Near-chromosome-level genome assembly and transcriptome analysis of the Ural owl, Strix uralensis PALLAS, 1771

Near-chromosome-level genome assembly and transcriptome analysis of the Ural owl, Strix uralensis PALLAS, 1771 | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Near-chromosome-level genome assembly and transcriptome analysis of the Ural owl, Strix uralensis PALLAS, 1771 View ORCID Profile Sven Winter , View ORCID Profile René Meißner , View ORCID Profile Martin Grethlein , View ORCID Profile Gerrit Wehrenberg , View ORCID Profile Angelika Kiebler , View ORCID Profile Andrea X. Silva , View ORCID Profile Natalia Reyes Escobar , View ORCID Profile Suany M. Quesada Calderón , View ORCID Profile Ana V. Suescún , View ORCID Profile Luis Guzman Belmar , View ORCID Profile Stefan Prost doi: https://doi.org/10.1101/2025.03.26.645461 Sven Winter 1 Research Institute of Wildlife Ecology, University of Veterinary Medicine Vienna , Vienna, Austria 2 Faculty of Science and Technology, University of the Faroe Islands , Tórshavn, The Faroe Islands 3 Ecology and Genetics Research Unit, University of Oulu , Oulu, Finland 4 Senckenberg Biodiversity and Climate Research Centre , Frankfurt am Main, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sven Winter For correspondence: Stefan.Prost{at}oulu.fi Sven.Winter{at}vetmeduni.ac.at René Meißner 1 Research Institute of Wildlife Ecology, University of Veterinary Medicine Vienna , Vienna, Austria 3 Ecology and Genetics Research Unit, University of Oulu , Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for René Meißner Martin Grethlein 3 Ecology and Genetics Research Unit, University of Oulu , Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin Grethlein Gerrit Wehrenberg 3 Ecology and Genetics Research Unit, University of Oulu , Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gerrit Wehrenberg Angelika Kiebler 3 Ecology and Genetics Research Unit, University of Oulu , Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Angelika Kiebler Andrea X. Silva 5 AUSTRAL-omics, Vicerrectoría de Investigación, Desarrollo y Creación Artística, Universidad Austral de Chile , Valdivia, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrea X. Silva Natalia Reyes Escobar 5 AUSTRAL-omics, Vicerrectoría de Investigación, Desarrollo y Creación Artística, Universidad Austral de Chile , Valdivia, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Natalia Reyes Escobar Suany M. Quesada Calderón 5 AUSTRAL-omics, Vicerrectoría de Investigación, Desarrollo y Creación Artística, Universidad Austral de Chile , Valdivia, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Suany M. Quesada Calderón Ana V. Suescún 5 AUSTRAL-omics, Vicerrectoría de Investigación, Desarrollo y Creación Artística, Universidad Austral de Chile , Valdivia, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ana V. Suescún Luis Guzman Belmar 5 AUSTRAL-omics, Vicerrectoría de Investigación, Desarrollo y Creación Artística, Universidad Austral de Chile , Valdivia, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luis Guzman Belmar Stefan Prost 3 Ecology and Genetics Research Unit, University of Oulu , Oulu, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stefan Prost For correspondence: Stefan.Prost{at}oulu.fi Sven.Winter{at}vetmeduni.ac.at Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract The Ural owl ( Strix uralensis ) is a large member of the Strigidae family and inhabits Eurasian forests ranging from Germany to Japan. However, it faces increased range reduction, particularly at its southwestern distribution edges. Despite being considered “Least Concern” by the IUCN, local populations have become threatened in Central Europe due to severe habitat loss. Reintroduction programs aim to restore these populations by closing distribution gaps and facilitating natural recolonization of suitable habitats. To support these efforts, genomic resources have become an established tool to assess genetic diversity, geographic structure, and potential inbreeding, crucial for maintaining the genetic health and adaptability of newly established