Content uploaded by Songnian Hu
Author content
All content in this area was uploaded by Songnian Hu on Nov 27, 2019
Content may be subject to copyright.
Available via license: CC BY 4.0
Content may be subject to copyright.
Jiang et al. Horticulture Research (2019) 6:128 Horticulture Research
https://doi.org/10.1038/s41438-019-0215-6 www.nature.com/hortres
ARTICLE Open Access
The apricot (Prunus armeniaca L.) genome
elucidates Rosaceae evolution and beta-carotenoid
synthesis
Fengchao Jiang
1,2
,JunhuanZhang
1,2
,SenWang
3
,LiYang
1,2
, Yingfeng Luo
3
, Shenghan Gao
3
, Meiling Zhang
1,2
,
Shuangyang Wu
3
, Songnian Hu
3
, Haoyuan Sun
1,2
and Yuzhu Wang
1,2
Abstract
Apricots, scientifically known as Prunus armeniaca L, are drupes that resemble and are closely related to peaches or
plums. As one of the top consumed fruits, apricots are widely grown worldwide except in Antarctica. A high-quality
reference genome for apricot is still unavailable, which has become a handicap that has dramatically limited the
elucidation of the associations of phenotypes with the genetic background, evolutionary diversity, and population
diversity in apricot. DNA from P. armeniaca was used to generate a standard, size-selected library with an average DNA
fragment size of ~20 kb. The library was run on Sequel SMRT Cells, generating a total of 16.54 Gb of PacBio subreads
(N50 =13.55 kb). The high-quality P. armeniaca reference genome presented here was assembled using long-read
single-molecule sequencing at approximately 70× coverage and 171× Illumina reads (40.46 Gb), combined with a
genetic map for chromosome scaffolding. The assembled genome size was 221.9 Mb, with a contig NG50 size of
1.02 Mb. Scaffolds covering 92.88% of the assembled genome were anchored on eight chromosomes. Benchmarking
Universal Single-Copy Orthologs analysis showed 98.0% complete genes. We predicted 30,436 protein-coding genes,
and 38.28% of the genome was predicted to be repetitive. We found 981 contracted gene families, 1324 expanded
gene families and 2300 apricot-specific genes. The differentially expressed gene (DEG) analysis indicated that a change
in the expression of the 9-cis-epoxycarotenoid dioxygenase (NCED) gene but not lycopene beta-cyclase (LcyB) gene
results in a low β-carotenoid content in the white cultivar “Dabaixing”. This complete and highly contiguous
P. armeniaca reference genome will be of help for future studies of resistance to plum pox virus (PPV) and the
identification and characterization of important agronomic genes and breeding strategies in apricot.
Introduction
The Rosaceae family provides most of the world’s well-
known temperate fruit crops classified as pome and stone
fruits according to their fruit morphology. With the global
tendency of consumers’purchasing preferences to shift
from large-scale commodity fruit crops (e.g., apples,
citrus, and pears) to smaller unique fruit crops with
increased nutritional value and a pleasing flavor, the rapid
development of stone fruit crops (e.g., apricots, cherries,
peaches and plums) has come to the forefront
1–3
.
Apricot (Prunus armeniaca L.), which is now generally
accepted as a fruit of Chinese origin with a growing his-
tory of more than 3000 years in China
4,5
, has been widely
grown throughout the world except for Antarctica due to
its early harvesting season, unique aroma, delicious taste,
high nutritional value and multiple uses. The rich diver-
sity of apricot germplasms indicates that apricots can be
grown even more widely and provide a higher proportion
of the world’s fruit production. Since the early 2000s, the
© The Author(s) 2019
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, whi ch permits use, sharing, adaptation, distribution and reproduction
in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a linktotheCreativeCommons license, and indicate if
changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If
material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Correspondence: Haoyuan Sun (sunhyhnus@126.com) or Yuzhu Wang
(chinabjwyz@126.com)
1
Beijing Academy of Forestry and Pomology Sciences, 100093 Beijing, PR China
2
Apricot Engineering and Technology Research Center, National Forestry and
Grassland Administration, 100093 Beijing, PR China
Full list of author information is available at the end of the article.
These authors contributed equally: Fengchao Jiang, Junhuan Zhang,
Sen Wang
1234567890():,;
1234567890():,;
1234567890():,;
1234567890():,;
fruit production and harvested orchard area of apricot
have both increased on a worldwide basis, with a 7.9
million Mt fruit tonnage and 536,072 ha orchard area
being recorded in 2017, respectively. Compared with
2002, the world production and harvested area in 2017
were increased by 196.5% and 32.2%, respectively. The
production of apricot and other Prunus species in Europe
is currently subjected to severe damaged from sharka
disease caused by the plum pox virus (PPV)
5,6
. PPV is the
most important viral disease affecting a number of Prunus
species, including apricot
7
. Therefore, it is necessary to
construct an apricot genome to localize PPV resistance-
related genes to guide the PPV resistance breeding of
apricots.
Apricot fruits are enriched in β-carotene, which repre-
sents 60−70% of the total carotenoid content
8,9
and gives
the fruit its characteristic orange color
10
.β-carotene is the
main precursor of vitamin A, one of the most important
functional ingredients in apricots. Vitamin A is an
essential nutrient for humans because it cannot be syn-
thesized within the body. Thus, as a good source of
β-carotene, apricots are highly beneficial for human
health
11,12
. In addition to its nutritional characteristics,
apricot fruit also presents some pharmacological sig-
nificance due to its high antioxidant content. Apricot and/
or β-carotene treatment is believed to be effective for
preventing the impairments caused by oxidative stress,
methotrexate-induced intestinal damage and nephro-
toxicity
13,14
. Therefore, the generation of new apricot
genotypes with higher levels of β-carotene in the fruit is a
promising breeding objective. Understanding the regula-
tion of β-carotenoid metabolism and biosynthesis will
allow breeders to develop more effective methodologies
for increasing β-carotenoid content and consequently
achieving the breeding goal.
Over the last 20 years, genomics research in Rosaceae,
and especially in Prunus, has made great advances
15
. Due
to the small size and simplicity of stone fruit tree gen-
omes, they are considered ideal candidates for the pro-
motion of association genetics approaches based on
whole-genome sequence genotyping and genome-wide
selection. Now that peach
16
, mume
17
, cherry
18
and
almond
19
genome sequences are available, genome-level
comparative analysis between multigenome sequences
within a family has become possible, which provide
valuable evolutionary insights and allow the transfer of
knowledge between species. In this study, we aimed to
construct a complementary apricot genome cv.
“Chuanzhihong”using third-generation PacBio technol-
ogy combined with second-generation Illumina data to
understand Rosaceae evolution, particularly the evolution
of genes contributing to combating PPV in Prunus and to
beta-carotenoid synthesis. A high-quality apricot refer-
ence genome sequence will afford great opportunities for
further research on germplasm diversity, evolution and
breeding.
