;base64,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","orcid":"","institution":"Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)","correspondingAuthor":true,"prefix":"","firstName":"William","middleName":"","lastName":"Bardales","suffix":""}],"badges":[],"createdAt":"2025-04-24 00:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6515928/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6515928/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-29107-x","type":"published","date":"2025-12-21T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81737562,"identity":"dfe35bc7-20cf-42ef-a1aa-8ecf0d420fe7","added_by":"auto","created_at":"2025-04-30 22:45:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":265033,"visible":true,"origin":"","legend":"\u003cp\u003eMitochondrial genome map of the Peruvian Paso Horse (\u003cem\u003eE. caballus\u003c/em\u003e), with a total length of 16,617 bp. Different genomic elements are represented using various colors according to the legend. Genes encoded on the heavy strand are positioned outside the circle, while those on the light strand are placed inside. The innermost gray ring represents the (G + C) content, where darker areas indicate higher (G + C) percentages.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6515928/v1/0b3a366310c808b2b559ee7f.png"},{"id":81737560,"identity":"3774283d-8873-4fc9-bc2d-61cbdec99377","added_by":"auto","created_at":"2025-04-30 22:45:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":194879,"visible":true,"origin":"","legend":"\u003cp\u003eRelative synonymous codon usage (RSCU) (A) and codon distribution (B) of protein-coding genes (PCGs) in the mitochondrial genomes Peruvian Paso Horse (\u003cem\u003eE. caballus\u003c/em\u003e). CDpT represents codons per thousand codons.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6515928/v1/a9e5186998c8eb2826813f08.png"},{"id":81737561,"identity":"0b27c0a1-affa-449c-896d-a39ff4778a27","added_by":"auto","created_at":"2025-04-30 22:45:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":342474,"visible":true,"origin":"","legend":"\u003cp\u003eThe predicted secondary structures of the 22 transfer RNA (tRNA) genes in Peruvian Paso Horse are shown.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6515928/v1/668aeec0898c06c3619dcc78.png"},{"id":81737698,"identity":"3f07ef13-10fa-4d9e-81d1-f2539d5e1a60","added_by":"auto","created_at":"2025-04-30 22:53:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":491727,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum Likelihood phylogenetic tree of 682 horses inferred from mitochondrial genomic sequences. Bootstrap is shown only for branches with \u0026gt;70% support. Representative images of five breeds belongs a PPH group in lateral profile: Westphalian (Photograph: Association of Westphalian Horse Breeders, https://westfalenpferde.de/en/), Maremmano (Source: Associazione Nazionale Allevatori del Cavallo Maremmano, Italy. http://www.anamcavallomaremmano.com), Peruvian Paso Horse (Source: UNTRM) Holstein (Source: \u0026nbsp;Holsteiner Verband, Germany (https://www.holsteiner-verband.de and Shagya-Arab (Source: Bábolna Nemzeti Ménesbirtok és Tangazdaság Zrt., Hungría, https://babolnamenes.hu/lovak/termekkategoria/5181-gazal-xxii/)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6515928/v1/39273014edfc431ed87bc19a.png"},{"id":98814081,"identity":"71dd5e2d-b670-4495-b749-4a3a6449747f","added_by":"auto","created_at":"2025-12-22 16:10:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2105346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6515928/v1/4af632bb-f739-4800-8fb4-8c5d93b3de07.pdf"},{"id":81737769,"identity":"6180c57d-ae58-42d8-942b-5469d40a0610","added_by":"auto","created_at":"2025-04-30 23:01:40","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":676060,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialSaldanaetal.zip","url":"https://assets-eu.researchsquare.com/files/rs-6515928/v1/9bc6e9ade2e1dd92737e5aaa.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mitochondrial Genome Assembly of the Peruvian Paso Horse Through PacBio Long-Read Sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince horses were first brought to America by Spanish settlers, many local breeds have taken root in the New World. These breeds are the result of the mix of different horses that came from Europe. Over 400 years of natural selection, the creole horse in various parts of South America has adapted to its surroundings [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Breeders have focused on improving traits like looks, stamina, strength, and gait in local horses. The Peruvian Paso Horse (PPH) is a breed native to Peru [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] recognized as part of Peru's cultural heritage [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The PPH is renowned for its distinctive gait, known as the \"paso llano.