populations. Here, we present a de novo genome assembly and transcriptome of the Ural owl based on ONT long-reads, Omni-C Illumina short-reads, and RNASeq data. The final assembly has a total length of 1.26 Gb, of which 96.37 % are anchored into the 41 largest scaffolds. The contig and scaffold N50 values of 88.6 Mb and 21.7 Mb, respectively, a BUSCO/compleasm completeness of 97.5 %/99.65 % and k-mer completeness of 95.18 %, emphasize the high quality of this assembly. Furthermore, annotation of the assembly identified 17,650 genes and a repeat content of 12.48 %. This new highly contiguous and chromosome-scale assembly will greatly benefit Ural owl conservation management by informing reintroduction programs about the species’ genetic health and contributing a valuable resource to study genetic function in greater detail across the whole Strigidae family. Introduction Owls (Strigiformes) are primarily nocturnal birds of prey, known for their exceptional low-light vision and excellent hearing capabilities. Unlike most orders of birds, owls primarily rely on their hearing for hunting, and acoustically detected prey is captured in an almost silent approach, made possible by specialized wing anatomy ( Konishi, 1973 ). Today, the Strigiformes consist of approximately 250 extant species, classified into two distinct families: Tytonidae, or barn owls, with 19 species, and Strigidae, or true owls, with approximately 235 species ( Ackerman, 2024 ; Penhallurick, 2002 ). Within the Strigidae, the Ural owl Strix uralensis ( Fig. 1 ) is a large member of the wood owls ( Strix ) with a round body and an exceptionally long tail ( Voous, 1964 ). The species prefers old-growth primary forests and requires sufficient open areas without underbrush, allowing for unobstructed hunting, especially during the rearing of its young (Tutiš et al., 2009). Its usual prey ranges from small rodents to medium-sized lagomorphs and varies depending on the populated habitat, with the most frequently occurring prey being the most commonly hunted ( Korpimäki & Sulkava, 1987 ). Throughout Eurasia, Ural owls exhibit a wide distribution across various forest habitats, extending from Scandinavia and parts of Central Europe to Japan (BirdLife International, 2021 ). Download figure Open in new tab Figure 1. A roosting Ural owl ( Strix uralensis ) of the reintroduced Central European population in the Viennese Forest. ©René Meißner The Ural owl is safeguarded under the EU Birds Directive Annex I, the Bern Convention on the Conservation of European Wildlife and Nature Habitats Annex I and II and listed on CITES Appendix II (see https://eunis.eea.europa.eu/species/1289 ). Although it is currently not considered a threatened species globally by the IUCN (BirdLife International, 2021 ), it has lost significant portions of its former range, particularly at its southwestern distribution edges. Historically, the species was also present in the low mountain ranges of Germany and the northern Alpine slopes of Switzerland, indicating a previously more continuous distribution that connected the Carpathian and Dinaric populations with the Northern European populations ( Goffette et al., 2016 ). While still common in Fennoscandia, the loss of nesting sites due to forest management has resulted in threatened populations and local extinction in Southern Scandinavia and Central Europe ( Scherzinger, 1987 ). Therefore, establishing new populations in the Ural owl’s former range and reinforcing existing ones is vital to ensure genetic exchange within the remaining metapopulations to secure the species ’long-term survival ( Hausknecht et al., 2014 ). A crucial component of this effort is human-aided reintroduction, which has been conducted in Central Europe since the 1980s, for instance, in Germany and Austria ( Engleder, 2003 ). Such projects aim to close distribution gaps and enable the Ural owl to recolonize suitable habitats on its own ( Scherzinger, 1987 ). To support these efforts, high-quality chromosome-level genome assemblies play an important role in species conservation ( Paez et al., 2022 ) and provide an invaluable