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Abstract and Figures

Cervids are distinguished by the shedding and regrowth of antlers. Furthermore, they provide insights into prion and other diseases. Genomic resources can facilitate studies of the genetic underpinnings of deer phenotypes, behavior, and disease resistance. Widely distributed in North America, the white-tailed deer (Odocoileus virginianus) has recreational, commercial, and food source value for many households. We present a genome generated using DNA from a single Illinois white-tailed sequenced on the PacBio Sequel II platform and assembled using Wtdbg2. Omni-C chromatin conformation capture sequencing was used to scaffold the genome contigs. The final assembly was 2.42 Gb, consisting of 508 scaffolds with a contig N50 of 21.7 Mb, a scaffold N50 of 52.4 Mb, and a BUSCO complete score of 93.1%. Thirty-six chromosome pseudomolecules comprised 93% of the entire sequenced genome length. A total of 20,651 predicted genes using the BRAKER pipeline were validated using InterProScan. Chromosome length assembly sequences were aligned to the genomes of related species to reveal corresponding chromosomes. Subject Area: Genome Resources
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Journal of Heredity, 2022, 113, 479–489
https://doi.org/10.1093/jhered/esac022
Advance access publication 3 May 2022
Genome Resources
Received December 13, 2021; Accepted May 5, 2022
Genome Resources
A De Novo Chromosome-Level Genome Assembly of the
White-Tailed Deer, Odocoileus Virginianus
Evan W.London , Alfred L.Roca , Jan E.Novakofski and Nohra E.Mateus-Pinilla
From the Illinois Natural History Survey-Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
(London, Roca, Novakofski, and Mateus-Pinilla); Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL
61801, USA (London, Roca, Novakofski, and Mateus-Pinilla); and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-
Champaign, Urbana, IL 61801, USA (Roca).
Address Correspondence to A.L. Roca at the above address, or e-mail: roca@illinios.edu.
Address Correspondence to N.E. Mateus-Pinilla at the above address, or e-mail: nohram@illinois.edu.
Corresponding Editor: Klaus-PeterKoepfli
Abstract
Cervids are distinguished by the shedding and regrowth of antlers. Furthermore, they provide insights into prion and other diseases. Genomic
resources can facilitate studies of the genetic underpinnings of deer phenotypes, behavior, and disease resistance. Widely distributed in North
America, the white-tailed deer (Odocoileus virginianus) has recreational, commercial, and food source value for many households. We present
a genome generated using DNA from a single Illinois white-tailed sequenced on the PacBio Sequel II platform and assembled using Wtdbg2.
Omni-C chromatin conformation capture sequencing was used to scaffold the genome contigs. The final assembly was 2.42 Gb, consisting
of 508 scaffolds with a contig N50 of 21.7 Mb, a scaffold N50 of 52.4Mb, and a BUSCO complete score of 93.1%. Thirty-six chromosome
pseudomolecules comprised 93% of the entire sequenced genome length. A total of 20 651 predicted genes using the BRAKER pipeline
were validated using InterProScan. Chromosome length assembly sequences were aligned to the genomes of related species to reveal corre-
sponding chromosomes.
Key words: annotation, haploid, Illumina, non-model species, Omni-C, Pacific Biosciences
The white-tailed deer (Odocoileus virginianus) is 1 of 5 spe-
cies within the deer family Cervidae that is native to the United
States, along with the mule deer (Odocoileus hemionus),
moose (Alces americanus), caribou (Rangifer tarandus), and
elk (Cervus canadensis). White-tailed deer are the most wide-
spread of all Capreolinae (New-world deer), with a range
extending from the Arctic Circle in Canada to Peru and
Bolivia (Hewitt 2011). In the United States (USA) deer hunt-
ing is a growing industry, accounting for $20 billion of value
added to the GDP in 2016 (Allen et al. 2018). Additionally,
there were 3172 deer farms operating in the United States
with an estimated value of $50 million in meat and ani-
mal product sales as of 2017 (USDA National Agricultural
Statistics Service 2019).
Reference genomes are currently available for 3 North
American deer species; mule deer (Lamb et al. 2021), Rocky
Mountain elk (Masonbrink et al. 2021), and white-tailed deer
(Odocoileus virginianus texanus) (Seabury et al. 2011). Using
third-generation sequencing (3GS), the Rocky Mountain
elk and mule deer genomes have been resolved at the chro-
mosome level (Lamb et al. 2021; Masonbrink et al. 2021).
A chromosome-level assembly sequence is a reasonably
complete pseudo-molecule with some gaps but consisting
primarily of sequenced bases (Genome Reference Consortium
2021). The Rocky Mountain elk and mule deer genomes were
both generated using Pacic Biosciences (PacBio), Illumina,
and Hi-C sequencing with both assemblies consisting of 35
chromosome-scale scaffolds (Lamb et al. 2021, Masonbrink
et al. 2021). However, 3GS was not yet available when the
Seabury et al. assembly was generated for white-tailed deer
(Seabury et al. 2011). The existing white-tailed deer genome
consists of >17 000 small scaffolds generated using sec-
ond-generation sequencing (2GS).