Methods
Plant materials
The P. armeniaca L plant (“Chuanzhihong”) is native to
Hebei Province and has a cultivation history of more than
300 years. It is known as “Chuanzhihong”because of its
red color and fruitfulness. “Chuanzhihong”fruit with
good comprehensive cultivation characteristics of red
color, high yield, disease resistance, and late maturation is
a major variety in the northern region. The fruit of
“Chuanzhihong”presents good commodity value because
it can be stored for more than 10 days under normal
conditions and is suitable for long-distance transporta-
tion. Leaves of “Chuanzhihong”at an early-to-mid
developmental stage were collected for genome sequen-
cing in the Yanqing District (40°35′N and 116°11′E), in
the north of Beijing, China, and were immediately frozen
in liquid nitrogen and stored at −80 °C until DNA was
extracted.
Two Chinese apricot varieties, “Chuanzhihong”(yellow-
fleshed fruit) and “Dabaixing”(white-fleshed fruit), from
the garden of the Beijing Academy of Forestry and
Pomology Sciences, Beijing, China, were selected for RNA
sequencing to analyze the biosynthesis of β-carotene.
Samples were collected at four developmental stages:
green fruit with a soft kernel (G1), green fruit with a hard
kernel (G2), color-returning fruit (CT) and fully ripe fruit
(FR). Three biological replicates were performed, and ten
almost identical fruits were sampled at every stage for
each replicate. After the peel was removed, the pulp was
quickly cut into small pieces and frozen with liquid
nitrogen, then stored at −80 °C until RNA was extracted.
The stamen and seed tissues of “Chuanzhihong”from the
garden were used for RNA sequencing. All RNA data
were used for transcriptome-based gene prediction.
DNA and RNA sequencing
DNA sequencing
For PacBio sequencing, a DNA Template Prep Kit 1.0
was used to generate single-molecule real-time (SMRT)
bell genomic libraries. DNA fragments of ~20 kb were
obtained using a Covaris g-Tube, and the distribution of
the fragments was assessed using a Bioanalyzer 2100 12 K
DNA Chip assay. The quality and quantity of the SMRT
Bell template (>10 kb) were checked by using an Agilent
Bioanalyzer and a Qubit fluorometer, respectively.
According to the manufacturer’s instructions, the PacBio
Binding Kit 2.0 was used to generate a ready-to-sequence
SMRTbell-Polymerase Complex. The genomic DNA was
sequenced using SMRT Cells v3.0 with a yield of 16.54 Gb
of subreads (Table S1). For short-read sequencing, 150,
180 and 500 bp insert libraries were constructed
Jiang et al. Horticulture Research (2019) 6:128 Page 2 of 12
according to the manufacturer’s instructions, and
40.46 Gb of Illumina sequences was generated using the
Illumina HiSeq 2000 platform (Table S2).
RNA sequencing
RNA from G1, G2, CT and FR fruits (two cultivars
“Chuanzhihong”and “Dabaixing”) was extracted using the
RNAprep Pure Plant Kit (Polysaccharides & Poly-
phenolics-rich). The samples were processed using an
RNA library preparation kit and then sequenced on the
Illumina HiSeq 2000 platform. The RNA from the stamen
and seed tissues of “Chuanzhihong”was extracted using
the NEBNext Poly (A) mRNA Magnetic Isolation Module,
and libraries were prepared using the NEBNext mRNA
Library Prep Master Mix Set. Then, paired-end sequen-
cing with a read length of 100 bp was conducted on the
HiSeq 2500 platform.
Genome size estimation and heterozygosity
The genome size of P. armeniaca was estimated using
GenomeScope
20
. The quality of the Illumina reads was
estimated using the FastQC program
21
. Adapter sequen-
ces, PCR duplicates, contaminants, and low-quality
sequences (Phred score < 30) were removed using
fastp
22
. The analysis of optimal kmer size was performed
by using KmerGenie
23
with chloroplast and mitochondrial
sequences being removed from the high-quality clean
reads. Then, the best k-mer was used for kmer count
analysis with Jellyfish
24
. After converting the k-mer
counts into a histogram format, the k-mer distribution
was analyzed to estimate genome size and heterozygosity.
De novo genome assembly
We first estimated the error rate of the long reads
obtained from the PacBio platform using Illumina paired-
end reads. We applied the Canu
25
pipeline to assemble
the long reads and super-reads obtained from
MaSuRCA
26
into contigs with the following flow para-
meters: genomeSize =300 m, corOutCoverage =100,
minReadLength =1000, minOverlapLength =1000,
ErrorRate =0.064 and batOptions. One copy of the con-
tigs from heterozygous regions was retained by using
Purge_Haplotigs
27
. We then further mapped the Illumina
paired-end reads to the filtered contigs using bwa-mem
28
and polished the contigs with Pilon
29
. We constructed the
linkage map (https://doi.org/10.1139/CJPS-2018-0177)
and organized the contigs into pseudochromosomes with
JCVI allmaps
30
and SLAF markers.
Transcriptome assembly
The quality control (base correction, adapter trimming
and read filtering) of the RNA-seq data from leaf, fruit,
stamen and seed tissues (28 libraries in total) was per-
formed by using the software fastp with the default
parameters, after which two approaches were used to
reconstruct transcripts: de novo assembly and reference-
guided assembly. In the de novo assembly, Trinity
31
was
used to reconstruct transcripts from the RNA-seq reads.
In the genome-referenced assembly, high-quality RNA-
seq reads were mapped to the genomes using Hisat2
32,
and transcripts were built by using Stringtie
33
with the
default settings. CD-HIT
34
was used to cluster highly
similar transcripts for de novo assembly with the default
parameters.
Evaluation of the assembled genome
We first mapped the Illumina paired-end reads to the
genome using bwa-mem with the default parameters.
Second, RNA-seq data from leaf, fruit, stamen and seed
tissues were aligned using Hisat2 with the default settings.
Finally, we used BUSCO
35
to examine the single-copy
orthologs (1375, Species: Arabidopsis) with OrthoDB
36
v10.
Repeat element identification
The library of species-specific repeats was constructed
using RepeatModeler (http://www.repeatmasker.org) with
the default parameters, and RepeatMasker
37
was used to
identify repeat elements with the specific library and the
default library from the RepeatMasker database (http://
www.Repeatmasker.org). Long terminal repeat (LTR)
retrotransposons were detected with LTR-Finder
38
and
Inpactor
39
. The phylogenetic tree was constructed to
estimate the insertion times of LTR retrotransposons
using the Inpactor pipeline.
Noncoding RNA prediction
The tRNA genes were detected with tRNAscan-SE
40
using general eukaryote parameters. Ribosomal RNA
(rRNA) genes were identified with the program
RNAammer
41
. For miRNA prediction, we aligned the
mature miRNA sequences in miRBase (www.mirbase.org)
against the P. armeniaca genome with an e-value < 1e-5
and identity > 95%. The candidate sequences were
extracted from the aligned regions with an extension of 90
nucleotides flanking each side and were used to predict
RNA secondary structures by using RNAfold
42
with the
default parameters. According to the RNAfold analysis,
the candidate miRNAs were selected using the following
criteria: (1) candidate sequences were located in one of
the hairpin precursor arms, (2) the minimum free energy
for the hairpin structures was −20 kcal/mol, and (3) the
hairpins were located in intergenic regions or introns.
snRNAs and other ncRNAs were predicted using Infer-
nal
43
with the Rfam database (http://rfam.xfam.org/).