resource for informing and planning reintroduction programs ( He et al., 2016 ). As highly contiguous genome references, assemblies heighten the value of short-read data, the most common genomic resource in conservation (B. R. Wright et al., 2020 ). Genomes further enable the assessment of genetic diversity, population structure, and subspecies, which are critical for maintaining the genetic health and adaptability of newly established populations ( Formenti et al., 2022 ). Detecting inbreeding and emerging bottlenecks is especially important for reintroduction programs, as they guide the selection of individuals that maximize genetic variability ( He et al., 2016 ). Furthermore, the detailed genetic information aids in understanding the species’ evolutionary history and adaptive traits, supporting the development of effective management strategies ( Prost et al., 2022 ). Although fragmented scaffold assemblies might initially seem sufficient to provide genomic information comparable to chromosome-level assemblies for conservation genomic analyses, recent studies have shown that more contiguous assemblies enable better estimation of genetic diversity, improved localization, and visualization of regions with low heterozygosity within genomes, so-called runs of homozygosity, and allow the estimation of other important genetic parameters such as the amount of potentially harmful mutations in a genome, called mutational load ( Totikov et al., 2021 ; von Seth et al., 2021 ). Here, we present a chromosome-level genome assembly, aiding our understanding of Ural owl populations and ultimately supporting conservation strategies for the species. Methods Biological Materials In this study, we sequenced the genome of a Ural owl Strix uralensis (Voucher No. OV.35916 ( http://id.zmuo.oulu.fi/OV.35916 ), GBIF entry: https://www.gbif.org/occurrence/4463254306 ) from muscle tissue stored at - 20°C at the frozen collection of the Zoological Museum of the University of Oulu. The female individual was found dead (famished) on the 29th of March 2001 in the municipality of Kuhmo near Lentiira, Finland, on the eastern side of southern Lake Änättijärvi. In addition, we used tissue samples from two additional specimens from the frozen collection of the pathology department at the University of Veterinary Medicine Vienna, Austria, for RNA extraction: brain, kidney, muscle, and heart tissue from the female juvenile specimen AC/963/14 (3442), and liver tissue from the adult female specimen Z/677/21 (9679). Nucleic acid library preparation We extracted high molecular weight (HMW) genomic DNA from the frozen muscle tissue of the sample (Voucher No. OV.35916) with the Monarch® HMW DNA Extraction Kit for Tissue (New England Biolabs, Ipswich, MA, USA) following the manufacturer’s protocol. The tissue was homogenized using the included Monarch Pestle Tubes and pestle. A second extraction was performed using the MagAttract HMW DNA kit (Qiagen, Hilden, Germany). The purity, quantity, and molecular weight of the DNA extract were checked using a NanoDrop Microvolume Spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA), a Qubit Fluorometer with the Qubit Broad Range, dsDNA Quantification Assay Kit (Thermo Fisher Scientific Inc., Waltham, MA, USA), and an agarose gel electrophoresis, respectively. In total, three long-read DNA libraries were prepared using the Oxford Nanopore Technologies (ONT, Oxford, UK) Ligation Sequencing Kit V14 (SQK-LSK114) with some modifications. Prior to library preparation, we sheared 2.9 µg of HMW DNA in a volume of 100 µl elution buffer by passing the solution through a 30G needle seven times, interspersed with a quick vortexing. We increased the incubation time during end-prep to 30 min for both temperatures and increased the elution times of the magnetic bead clean-up to 10 min after end-prep and 15 min at 37 °C after adapter ligation. In addition, we generated a 150 bp paired-end chromatin conformation capture library from the frozen tissue using the Dovetail® Omni-C® Kit (Dovetail Genomics, part of Cantana Bio, LLC, Scotts Valley, CA, USA) and the NEBNext® Ultra™ II DNA Library Prep Kit for Illumina® according to the manufacturer’s protocols. To generate mRNA evidence for the annotation of the genome assembly and to generate a transcriptome assembly, we extracted RNA from the brain, kidney, muscle, heart, and liver tissue with the Quick RNA Miniprep Plus Kit (Zymo Research, Orange, CA, USA). The extracted RNA was sent to Novogene Europe (Cambridge, UK) for 150 bp paired-end library preparation and sequencing. DNA/RNA Sequencing and Genome Assembly From the three long-read ONT libraries, we initially sequenced two on the MinION Mk1c on a FLO-MIN114 R10.4.1 flow cell. The final library was sequenced on the PromethION 2 Solo (P2S) on a FLO-PRO114M R10.4.1 flow cell. The Omni-C library and the RNAseq libraries were sequenced at Novogene Europe (Cambridge, UK) on the Illumina Novaseq X platform. The raw ONT data were basecalled with Dorado v.0.5.2 ( Oxford Nanopore Technologies Ltd., 2022 ) using the super-high-accuracy basecalling model in duplex mode. Read quality and length were checked with Nanoplot v.1.42 (De Coster et al., 2018). The genome was assembled using the full ONT dataset (duplex + simplex reads) with Flye v.2.9.3 ( Kolmogorov et al., 2019 ), including one iteration of long-read polishing. The contigs of the draft assembly were anchored into chromosome-scale scaffolds using the chromatin conformation information of the Omni-C data. We followed the Arima Hi-C mapping pipeline used by the Vertebrate Genome Project ( https://github.com/VGP/vgp-assembly/blob/master/pipeline/salsa/arima_mapping_pipeline.sh ) to filter and map the reads to the assembly. The reads were trimmed using fastp v.0.20.0 ( Chen et al., 2018 ) and mapped to the assembly using bwa-mem v.0.7.17 ( Li, 2013 ). Mapped reads were filtered based on mapping quality, read quality, and CIGAR strings with samtools v.1.18 ( Li et al., 2009 ). Duplicated reads were removed using Sambamba v.1.0.1 ( Tarasov et al., 2015 ). The mapped and filtered reads were subsequently used for proximity-ligation-based scaffolding with YaHS v1.1 ( Zhou et al., 2022 ). Hi-C contact maps and assembly files used for manual curation in JuiceBox v.1.11.08 ( Durand et al., 2016 ) were generated using JuicerTools v.1.22.01 ( Durand et al., 2016 ). To improve the contig-level contiguity of the scaffolded assembly, we ran TGS-GapCloser v.2.0.0 ( Xu et al., 2020 ) with the long-read ONT data followed by one iteration of short-read polishing with pilon v.1.24 ( Walker et al., 2014 ) using the high-quality Omni-C data to improve base-level accuracy. We repeated scaffolding, gap-closing, and short-read polishing once to improve the assembly after manual corrections. In addition, we used GetOrganelle v.1.7.7.1 ( Jin et al., 2020 ) to assemble the mitochondrial genome from the short-read Omni-C data. Assembly QC & Synteny analyses To assess the quality of the assembly, we calculated assembly statistics with Quast v.5.0.2 ( Gurevich et al., 2013 ) and ran a gene set completeness analysis with both BUSCO v.5.4.7 ( Manni et al., 2021 ) and compleasm v.0.2.6 ( Huang & Li, 2023 ) using the aves_odb10 dataset. In addition, we estimated assembly completeness and the base-level error rate based on 21-mer counts generated with meryl v.1.4.1 using merqury v.1.3 ( Rhie et al., 2020 ). To assess potential contamination, we first mapped the ONT long-reads, as well as the Omni-C illumina short-reads (separately) to the assembly using minimap2 v.2.28 ( Li, 2018 ) and bwa-mem v.0.7.17 ( Li, 2013 ), respectively and calculated mapping statistics with QualiMap v. 2.3 ( Okonechnikov et al., 2016 ). We also used BLASTN v.2.11+ ( Camacho et al., 2009 ) to assign taxon information to each of the scaffolds and contigs of the assembly. The resulting mapping files, as well as the output of BLASTN, were then combined into a blobplot with Blobtoolkit v. 3.5.2 ( Challis et al., 2020 ). Synteny between the final Ural owl assembly and the available genome of Strix occidentalis (GCA_030819815.1), was analyzed with JupiterPlot v.1.1 ( Chu, 2018 ). Transcriptome assembly The transcriptome of the Ural owl was assembled