\" This natural four-beat lateral movement allows each hoof to touch the ground independently and at regular intervals, providing an exceptionally smooth ride without the vertical movement typical of other gaits. Additionally, the Peruvian Paso Horse exhibits a unique forelimb motion called \"termino,\" characterized by an elegant lateral rotation of the front legs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous studies have looked at its reproduction [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and heritability [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, research on the Peruvian Paso horse\u0026rsquo;s genomics is still scarce. Genetically characterizing populations and understanding the structure of the horse genome is a valuable tool for conserving breeds, as well as for guiding future reproductive strategies and management plans [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Mitochondrial genomes (mitogenomes) of animals are generally 14\u0026ndash;20 kb long circular molecules and it\u0026acute;s higher mutation rate compared to nuclear DNA, combined with the varying degrees of conservation across its regions, enables the identification of different levels of phylogenetic relationships among species [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In horses mtDNA is routinely used at both the population and species levels in studies of phylogeography [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], phylogenetics [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and paleogenomics [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, mtDNA genome includes specific complex regions, particularly repetitive sequences and segmental duplications, sometimes located within the control region (CR), which have traditionally posed difficulties in resolution. In theory, when repeat sequences exceed the length of sequencing reads, assembly is restricted by the limits of these repetitive elements [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNext-generation sequencing (NGS) approaches such as sequencing by synthesis (e.g., Illumina sequencers, San Diego, CA, USA) and semiconductor sequencing (e.g., Ion Torrent systems from Thermo Fisher Scientific) have been widely used for omic studies. However, they are constrained by the limited length of the DNA fragments they can process, which complicates the accurate assembly of sequencing reads. New whole-genome sequencing (WGS) strategies based on single-molecule, long-read sequencing technologies have successfully improved the quality of genome assemblies, particularly in repetitive regions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. With the ability to generate reads of at least 10\u0026ndash;20 kbp in length, single-molecule DNA sequencing has the potential to capture entire mtDNA genomes in a single read.\u003c/p\u003e \u003cp\u003eIn this study, we present, the first mitochondrial genome of the Peruvian Paso Horse, sequenced using PacBio long-read technology. Our study contributes to a deeper understanding of the maternal lineage of the Peruvian Paso Horse, providing valuable insights for phylogenetic analyses, breed conservation, future genomic research and conservation programs.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Genome organization\u003c/h2\u003e \u003cp\u003eThe mitochondrial genome was sequenced using PacBio HiFi technology yielding a total of 14,549,851 HiFi reads and 239.18 Gbp of data, with an average read length of ~\u0026thinsp;16.4 kb and a median quality score of Q35. The average coverage was approximately 14.4\u0026nbsp;million, enabling highly accurate de novo assembly and variant analysis. The assembled mtDNA has a length of 16,617 bp. This genome comprises 13 protein-coding genes, 22 tRNA genes, and 2 rRNA genes and one control region (D-loop) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The heavy (H) strand harbored the majority of genes, (12 protein-coding genes and 14 tRNA), while the light (L) strand housed ND6 and eight tRNAs (Gln, Ala, Asn, Cys, Tyr, Ser, Glu, and Pro). The elemental composition of this genome was distributed as follows: 24.44% Adenine (A), 25.13% Thymine (T), 25.62% Cytosine (C), and 24.81% Guanine (G). The complete mitochondrial genome sequence has been deposited in the GenBank database under accession number PQ663616. The corresponding BioProject and BioSample identifiers are PRJNA1149496, SAMN43249583 respectively.\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\u003eGene organization of the mitochondrial genome of Peruvian Paso Horse.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleotide Positions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrand*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCodon\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRN\u003c/em\u003e\u003csup\u003e\u003cem\u003ePhe\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003errnS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71-1046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eVal\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1047\u0026ndash;1113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003errnL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1114\u0026ndash;2693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eLeu2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2694\u0026ndash;2768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2771\u0026ndash;3727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eIle\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3727\u0026ndash;3795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