Advanced Applications of
Next-generation Sequencing
Technologies to Orchid Biology
Chuan-Ming Yeh1†, Zhong-Jian Liu2,3,4† and Wen-Chieh Tsai5,6,7*
1
Division of Strategic Research and Development, Graduate School of Science and Engineering, Satitama University,
Saitama, Japan.
2
Shenzhen Key Laboratory for Orchid Conservation and Utilization, The National Orchid Conservation Center of
China and The Orchid Conservation and Research Center of Shenzhen, Shenzhen, China.
3
The Center for Biotechnology and BioMedicine, Graduate School at Shenzhen, Tsinghua University, Shenzhen,
China.
4
College of Arts, College of Landscape Architecture, Fujian Agriculture and Forestry University, Fuzhou, China.
5
Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan, Taiwan.
6
Department of Life Sciences, National Cheng Kung University, Tainan, Taiwan.
7
Orchid Research and Development Center, National Cheng Kung University, Tainan, Taiwan.
*Correspondence: tsaiwc@mail.ncku.edu.tw
These authors contributed equally
†
https://doi.org/10.21775/cimb.027.051
Abstract
Next-generation sequencing (NGS) technologies
are revolutionizing biology by permitting transcriptome sequencing, whole-genome sequencing
and resequencing, and genome-wide single nucleotide polymorphism profiling. Orchid research
has benefited from this breakthrough, and a few
orchid genomes are now available; new biological
questions can be approached and new breeding
strategies can be designed. The first part of this
review describes the unique features of orchid
biology. The second part provides an overview
of the current NGS platforms, many of which are
already used in plant laboratories. The third part
summarizes the state of orchid transcriptome
and genome sequencing and illustrates current
achievements. The genetic sequences currently
obtained will not only provide a broad scope for
the study of orchid biology, but also serves as a
starting point for uncovering the mystery of orchid
evolution.
Curr. Issues Mol. Biol. Vol. 27
Introduction
The Chinese have been cultivating fragrant Cymbidium species since 500 bc. The earliest book on
record about orchids is Shen Nung Pen Tsao Ching,
published during the Han dynasty. This book refers
to well-known orchids used as popular medicines,
including Dendrobium, Gsatrodia, and Bletilla. It
is generally agreed that the term orchid was first
used by the Greek philosopher Theophrastus in
his inquiry into plants (Arditti, 1992). Orchid
cultivation and growth became popular in the late
eighteenth century in Europe. Voyages around the
world were sponsored by the wealthy to collect
orchids, herbarium species, and other exotic plants.
Merchants, government officials, sea captains, plant
collectors, explorers, privateers, and other travellers began sending plants to their home countries
soon after they discovered them. Some of these
orchids were sent to botanical gardens; others
reached private growers. Subsequently, the landed
gentry, the wealthy, and commercial firms started
52
|
Yeh et al.
to accumulate orchid collections. In 1794, 15 epiphytic orchids were cultivated at Kew Gardens in
London. To satisfy the needs of growers, large numbers of collectors were sent to faraway places. These
collectors destroyed millions of plants, discovered
many new species, and suffered and died from
diseases and deprivation, but sent many orchids to
England. By about 1820, it became possible to heat
greenhouses with hot water flowing through pipes.
These advances permitted growers to simulate
what they considered to be appropriate conditions
for orchid culture – heat and humidity. Improved
methods such as lower temperature, better ventilation and potting contributed to higher survival of
the orchids and became even more popular.
The family Orchidaceae is the largest family of
flowering plants and the number of species may
exceed 25,000 (Atwood, 1986). Like all other living
organisms, present-day orchids have evolved from
ancestral forms as a result of selection pressure and
adaptation. They show a wide diversity of epiphytic
and terrestrial growth forms and have successfully
colonized almost every habitat on earth. Factors
promoting orchid species richness include specific
interaction between the orchid flower and pollinator (Cozzolino and Widmer, 2005), sequential and
rapid interplay between drift and natural selection
(Tremblay et al., 2005), obligate interaction with
mycorrhiza (Otero and Flanagan, 2006), and epiphytism which is true for most of all orchids and
probably two-thirds of the epiphytic flora of the
world. The radiation of the orchid family has probably taken place in a comparatively short period
as compared with that of most flowering plant
families, which had already started to diversify in
the Mid-Cretaceous period (Crane et al., 1995).