Gene prediction
Transcriptome-based, homology-based and ab initio
prediction methods were applied to predict gene models.
Jiang et al. Horticulture Research (2019) 6:128 Page 3 of 12
For transcriptome-based prediction, the nonredundant
and full-length transcripts from the de novo assembly
were aligned to the genome to resolve gene structures
using PASA
44
. The transcripts from the genome-
referenced assembly were applied to obtain reliable
transcripts with the longest open reading frames using
TransDecoder. For homology-based prediction, the pro-
tein sequences of Amygdaloideae genomes were aligned
to the genome by SPLAN
45
with the default settings. The
alignments were extended by 1 kb on each side of the hits
to identify start and (or) stop codons. For ab initio gene
prediction, the training sets (the transcripts obtained from
transcriptome-based prediction with complete 5′UTRs
and 3′UTR) were used to generate a hidden Markov
model (HMM) for ab initio gene prediction. Augustus
46
and SNAP
47
were employed to predict gene models in the
repeat-masked genome. We used the gene models
obtained from the three approaches to generate con-
sensus gene models with EVidenceModeler
44
.We
polished the gene models using full-length transcripts
according to the following steps: (1) We rectified the
potential mistakes caused by frame-shift problems. (2)
Priority was given in the following order: SPALN (Con-
served), PASA (Expressed), and EVM (Predicted). (3) If
genes overlapped on the same strand, the longer one was
retained. (4) Gene overlaps with different strand orienta-
tions were allowed. (5) Gene nesting in another gene’s
intron was allowed. Finally, we identified the UTRs and
alternative splicing of the models with PASA.
Gene functions
Gene functions were assigned by searching the pre-
dicted proteins against public databases by using
BLASTP
48
with e-value < 1e-5, including the UniProt and
the KEGG (Kyoto Encyclopedia of Genes and Genomes)
databases. We aligned the proteins against the InterPro
database using InterProScan
49
to identify protein domains
and transmembrane helices and to assign gene ontology
(GO) terms. Transcription factor (TF) identification was
carried out using an online web resource (http://planttfdb.
cbi.pku.edu.cn/prediction.php).
Gene families and synteny
We collected the protein sequences from Prunus
armeniaca L. and 13 other species (Prunus persica (L)
Batsch, Prunus avium (L) L., Prunus mume (mei), Prunus
dulcis Miller., Malus domestica Borkh., Fragaria vesca L.,
Rosa chinensis Jacq., Vitis vinifera L., Pyrus bretschneideri
Rehder., Populus trichocarpa Torr., Oryza sativa L.,
Arabidopsis thaliana (L.) Heynh. and Amborella tricho-
poda Baill.) to analyze gene families. An all-to-all BLASTP
analysis of proteins with a length ≥30 aa was performed
with an e-value < 1e-5. According to the BLASTP results,
paralogous and orthologous genes were identified by
using the software OrthoFinder
50
with an inflation of 1.5.
The all-to-all BLASTP results between P. armeniaca and
P. persica,P. armeniaca and P. dulcis, and P. armeniaca
and P. avium were extracted, and the orthologous gene
blocks on the chromosomes or pseudomolecules were
identified using the software MCscanX with the default
parameters
51
.
Phylogenetic reconstructions and divergence time
estimation
Phylogenetic construction was performed based on 269
single-copy genes extracted from the gene family analysis.
We utilized MAFFT
52
to construct protein alignments for
each single-copy gene family and removed gaps from the
alignments using the program trimAL
53
. The protein
alignments with a length ≥30 aa were concatenated for
subsequent analyses. The best substitution model for the
alignment was estimated by using ModelFinder
54
with the
default settings. The maximum likelihood tree was con-
structed using IQ-TREE
55
with a Best-fit model of JTT +
F+R3 and 1000 bootstrap replicates.
The divergence time of each node in the phylogenetic
tree was estimated based on the JC69 model in the
MCMCTree program from the PAML package
56
. The use
data parameter was set to 1 for the calculation of the
likelihood function in a normal way. For the clock para-
meter, the correlated rates were used following a log-
normal distribution. In total, the MCMCTree was run for
1,000,000 generations, with a burn-in 10,000 iterations to
a stable state. Three reported divergence times were used
as a calibration. The divergence times between the
Amygdaleae and Maleae
57
(48.4 Ma), P. trichocarpa and
A. thalian
58
(100−120 Ma) and monocots and eudicots
59
(~240 Ma) were used as calibrators.
Gene family expansion and contraction analysis
The gene family count profile used as the input file for
CAFE
60
was obtained with the program OrthoFinder. The
phylogenetic tree generated by IQ-TREE was converted to
an ultrametric tree using r8s
61
. The λvalue was estimated
with the CAFE program to identify the expansion or
contraction of gene families based on a stochastic birth
and death process model. We only considered gene
families that were significantly expanded or contracted
with pvalues smaller than 0.01. We considered both
expansion and contraction compared to the RCAs (recent
common ancestors) of species. We considered a gene
family to be unchanged if the species and its RCA
exhibited the same gene copy.
Synonymous substitutions per synonymous site (K
s
)
distribution
Orthologous genes and paralogous genes among P.
armeniaca,P. persica,P. avium,P. mume, M. domestica
Jiang et al. Horticulture Research (2019) 6:128 Page 4 of 12
and P. bretschneideri were extracted from the gene
families. Protein alignments were constructed by using
MAFFT, and the corresponding CDS (nucleotide
sequences) alignments were converted. Then, the K
s
value
for each pair of orthologous genes and paralogous genes
was calculated using codeml (CodonFreq =2, runmodel
=−2) in the PAML package.
RNA-seq and WGCNA analysis
In genome-referenced mapping, the high-quality reads
from RNA-seq data (“Chuanzhihong”and “Dabaixing”,
G1, G2, CT and FR) were aligned to the genome by using
Hisat2 and transcripts of each sample were built by using
Stringtie with the default parameters. The fragments per
kilobase of transcript per million fragments mapped
(FPKM) for each gene were calculated using Stringtie with
the -G parameter employing the gff3 genome as the
reference. The differentially expressed genes (DEGs) were
analyzed using the edgeR R package (FDR < 0.05, logFC ≥
1)
62
. The expression levels of the genes involved in car-
otenoid metabolism and genes encoding transcription
factors were used to construct the correlation network by
using the WGCNA R package
63
.
Results
Genome assembly
The genome of apricot (P. armeniaca L) (2n=16) is
small but highly heterozygous. The genome size and
fraction of heterozygosity in P. armeniaca were estimated
to be 220.36−220.56 Mb and 0.900−0.902%, respectively,
according to evaluation with GenomeScope (best k-mer =
61, obtained with KmerGenie). After the purging of
haplotigs, we obtained a haplotype assembly with 444
contigs, and its size was 221.9 Mb, with a contig
NG50 size of 1.02 Mb (Table 1). A total of 92.88% of the
assembly was anchored to eight linkage groups using
linkage maps, and the pseudomolecules ranged in size
from 18.6 to 43.0 Mb (Fig. 1, Table S3). The Illumina
reads were remapped to the genome, and single
nucleotide polymorphisms were called to estimate the
level of heterozygosity (0.96%). It can be seen from Fig. 1
that the gene density and GC content were uniformly
distributed on eight chromosomes, but the repetition
density was not uniformly distributed either in the whole
genome or on each chromosome.