Third-generation sequencing, such as PacBio, allows for
continuous reads of single molecules of DNA ranging in size
from 1 to 50kb (English et al. 2012). Long reads allow for
greater overlaps between DNA sequences, resolution of long
repeat elements, and the reconstruction of contigs (Pollard
et al. 2018). A technique based on chromosome conforma-
tion capture, Hi-C (Omni-C) sequencing, is utilized to map
associations between sequences originating from the same
chromosome (Belton et al. 2012). Applying both 3GS (PacBio)
and 2GS sequencing (Illumina, Hi-C) techniques allows for
the construction of higher resolution genome assemblies be-
cause the high accuracy of 2GS short-reads corrects errors in
3GS long-read sequencing (Mahmoud et al. 2019).
© The American Genetic Association. 2022.
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480 Journal of Heredity, 2022, Vol. 113, No. 4
Having a high-quality genome assembly can empower fur-
ther studies and genomic resequencing projects at the popu-
lation level (Fuentes-Pardo et al. 2017). The ranched/farmed
white-tailed deer industry is relatively small but has been
expanding within the United States, thus creating a demand
for genomic resources that can be used to study heritable
traits such as body size, antler rejuvenation (Jamieson et al.
2020), and resistance to pathogens (Seabury et al. 2020).
A more complete, 3GS white-tailed deer genome will facil-
itate future research studies into additional genes that may
play a role in diseases of cervids (Masonbrink et al. 2021),
including white-tailed deer. For example, all native cervid
species in North America are susceptible to chronic wasting
disease (CWD), a transmissible spongiform encephalopathy
(Rivera et al. 2019), linked to genetic variation in the PRNP
gene (Robinson et al. 2012; Brandt et al. 2018; Güere et al.
2020). Additionally, according to recent genome-wide associ-
ation studies (GWAS), other non-PRNP loci may play a role
in CWD (Seabury et al. 2020), as is the case for other prion
diseases such as Creutzfeldt-Jakob disease (Jones et al. 2020).
Furthermore, having a chromosome-level assembly com-
parative genomics across species. Extrapolation of linkage
is dependent on relative chromosome location, and chromo-
some arrangements may differ across species (Potter et al.
2017). Knowing the chromosome identities in white-tailed
deer will allow for the evaluation of gene relationships within
and across chromosomes (Kong et al. 1997).
Therefore, there is a need to build on the resolution of the
white-tailed deer genome using 3GS and Hi-C scaffolding
technologies. The primary aim of this study is to create a de
novo chromosome-level deer genome by integrating resources
from both 2GS and 3GS platforms as well as Hi-C sequencing
for scaffolding. Additionally, chromosome comparisons will
be made between white-tailed deer and other mammal species
to identify homologous chromosomal regions.
Methods
Biological Sample Collection
A muscle tissue sample was selected from the Illinois tissue re-
search archive used in previous CWD genetic studies (Brandt
2018; Rivera 2019; Ishida 2020) . Illinois white-tailed deer
was traditionally classied as Odocoileus virginianus bore-
alis, although the population is an admixture of deer relocated
from adjacent regions (Pietsch 1954; Perrin-Stowe 2020).
The criteria for choosing the sample included: being stored
for fewer than 5 months, cold-weather eld conditions during
sample collection, and sustained storage at −20 °C. A Male
was chosen to sequence both X and Y chromosomes. The
selected male white-tailed deer originated from Jo Daviess
County and was sampled in February 2020.
Nucleic Acid Library Preparation
Circulomics Nanobind High-Molecular-Weight DNA
Extraction
High-molecular-weight DNA was extracted from 0.5 g
of muscle tissue using the Nanobind Tissue Big DNA Kit
(Curculomics, Baltimore MD). Briey, tissue was disrupted
using a tight-tting 1.0-mL Dounce homogenizer before lysing
with proteinase K. Following homogenization, the solution
was centrifuged at 3000 × g at 4 ° C for 5min to pelletize de-
bris and proteins. The supernatant containing the DNA was
transferred to a low-bind microfuge tube. Isopropanol and
the magnetic Nanobind disk were then added to the super-
natant and gently mixed. The disk, containing bound DNA,
was washed 3 times using a magnetic tube rack to prevent
DNA shearing. Finally, DNA was eluted from the disk using
75 µL of elution buffer. The resulting DNA concentration
was quantied using a Qubit 4 uorometer (ThermoFisher
Scientic, Waltham, MA), and the DNA length was quantied
using a Fragment Analyzer™ (Advanced Analytical
Technologies, Inc.).