The time of origin of orchids is in dispute, although
Dressler suggests that they originated 80 to 40 million years ago (Mya; late Cretaceous to late Eocene)
(Dressler, 1981). Recently, the origin of the
Orchidaceae was dated with a fossil orchid and its
pollinator. The authors showed that the most recent
common ancestor of extant orchids lived in the late
Cretaceous (76 to 84 Mya) (Ramírez et al., 2007).
They also suggested the largest orchid subfamilies,
which together represent > 95% of living orchid
species, began to diversify early in the Tertiary (65
Mya) (Ramírez et al., 2007).
According to molecular phylogenetic studies, Orchidaceae comprises five subfamilies:
Curr. Issues Mol. Biol. Vol. 27
Apostasioideae, Cypripedioideae, Vanilloideae,
Orchidoideae and Epidendroideae. The Apostasioideae is considered the sister group to other
orchids. Vanilloideae diverged just before Cypripedioideae. Both subfamilies have relatively low
numbers of genera and species. Most of the
taxonomic diversity in orchids is in two recently
expanded sister subfamilies: Orchidoideae and
especially Epidendroideae (Górniaka et al., 2010).
Orchids are known for their diversity of specialized
reproductive and ecological strategies (Tsai et al.,
2014). For successful reproduction, the production
of labellum and gynostemium (a fused structure
of androecium and gynoecium) to facilitate pollination is well documented and the co-evolution
of orchid flowers and pollinators is well known
(Schiestl et al., 2003). In addition, the especially
successful evolutionary progress of orchids may
be explained by mature pollen grains packaged
as pollinia, pollination-regulated ovary/ovule
development, synchronized timing of micro- and
mega-gametogenesis for effective fertilization, and
the release of thousands or millions of immature
embryos (seeds without endosperm) in a mature
capsule (Yu and Goh, 2001). However, despite their
unique developmental reproductive biology, as well
as specialized pollination and ecological strategies,
orchids remain under-represented in molecular
studies relative to other species-rich plant families
(Peakall, 2007). The reasons may be associated
with the large genome size, long life cycle, and inefficient transformation system of orchids (Hsiao et
al., 2011b).
During the last 30 years DNA sequencing has
completely changed our vision of biology and
particularly plant biology. It has been possible
to characterize a large number of genes by their
nucleotide sequences, thus providing a shortcut
to the corresponding protein sequences and their
functions. Information on gene polymorphisms
has facilitated genetic mapping, gene cloning and
the understanding of evolutionary relationships
and has allowed for the initiation of biodiversity
studies. The most popular sequencing method has
been the Sanger method (Sanger et al., 1977a).
When combined with the use of robotics, bioinformatics, computer databases and instrumentation,
the method has allowed for sequencing larger
DNA fragments and, finally, complete genomes.
As a result, a series of landmark genomes was
Next-generation Sequencing Technologies in Orchid Biology
obtained, such as Caenorabditis elegans, Drosophila
melanogaster, Arabidopsis thaliana, Homo sapiens
and Oryza sativa (International Human Genome
Sequencing Consortium, 2004; International Rice
Genome Sequencing Project, 2005; The Arabidopsis Genome Initiative, 2000). The deciphering of
these genomes led to the era of functional genomics
and completely modified biological investigation.
However, this technology remained tedious and
expensive. These limiting factors stimulated the
development and commercialization of nextgeneration sequencing (NGS) technologies, as
opposed to the automated Sanger method, which
is considered a first-generation technology. When
coupled with the appropriate computational algorithms, the development of NGS technologies has
opened new avenues on a genome-wide scale to
radically alter our understanding of biology that
could not be answered with classical sequencing.