eATC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eGln\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3793\u0026ndash;3865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eMet\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3868\u0026ndash;3936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3937\u0026ndash;4977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eTrp\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4976\u0026ndash;5045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eAla\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5051\u0026ndash;5119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eAsn\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5132\u0026ndash;5187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRep_origin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5188\u0026ndash;5225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eCys\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5226\u0026ndash;5291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eTyr\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5292\u0026ndash;5358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5360\u0026ndash;6904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eSer\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6902\u0026ndash;6970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eAsp\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6979\u0026ndash;7045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7046\u0026ndash;7729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eLys\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7733\u0026ndash;7800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7802\u0026ndash;8005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7963\u0026ndash;8643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8643\u0026ndash;9425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eGly\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9427\u0026ndash;9495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9496\u0026ndash;9852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eArg\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9843\u0026ndash;9911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND4L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9913\u0026ndash;10209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10203\u0026ndash;11579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eHis\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11581\u0026ndash;11649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eSer2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11650\u0026ndash;11709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eLeu\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11711\u0026ndash;11780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11787\u0026ndash;13601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13585\u0026ndash;14112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eGlu\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14113\u0026ndash;14181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCytB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14186\u0026ndash;15325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003eThr\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15326\u0026ndash;15398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etRNA\u003c/em\u003e\u003csup\u003e\u003cem\u003ePro\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15400\u0026ndash;15465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eD-loop\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15466\u0026ndash;16617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2. Protein Coding Genes (PCGs) and Codon Usage\u003c/h3\u003e\n\u003cp\u003eTen of the protein-coding genes (\u003cem\u003eND1, COX1, COX2, ATP8, ATP6, COX3, ND4, ND4L, ND5, ND6\u003c/em\u003e, and \u003cem\u003eCYTB\u003c/em\u003e) use ATG as the start codon, while two genes (\u003cem\u003eND2\u003c/em\u003e and \u003cem\u003eND3\u003c/em\u003e) initiate with ATA. Regarding stop codons, four genes (\u003cem\u003eND1, ND2, ATP8\u003c/em\u003e and \u003cem\u003eND3\u003c/em\u003e) have the complete TAG stop codon, while \u003cem\u003eCOX1, COX2, ATP6, ND4L, ND5 and ND6\u003c/em\u003e terminates with TAA and \u003cem\u003eCYTB\u003c/em\u003e with AGA. Additionally, ND4 exhibit an incomplete TA(A) stop codon, whereas COX3 has a truncated T(AA) stop codon. PCG also exhibit an AT bias, with AT content ranging from 54.6% in \u003cem\u003eCytB\u003c/em\u003e to 64.2% in \u003cem\u003eATP8\u003c/em\u003e. Additionally, the length of the PCGs varies significantly, from 204 bp in \u003cem\u003eATP8\u003c/em\u003e to 1815bp in \u003cem\u003eNAD5\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The amino acid (AA) codon usages were assessed by calculating relative synonymous codon usage (RSCU) values in 13 PCGs. A total of 3645 codons were encoded by 13 PCGs, and the most frequently used codons were CUA (7.81%), AUC (5.6%), and AUA (5.13%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The analysis of the AA components and their codon usage revealed that the codons encoding Leu(CUA) represented over 100 codons per thousand (CDpT), AUA (isoleucine), AUC (isoleucine), AUA (isoleucine), and UCC (Phenilalanine) were the most frequently used codons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\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\u003eFeatures of the protein-coding genes identified in the mitochondrial genome of Peruvian Paso Horse.