Third- and Second-Generation DNA Sequencing
To generate long-read sequencing libraries, DNA was sheared
with a gTube to an average fragment length of 30kb prior
to conversion into a library following the SMRTbell Express
Template Prep Kit 2.0 protocol from Pacic Biosciences. The
library was sequenced on 2 SMRT cells on a PacBio Sequel
II with 24-hr movies. Short-read shotgun genomic libraries
were prepared using the Hyper Library construction kit from
Kapa Biosystems (Roche, Penzberg, Germany). Libraries were
sequenced on the Illumina NovaSeq 6000 equipped with an
SP owcell using 2 × 150 bp paired-end reads. Chromatin
conformation capture sequencing libraries were prepared
using the Omni-C kit from Dovetail Genomics. Libraries were
pooled; quantitated by qPCR and sequenced on the Illumina
NovaSeq 6000 equipped with an SP owcell using 2×150 nt
paired-end reads. Library preparation and sequencing were
conducted by the Roy J. Carver Biotechnology Center of the
University of Illinois at Urbana-Champaign (UIUC).
Pre-processing Reads
Sequencing reads that were much shorter or longer than the
expected read length for 3GS were removed from the read
data sets before genome assembly to reduce misassembles
and false contigs. Specically, PacBio long reads >5000bp
(Hufnagel et al. 2020) were retained from the data sets using
Fastp (Chen et al. 2018) to improve the nal assembly, and
reads greater than 50kb were removed to reduce the poten-
tial for chimeric molecular sequencing templates. All 2GS
Adaptor sequences were removed using bcl2fastq (https://
support.illumina.com/downloads/bcl2fastq-conversion-
software-v2-20.html). Thereafter, sequences were ltered for
a minimum read length of 50 bp and a minimum PHRED
score of 30 to ensure short read accuracy using Fastp. Omni-C
reads were subsampled using Seqtk (https://github.com/lh3/
seqtk) to include only 300 million read pairs based on the
recommended protocol provided by Dovetail Genomics for
the Omni-C kit (Dovetail Genomics, Scotts Valley, CA). We
list the programs and versions used throughout the assembly
and analysis pipeline in (Table 1).
De Novo Genome Assembly and Error Correction
Assembly with Wtdbg2
Filtered PacBio reads were assembled using the Wtdbg2
assembler (Ruan and Li 2020) at successive coverage
threshold intervals: 50×, 70×, 90×, 100×, 110×, 135×, and
150× (Supplementary Table S1). Furthermore, analysis was
conducted using the 90x threshold coverage assembly be-
cause the increase of coverage threshold from 70× to 90×
produced the most substantial gains in “longest contig
length” (Supplementary Table S1), while limiting excess cov-
erage from the higher error-rate long reads. Genome assembly
Journal of Heredity, 2022, Vol. 113, No. 4 481
and polishing were conducted on the Biocluster at the Carl R.
Woese Institute for Genomic Biology at UIUC.
Error Correction
Two polishing steps were performed using long and short
sequencing reads to improve assembly accuracy. Wtdbg2
performed a single round of consensus polishing (Ruan and
Li 2020) using the binned sequences. An additional round
of long-read consensus polishing was completed by aligning
PacBio reads to the 90× Wtdbg2 assembly using the Arrow
algorithm from the PacBio SMRTLink software package
(Pacic Biosciences). Short-read consensus polishing rounds
were conducted with the ltered Illumina reads using Pilon
(Walker et al. 2014) in conjunction with UniCycler (Wick et
al. 2017) to execute 10 iterative rounds of Pilon polishing.
Pilon polishing with a single round was also conducted to
address potential overcorrection. The assembly was examined
using BLAST+ for contaminant sequences that may have been
introduced through the DNA extraction sequencing process.
The core UniVec database (http://ftp.ncbi.nih.gov/pub/
UniVec) was downloaded from NCBI on March 10, 2021,
and a nucleotide BLAST search was performed against the
assembly contigs. All contaminant sequences were excised.
The contig from which each was excised was split at the re-
moval sites into 2 separate contigs (NCBI 2016). Excision of
contaminating bp was performed using the Emacs text ed-
itor using the start and end positions of the alignment output
from the nucleotide BLAST search. Furthermore, the genome
was assessed for potential contamination during nal submis-
sion to the GenBank Genome database.
Genome Deduplication and Scaffolding
Removal of Haplotigs and Artifacts
The software Purge-Haplotigs (Roach et al. 2018) was used
to remove haplotig and artifact assembly fragments. Artifacts
were dened as contigs with greater than 80% of their se-
quence being above the high or below the low sequencing cov-
erage thresholds. The threshold of 80% (default setting) was
previously shown to be sufcient to purge putative artifacts
and organelle contigs from the assembly (Roach et al. 2018).
Contig coverage histograms were generated by aligning the
ltered PacBio reads to the assembly using Minimap2 (v.)
(Li 2018). The histogram (Supplementary Figure S1A) was
generated using the purge_haplotigs hist command with
the long-reads aligned back to the genome in a BAM le as
input. The low coverage threshold was set to 15, and the
high coverage threshold was set to 190. The midpoint cov-
erage between the haploid and diploid peaks was set to 55.
Coverage thresholds were derived from the histogram peaks
in Supplementary Figure S1A and their midpoint.