from the RNAseq data derived from the five different tissue samples. We first combined all RNAseq data into a single dataset before k-mer based read correction with Rcorrector v.1.0.7 ( Song & Florea, 2015 ) and subsequent removal of all unfixable read pairs with the python script FilterUncorrectabledPEfastq.py ( https://github.com/harvardinformatics/TranscriptomeAssemblyTools/ ). Next, we trimmed adaptors and low-quality bases from the filtered dataset using TrimGalore v.0.6.10 ( Krueger, 2015 ) and removed unwanted rRNA reads (e.g., those from microorganisms) by mapping the data to the SILVA rRNA database v. 138.1 ( Quast et al., 2013 ) using bowtie2 v.2.5.3 ( Langmead & Salzberg, 2012 ), keeping only paired and unmapped reads. This final dataset was then used to assemble the transcriptome using Trinity v.2.15 ( Grabherr et al., 2011 ). To evaluate the quality of the transcriptome, we generated assembly statistics with the TrinityStats.pl script as part of Trinity and with Quast v.5.0.2 ( Gurevich et al., 2013 ). Furthermore, we quantified read support by mapping the filtered reads back to the final assembly using bowtie2 and checked for completeness using BUSCO v.5.4.7 ( Manni et al., 2021 ) in transcriptome mode and compleasm v.0.2.6 ( Huang & Li, 2023 ). Repeat and Gene annotation Repeats in the genome assembly were masked in a three-step process. First, we masked known repeats for birds (‘ -species aves ’) based on the Repbase (release 20181026) ( Bao et al., 2015 ) and Dfam (release 3.1-rb20181026) ( Storer et al., 2021 ) databases with RepeatMasker v.4.1.0 ( Smit et al., 2015a ). Next, we identified the remaining repeats in the assembly de novo using RepeatModeler v.2.0.1 (Smit et al., 2015b). The resulting de novo repeat library was used in a second iteration of repeat masking to mask the remaining repeats in the assembly. We combined both Repeatmasker repeat tables into a single table to represent the entirety of masked repeats in the assembly (Supplementary Material S1). Genes in the masked assembly were predicted based on homology with GeMoMa v.1.9 ( Keilwagen et al., 2018 ) using the following eight annotated assemblies as evidence: Chicken ( Gallus gallus ) GCF_016699485.2, Japanese quail ( Coturnix japonica ) GCF_001577835.2, Burrowing owl ( Athene cunicularia ) GCF_003259725.1 ( Mueller et al., 2018 ), Common barn owl ( Tyto alba ) GCF_018691265.1 ( Cumer et al., 2022 ), Speckled mousebird ( Colius striatus ) GCF_028858725.1, California Condor ( Gymnogypus californianus ) GCF_018139145.2 ( Robinson et al., 2021 ), Red-fronted tinkerbird ( Pogoniulus pusillus ) GCF_015220805.1, Downy woodpecker ( Dryobates pubescens ) GCF_014839835.1. In addition, the corrected and trimmed RNAseq data of the five different tissues were mapped against the masked reference with STAR v.2.7.9a ( Dobin et al., 2013 ) and used as extrinsic evidence during the annotation. The proteins predicted by GeMoMa were further annotated by a BLASTP v2.15.0+ ( Camacho et al., 2009 ) search against the SwissProt database (release 04-2024, The UniProt Consortium, 2019 ) applying a e -value cutoff of 10 -6 . We also annotated GeneOntology (GO) terms, domains, and motifs with InterProScan v.5.64-96.0 ( Jones et al., 2014 ). Results Genome sequencing and assembly Sequencing on the ONT MinION Mk1c and PromethION P2S together generated a total of 49.24 Gb or approximately 39-fold sequencing depth of long-read data after base-calling, of which 37.37 Gb were simplex, and 5.74 Gb were duplex reads. The combined long-read dataset had a mean base quality of 14.7, a median base quality of 20.1, and a mean read length of 7,209.9 bp. The final assembly ( S3B_Suralensis_v1.3.3 ) after initial assembly with Flye, proximity-ligation scaffolding, gap-closing, and removal of potential contamination, had a total length of 1.26 Gb across 1,876 scaffolds/contigs, including one contig for the mitochondrial genome, with scaffold and contig N50 values of 88.65 Mb and 19.98 Mb, respectively, and no remaining signs of contamination ( Table 2 A, Fig. 2 A-C). The largest 42 scaffolds, likely corresponding to