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene Length (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u0026thinsp;+\u0026thinsp;T Content (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStart/Stop Cod on\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProtein Length (aa)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/TAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA/TAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/TAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/TAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/TAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/TAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/T--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATA/TAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND4L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/TAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/TA-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/TAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/TAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCytB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG/AGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e379\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 \u003c/p\u003e\n\u003ch3\u003e2.3. rRNA, tRNA, and Non-Coding Genomic Regions\u003c/h3\u003e\n\u003cp\u003eThe two rRNA genes (12S and 16S) had a combined length of 2,556 bp and were flanked by tRNA\u003csup\u003e\u003cem\u003ePhe\u003c/em\u003e\u003c/sup\u003e and tRNA\u003csup\u003e\u003cem\u003eLeu2\u003c/em\u003e\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 22 tRNA genes were identified, spanning 1,500 bp, with lengths ranging from 56 bp (tRNA\u003csup\u003e\u003cem\u003eAsn\u003c/em\u003e\u003c/sup\u003e) to 75 bp (tRNA\u003csup\u003e\u003cem\u003eLeu2\u003c/em\u003e\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The heavy (H) strand encoded 14 tRNA genes, while the light (L) strand encoded eight. All tRNA genes exhibited a typical cloverleaf secondary structure, except for tRNA\u003csup\u003e\u003cem\u003eSer\u003c/em\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Non-coding regions included the origin of replication, intergenic spacers, and the control region. The origin of replication was 38 bp long and located between tRNA\u003csup\u003e\u003cem\u003eAsn\u003c/em\u003e\u003c/sup\u003e and tRNA\u003csup\u003e\u003cem\u003eCys.\u003c/em\u003e\u003c/sup\u003e Additionally, the control region (D-loop) measured 1152 bp and was flanked by tRNA\u003csup\u003e\u003cem\u003ePro\u003c/em\u003e\u003c/sup\u003e and tRNA\u003csup\u003e\u003cem\u003ePhe\u003c/em\u003e\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e2.3. Phylogenetic Analysis\u003c/h3\u003e\n\u003cp\u003eOur phylogenetic trees resolved two main clades (C1 and C2) and a grade topology. Clade 2 (with \u0026gt;\u0026thinsp;70% support). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the ML tree grouping the horses based on their regions of origin. This tree shows that the PPH is most related to south and central European representatives. Breeds included in the well-supported clade of PPH are Maremmano, Westphalian, Holsteiner, Shagya - Arab and two individuals from Germany and Serbia. Moreover, the clade also comprises individuals from the Thoroughbred breed originating in Asia and Oceania, along with Kinsky horses and other representatives from Central Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On the other hand, no clear pattern was observed linking horse evolutionary relationships to either continent of origin (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eThe Peruvian Paso Horse is a breed of significant cultural and genetic value, renowned for its distinctive gait and well-defined lineage. The assembly and annotation of the Peruvian Paso Horse (PPH) mitochondrial genome using PacBio Long-Read technology yielded a high-quality circular contig of 16,617 bp, whose organization aligns with the classical structure reported in other equine mitogenome studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This genetic arrangement, with the majority of genes located on the heavy (H) strand and a smaller number on the light (L) strand, reflects the conserved pattern in \u003cem\u003eEquus caballus\u003c/em\u003e mitogenomes and suggests stability in genomic organization throughout evolution [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].The nearly balanced nucleotide composition\u0026mdash;24.44% Adenine, 25.13% Thymine, 25.62% Cytosine, and 24.81% Guanine\u0026mdash;indicates a stable profile similar to that observed in other studies of equine mitochondria, which may be associated with a low mutation rate and efficient maintenance of mitochondrial genome integrity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The correct assembly circularization and annotation of the mitogenome, demonstrating its