Table 1. Bioinformatics software used for assembly and analysis
Software Version
Assembly and error correction
Long-read ltering Fastp 0.20.0
De novo Assembly Wtdbg2 2.5
Contig polishing (long reads) https://github.com/PacicBiosciences/gcpp 8.0.0
Short-read pre-processing Bcl2fastq2 2.20
Short-read ltering Fastp 0.20.0
Contig polishing (short reads) Pilon 1.2.2
Contig deduplication Purge-Haplotigs 1.1.1
Contamination screen BLAST+ 2.10.1
Scaffolding
Omni-C™ read ltering https://github.com/lh3/seqtk 0.3.0
Arima genomics mapping pipeline https://github.com/lh3/bwahttp://samtools.sourceforge.net/
https://github.com/broadinstitute/picard
0.7.17
1.11
2.10.1
Omni-C™ scaffolding SALSA2 2.2
Omni-C™ contact map Juicebox
HiCExplorer
1.11.08
2.2.1.1
Scaffold deduplication Purge-Haplotigs 1.1.1
Benchmarking
Genome completeness BUSCO 4.1.4
Synteny with other species https://github.com/JustinChu/JupiterPlot 1.0
Annotation
Repeat assessment RepeatMasker 4.1.1
Protein alignments ProtHint 2.5.0
RNA alignments STAR 2.7.6a
Gene prediction BRAKER 2.1.6
Prediction ltering Interproscan 5.52-86
Software presented in relative order of use in the pipeline. See citations in-text.
482 Journal of Heredity, 2022, Vol. 113, No. 4
Arima Genomics Mapping Pipeline
Subsampled Omni-C paired reads were aligned to the
deduplicated contig assembly using bwa index and bwa
mem (Li and Durbin 2009). Aligned read pairs were sorted
by position using SAMtools (Li et al. 2009) and ltered for
5' ends using the lter_ve_end.pl script (https://github.com/
ArimaGenomics/mapping_pipeline). Reads were also ltered
with SAMtools using a minimum mapping quality of 10.
Read groups and duplicate reads were added using Picard
(https://github.com/broadinstitute/picard).
Scaffolding, Contig Reassignment, and Haplotig
Removal
Mapped Omni-C reads were used as input for Salsa (Ghurye
et al. 2019) scaffolding. Salsa was run in correction mode,
allowing the use of mapping information to detect mis-
assemblies in the input contigs. The contacts between scaffolds
were visualized using Juicebox (Robinson et al. 2018). The
second round of deduplication was conducted using Illumina
paired-end reads using Purge-Haplotigs (Roach et al. 2018).
In short, Illumina reads were aligned to scaffolds using
Minimap2 and a read-depth histogram was created using the
purge_haplotigs hist command (Supplementary Figure S1B).
Scaffolds were ltered based on a low coverage threshold
of 5, and a high coverage threshold of 90. The midpoint
threshold was set to 25. Coverage thresholds were derived
from the histogram in Supplementary Figure S1B peaks and
their midpoint.
Chromosome-Level Pseudomolecule Curation
The scaffold chromatin contact matrix was visualized with
HiCExplorer (Ramirez et al. 2018) and specic scaffold–
scaffold contact graphs were examined using Juicebox.
Based on the contact graphs, scaffolds were joined into
pseudomolecules when orientation could be determined. The
orientation of the largest scaffold in each pseudomolecule
was assumed to be in the forward direction. Smaller scaffolds
were reversed as necessary based on the contact informa-
tion. Final chromosomes were aligned to the chromosome
assemblies of 6 other species using MiniMap2. The species
in order of largest to smallest chromosome number were
Cervus canadensis (GCA_019320065.1, Masonbrink et al.
2021), Cervus nippon (GWHANOY00000000, Xiumei et
al. 2021), Cervus elaphus (GCA_002197005.1, Bana et al.
2018), Bos taurus (GCA_000003205.1, Mehta et al., 2009),
Ovis aries (GCA_011170295.1, Li et al. 2021), and Homo
sapiens (GCA_000001405.28, Schneider et al. 2017). The
species C. canadensis, C. nippon, and C. elaphus have 68
autosomes; whereas B. taurus and O. aries have 58 and 52
autosomes, respectively. All species had sequences for both
X and Y chromosomes except for C. nippon, for which the
Y-chromosome sequence was not available at the time of pub-
lication. Sex chromosomes were determined based on align-
ment with the other species.
Genome Annotation
Genomic annotation used multiple available databases
for gene prediction. Gene models were predicted using the
BRAKER annotation pipeline with transcript and protein ev-
idence via GeneMark ETP+ (Altschul et al. 1990; Lomsadze
et al. 2005; Stanke et al. 2008; Camacho et al. 2009; Barnett
et al. 2011; Hoff et al. 2016, 2019). RNA alignments were
examined using GeneMark (Lomsadze et al. 2014). Proteins
were aligned to the genome using ProtHint (Brůna et al.
2020), which combines the Splan (Gotoh 2008; Iwata and
Gotoh 2012) and DIAMOND (Buchnk et al. 2015) protein
aligners. Prior to annotation, the genome was masked with
RepeatMasker (Smit et al. 2013) using Cetartiodactyla and
ancestral repeat sequences in the RepBase Update repeat da-
tabase (Bao et al. 2015). Cetartiodatyla includes cetaceans
and even-toed ungulates (Price et al. 2005). Soft-masking of
repeat sequences using RepeatMasker was used to increase
annotation speed and accuracy (Hoff et al. 2019).