Assembly validation
To assess the quality of the apricot genome, we aligned
the Illumina clean data to the apricot genome and
obtained a mapping ratio of 99.36%. We also quantified
the coverage by the PacBio data, which was 99.87%. In
addition, we aligned resequenced Prunus sibirica Illumina
paired-end reads (SRR5046735) to the assembled genome
and found that 98.69% of the reads could be mapped
58
.
The alignment rates of the RNA-seq reads from three
different tissues (flower, fruit and seed) were approxi-
mately 91.45 and 96.45% (Table S4). The BUSCO analysis
showed that 98.0% of the complete genes could be
detected in the assembly (Table S5).
Repeat annotation
Among the predicted repeats in the apricot genome,
long terminal repeats (LTR) comprised the largest pro-
portion (13.43%) (Table S6). Unclassified elements ranked
second, accounting for 12.17% of the genome. The DNA
class repeat elements comprised 9.50% of the genome.
Altogether, 38.28% of the genome was predicted to be
repetitive. The phylogenetic tree of LTR retrotransposons
showed that the repeat elements were clustered into four
groups: Gypsy,Copia, retrovirus and others (Fig. S3). The
mean divergence times of Gypsy and Copia were 0.97 and
0.88 Mya (million years ago), and both groups exhibited
recent active transposition events (Fig. S4). Altogether,
38.28% of the genome was predicted to be repetitive,
which is comparable with the repeat content observed in
mume (45.0%) and sweet cherry (43.8%) but higher than
that observed in peach (29.6%).
Gene prediction and functional annotation
A total of 30,436 protein-coding genes were predicted,
with an average transcript length of 1641 bp, by using a
combination of homology-based, ab initio and
transcriptome-based prediction methods (Table S7). The
average gene density of apricot was 137 genes per Mb,
which is higher than in peach (122 genes per Mb), mume
(132 genes per Mb), sweet cherry (87 genes per Mb) and
almond (112 genes per Mb). We identified 905 ribosomal
RNAs (5S, 5.8S, 18S and 28S), 488 transfer RNAs,
353 small nuclear RNAs and 278 microRNAs (Table S8).
The proportions of all gene models annotated to the Nr,
Pfam
59
, KEGG, GO, UniProt and Trans Membrane pre-
diction (TMHMM) databases were 99.17%, 86.07%,
43.40%, 54.59%, 71.53% and 23.51%, respectively (Table
Table 1 Genome features of P. armeniaca.
Assembly Pseudomolecules
Size (bp) 221,901,797 206,096,285
Number 444 8
NG50 (bp) 1,020,063 25,125,992
N50 (bp) 1,018,044 25,125,992
GC content (%) 37.6% 37.42%
Maximum size (bp) 5,999,228 42,984,470
Minimum size (bp) 1159 18,857,615
Mean size (bp) 499,724 25,762,035
Jiang et al. Horticulture Research (2019) 6:128 Page 5 of 12
S9). We also detected 1363 transcription factors in the
apricot genome (Table S10).
Genome evolution
The phylogenetic tree of apricot and related species was
constructed using the maximum likelihood method, and
the divergence time among branches of the tree was
estimated (Fig. 2, Figs. S5, S6). The phylogenetic tree
indicated that apricot was more closely related to
P. mume (Japanese apricot) and that the ancestor of the
two species split ~5.53 million years ago. The estimated
divergence time of the ancestor of sweet cherry was
relatively distant in the four Prunus species, at 10.92
million years (Fig. 2, Fig. S6).
A collinear analysis of the three closely related Prunus
species (P. armeniaca,P. persica and P. dulcis) was per-
formed, and the results showed that the three species
exhibited high collinearity. A total of 16,780 and 13,094
B
M
0
6MB
12MB
18MB
24MB
LG1
0MB
6MB
12MB
18MB
24MB
30MB
36MB
42MB
LG2
0MB
6MB
12MB
18MB
24MB
LG3
0MB
12MB
18MB
6MB
24MB
LG4
0MB
6MB
12MB
18MB
24MB
LG5
0MB
6MB
12MB
18MB
LG6
0MB
6MB
12MB
18MB
LG7
0MB
6MB
12MB
18MB
LG8
1
2
3
4
5
Fig. 1 Genetic structure and variant density of apricot. (1) Pseudomolecules; (2) gene density; (3) GC content (per 100 Kb); (4) repeat density (per
100 Kb); (5) heatmap of the G1, G2, CT and FR stages.
Jiang et al. Horticulture Research (2019) 6:128 Page 6 of 12
apricot genes were located in collinear blocks between
apricot and peach and between apricot and almond,
respectively (Fig. S7). Functional annotation showed that
these collinear genes were mainly involved in the basic
needs of organisms, including energy and other types of
metabolism (Fig. S8).
Gene family analysis showed that during the evolution of
apricot, 1324 gene families expanded, while 981 families
contracted and produced 2300 apricot-specific genes (Fig.
2, Table S11). The genes from the expanded families were
mainly enriched in phenylpropanoid biosynthesis
(p=0.0018) and flavonoid biosynthesis (p=0.0019) (Fig.
S8, Table S12). In addition, the citrate synthase family was
expanded, with three copies in the apricot genome (the
additional copy came from the recent species-specific
tandem duplication event) and two in other species in the
Prunus genus. Gene expression analysis indicated that the
three citrate synthase-encoding genes were highly expres-
sed during fruit development. The functions of the apricot-
specific genes were enriched in transport (sulfate transport
(p=6.0404e-9), anion transport (p=6.0985e-6), trans-
membrane transport (p=5.1224e-5), and oxidation
reduction (p=1.7853e-5)). A total of 361 apricot-specific
genes were transcriptionally active during at least one of
the four fruit developmental stages, indicating that these
genes may play an important role in the growth and
development of apricot (Fig. S8, Table S13).
Although apricot has not experienced whole-genome
duplication events, as observed in apple and pear (Fig.
S10), there were many large segmental duplication regions
in the apricot genome. We identified 290 gene blocks in
the apricot genome, involving a total of 2794 genes. These
genes were mainly enriched in pathways of plant
−pathogen interactions (p=0.00029) and phenylpropa-
noid biosynthesis (p=0.0011) (Fig. S8, Table S14).
MATHd evolution of PPV in Prunus
The comparative analysis of MATHd-orthologous
regions within related Prunus species (P. armeniaca,
P. avium, P. mume and P. persica) was performed to detail
the evolutionary history of these important gene clusters
(Fig. 3a). These species exhibited different copy numbers
within these regions; P. armeniaca exhibited 6, P. mume
7, P. persica 7 and P. avium 12. Phylogenetic tree analysis
suggested that species-specific tandem duplication and
perhaps gene loss events contributed to the architectural
composition of these orthologous syntenic regions (Fig.
3b).
Carotenoid metabolism
Carotenoids play an important role in plant photo-
synthesis and lipid peroxidation and impact the color
traits of plant fruits. We analyzed the dynamic changes in
gene expression levels in four stages of apricot pulp (G1,
Fig. 2 Phylogenetic tree and gene family changes of apricot and related species.