Sequencing platforms
Sanger sequencing, developed by Sanger and his
colleagues in 1977 based on the chain-termination
method, is predominantly employed for DNA
sequencing in the following 30 years (Sanger et
al., 1977b). It was first commercialized by Applied
Biosystems to launch the automatic sequencing
machine, AB370, in 1987. By adopting capillary
electrophoresis, the sequencing became faster and
more accurate (Liu et al., 2012; Illumina 2016).
Although Sanger sequencing was applied to complete the genome projects of human and several
model organisms including the first sequenced
plant, Arabidopsis thaliana, it took plenty of time,
cost and resources (The Arabidopsis Genome
Initiative, 2000; International Human Genome
Sequencing Consortium, 2004; van Dijk et al.,
2014). Therefore, National Human Genome
Research Institute (NHGRI) initiated a funding
programme which aimed to reduce the cost of
human genome sequencing to 1000 US dollars
within 10 years. It prompted NGS development to
create sequencers with fast, cheap, easy-to-operate
and accurate features (Liu et al., 2012; van Dijk et
al., 2014).
The 454 Genome Sequencer employing the
pyrosequencing method is the first commercial
NGS platform released by 454 Life Sciences in 2005
(Margulies et al., 2005), followed by the Genome
Curr. Issues Mol. Biol. Vol. 27
Analyser in 2006 from Solexa, which was purchased
by Illumina 1 year later (Liu et al., 2012). The
SOLiD (Sequencing by Oligo Ligation Detection)
platform is the third NGS technology developed by
Applied Biosystems (now Thermo Fisher) in 2007
(Valouev et al., 2008). These Solexa (Illumina)
and SOLiD sequencers generated short reads with
only 35-bp lengths comparing to the 110-bp reads
by the 454 system. However, their numbers of
reads (30 and 100 million reads, respectively) are
much larger than that of the 454 sequencer (200
thousand reads). In 2010, PGM (Personal Genome
Machine), the first NGS platform developed by
semiconductor technology which generates 100-bp
reads, was released by Ion Torrent (now Thermo
Fisher). Without using optical-sensing device,
the detection for sequencing by Ion Torren PGM
is based on measuring the change of pH during
nucleotide incorporation (Rothberg et al., 2011;
Liu et al., 2012). These unique features make PGM
a smaller size with higher speed and lower cost (Liu
et al., 2012; van Dijk et al., 2014).
The sequencing approaches of these short-read
NGS platforms can be classified to the sequencing by ligation (SBL) employed by SOLiD and
the sequencing by synthesis (SBS) adopted by
Illumina, 454 and Ion Torren. The SBS can be further distinguished to cyclic reversible termination
(CRT) for Illumina and single-nucleotide addition
(SNA) for 454 and Ion Torren (Goodwin et al.,
2016). Basically, the NGS systems employing the
SBS approaches are similar to the first-generation
sequencing in that the fluorescently labelled dNTPs
(deoxyribonucleotide triphosphates) are incorporated into a DNA template by DNA polymerase. The
incorporated nucleotides are then detected by fluorophore excitation during sequencing (Voelkerding
et al., 2009; Illumina, 2016). For the SBL approach,
instead of DNA polymerase, it relies on DNA ligase
to determine the underlying sequence of the template DNA. The fluorescently labelled interrogation
probes hybridize to the template DNA followed by
ligation and imaging. The identity of the bases in
the interrogation probes is indicated by the emission spectrum of the fluorophore (Shendure et al.,
2005; Goodwin et al., 2016). The major improvements of NGS leading to high throughput and
sequencing rate include preparation of NGS libraries in a cell free system instead of bacterial cloning,
parallel proceeding of a large number of sequencing
|
53
54
|
Yeh et al.
reactions and direct detection of sequencing output
without performing electrophoresis (van Dijk et
al., 2014; Chaitankar et al., 2016). Compared to
the conventional sequencing, second-generation
sequencing technologies generate vast amounts of
shorter reads, ranging from 35 to 700 bp, making
it possible to produce massive sequencing data in
a much shorter time (Goodwin et al., 2016). The
current NGS systems can complete human genome
sequencing within one day, which consumed 15
years before (Illumina, 2016).