Available RNA-seq data for white-tailed deer were
downloaded from the NCBI Sequence Read Archive. RNA-
Seq data have been generated in previous studies from mul-
tiple tissue types including retropharyngeal lymph node
(SRX4604241), liver (SRX2175788, SRX2175791), antler
(SRX2175789), bone (SRX2175790), lung (SRX2175792),
brain (SRX2175793), muscle (SRX2175794), testis
(SRX2175795, SRX2175797), and pedicle (SRX2175796).
All RNA reads were trimmed using Trim Galore (Martin
et al. 2011) using the default settings to remove adapter
sequences and sequences with an average Phred score below
30. Following trimming, RNA was aligned to the genome
using STAR (Dobin and Gingeras 2015) and sorted into bam
les using SAMtools (Li et al. 2009). All RNA dataset BAM
les were then merged into a single input le for BRAKER.
Following the guidance of BRAKER pipeline D (https://
github.com/Gaius-Augustus/BRAKER), protein sequences
from humans (n = 20 396) and artiodactyls (n = 8 931)
present in the SwissProt database (Boutet et al. 2007) were
used as evidence from “closely related” species. Vertebrate
protein sequences present in the orthologous gene database,
OrthoDB (n = 4937339) (Kriventseva et al. 2019), were used
as evidence from more distantly related species. All protein
sequences were aligned to the genome using the ProtHint
pipeline within GeneMark-EP (Brůna et al. 2020), which
provides an output le that BRAKER can use to incorporate
protein information. BRAKER merges the external evidence
from RNA-Seq and protein alignments for use as input to
the Augustus gene prediction software (Stanke et al. 2006;
Keller et al. 2011), which outputs the nal general feature
format les containing the locations and features of predicted
genes. Only genes supported by RNA and protein sequence
data were used for further analysis.
The longest coding sequences for each supported gene
predicted by BRAKER were translated into amino acids and
queried against the InterProScan Gene3D and Pfam protein
databases (Jones et al. 2014; Lewis et al. 2018; Blum et al. 2021;
Mistry et al. 2021). Sequences with matches were retained
within the BRAKER annotation le and predicted genes
without corresponding matches were removed. Additionally,
retroelements with identied reverse-transcriptase domains
were removed from the protein-coding gene annotations.
Assessing Completeness and Synteny
To assess the completeness of the assembly, BUSCO (Manni
et al. 2021) searches were conducted following successive
steps of the assembly and analysis pipeline. All BUSCO
searches were conducted using the Cetartiodactyla lineage
dataset from OrthoDB (Kriventseva et al., 2019). Synteny
between the white-tailed deer pseudomolecule assembly and
the Rocky Mountain elk assembly (GCA_019320065) was
Journal of Heredity, 2022, Vol. 113, No. 4 483
visualized using JupiterPlot (https://github.com/JustinChu/
JupiterPlot). The software performs alignments between ref-
erence chromosomes and query scaffolds using Minimap2,
runs in assembly mode drawing the alignment links in a cir-
cular diagram.
Results
Sequencing and Assembly
3GS, 2GS, and Omni-C Sequencing Metrics
Genomic sequencing used 13 µg of DNA with an average
fragment length of 54.8 kb. Two single-molecule real-
time sequencing cells produced 32 932 198 reads (cell 1:
13756097; cell 2: 19176 101) for a total of 390.6 gigabases
(Gb) of DNA sequence (cell 1: 174.6 Gb; cell 2: 215.9 Gb).
Long reads used for assembly were between 5 and 50kb in
length and totaled 23002345bp (cell 1: 9554592; cell 2:
13447753), covering 345.9 Gb of sequence (cell 1: 153.6
Gb; cell 2: 192.3 Gb). A single lane of paired-end Illumina
sequencing produced 1049534 322 paired-end reads for a
total of 157.4 Gb of DNA sequence. Short reads used for
error correction and deduplication totaled 1 004 706 776;
thus representing 149.2 Gb of the total sequence. A single
lane of paired-end Illumina sequencing of the Omni-C library
produced a total of 967979 604 paired reads for a total of
145.1 Gb of DNA sequence. The 600 million paired-end
reads were subsampled from the total Omni-C sequencing
output for a total of 90 Gb of sequence.
Contig- and Scaffold-Level Assembly with
Deduplication
The 90x coverage Wtdbg2 assembly represented a plateau in
assembly quality while limiting the input of “noisy” long reads
and was used for further analysis (Supplementary Table S1).
Wtdbg2 produced 5 506 contigs from ltered PacBio reads.