Jiang et al. Horticulture Research (2019) 6:128 Page 7 of 12
G2, CT and FR) (Fig. 4). Differentially expressed gene
analysis showed that 2532, 4708, and 2033 genes differed
between G1 and G2, G2 and CT, and CT and FR,
respectively, and 1, 9, and 3 genes were involved in car-
otenoid metabolism (Fig. 4, Fig. S11, Table S15A, B, C).
During the G2 to CT phase of fruit ripening, the
expression levels of the genes changed more significantly,
especially those of genes related to beta-carotene synth-
esis. The gene encoding the enzyme LcyB (lycopene beta-
cyclase), which is the key enzyme in the synthesis of
carotene, was significantly upregulated, indicating that
beta-carotene is rapidly synthesized during the CT phase.
The genes encoding PSY (15-cis-phytoene synthase) and
ZDS (zeta-carotene desaturase), which play an important
role in the synthesis of the precursors of beta-carotene,
were also upregulated between G2 and CT. Although the
carotene content increased, there was no significant
change in beta-carotene synthesis-related gene expression
between CT and FR (Fig. 5), which revealed that high
expression of the LcyB gene results in the accumulation of
carotenoids.
We further explored the transcription factors involved
in the regulation of beta-carotene synthesis through
coexpression network analysis as described in WGCNA
(Figs. S12, 13). We identified 95 transcription factors
involved in the regulation of carotenoid metabolic path-
ways, 12 of which were related to the LcyB gene (Table
S16). Among the 11 transcription factors, 9 were involved
in positive regulation, and 3 were involved in negative
regulation (Table S17).
The most important difference in the color of the white
and yellow cultivars was the beta-carotene content, but
DEG analysis showed that no obvious changes in the
genes related to beta-carotene synthesis (Fig. S14). We
found that the expression of the gene encoding the
enzyme NCED (9-cis-epoxycarotenoid dioxygenase),
which catalyzes the synthesis of 9-cis-neoxanthin-syn-
thesized xanthoxin, was very active, and its expression
P. armeniaca LG2 (8.28-8.32Mb)
P. mume LG2 (33.32-33.41Mb)P. persica Chr01 (8.66-8.60Mb)
P. avium NW_018921264 (0.58-0.69Mb)
(A)
(B)
0.2
LOC103323307
LOC107880359
LOC103323272
PARG04361
LOC103323306
LOC103323274
PARG04357
LOC103323308
LOC110752375
LOC110752311
PARG04360
LOC110751749
LOC103323270
LOC110752344
LOC110751995
LOC110752353
LOC110751960
Prupe.1G108000.1
Prupe.1G107400.1
PRUAV|rna863
LOC110751988
Prupe.1G107300.1
Prupe.1G107900.1
PARG04358
Prupe.1G107600.1
Prupe.1G107700.1
PRUAV|rna860
PARG04359
Prupe.1G107800.1
Prupe.1G107500.1
Prupe.1G107200.1
LOC103323305
PARG04356
LOC110752411
LOC110751759
Fig. 3 MATHd proteins within apricot, P. mume,P. persica and P. avium.aOrthologous regions; bthe phylogenetic tree of MATH proteins from
apricot (blue), P. mume (green), P. persica (pink) and P. avium (dark blue).
Jiang et al. Horticulture Research (2019) 6:128 Page 8 of 12
level was significantly different between the two cultivars
“Chuanzhihong”and “Dabaixing”(Fig. S14). In the white
cultivar, the synthesis and decomposition of carotene
were balanced, and the newly synthesized carotenoids
were converted into xanthoxin, the precursor of abcisate,
through enzyme catalysis, especially by NCED, halting the
accumulation of carotene.
Discussion
Fruit trees usually exhibit high heterogeneity, which
makes it more difficult to obtain high-quality complete
genomes, and this effect is particularly evident in the
released genomes of stone fruit trees. In the assembly of
the apricot genome, to overcome the problem caused by
heterozygosity, we first assembled the Illumina data into
super-reads by using MaSuRCA, and super-reads and
corrected PacBio subreads were then assembled into
contigs constituting the diploid genome. After purging
haplotigs, the apricot genome N50 size was 1,018,044 bp,
and the contig N50 sizes of peach, cherry, mume and
almond were 294, 276, 31,772 and 77,040 bp, respectively,
which suggested that the P. armeniaca reference genome
was the most contiguous among the sequenced stone fruit
trees
16–19
. The BUSCO analysis showed that 98.0%
complete genes were detected in the assembly, which
indicated that the genome quality of apricot was better
than those of the other published stone fruit genomes
16–19
.
In brief, these results indicate that the genome of apricot
is relatively accurate and complete among the available
genomes of stone fruit trees.
Plum pox virus, also known as sharka, is a linear single-
stranded RNA virus that affects Prunus species. The
selection of PPV-resistant genetic resources in Prunus
germplasms is an important approach for resistance
breeding that is currently effective against PPV; with the
breakthroughs regarding anti-PPV genes, biotechnologi-
cal strategies have been applied or may be exploited to
IPP
DMAPP
GGPP
15-cis-Phytoene
all-trans-zeta-Carotene
all-trans-Lycopene
delta-Carotene
alpha-Carotene
Lutein
gamma-Carotene
beta-Carotene
Zeaxanthin
Violaxanthin
9'-cis-Neoxanthin
Xanthoxin
Abscisic aldehyde
Abscisic acid
IPPI
GGPS
PSY
PDS
ZDS
lcyB
Zeinoxanthin
CYP97A3
CYP97C1
lcyB
crtZ
ZEP
VDE
9-cis-Violaxanthin
NCED
NCED
ABA2
AAO3
NSY
lcyE lcyB
Fig. 4 Carotenoid metabolism pathway of apricot. The expression
levels of genes are shown in a bar chart with different colors
corresponding to different stages. A black asterisk indicates a
significant change (p≤0.05) between two adjacent stages.
Fig. 5 Changes in the color and beta-carotenoid content of
apricot fruits in different developmental stages.
Jiang et al. Horticulture Research (2019) 6:128 Page 9 of 12
confer PPV resistance
64,65
. Recent studies have shown
that apricot resistance to PPV is associated with the
pseudogenization/downregulation of two tandemly
duplicated MATHd genes
66
. By comparing the changes in
MATHd orthologues in Prunus, we found that the asso-
ciated regions were vertically inherited from the ancestor
of Prunus species and that at least two tandemly arrayed
copies have been retained in each species; the loss of the
MATHd genes may result in susceptibility to PPV. Con-
sidering these results together, it will be interesting to test
the roles of these genes in PPV infection through mole-
cular studies, genetic association studies and molecular
breeding within other Prunus species.
The balance of the biosynthesis and decomposition of
β-carotene may contribute to the color of apricot fruit.