Although these short-read sequencing platforms
have created a new sequencing era in the past
decade, the short read lengths give rise to a new
obstacle in genome assembly, such as improper discard of repeated sequences and assembling in wrong
locations or orientations. It is required to develop
novel computational algorithms for data analysis
(Baker, 2012; van Dijk et al., 2014). However, it
is increasingly obvious that genomes are highly
complex because of having many long repetitive
elements, copy numbers and structural variations.
The short-read NGS technologies are insufficient
to resolve these complex elements (Goodwin et al.,
2016). ‘Very long, very high-quality reads will do
wonders for assembly, and fix many of these issues’,
says Adam Felsenfeld, the director of the LargeScale Sequencing Program at the NHGRI (Baker,
2012). Although we are still not there yet, the socalled third-generation sequencing approaches can
now provide an alternative choice (Goodwin et al.,
2016). The read lengths of the current long-read
sequencing can reach to several kilobases, allowing to span complex or repetitive regions with a
single continuous read. In addition, it is also helpful
to identify the precise connectivity of exons and
discern gene isoforms in transcriptomic research
because it can span entire mRNA transcripts. No
PCR amplification step before sequencing is one
of the main characteristics of the third-generation
sequencing technologies. The other is that the
sequencing reaction can be detected in real time
whether in PacBio by fluorescence or in Nanopore
by electric current (Liu et al., 2012). Although the
more expensive cost and relatively lower throughput than second-generation sequencing currently
limit their widespread application, ultra-long-read
sequencing technologies with high outputs and low
Curr. Issues Mol. Biol. Vol. 27
prices could be expected in the near future. In this
review article, the current development and application of second- and third-generation sequencing
platforms along with their benefits and drawbacks
are introduced and discussed (Table 3.1).
Roche 454
The 454 Life Sciences, purchased by Roche in 2007,
released the first NGS platform, the 454 Genome
Sequencer, in 2005 (Margulies et al., 2005). The
sequencing workflow is initiated by preparation
of a sequencing library from the source nucleic
acids. First, the long DNA or RNA molecules
are fragmented into a suitable size (around 50 to
500 bp). The fragments are next fused with specific
adapters followed by a size selection step to enrich
molecules with a desired size and to remove the
free adapters. The templates are adhered to microbeads through adaptors and amplified by emulsion
PCR
(http://454.com/products/technology.
asp; Egan et al., 2012; van Dijk et al., 2014). This
sample preparation process is similar to the subsequently developed NGS platforms, Ion Torrent
and SOLiD. For the 454 pyrosequencing, the templates is denatured and incubated with sequencing
reagents including DNA polymerase, dNTP and
several enzymes. Pyrophosphates are released and
converted to ATP after the appropriate dNTPs are
incorporated into the new strand by DNA polymerase. ATP in turn reacts with luciferase to release
oxyluciferin fluorescence detected by a CCD
(charge-coupled device) camera (Egan et al., 2012;
Goodwin et al., 2016).
The Roche 454 currently offers two pyrosequencing platforms including the bench top GS
Junior+ (the upgraded version of GS Junior) and
FLX+ system (www.454.com). The advantages of
the 454 platforms include the relatively fast run
times and the long read lengths with maximum
of 1 kb (Table 3.1). The generated long reads are
helpful for mapping to a reference genome, de novo
genome assembly or metagenomics applications.
However, it possesses the drawbacks of the relatively low throughput, high reagent cost and high
error rates in homopolymer repeats. In addition,
an announcement that Roche will shut down 454
and stop the supporting services for the sequencing
platform should be noticed (van Dijk et al., 2014).