the expected number of haploid chromosomes (including a partial W chromosome) ( Sasaki et al., 1994 ), contain 96.42 % of the total assembly length, resulting in an L50 of five. S3B_Suralensis_v1.3.3 showed high gene set completeness with BUSCO and compleasm scores of 97.5 % and 99.65 % complete BUSCO genes of the aves_odb10 dataset with only 0.5% and 0.28 % duplicated genes, respectively ( Table 2 B). Furthermore, Merqury estimated a k-mer completeness of 95.18 % with a QV score of 39.45, corresponding to an error rate of 0.0001. Download figure Open in new tab Figure 2. Assembly quality assessment and mitochondrial genome of S3B_Suralensis_v1.3.3 and synteny with S. occidentalis . (A)Hi-C (Omni-C) contact density map depicting the 42 chromosome-level scaffold and additional small scaffolds of S3B_Suralensis_v1.3.3 . (B) BlobPlot analysis comparing GC content (x-axis) and sequence coverage of the Omni-C data (y-axis). The color of th “blobs” representing each scaffold corresponds to the taxonomic assignment based on NCBI’s nucleotide database. (C) Graphical representation of the annotated mitochondrial genome of S. uralensis isolate S3B. (D) Circos plot generated with JupiterPlot comparing the synteny of S3B_Suralensis_v1.3.3 (left) with its close relative S. occidentalis (right). Colored ribbons between scaffolds indicate syntenic regions. Scaffolds are sorted by size from the largest (bottom) to the smallest (top). View this table: View inline View popup Table 2. Assembly statistics and gene set completeness scores Transcriptome assembly The final transcriptome assembly based on 39.7 Gb of RNAseq data has a total length of 333.2 Mb with a contig N50 of 2,792 bp, a total number of trinity ‘genes ’ and transcripts of 241,711 and 318,498, respectively ( Table 2 A). BUSCO found 88.2 % complete orthologous genes of the aves_odb10 dataset, with 28.0 % being single copy, 60.2 % duplicated, and 9.0 % missing ( Table 2 B). Compleasm identified 88.16% complete genes, of which 33.07 % were single-copy, 55,09 % duplicate genes, and 8.54% missing genes ( Table 2 B). Annotation Repeat annotation A total of 156.83 Mb (12.48 %) of the final assembly was classified as repeats (Supplementary Material S1). Specifically, interspersed repeats make up most of the repeats (10.39 %), of which Long Interspersed Nuclear Elements (LINEs) are the most common repeat elements at 4,90 %, followed by Long Terminal Repeats (LTRs) with 1.92 %. An additional 2.91 % of the assembly was identified as unknown or unclassified interspersed repeats. Of the non-interspersed repeats, simple repeats are the most common, spanning 1.14 % of the assembly. Gene annotation The homology-based gene prediction with GeMoMa identified 17,650 genes spanning 328.2 Mb of the assembly, with a median gene length of 8,985 bp. BUSCO and compleasm analyses indicate high completeness of the annotation with 97.3 % complete orthologous (single copy and duplicates) of the aves_odb10 dataset found in the annotation with BUSCO and 96.77 % identified by compleasm ( Table 2 B). Of the 44,008 predicted proteins, 43,016 (97.74 %) could be matched to entries within the Swiss-Prot database, while InterProScan assigned a functional annotation to 43,842 (99.62%) proteins. At least one Gene Ontology (GO)-term was assigned to 33,608 (76.37 %) proteins, and 39,328 (89.37 %) proteins were assigned to the reactome. Discussion The high-quality near chromosome-level assembly of the Ural owl presented in this study represents a valuable genomic resource for the species and the Stringiformes. With highly contiguous scaffolds and gene set completeness, the assembly offers a crucial genomic base for future population and conservation genetics studies (B. Wright et al., 2019 ) and could help to shed light on the still difficult to resolve phylogenetic position of the Strigiformes within the Telluraves ( Stiller et al., 2024 ). The assembly’s quality is evidenced by its high BUSCO scores and robust annotation, which highlights its suitability for in-depth genetic analysis and its role in understanding complex evolutionary processes. Moreover, detailed