Deduplication of the contig assembly produced 984 haplotigs
and 2103 artifact sequences for a total of 27.9 and 34.2Mb,
respectively. A single contig was found to have a contamina-
tion vector based on a BLAST search of the UniVec database
and no contamination was found by GenBank submission
staff. The nal contig assembly consisted of 2420 contigs with
a total length of 2461348 864 bp. The N50 of the contig
assembly was 21.7Mb with an L50 of 32 contigs (Table 2
and Figure 1A). Scaffolding by Salsa with Omni-C reads was
able to join 312 contigs into 156 scaffolds. Additionally, Salsa
detected 8 contigs with mis-assemblies based upon Omni-C
mapping information. Misassembled contigs were separated
into 16 sequences before being joined into scaffolds. The N50
of the scaffold assembly was 51.4 Mb, with an L50 of 18
sequences (Figure 1A). A strong diagonal “self-associated”
signal was observed in the Hi-CExplorer plot of the Omni-C
contact matrix (Figure 1B), with minimal non-self associations.
Deduplication of the scaffold assembly with Purge-Haplotigs
revealed 637 duplicated haplotype scaffolds and 972 artifact
scaffolds for a total of 17.9 and 18.6Mb, respectively. The
nal scaffold assembly consisted of 191 contigs joined into
508 scaffolds with a total un-gapped length of 2.42 Gb (2 424
791 208bp). A total of 36 scaffolds were joined into 12 chro-
mosome groups based on HiC associations and the remaining
24 chromosomes consisted of single scaffolds (Table 3 and
Figure 1C). The 36 chromosome pseudomolecules had an
ungapped length of 2258487866bp (Table 3), representing
93% of the complete genomic sequence assembled in this
study. The number of annotated genes per chromosome and
the corresponding chromosomes of other species are shown
in Table 3. The number of annotated genes per chromo-
some and corresponding chromosomes of other species are
shown in Table 3. Chromosomal ssions were inferred if mul-
tiple chromosomes in the Odocoileus virginianus assembly
aligned to the same chromosome in another organism Gray
cells in (Table 3). Similarly, fusions were inferred if a single
Table 2. Assembly statistics and BUSCO scores for white-tailed deer
O. v. borealis(contig level) O. v. borealis(scaffold-level)
Total length (bp) 2461348864 2424946708
Number of sequences 2420 508
Number of “N” gaps n/a 311
% “N” n/a 0.006%
Largest sequence (bp) 108025303 108602581
Smallest sequence (bp) 1939 2657
Average length (bp) 1017086.3 4773517.1
N50 (bp) 21776300 52482646
L50 (# of sequences) 32 18
N90 (bp) 3308695 10477849
L90 (# of sequences) 134 49
BUSCO (n = 13335)
C: complete 93.2% (12433) 93.2% (12424)
S: single copy 90.9% (12128) 91.0%(12129)
D: duplicated 2.3% (305) 2.2% (295)
F: fragmented 0.4% (53) 0.4% (51)
M: missing 6.4% (849) 6.4% (860)
Single-copy orthologous genes from the 22 species in the Cetartiodactyla lineage dataset.
484 Journal of Heredity, 2022, Vol. 113, No. 4
chromosome in the Odocoileus virginianus assembly aligned
to multiple chromosomes in another organism Bolded cells
in(Table 3).
Genome Analysis
Annotation of Genes and Repetitive Elements
RepeatMasker identied 3499765 total interspersed repeti-
tive elements in the Ovbo_1.0 assembly occupying a total of
1034014200bp. The genome had an average repeat density
of 42.69% per scaffold, with the largest 36 scaffolds having
a repeat density of 42.09%. Initial analysis using BRAKER
predicted 46 152 complete genes, of which 37 684 were
supported by external RNA or protein evidence. Validation
of gene predictions with InterProScan supported 26 648
predicted genes. Of these supported genes, 5997 contained
reverse transcriptase domains and were removed from the an-
notation set, for a nal count of 20651 protein-coding genes
(Table 3).
Assessing Completeness Using BUSCO and
Synteny
Initial BUSCO scores following assembly by Wtdbg2 were
89.6% complete genes (88.0% single-copy; 1.6% duplicated)
and BUSCO was re-run following each step of analysis
(Supplementary Table S2). The nal BUSCO scores following
scaffold deduplication were 93.2% complete genes (91.0%
single-copy, 2.1% duplicated). Synteny comparisons between
the white-tailed deer and Rocky Mountain elk assemblies
showed single chromosomal ssion of the Rocky Mountain
elk chromosome 1 into the white-tailed deer chromosomes
12 and 17 (Table 3 and Supplementary Figure S2). Despite
the greatly enhanced contiguity of the reference genome as-
sembly achieved herein via 3GS sequencing, it should also be
noted that the BUSCO scores from Seabury et al. 2011 are
higher (93.7%) than those achieved in this study (93.2%);
thereby reecting the quality and precision of the previous
2GS assembly.