Next-generation Sequencing Technologies in Orchid Biology
Table 3.1 Comparison of performance of the current second- and third-generation sequencing platforms
Company
Platform
Sequencing
chemistry
Maximum
output
Maximum
reads per run
Maximum read
length
Run time
Roche (454)
GS Junior+
SBS (SNA)
70 Mb
~0.1 million
1000 bp
18 hours
GS FLX+
SBS (SNA)
700 Mb
~1 million
1000 bp
23 hours
MiniSeq
SBS (CRT)
8 Gb
25 million
2 × 150 bp
4–24 hours
MiSeq
SBS (CRT)
15 Gb
25 million
2 × 300 bp
4–55 hours
NextSeq
SBS (CRT)
120 Gb
400 million
2 × 150 bp
12–30 hours
HiSeq
SBS (CRT)
1.5 Tb
5 billion
2 × 150 bp
7 hours – 6 days
HiSeq X
SBS (CRT)
1.8 Tb
6 billion
2 × 150 bp
< 3 days
SOLiD 5500
SBL
320 Gb
~1.4 billion
50 or 75 bp
10 days
Ion PGM
SBS (SNA)
2 Gb
5.5 million
200 or 400 bp
4–7.3 hours
Ion Proton
SBS (SNA)
10 Gb
80 million
200 bp
2–4 hours
Illumina
Thermo Fisher
Ion S5
SBS (SNA)
15 Gb
80 million
200 or 400 bp
2.5–4 hours
SeqLL (Helicos)
Heliscope
SMS
35 Gb
~1 billion
55 bp
8 days
Pacific
Biosciences
PacBio RS II
SMRT
1 Gb*
~55,000
60 kb
0.5–6 hours
Sequel
SMRT
7 Gb*
~370,000
60 kb
0.5–6 hours
MinION
SMRT
42 Gb
4.4 million
230–300 kb
< 2 days
SMRT
12 Tb
1250 million
230–300 kb
< 2 days
Oxford Nanopore
PromethION
The data are obtained from the homepages of each company and two recent review articles (Chaitankar et al.,
2016; Goodwin et al., 2016). *Output per SMRT cell (Number of SMRT cell is 1–16). bp, base pairs; CRT, cyclic
reversible termination; Gb, gigabase pairs; kb, kilobase pairs; Mb, megabase pairs; SBL, sequencing by ligation;
SBS: sequencing by synthesis; SMRT, single-molecule real-time; SMS, single-molecule sequencing; SNA:
single-nucleotide addition; Tb, terabase pairs.
Illumina
The Illumina sequencing platform, the most widely
adopted in the industry, employs the CRT approach
of SBS methodology (Liu et al., 2012; Goodwin et
al., 2016). The sequencing steps include library
preparation, cluster generation and SBS. The
polynucleotide samples are randomly fragmented
followed by adapter ligation at both 5′ and 3′ ends to
prepare sequencing library. After adapter ligation,
PCR amplification and gel purification are performed. The library is then loaded into a flow cell
for cluster generation. The nucleotide fragments
are immobilized on the flow cell surface through
hybridization of oligos and adapters. By bridge
amplification, each fragment is amplified into a
clonal cluster. The flow cell is in turn incubated with
the sequencing reagents and the four fluorescent
dNTPs bound with reversible terminators. The
incorporated bases are identified according to emission wavelength and intensity (Illumina, 2016).
Solexa, acquired by Illumina in 2007, launched
its first sequencer (the Genome Analyser) which
can generate roughly 1 gigabase (Gb) of data per
Curr. Issues Mol. Biol. Vol. 27
sequencing run in 2006. Following the Genome
Analyser, the Illumina sequencers with different application and sequencing power have been
developed. The current platforms include MiniSeq,
MiSeq, NextSeq, HiSeq Series and HiSeq X Series
(www.illumina.com). The outputs range from 1.8
to 7.8 Gb for targeted sequencing studies by the
benchtop MiniSeq system to 1.6 to 1.8 terabase
(Tb) for population-scale studies by the HiSeqX
Series (Table 3.1). The HiSeqX Ten, released in
2014, is currently the sequencer with the highest
throughput. It was upgraded to sequence 1.8 Tb
per run, leading to the possibility of sequencing
over 45 human genomes in a single day. In addition,
the cost was down to approximately US$1000 for
one human genome. It takes genome sequencing
entering a period to see the differences among
thousands of people and discover the critical genes
causing cancer or other diseases (Illumina, 2016).