gene annotation enables future studies on gene function and the species ’adaptive capacity ( King et al., 2003 ). Furthermore, this genome will greatly aid Ural owl conservation and provide insights into genetic diversity, population structure, and adaptive traits, key factors in evaluating wild populations and identifying potential source populations for successful reintroduction programs ( He et al., 2016 ). Similar to the reference genomes developed for other non-model species, this Ural owl assembly will support a range of further applications, including identifying genes linked to adaptive traits in the species, monitoring population-level genetic diversity, and ensuring the genetic health of reintroduced populations ( He et al., 2016 ; Paez et al., 2022 ). High-quality references are essential for developing effective conservation strategies, especially for species experiencing habitat loss or fragmentation. Although obtaining high-quality genome assemblies for non-model species can be challenging due to technical and financial limitations, advances in sequencing technologies and bioinformatics make these resources more accessible and economically feasible ( McMahon et al., 2014 ). Collaboration between institutions and global genome initiatives can further accelerate the development of genomic resources for conservation purposes ( Formenti et al., 2022 ). Such collaborations allow conservation managers to focus on adaptive management while generating high-quality genomic data to inform real-time decision-making ( Bernos et al., 2020 ). In summary, the new Ural owl genome assembly provides an essential tool for conservation efforts by enabling detailed genetic analysis and population monitoring. This resource will play a key role in supporting species reintroduction and long-term population viability, ensuring genetic diversity, and identifying adaptive traits critical for the species’ future survival in the wild. As global biodiversity faces increasing threats, the development and application of genomic resources will be crucial in shaping effective conservation strategies for endangered species and will aid in impeding global biodiversity loss. Funding SP, GW, and this work were supported by the Biodiverse Anthropocenes Research Programme of the University of Oulu, funded by the Research Council of Finland PROFI6 funding (2021-2026). SW and RM were further supported by the VisitANTS Come-and-GOulu Travel Grant. SW, AXS, NRE, SMQC, AVS, LGB, and SP were supported by the Chilean National Agency for Research and Development (ANID) FOVI-220196 grant. Data Availability All primary data (DNA/RNA sequences) underlying these analyses, the genome assembly, and the transcriptome have been deposited under GenBank BioProject PRJNA1140424. The final genome assembly, transcriptome assembly, repeat masked assembly and annotation results are deposited at Dryad ( a link will be provided during the revision ). View this table: View inline View popup Table 1. Software and versions used to generate the Strix uralensis assembly and transcriptome. Supplementary Material S1: Repeat table generated by RepeatMasker Acknowledgments We thank Anna Kübber-Heiss and Helmut Dier from the pathology department of the University of Veterinary Medicine, Vienna, for access to their frozen tissue sample collection, and Pasi Laakso and Ilmari Mäkisalo for collecting and providing the reference specimen in Kuhmo, Finland. References ↵ Ackerman , J . ( 2024 ). What an Owl Knows: The New Science of the World’s Most Enigmatic Birds . Penguin Group . ↵ Bao , W. , Kojima , K. K. , & Kohany , O . ( 2015 ). Repbase Update, a database of repetitive elements in eukaryotic genomes . Mobile DNA , 6 ( 1 ), 11 . doi: 10.1186/s13100-015-0041-9 OpenUrl CrossRef PubMed ↵ Bernos , T. A. , Jeffries , K. M. , & Mandrak , N. E . ( 2020 ). Linking genomics and fish conservation decision making: A review . Reviews in Fish Biology and Fisheries , 30 ( 4 ), 587 – 604 . doi: 10.1007/s11160-020-09618-8 OpenUrl CrossRef ↵ BirdLife International . ( 2021 ). 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