First, β-carotene, as one of the metabolites important for
fruit quality, contributes to the yellow color of apricot
fruit
67,68
. Among fruit pigments, anthocyanins are the
main typical pigments contributing to the red, pink or
violet coloration of some fruit, such as apple, tomato,
purple sweet potato, and strawberry
69–76
. However, in
apricot fruit, β-carotene is detectable and shows sig-
nificantly higher levels in the yellow-flesh of “Chuanzhi-
hong”than in the white-flesh of “Dabaixing”. These
results showed that β-carotene is the main pigment of
apricot with yellow flesh (Fig. 5), which is supported by
studies from Curl
77
and Roussos et al.
10
There are also
many other fruit species with high levels of β-carotene,
such as citrus, carrot, mango, papaya, and tomato
73,78
.
Moreover, the expression patterns of β-carotene
synthesis genes in plants are tissue- and stage-dependent.
Psy, pds, zds, lcy-e, crt-b, zep and necd3 are all expressed
in coffee leaves, flowers and shoots, but the transcript
levels differ among the three tissues
79
. For tomato, citrus,
watermelon and other fruit-type crops, the genes related
to β-carotene biosynthesis appear to show the highest
transcript levels, and rapid β-carotene accumulation is
mainly observed in the nearly mature stage
80–82
. Lyco-
pene and β-carotene rapidly accumulate in the flesh of
Cara citrus fruit during the two stages of fruit enlarge-
ment and fruit ripening. The present study indicated that
the G2 to CT stages may be the corresponding key period
in apricot. As described in the literature, the color change
from green to red or yellow is very important in fruit
development as anthocyanins or carotenoids accumulate,
in addition to sugar, ABA and ETH, to promote fruit
ripening
83–85
. A similar result was observed in this study
(Fig. 5). During the G2 to CT phase of fruit ripening, the
expression levels of genes changed more significantly,
especially those of genes related to β-carotene synthesis.
Multiple metabolic pathways are involved in this stage to
affect fruit development and ripening, including
β-carotene biosynthesis. The β-carotene content of
“Chuanzhihong”rapidly increased to a high level
beginning in the CT stage and reached its highest level at
the FR stage, which indicates maturity of the fruit.
Furthermore, all the DEGs related to the β-carotene
biosynthesis pathway were analyzed. In apricot, lcy-b may
make an important contribution to yellow flesh develop-
ment in the ripening fruit of “Chaunzhihong”. As shown
in Fig. S10, lcy-b was significantly upregulated, indicating
that β-carotene was undergoing rapid synthesis during the
CT phase, which is similar to the change trend of flesh
color. In contrast, among the other three important genes
(psy, pds, zds) for β-carotene biosynthesis, the gene
expression levels were not constant with the accumula-
tion of β-carotene. All of the previous studies indicated
that the gene expression changes controlling β-carotene
biosynthesis in plants among different species or different
varieties are very complex. However, it is interesting that,
in the white cultivar “Dabaixing”, the transcript level of
the NCED gene is much higher during fruit development,
especially in the last two stages of CT and FR, which is
contrary to what is observed in the yellower cultivar
“Chuanzhihong”(Fig. S13). The newly synthesized car-
otenoids are rapidly converted into xanthoxin, the pre-
cursor of abcisate, through enzyme catalysis, especially
that by NCED, halting the accumulation of carotene.
Thus, the balance of the biosynthesis and decomposition
of β-carotene may contribute to the color of apricot fruit,
providing the first report of the likely mechanism of fruit
color development in apricot. Further research with
respect to the characterization and functional identifica-
tion of lcy-b and NCED as well as the possible tran-
scriptional regulation mechanism of β-carotene in apricot
will be carried out, which is valuable for basic research
and the future breeding of new apricot varieties rich in
β-carotene.
Conclusions
In this study, we first report the sequencing, assembly
and annotation of the apricot (Prunus armeniaca L)
genome, along with the significant evolutionary features
of the main Prunus species. The MATHd genes were
shown to be vertically inherited from the ancestor of the
Prunus species and retained at least two tandemly arrayed
copies in apricot, cherry, mume and peach species. The
NCED (9-cis-epoxycarotenoid dioxygenase) gene, not
LcyB (lycopene beta-cyclase), results in a low β-carotenoid
content in the white cultivar “Dabaixing”. The
chromosome-scale assembly of apricot will provide more
important gene resources for future studies on stone fruit
crops, which is also valuable for efficiently screening
functional genes related to agronomic traits as well as for
GWAS (genome-wide association study) analysis and fine
QTL mapping. Taken together, the results of this study
indicate that it is feasible to use this genome as a tool for
improving breeding strategies.
Jiang et al. Horticulture Research (2019) 6:128 Page 10 of 12
Acknowledgements
This work was supported by the research of the National Key R&D Program of
China (2018YFD1000606-4), the Beijing Academy of Agriculture and Forestry
Fund for Young Scholars (QNJJ201702, QNJJ201925), the National Natural
Science Foundation of China (31401836), and the Municipal Natural Science
Foundation of Beijing (6162012).
Author details
1
Beijing Academy of Forestry and Pomology Sciences, 100093 Beijing, PR
China.
2
Apricot Engineering and Technology Research Center, National
Forestry and Grassland Administration, 100093 Beijing, PR China.
3
CAS Key
Laboratory of Genome Sciences and Information, Beijing Institute of Genomics,
Chinese Academy of Sciences, 100101 Beijing, China
Author contributions
Y.W. and H.S. conceived and designed the project. J.Z. collected and extracted
the genomic DNA and fruit RNA with assistance from F.J., L.Y., H.S. and M.Z. S.H.
and Y.L. supervised the bioinformatics analysis; S.W., F.J. and S.G. assembled the
apricot genome; F.J. and S.W. performed gene annotation and gene family
analysis; S.W., F.J. and Y.L. performed the analysis of transcriptome and PPV-
resistant genes; F.J., J.Z. and S.W. wrote the draft manuscript. All authors read
and approved the final version of the manuscript.
Data availability
The data that support the findings of this study have been deposited in the
CNSA (https://db.cngb.org/cnsa/) of CNGBdb with accession number
CNP0000755. The genome assembly data have been deposited in genome
database for Rosaceae (www.rosaceae.org).
Conflict of interest
The authors declare that they have no conflict of interest.
Supplementary Information accompanies this paper at (https://doi.org/
10.1038/s41438-019-0215-6).
Received: 21 June 2019 Revised: 9 October 2019 Accepted: 23 October
2019
References
1. Folta, K. M. & Gardiner, S. E. Genetics and Genomics of Rosaceae (Springer, 2009).
2. Benichou, M. et al. Postharvest technologies for shelf life enhancement of
temperate fruits. in
Postharvest Biology and Technology of Temperate Fruits (eds Shabir Ahmad,
M., Manzoor Ahmad, S. & Mohammad Maqbool, M.) 77−100 (Springer, 2018).
3. Kearney, J. Food consumption trends and drivers. Philos.Trans.R.Soc.B:Biol.
Sci. 365, 2793–2807 (2010).
4. De Candolle, A. Origin of Cultivated Plants (Reprint 1964). (Hafner Publishing,
New York, 1886).
5. Faust,M.,Suranyi,D.&Nyujto,F.Originanddisseminationofapricot.Hortic.
Res. 22,225–260 (1998).
6. Zhebentyayeva, T., Ledbetter, C., Burgos, L. & Llácer, G. Apricot. Fruit Breeding
415−458 (Springer, 2012).