robustness and efficiency for obtaining complete mitochondrial sequences from long-read data. This approach offers a significant advantage over short-read methods, particularly for resolving complex regions such as the D-loop and repetitive sequences [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe analysis of protein-coding genes (PCGs) and codon usage in the mitochondrial genome of the Peruvian Paso Horse (CPP) reveals implications about evolution and translational efficiency in equine mitochondria. Codon usage bias (CUB) is influenced by multiple factors such as mutation pressure, natural selection, GC content, nucleotide skewness, gene expression levels, protein secondary structure, biochemical properties, transcription, and open reading frame length [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the main drivers of CUB are natural selection and mutation pressure acting on the background nucleotide composition [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consequently, certain synonymous codons are favored over others, which in turn can promote adaptive evolution [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The start codon ATG and stop codon TAA were the most abundant in the PPH mitocondrial genome, like in previous studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. \u003cem\u003eND4\u003c/em\u003e exhibits an incomplete stop codon (TA[A]), and \u003cem\u003eCOX3\u003c/em\u003e shows a truncated codon (T[AA]). This variability in start and stop codons is consistent with other other vertebrates [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and equines mitogenomes may be related to post-transcriptional mechanisms that complete incomplete codons during mRNA maturation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These incomplete stop codons are completed through polyadenylation at the 3' end of the mRNA, thereby converting them into functional stop codons [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The analysis of codon usage, based on relative synonymous codon usage (RSCU) values, revealed that the most frequent being CUA (7.81%), AUC (5.6%), and AUA (5.13%). In particular, the high frequency of the CUA codon for leucine, along with the elevated utilization of codons for isoleucine (AUC and AUA), suggests that there is an optimization in codon usage that could reflect the relative abundance of the corresponding tRNAs and an adaptation to maximize the efficiency of mitochondrial protein synthesis. This pattern of codon usage bias is fundamental for understanding the evolution of the translational machinery in mitochondria [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe ribosomal RNA genes (12S and 16S) in the PPH mitochondrial are flanked by tRNA\u003csup\u003e\u003cem\u003ePhe\u003c/em\u003e\u003c/sup\u003e and tRNA\u003csup\u003e\u003cem\u003eLeu2\u003c/em\u003e\u003c/sup\u003e. This organization is consistent with the mitochondrial genomes of other mammals, where rRNA genes are highly conserved and play a crucial role in mitochondrial protein synthesis as integral components of the small (12S) and large (16S) ribosomal subunits [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This conserved structure is essential for maintaining translational efficiency in mitochondria, as previously reported in mammalian species such as \u003cem\u003eRattus norvegicus\u003c/em\u003e [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Most tRNAs exhibit a typical cloverleaf secondary structure, except for tRNA\u003csup\u003e\u003cem\u003eSer\u003c/em\u003e\u003c/sup\u003e, which lacks the D-arm, a common feature in mammalian mitogenomes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This variation in secondary structure may influence the efficiency of aminoacylation and translation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe non-coding regions include the origin of replication, intergenic spacers, and the control region (D-loop). Overall, the organization and characteristics of the non-coding regions in the PPH mitochondrial genome exhibit a high degree of conservation with other mammalian mitogenomes, indicating strong evolutionary constraints to maintain essential functions in gene expression and mitochondrial DNA replication [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This structural conservation highlights the importance of mitochondrial genome stability in equine evolution and suggests potential applications in phylogenetics and breed conservation.