Discussion
Our chromosome-level assembly of the white-tailed deer
genome will serve as a valuable resource for future rumi-
nant and cervid research including molecular phylogeny
and comparative evolutionary studies. By employing mul-
tiple sequencing technologies, including Illumina short-
reads, Omni-C reads, and Pacic Biosciences long reads,
Figure 1. Contig, scaffold, and chromosome-level assemblies of the white-tailed deer genome. (A) Scaffolds are arranged by size (bottom) and their
component contigs are arranged by scaffold (top). The largest scaffolds representing 50% (orange) and 90% (orange + red) of the assembly are
indicated with color, leaving the remaining 10% of the assembly (black + gray). Scaffolds below 3Mb (gray) are not visually separated. The number of
contigs per scaffold is presented in Table 3. (B) Scaffold contact map generated from chromatin conformation capture Omni-C sequencing and visualized
with HiCExplorer. Scaffold-scaffold contacts are shown increasing from blue to white, to red, and the strong diagonal signal represents scaffold self-
association based on nuclear proximity. (C) Contact map for chromosome-sized pseudomolecules sequences manually curated into chromosomes.
Journal of Heredity, 2022, Vol. 113, No. 4 485
Table 3. Genome annotations and homology for the 36 chromosome pseudomolecules of white-tailed deer
Chrom. ID Ungapped length (bp) No. of gaps No. of genes No. of repeats Cervus canadensis aCervus elaphus Cervus nippon aBos taurus aOvis aries Homo sapiens
1 108600581 4 721 174661 3 18 4 4 4 7
2 102048420 2 929 173101 5 11 6 11 1 9
3 100279162 1 1253 169281 4 9 5 7 5 5
4 93958800 7 628 158735 7 19 8 1 1 3
5 93570283 16 956 164814 2 20 3 3 1 1
6 89349494 3 813 150017 6 12 7 10 7 15
7 85956676 3 583 141968 8 15 9 26/28 22 10
8 80668930 2 385 133362 9 30 10 12 10 13
9 78136789 7 685 134814 10 23 1 13 13 20
10 73421497 8 704 130149 11 1 11 15 15 11
11 72668630 4 589 123588 13 14 13 16 12 1
12 68288379 2 574 118932 1 16 2 17 17 22
13 68111889 5 304 109301 15 33 14 2 2 2
14 67564244 4 319 117421 16 25 15 20 16 5
15 66412021 3 332 115247 12 21 12 14 9 8
16 61986249 6 472 107329 17 13 16 21 18 15
17 60095371 8 1077 101312 1 5 2 19 11 17
18 57744214 5 336 93 516 14 29 18 8 2 9
19 57482545 4 252 93 221 20 28 26 9 8 6
20 57216540 5 1059 98 539 18 4 1 18 14 16/19
21 56275464 4 254 91 774 19 6/17 17 6 6 4
22 55840698 2 246 91 928 23 27 21 24 23 18
23 53708925 2 546 96 459 21 22 20 5 3 12
24 52991459 1 465 88 100 25 3 23 5 3 12
25 51970072 1 210 88 431 27 31 24 1 1 21
26 47961987 1 376 76 790 22 24 19 22 19 3
27 45470101 4 603 74 352 28 7 25 23 20 6
28 44772963 4 506 77 294 29 2 28 29 21 11
29 43582846 2 249 81 117 26 6 27 6 6 4
30 43498002 3 426 77 730 24 33 22 2 2 1
31 43483628 4 329 77 828 30 16 29 8 2 9
32 41958503 5 221 66 679 31 32 30 27 26 8
33 40612519 2 659 77 187 32 10 31 25 24 16
34 35913106 0 238 57 077 33 26 32 9 8 6
X 54563062 18 340 97 953 X X X X X X
Y 2343217 3 11 4570 Y Y bX X X
486 Journal of Heredity, 2022, Vol. 113, No. 4
the contiguity, and accuracy of the assembly were able to
surpass those of previously generated Capreolinae (New
World deer) genomes; Rangifer tarandus (GCA_014898785),
and Odocoileus hemionus (GCA_004115125). This work,
resulting in the Ovbor_1.0 assembly, used currently avail-
able long-read 3GS and Omni-C technologies to produce a
scaffold N50 of 52 Mb, which is 60 times longer than the
scaffold N50 of the existing Odocoileus virginianus texanus
assembly (GCA_002102435; Seabury et al. 2011) generated
before 3GS became available. The current assembly has an
average of fewer than 5 gaps per chromosome and will serve
as a valuable reference genome for genomic studies in white-
tailed deer and other cervids.
The assembly produced during step 1 by Wtdbg2 produced
large contiguous sequences, with an NG50 of 21Mb. This
is comparable to the human Wtdbg2 assembly presented by
Ruan and Li (2020), which had an NG50 of 18Mb. Arrow
long-read polishing was able to extend and error-correct
contigs produced by Wtdbg2. Pilon polishing was performed
iteratively; however, the most accurate error correction was
complete after a single iteration (Supplementary Table S2).
This may be compared with the study by Nguyen et al. (2020)
where 4 rounds of pilon polishing were required, following the
use of Oxford Nanopore technology. Oxford Nanopore reads
use complex electrical signals, and long-range errors can occur
(Rang et al. 2018). By contrast, in PacBio reads, errors are
characterized by insertions and deletions, and a single round
of pilon polishing produced an assembly with a higher BUSCO
score (Supplementary Table S2). Furthermore, each round of
pilon polishing can take 1– 2 days to complete depending on
the size of the genome; thereby reducing the time spent on
error correction and improving overall pipeline efciency.