7. Zuriaga, E. et al. Genomic analysis reveals MATH gene (s) as candidate (s) for P
lum pox virus (PPV) resistance in apricot (P runus armeniaca L.). Mol. Plant
Pathol. 14, 663–677 (2013).
8. Sass-Kiss, A., Kiss, J., Milotay, P., Kerek, M. & Toth-Markus, M. Differences in
anthocyanin and carotenoid content of fruits and vegetables. Food Res. Int. 38,
1023–1029 (2005).
9. Dragovic-Uzelac,V.,Levaj,B.,Mrkic,V.,Bursac,D.&Boras,M.Thecontentof
polyphenols and carotenoids in three apricot cultivars depending on stage of
maturity and geographical region. Food Chem. 102, 966–975 (2007).
10. Roussos,P.A.etal.Apricot(Prunus armeniaca L.). in Nutritional composition
of fruit cultivars (eds Simmonds, M. & Preedy, V.) 19-48 (Academic press,
2016)
11. Akin, E. B., Karabulut, I. & Topcu, A. Some compositional properties of main
Malatya apricot (Prunus armeniaca L.) varieties. Food Chem. 107,939–948
(2008).
12. Ali,S.,Masud,T.&Abbasi,K.S.Physico-chemicalcharacteristicsofapricot
(Prunus armeniaca L.) grown in northern areas of Pakistan. Sci. Horticulturae
130,386–392 (2011).
13. Vardi,N.etal.Potentprotectiveeffectofapricotandβ-carotene on
methotrexate-induced intestinal oxidative damage in rats. Food Chem. Toxicol.
46,3015–3022 (2008).
14. Vardi,N.,Parlakpinar,H.,Ates,B.,Cetin,A.&Otlu,A.Theprotectiveeffectsof
Prunus armeniaca L (apricot) against methotrexate-induced oxidative damage
and apoptosis in rat kidney. J. Physiol. Biochem. 69,371–381 (2013).
15. Shulaev, V. et al. Multiple models for Rosaceae genomics. Plant Physiol. 147,
985–1003 (2008).
16. Verde, I. et al. The high-quality draft genome of peach (Prunus persica)iden-
tifies unique patterns of genetic diversity, domestication and genome evo-
lution. Nat. Genet. 45, 487 (2013).
17. Zhang, Q. et al. The genome of Prunus mume.Nat. Commun. 3, 1318 (2012).
18. Shirasawa, K. et al. The genome sequence of sweet cherry (Prunus avium)for
use in genomics-assisted breeding. DNA Res. 24,499–508 (2017).
19. Sánchez-Pérez, R. et al. Mutation of a bHLH transcription factor allowed
almond domestication. Science 364, 1095–1098 (2019).
20. Vurture,G.W.etal.GenomeScope:fastreference-freegenomeprofiling from
short reads. Bioinformatics 33,2202–2204 (2017).
21. Andrews, S. FastQC. A quality control tool for high throughput sequence data.
http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).
22. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ pre-
processor. Bioinformatics 34,i884–i890 (2018).
23. Chikhi, R. & Medvedev, P. Informed and automated k-mer size selection for
genome assembly. Bioinformatics 30,31–37 (2013).
24. Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel
counting of occurrences of k-mers. Bioinformatics 27,764–770 (2011).
25. Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-
mer weighting and repeat separation. Genome Res. 27,722–736 (2017).
26. Zimin, A. V. et al. The MaSuRCA genome assembler. Bioinformatics 29,
2669–2677 (2013).
27. Roach, M. J., Schmidt, S. A. & Borneman, A. R. Purge Haplotigs: allelic contig
reassignment for third-gen diploid genome assemblies. BMC Bioinforma. 19,
460 (2018).
28. Li, H. & Durbin, R. Fast and accurate short read alignment with
Burrows–Wheeler transform. Bioinformatics 25,1754–1760 (2009).
29. Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial
variant detection and genome assembly improve ment. PLoS ONE 9,
e112963 (2014).
30. Tang, H., Krishnakuar, V. & Li, J. jcvi: JCVI utility libraries. Zenodo.https://doi.org/
10.5281/zenodo.31631 (2015).
31. Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq
using the Trinity platform for reference generation and analysis. Nat. Protoc. 8,
1494 (2013).
32. Kim,D.,Langmead,B.&Salzberg,S.L.HISAT:afastsplicedalignerwithlow
memory requirements. Nat. Meth. 12, 357 (2015).
33. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome
from RNA-seq reads. Nat. Biotechnol. 33, 290 (2015).
34. Fu,L.,Niu,B.,Zhu,Z.,Wu,S.&Li,W.CD-HIT:acceleratedforclusteringthenext-
generation sequencing data. Bioinformatics 28,3150–3152 (2012).
35. Simão,F.A.,Waterhouse,R.M.,Ioannidis,P.,Kriventseva,E.V.&Zdobnov,E.M.
BUSCO: assessing genome assembly and annotation completeness with
single-copy orthologs. Bioinformatics 31,3210–3212 (2015).
36. Kriventseva,E.V.etal.OrthoDBv10:sampling the diversity of animal, plant,
fungal, protist, bacterial and viral genomes for evolutionary and functional
annotations of orthologs. Nucleic Acids Res. 47,D807–D811 (2018).
37. Tarailo‐Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive ele-
ments in genomic sequences. Curr. Protoc. Bioinforma. 25, 14 (2009). 4.10. 11-
14.10.
38. Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length
LTR retrotransposons. Nucleic Acids Res. 35,W265–W268 (2007).
39. Orozco-Arias, S. et al. Inpactor, integrated and parallel analyzer and classifier of
LTR retrotransposons and its application for pineapple LTR retrotransposons
diversity and dynamics. Biology 7,32(2018).
40. Lowe, T. M. & Chan, P. P. tRNAscan-SE On-line: integrating search and
context for analysis of transfer RNA genes. Nucleic Acids Res. 44,
W54–W57 (2016).
41. Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal
RNA genes. Nucleic Acids Res. 35,3100–3108 (2007).
Jiang et al. Horticulture Research (2019) 6:128 Page 11 of 12
42. Lorenz, R. et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6,26(2011).
43. Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology sear-
ches. Bioinformatics 29, 2933–2935 (2013).
44. Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVi-
denceModeler and the Program to Assemble Spliced Alignments. Genome
Biol. 9, R7 (2008).
45. Iwata, H. & Gotoh, O. Benchmarking spliced alignment programs including
Spaln2, an extended version of Spaln that incorporates additional species-
specificfeatures.Nucleic Acids Res. 40,e161–e161 (2012).
46. Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts.
Nucleic Acids Res. 34,W435–W439 (2006).
47. Korf, I. Gene finding in novel genomes. BMC Bioinforma. 5,59(2004).
48. Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein
database search programs. Nucleic Acids Res. 25,3389–3402 (1997).
49. Jones, P. et al. InterProScan 5: genome-scale protein function classification.
Bioinformatics 30,1236–1240 (2014).
50. Emms, D. M. & Kelly, S. OrthoFinder: solving fundamental biases in whole
genome comparisons dramatically improves orthogroup inference accuracy.