\u003c/p\u003e \u003cp\u003eThe phylogenetic analysis based on maximum likelihood identified three principal clades that provide valuable insights into the maternal lineage of the PPH and its evolutionary relationships with other equine breeds. Traditionally, it has been held \u0026mdash;based on historical documents\u0026mdash; that the Peruvian Paso Horse descends from horses introduced to Peru by the Spanish in the 16th century, specifically from Andalusian and Barb breeds. The PPH may have evolved under particular Peruvian ecological conditions and management practices, giving rise to the current breed [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In our phylogenomic tree, the PPH was placed within cluster 2, displaying a close genetic relationship with individuals from Southern European breed (Maremmano) and five Central European breeds: Westphalian, German, Serbian, Holsteiner, and Shagya-Arab. These findings suggest a shared genetic ancestry, likely of Mediterranean origin, which aligns with historical records concerning the development of the breed [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Moreover, this clade also included individuals from Asia and other representatives from Central Europe. This finding is consistent with previous phylogenetic studies that have detected genetic signals from ancient Asian horse lineages in various modern breeds [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The presence of such Asian mitochondrial haplotypes may be attributed to historical horse trade routes or introgression events that occurred before or during early colonial expansion [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Interestingly, the accessions reported for the Andalusian breed, (JN398430 and JN398443.1) was more closely related to individuals from Asian (Chinese and Mongolian), Central European (Polish, Maremmano, and Hungarian Coldblood), Middle Eastern (Iranian), and Northern Europe breeds (Exmoor Pony) rather than to the PPH, suggesting a more complex pattern of ancestry and gene flow than previously assumed. The presence of European equine mitochondrial haplotypes in South American breeds has been reported in previous studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], further supporting this genetic connection. Similarly, results consistent with our findings and supporting the same dominant topology can be obtained using both control region (D-loop) sequences and complete mitochondrial genome data [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, no clear pattern was observed linking horse evolutionary relationships to either breed classification or geographic origin. The application of genomic tools has profoundly reshaped our understanding of domestication, ancestry, and the diversification of domesticated species, spanning both crops and animals. High-throughput sequencing technologies and advanced phylogenomic methods have clarified the complex origins of staple crops [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In this context, our mitogenomic analysis of the Peruvian Paso Horse (PPH) contributes to a broader understanding of breed origin, lineage diversification, and conservation priorities. The integration of long-read sequencing and mitochondrial phylogenetics provides a high-resolution view into the maternal ancestry of this culturally iconic breed, echoing the advances achieved in crop evolution studies. The complete mitochondrial genome generated here represents a valuable genomic resource for future research in equine genetics, the conservation of native breeds, and mitochondrial evolutionary studies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSample collected and Genomic DNA Sequencing\u003c/h2\u003e \u003cp\u003eA representative individual of the PPH breed names \u0026ldquo;Amunet\u0026rdquo; (Register Number YN-19315 in the National Association of Peruvian Paso Horse Breeders, born in 2013) residing at the Experimental Station of Luya (Amazonas, Peru) of UNTRM, was selected for sampling. Blood was collected using a vacutainer containing EDTA as an anticoagulant and was immediately transport to the laboratory. The experimental procedure was approved by the Institutional Research Ethics Committee of UNTRM in concordance with Peruvian National Law No. 30407: \u0026ldquo;Animal Protection and Welfare\u0026rdquo;. High molecular weight DNA extraction, PacBio HiFi Library and SMRTcell PacBio sequencing was prepared in Biotechnology Center of the University of Wisconsin-Madison, USA. Briefly, Nanobind CBB DNA Kit (PacBio) was used to extract high molecular weight DNA. The quality of the extracted DNA was measured on a NanoDrop\u0026trade; One instrument (ThermoFisher Scientific). Quantification of the extracted DNA was measured using the Qubit\u0026trade; dsDNA High Sensitivity kit (ThermoFisher Scientific). A Pacific Biosciences HiFi library was prepared according to PN 102-166-600 Version 04 (Pacific Biosciences). Library quality was assessed using the Agilent FemtoPulse System. Library was quantified using the Qubit\u0026trade; dsDNA High Sensitivity kit. The library was sequenced on a PacBio Revio at GTAC@MGI at Washington University in St Louis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssembly and annotation\u003c/h3\u003e\n\u003cp\u003eThe PacBio subreads for the mitochondrial genome were filtered out with pbcommand v2.4.4 using de argument pbccs. MitoHiFi v3.2.3 program [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] was used for assembly, circularization and annotation. As a reference genome we used EquCab3 (NCBI: NC_001640.1) of \u003cem\u003eEquus caballus\u003c/em\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Graphical map of the mitogenome was generated by OGDRAW webserver (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://chlorobox.mpimp-golm.mpg.de/OGDraw.html\u003c/span\u003e\u003cspan address=\"https://chlorobox.mpimp-golm.mpg.de/OGDraw.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed 9 December 2024).