However, the HiSeq X Ten is a set of 10 HiSeq X
system with a price of US$10 million. In addition,
the low cost claimed by Illumina is an average that
can be reached by running full capacity of all HiSeq
|
55
56
|
Yeh et al.
X machines for one year. In other words, it is limited
to be used only when large institutions carry out
population-scale genome sequencing (Goodwin et
al., 2016). To overcome the limitations caused by
short-read sequencing, Illumina recently released a
Synthetic Long-Read Sequencing technology that
can generate reads of around 10 kb. By using this
technology, synthetically long fragments can be
constructed from shorter sequencing reads generated by the HiSeq platform for accurate genome
assembly and genome finishing (www.illumina.
com/products/by-type/sequencing-kits/libraryprep-kits/truseq-synthetic-long-read.html; Li et al.,
2015a).
Thermo Fisher Scientific
The SOLiD sequencing technology based on SBL
approach was developed by George Church and
his colleagues. It was published in 2005 for the
application in the resequencing of the Escherichia
coli genome and was later improved and released by
Applied Biosystems (now Thermo Fisher) in 2007
(Shendure et al., 2005; Voelkerding et al., 2009). The
sequencing procedures include library and template
preparation, bead deposition and sequencing. Two
types of libraries including fragment or mate-paired
library can be constructed according to different
research applications. A mixture of short fragments
flanked by adaptors are generated and attached to
beads followed by emulsion PCR amplification.
The beads with the amplified templates are then
immobilized onto a glass slide or FlowChip by
covalent attachment. SBL is begun by annealing a
primer to the complementary adapter. In contrast to
SBS mediated by polymerase (3′ hydroxyl group),
the primer offer a 5′ phosphate group for ligation
with one of the fluorescently labelled interrogation
probes which are octamers consisting of two specific bases followed by six degenerate bases. There
are 16 combinations for the first two specific bases
in the interrogation probes, such as AA, AT and
so on. In the first SBL step, these probes compete
for hybridization with the templates followed by
ligation with the primer and detection of the fluorescence signal. The fluorophore along with three
bases is in turn cleaved from the probe, leaving a
5-bp fragment with a 5′ phosphate group for the
next ligation of the interrogation probes. Multiple
cycles of these processes are performed to complete
one round of sequencing. The extension product is
Curr. Issues Mol. Biol. Vol. 27
then denatured and a second round of sequencing is started with a new primer complementary
to the n-1 position of the adapter. Sequencing by
five rounds of these primer resets (until n-4) completes each read and each base of the template is
sequenced twice (www.thermofisher.com/; Voelkerding et al., 2009). The accuracy rate of the SOLiD
system can reach 99.94% which is higher than
most other NGS systems because each base is read
by twice (Liu et al., 2012; Goodwin et al., 2016).
However, the very short read lengths (75 bp), the
much longer runtime (at least 6 days) and the less
well-developed kits of sample preparation limit its
wide application, such as for genome assembly and
structural variant detection (van Dijk et al., 2014;
Goodwin et al., 2016).