Genome Biol. 16, 157 (2015).
51. Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary
analysis of gene synteny and collinearity. Nucleic Acids Res. 40,e49–e49
(2012).
52. Katoh,K.&Standley,D.M.MAFFTmultiplesequencealignmentsoftware
version 7: improvements in performance and usability. Mol. Biol. Evol. 30,
772–780 (2013).
53. Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for
automated alignment trimming in large-scale phylogenetic analyses. Bioin-
formatics 25,1972–1973 (2009).
54. Kalyaanamoorthy,S.,Minh,B.Q.,Wong,T.K.,VonHaeseler,A.&Jermiin,L.S.
ModelFinder: fast model selection for accurate phylogenetic estimates. Nat.
Meth. 14, 587 (2017).
55. Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and
effective stochastic algorithm for estimating maximum-likelihood phylogenies.
Mol. Biol. Evol. 32, 268–274 (2014).
56. Yang, Z. PAML: a program package for phylogenetic analysis by maximum
likelihood. Bioinformatics 13,555–556 (1997).
57. Hohmann, N., Wolf, E. M., Lysak, M. A. & Koch, M. A. A time-calibrated road map
of Brassicaceae species radiation and evolutionary history. Plant Cell 27,
2770–2784 (2015).
58. Tuskan, G. A. et al. The genome of black cottonwood, Populus trichocarpa
(Torr. & Gray). Science 313,1596–1604 (2006).
59. Jaillon, O. et al. The grapevine genome sequence suggests ancestral hex-
aploidization in major angiosperm phyla. Nature 449, 463 (2007).
60. De Bie, T., Cristianini, N., Demuth, J. P. & Hahn, M. W. CAFE: a computational
tool for the study of gene family evolution. Bioinformatics 22, 1269–1271
(2006).
61. Sanderson, M. J. r8s: inferring absolute rates of molecular evolution and
divergence times in the absence of a molecular clock. Bioinformatics 19,
301–302 (2003).
62. Robinson,M.D.,Mccarthy,D.J.&Smyth,G.K.edgeR:aBioconductorpackage
for differential expression analysis of digital gene expression data. Bioinfor-
matics 26,139–140 (2010).
63. Langfelder,P.&Horvath,S.WGCNA:anRpackageforweightedcorrelation
network analysis. BMC Bioinforma. 9, 559 (2008).
64. Rubio, M. et al. Gene expression analysis of Plum pox virus (Sharka) suscept-
ibility/resistance in apricot (Prunus armeniaca L.). PLoS ONE 10, e0144670
(2015).
65. Ilardi,V.&Tavazza,M.Biotechnological strategies and tools for Plum pox virus
resistance: trans-, intra-, cis-genesis, and beyond. Front. Plant Sci. 6, 379 (2015).
66. Zuriaga,E.,Romero,C.,Blanca,J.M.&Badenes,M.L.Resistanceto
Plum Pox Virus (PPV) in apricot (Prunus armeniaca L.) is associated with down-
regulation of two MATHd genes. BMC Plant Biol. 18, 25 (2018).
67. García-Gómez, B. E., Salazar, J. A., Dondini, L., Martínez-Gómez, P. & Ruiz, D.
Identification of QTLs linked to fruit quality traits in apricot (Prunus armeniaca
L.) and biological validation through gene expression analysis using qPCR. Mol.
Breed. 39,28(2019).
68. García-Gómez, B. et al. Comparative analysis of SSR markers developed in
exon, intron, and intergenic regions and distributed in regions controlling fruit
quality traits in Prunus species: genetic diversity and association studies. Plant
Mol. Biol. Report. 36,23–35 (2018).
69. Winkel-Shirley, B. Flavonoid biosynthesis. A colorful model for genetics,
biochemistry, cell biology, and biotechnology. Plant Physiol. 126,
485–493 (2001).
70. Espley, R. V. et al. Red colouration in apple fruit is due to the activity of the
MYB transcription factor, MdMYB10. Plant J. 49,414–427 (2007).
71. Gonzali,S.,Mazzucato,A.&Perata,P.Purpleasatomato:towardshigh
anthocyanin tomatoes. Trends Plant Sci. 14,237–241 (2009).
72. Carbone, F. et al. Developmental, genetic and environmental factors affect the
expression of flavonoid genes, enzymes and metabolites in strawberry fruits.
Plant, Cell Environ. 32,1117–1131 (2009).
73. Pons, E. et al. Metabolic engineering of β-carotene in orange fruit increases its
in vivo antioxidant properties. Plant Biotechnol. J. 12,17–27 (2014).
74. Rinaldo,A.R.etal.Agrapevineanthocyanin acyltransferase, transcriptionally
regulated by VvMYBA, can produce most acylated anthocyanins present in
grape skins. Plant Physiol. 169, 1897–1916 (2015).
75. Borghesi, E. et al. Comparative physiology during ripening in tomato rich-
anthocyanins fruits. Plant Growth Regul. 80,207–214 (2016).
76. Yao,G.etal.Map‐based cloning of the pear gene MYB 114 identifies an
interaction with other transcription factors to coordinately regulate fruit
anthocyanin biosynthesis. Plant J. 92,437–451 (2017).
77. Curl, A. L. The carotenoids of apricots. J. Food Sci. 25,190–196 (1960).
78. Schweiggert,R.M.,Mezger,D.,Schimpf,F.,Steingass,C.B.&Carle,R.Influence
of chromoplast morphology on carotenoid bioaccessibility of carrot, mango,
papaya, and tomato. Food Chem. 135,2736–2742 (2012).
79. Simkin,A.J.,Kuntz,M.,Moreau,H.&Mccarthy,J.Carotenoidprofiling and
the expression of carotenoid biosynthetic genes in developing coffee grain.
Plant Physiol. Biochem. 48, 434–442 (2010).
80. Carrari, F. & Fernie, A. R. Metabolic regulation underlying tomato fruit devel-
opment. J. Exp. Bot. 57,1883–1897 (2006).
81. Alquezar,B.,Rodrigo,M.J.&Zacarías,L.Regulationofcarotenoidbiosynthesis
during fruit maturation in the red-fleshed orange mutant Cara Cara. Phy-
tochemistry 69,1997–2007 (2008).
82. Dou, Jl et al. Effect of ploidy level on expression of lycopene bio-
synthesis genes and accumulation of phytohormones during water-
melon (Citrullus lanatus) fruit development and ripening. J. Integr. Agr.
16, 1956–1967 (2017).
83. Ecarnot, M., Bączyk, P., Tessarotto, L. & Chervin, C. Rapid phenotyping of the
tomato fruit model, Micro-Tom, with a portable VIS–NIR spectrometer. Plant
Physiol. Biochem. 70,159–163 (2013).
84. Su, L. et al. Carotenoid accumulation during tomato fruit ripening is modu-
lated by the auxin-ethylene balance. BMC Plant Biol. 15, 114 (2015).
85. Wang, Q. H. et al. Transcriptome analysis around the onset of strawberry fruit
ripening uncovers an important role of oxidative phosphorylation in ripening.
Sci. Rep. 7, 41477 (2017).
Jiang et al. Horticulture Research (2019) 6:128 Page 12 of 12