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCodon Usage and tRNA Analysis\u003c/h2\u003e \u003cp\u003eA codon usage bias analysis was done using python script from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rhondene/Codon-Usage-in\u003c/span\u003e\u003cspan address=\"https://github.com/rhondene/Codon-Usage-in\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e To predict the secondary structure of each tRNA, we used the tRNAscan-SE website server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://lowelab.ucsc.edu/tRNAscan-SE/\u003c/span\u003e\u003cspan address=\"http://lowelab.ucsc.edu/tRNAscan-SE/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhylogenetic Analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePhylogenetic analyses were conducted using all available mitochondrial genomes (n\u0026thinsp;=\u0026thinsp;682) assigned to taxid 9796, with lengths ranging from 16,000 to 18,000 bp, retrieved from the NCBI database (Supplementary Table S2). As an outgroup, we included Equus zebra, a species of the genus Equus. Genome alignments were performed with MAFFT v7.205b [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and a maximum likelihood (ML) tree was inferred under the GTR\u0026thinsp;+\u0026thinsp;GAMMA model of nucleotide substitution. The best-scoring ML tree was selected, and node support was assessed using 1,000 nonparametric bootstrap replicates implemented in RAxML v8.2.11 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Additionally, genetic distances were estimated based on Prevosti\u0026rsquo;s coefficient [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and a neighbor-joining dendrogram was generated using the poppr v1.1.4 R package [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The grouping factor of the 682 individuals was: breed, region and continent. This information was obtained from NCBI and/or publications where those individuals were reported.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DNA sequence generated and analyzed during the current study are available in the NCBI repository under BioProject PRJNA1149496 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1149496). Other data from the study are available from the corresponding authors.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank El\u0026iacute;as Carranza and Maribel V\u0026aacute;squez for their valuable assistance in the care of the mare Amunet and for their support during the blood sample collection. We also thank \u0026Aacute;ngel Pilco and Mario Ernesto for their help with data submission. Our gratitude extends to the National Association of Peruvian Paso Horse Breeders (ANCPCPP) for their continued support and collaboration. In addition, we are grateful to Stefany Lobato and Adriana D\u0026iacute;az for their assistance in editing the figures included in this manuscript, and to Maili Mu\u0026ntilde;oz for her support with the logistical activities of the project. Also, the authors acknowledge the High-Performance Computational Cluster of the National University Toribio Rodr\u0026iacute;guez de Mendoza of Amazonas (UNTRM\u0026ndash;DATA SCIENCE) and the Bioinformatics High-Performance Computing Server of the Universidad Nacional Agraria La Molina for providing computational resources for data analysis. The authors thank the University of Wisconsin \u0026ndash; Madison Biotechnology Center\u0026rsquo;s DNA Sequencing Facility (Research Resource Identifier \u0026ndash; RRID:SCR_017759) for extracting HMW DNA and generating PacBio libraries. Finally, we thank the Genome Technology Access Center at the McDonnell Genome Institute at Washington University School of Medicine (Research Resource Identifier - RRID:SCR_001030) for help with genomics services. The Center is partially supported by NCI Cancer Center Support Grant #P30 CA91842 to the Siteman Cancer Center from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. This publication is solely the responsibility of the authors and does not necessarily represent the official view of NCR R or NIH.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.L.S.: designed the study, sample collection, sample processing, analyzed the data, writing-original draft, writing-review \u0026amp; editing, S.J.: designed the study, analyzed the data, writing-original draft, writing-review \u0026amp; editing, L.M.: Sample collection, sample processing, supervision, writing-original draft, Writing-review \u0026amp; editing, H.V.V: supervision, writing-original draft, writing-review \u0026amp; editing, J.L.M: supervision, funding acquisition, writing-original draft, writing-review \u0026amp; editing, C.I.A.: designed the study, sample processing, analyzed the data, supervision, writing-original draft, writing-review \u0026amp; editing, W.B: designed the study, sample collection, analyzed the data, writing-original draft, writing-review \u0026amp; editing. All authors discussed the methodologies, results, and read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the proyect \u0026ldquo;Creaci\u0026oacute;n del servicio de investigaci\u0026oacute;n y ense\u0026ntilde;anza en equinos en la Universidad Nacional Toribio Rodr\u0026iacute;guez de Mendoza de Amazonas\u0026rdquo; (Grant ID: CUI N\u0026deg; 2314510) of the Peruvian Goverment and \u0026quot;Tesis de Pregrado y Postgrado en Ciencia, Tecnologia e Innovacion Tecnologica\u0026quot; (Grant ID: PE501085176-2023) of the Programa Nacional de Investigacion Cient\u0026iacute;fica y Estudios Avanzados, PROCIENCIA.