Another well-known NGS platform purchased
by Thermo Fisher is Ion Torrent. Although the
Ion Torrent system adopts SBS methods similar to
most NGS technologies, it is a unique sequencing
platform employing an integrated complementary metal oxide semiconductor (CMOS) and an
ion-sensitive field-effect transistor (ISFET) as the
detection system (Goodwin et al., 2016). The detection is based on measurement of the pH change
resulting from proton release during nucleotide
incorporation but not fluorescence (Rothberg et al.,
2011). For sequencing, the chip is flooded with one
nucleotide each time and the incorporated nucleotide is in turn detected. If the incorrect nucleotide
is added, no voltage will be detected. In case two
nucleotides are added, there will be double voltage
(Liu et al., 2012). For different research requirements, several types of chips and instruments are
offered by Ion Torrent. Their throughputs range
from ~50 Mb to 15 Gb and the runtimes are from
2 to 7 hours (Table 3.1; Goodwin et al., 2016). The
Ion PGM is the first commercial sequencer released
by Ion Torrent, targeting to clinical applications
and small labs. It possesses features of higher speed,
lower cost and smaller size because it is not required
to perform fluorescence labelling and camera scanning (Liu et al., 2012). With the latest released
318 chips, the Ion PGM improves the output to
over 1 Gb. As to the higher-throughput Ion Proton
system, the adopted Proton-I chip is manufactured
by the 110 nm CMOS technology to increase the
number of wells to ~165 million. It can produce 60
to 80 million reads per run with an output of 10 Gb
(Egan et al., 2012; Buermans and den Dunnen,
Next-generation Sequencing Technologies in Orchid Biology
2014). Aiming to develop NGS platform for clinical
sequencing, Ion Torrent released its dedicated diagnostic instruments, the Ion PGM Dx and the Ion
S5 series. The S5 series coupled with the Ion Chef
library preparation and chip loading device could
be one of the platforms with the simplest operation. In this combination system, argon required in
other Ion Torrent instruments is unnecessary and
the plug-and-play protocols have been established.
However, the higher-throughput S5 devices along
with the Ion Proton have limitations for elucidating
long-range genomic or transcriptomic structure,
because they cannot be applied for paired-end
sequencing (Goodwin et al., 2016).
RNA without reverse transcription (Ozsolak et al.,
2009). It can prevent the biases from cDNA synthesis by using other RNA sequencing technologies.
Moreover, both SMS of short and long RNAs
can be done together without performing different sample manipulation steps (Ozsolak, 2016).
However, Helicos BioSciences filed for Chapter
11 bankruptcy in 2012 and the properties were
acquired by SeqLL in 2014. The SeqLL currently
offers customized services for quantitative RNA
and specialty DNA sequencing by using the True
Single Molecule Sequencing technology (tSMS) of
HeliScope Genetic Analysis System (http://seqll.
com).
Helicos Biosciences
The Heliscope, released by Helicos Biosciences, is
the first sequencer for single-molecule sequencing
(SMS) derived from the technology developed by
Braslavsky et al. (2003). It was considered as the
interface between second- and third-generation
sequencing. In this technology, DNA polymerase is
used to acquire sequence information during synthesis of the complementary strand of a single DNA
template. The SMS approach is attractive because
it can directly sequence nucleic acids in an unbiased manner and prepare samples in a simple way.
Without steps for cloning or PCR amplification, the
GC-content and size biases appeared in other NGS
could be avoided (Pushkarev et al., 2009; Thompson and Steinmann, 2010). The sample preparation
includes DNA fragmentation, addition of poly(A)
tail at the 3′ end and fluorescence labelling of the
final adenosine. The poly(A) tail of the DNA
templates are hybridized to Poly(dT) oligonucleotides randomly immobilized on a flow-cell surface
by covalent bonding. These random sequencing
positions are recorded by the fluorescence of the
captured DNA templates. Before sequencing gets
started, the fluorescent labels are cleaved and the
flow cells are incubated with DNA polymerase and
one of the four Cy5-labelled dNTPs. The ‘virtual
terminator’ included in each nucleotide can prevent a further incorporation. The excitation of Cy5
from the incorporated dNTP is in turn detected
at 647 nm. The process is repeated to determine
the next incorporation of nucleotides (Harris et
al., 2008; Goodwin et al., 2016). In addition to
single DNA and cDNA molecules, the Heliscope
is also the first system that can directly sequence
Pacific Biosciences
The PacBio RS platform, released by Pacific
Biosciences in 2010, is the first third-generation
sequencing platform employing the SingleMolecule Real-Time (SMRT) sequencing
technology. It enables parallel and real-time detection of thousands of single-molecule sequencing
reactions (Eid et al., 2009; Liu et al., 2012). The
SMRT technology was developed based on the
zero-mode waveguide (ZMW) technology published at Science in 2003 (Levene et al., 2003). In
the conventional approaches, pico- to nanomolar
concentrations of fluorophores are suitable for
optical observation of dynamics of individual
molecules. However, ligand concentration at micromolar is usually required for biological reactions
that make it necessary to reduce sample volume by
three orders of magnitude for optical observation of
single molecules. ZMWs are tiny nanoholes with a
diameter of 70 nm and depth of 100 nm in a metal
film. It was successfully applied to observe activity
of a single DNA polymerase molecule at micromolar concentrations with microsecond temporal
resolution (Levene et al., 2003; McCarthy, 2010).