BUSCO results indicated that only a small percentage of the
sequence was duplicated. During purging by Purge-Haplotigs,
it was only necessary to purge 98.6Mb of sequence, which was
less than 5% of the total genome length and was comprised of
putative artifact and haplotig sequences. By contrast, other ge-
nome assemblies require almost 50% of the genome sequence
to be purged (Roach et al. 2018). Thus, the assembly produced
by Wtdbg2 contained primarily collapsed haplotype sequences
without high levels of duplication. Furthermore, only 8 contigs
had missassemblies that were able to be detected by Salsa, (i.e.,
<0.1% of Wtdbg2 contigs), indicating that almost all contigs
were in the chromosome order implicated by Omni-C.
Our genome annotation produced by BRAKER and
validated with InterProScan expanded the set of annotations
on chromosome-sized sequences. This annotation will
provide further genomic context allowing for the assess-
ment of chromosomal rearrangements and evolutionary
relationships in white-tailed deer. Although annotations
were validated using human protein sequences, research
has shown that lineage-specic traits such as antler growth
have their genetic basis in genes (referred to as headgear
genes) that are shared across mammalian lineages; there-
fore, it is unlikely that InterProScan validation led to a loss
of lineage-specic genes. Some genes have been shown to be
under positive selection in ruminants with headgear traits
(OLIG1 and OTOP3), while others have been shown to be
highly expressed in headgear (i.e., SOX10, NGFR, ALX1,
VCAN, COL1A1) (Chen et al. 2019 and Wang et al. 2019).
This annotation information may also facilitate future syn-
tenic comparisons utilizing further gene-based synteny.
Chromosome ssions and fusions were detected between
Chrom. ID Ungapped length (bp) No. of gaps No. of genes No. of repeats Cervus canadensis aCervus elaphus Cervus nippon aBos taurus aOvis aries Homo sapiens
Placed 2258507266 18 869 3834577
Unplaced 166333442 - 1782 281882
Total 2424840708 20 651 4116459
Gray cells—multiple chromosomes in the Odocoileus virginianus assembly aligned to the same chromosome in another organism. Bold cells—a single chromosome in the Odocoileus virginianus assembly aligned
to multiple chromosomes in another organism.
aChromosomes (chrom.) for this species are not numbered in order of size.
bNo Y chromosome sequence available for Cervus nippon.
Table 3. Continued
Journal of Heredity, 2022, Vol. 113, No. 4 487
the white-tailed deer genome and the other species that
were compared (Table 3). Identication of chromosomal
arrangements will inform the assumptions made about gene
linkage and synteny.
Future genome-wide association studies will be able to
make alignments to the chromosome-level scaffolds of the
3GS Ovbor_1.0 white-tailed deer reference assembly. Having
a chromosome-level assembly with few gaps will empower
future population genomic sequencing to characterize genetic
diversity within the deer population that could identify un-
derlying genetic disease resistance loci and assist with current
conservation efforts.
Supplementary Material
Supplementary data are available at Journal of Heredity
online.
Funding
This project was supported by the U.S. Fish and Wildlife
Service [Federal Aid in Wildlife Restoration (W-146-R)]. With
additional funding from the Illinois Natural History Survey –
Prairie Research Institute and the Ofce of the Vice Chancellor
of Research, at the University of Illinois Urbana-Champaign.
Conflict of Interest
The authors declare there is no conict of interest.
Acknowledgments
We thank the Illinois Department of Natural Resources
biologists for their efforts in conducting surveillance for
chronic wasting disease and for allowing us to sample the an-
imal used in this study. We thank Dr Alvaro Hernandez and
the staff of the Roy J. Carver biotechnology center at UIUC
for their genomic sequencing and consultation. We thank
Kimberly Walden and Dr Christopher Fields of the UIUC
HPCBio facility for their consultancy and assistance with the
genome assembly pipeline. We thank Dr Julian Catchen of the
Department of Evolution, Ecology, and Behavior at UIUC for
providing insight into bioinformatics methods and analyses.
Additionally, we thank Dr Daniel Raudabaugh for his cour-
tesy reviews and genomic discussions.
Data Availability
This Whole Genome Shotgun project has been
deposited at DDBJ/ENA/GenBank under the acces-
sion JAJQKH000000000. Illumina, Omni-C, and PacBio
sequencing reads have been deposited in NCBI Sequence Read
Archive (SRR17118554, SRR17118555, SRR17162326,
SRR17162327). The GFF le produced by BRAKER is pro-
vided as data in Supplementary Material. The GFF le fol-
lowing validation by InterProScan is provided as data in
Supplementary Material.
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... Mean depth, estimated as the average number of times that each nucleotide was sequenced (Sims et al., 2014) was calculated as L * N / G, with L being the length of the reads, N being the total number of reads and G the length of the reference genome. Since no genome is available for the species we studied, data from the phylogenetically close white-tailed deer, Odocoileus virginianus, whose genome size is 2.4 GB (London et al., 2022) was used. The assembled contigs were used as an input for the microsatellite search using MSATCOMMANDER 1.0.8 ...
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