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKelly, L., Postiglioni, A., De Andr\u0026eacute;s, D. F., Vega-Pl\u0026aacute;, J. L., Gagliardi, R., Biagetti, R., \u0026amp; Franco, J. 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MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads. BMC Bioinformatics 24(1), 288 (2023).\u003c/li\u003e\n\u003cli\u003eKatoh K., Standley D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30(4), 772\u0026ndash;780 (2013).\u003c/li\u003e\n\u003cli\u003eStamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30(9), 1312\u0026ndash;1313 (2014)\u003c/li\u003e\n\u003cli\u003ePrevosti A., Ocana J., Alonso G. Distances between populations of Drosophila subobscura, based on chromosome arrangement frequencies. Theor. Appl. Genet. 45(6), 231\u0026ndash;241 (1975).\u003c/li\u003e\n\u003cli\u003eKamvar Z. N., Tabima J. F., Gr\u0026uuml;nwald N. J. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. \u003cem\u003ePeerJ\u003c/em\u003e\u003cstrong\u003e2\u003c/strong\u003e, e281 (2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mitogenome, NGS, bioinformatics, phylogenomic, equine","lastPublishedDoi":"10.21203/rs.3.rs-6515928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6515928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Peruvian Paso Horse (PPH) is a unique breed recognized for its distinctive gait and cultural significance. Despite its importance, its mitochondrial genome (mitogenome) remains largely uncharacterized. This study presents the first complete mitogenome of the PPH, which was assembled using PacBio long-read sequencing. The resulting high-quality circular mitogenome (16,617 bp) contains 13 protein-coding genes (PCGs), 22 tRNAs, 2 rRNAs, and a control region (D-loop). The Nucleotide composition was 24.44% A, 25.13% T, 25.62% C, and 24.81% G, consistent with other equine mitogenomes. Codon usage analysis revealed a bias toward CUA(Leu), AUC(Ile), and AUA(Ile), suggesting optimization for mitochondrial translation. The tRNA genes exhibited a typical cloverleaf secondary structure, except for tRNA\u003csup\u003e\u003cem\u003eSer\u003c/em\u003e\u003c/sup\u003e. The D-loop (1,152 bp) was positioned between tRNA\u003csup\u003e\u003cem\u003ePro\u003c/em\u003e\u003c/sup\u003e and tRNA\u003csup\u003e\u003cem\u003ePhe\u003c/em\u003e\u003c/sup\u003e, serving as the primary site for mitochondrial replication and transcription. Phylogenetic analysis placed the PPH within a European breed clade, closely related to Westphalian and Maremmano and two individuals from Germany and Serbia. with potential connections to Asian lineages. These findings highlight the effectiveness of long-read sequencing for resolving complex mitochondrial regions, providing a valuable resource for equine genetics, breed conservation, and evolutionary studies.\u003c/p\u003e","manuscriptTitle":"Mitochondrial Genome Assembly of the Peruvian Paso Horse Through PacBio Long-Read Sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 22:45:35","doi":"10.21203/rs.3.rs-6515928/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-17T04:56:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-15T06:09:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-14T15:27:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304669499973002736762808011997410486807","date":"2025-06-04T23:36:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157954187526260468755012551425832642059","date":"2025-06-04T15:55:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-26T09:13:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57723424870217771097511890931529194591","date":"2025-05-16T07:31:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T05:50:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47876764979270173697148968328373011988","date":"2025-05-14T06:49:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T23:32:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T13:50:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-13T12:32:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-13T04:16:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-24T00:30:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0df73aaf-9fd7-40a0-aa5e-3463cdd65402","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47645565,"name":"Biological sciences/Biotechnology/Animal biotechnology"},{"id":47645566,"name":"Biological sciences/Biotechnology/Genomics"}],"tags":[],"updatedAt":"2025-12-22T16:04:37+00:00","versionOfRecord":{"articleIdentity":"rs-6515928","link":"https://doi.org/10.1038/s41598-025-29107-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-21 15:57:39","publishedOnDateReadable":"December 21st, 2025"},"versionCreatedAt":"2025-04-30 22:45:35","video":"","vorDoi":"10.1038/s41598-025-29107-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-29107-x","workflowStages":[]},"version":"v1","identity":"rs-6515928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6515928","identity":"rs-6515928","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}