In the current PacBio RS II and Sequel systems,
each SMRT cell consists of 150,000 and 1 million
of ZMWs, respectively, with a single DNA polymerase at the bottom of each nanohole. The Sequel
system thus can produce seven times as many reads
as the PacBio RS II (www.pacb.com/products-andservices/pacbio-systems/). During sequencing
by synthesis, the DNA polymerase incorporates
one of the four nucleotides labelled by different
fluorescent dye into the complementary strand
of the template DNA. The signal is immediately
Curr. Issues Mol. Biol. Vol. 27
|
57
58
|
Yeh et al.
captured and recorded as a movie format by camera
inside the sequencer for real-time observation.
The dNTP-bound fluorophore is cleaved by DNA
polymerase before the next incorporation of dNTP
(McCarthy, 2010; Liu et al., 2012).
Comparing to the second-generation sequencers, the PacBio platforms have several advantages
including fast sample preparation (4 to 6 h), short
run times (0.5 to 6 h) and long read lengths. Without PCR step in the sample preparation, the bias and
error caused by PCR is reduced. In both of PacBio
RS II and Sequel systems, half of the reads are over
20 kb with an average of 10 kb making PacBio ideal
for genome assembly and improvement of the
existing draft genomes (Liu et al., 2012; www.pacb.
com/products-and-services/pacbio-systems/). In
addition, by using unique circular DNA templates,
the ones shorter than 3 kb can be sequenced multiple times to generate a consensus read of insert, the
so-called circular consensus sequence (Goodwin
et al., 2016). However, the PacBio platforms have
drawbacks of relatively low throughput and high
cost, currently limiting the range of applications
(van Dijk et al., 2014).
Oxford Nanopore
Oxford Nanopore is another third-generation
sequencing technology because it also sequences
single molecules in a real-time manner (van Dijk
et al., 2014). Instead of monitor of incorporations
or hybridizations of nucleotides employed by other
sequencing technologies, the Nanopore platform
can directly detect the nucleotide composition of
single-stranded DNAs (Goodwin et al., 2016). To
carry out sequencing, the bases are identified by
the change in electrical conductivity when a DNA
molecule is transited through a tiny biopore with
diameter in nanoscale (Clarke et al., 2009; Liu et al.,
2012).The detection of single molecules based on
the nanopore method has emerged from a PNAS
paper published in 1996 (Kasianowicz et al., 1996).
It was reported that single-stranded DNA and RNA
molecules can be driven by an electric field through
an ion channel formed by S. aureus α-hemolysin
across a lipid bilayer. When each polynucleotide
molecule translocates through the channel, it can
be detected by a transient decrease or block of ionic
conductance due to occupy of the pore’s volume.
Therefore, the possibility of direct and rapid
sequencing of single molecules of DNA or RNA by
Curr. Issues Mol. Biol. Vol. 27
further improving this nanopore method was proposed and investigated (Kasianowicz et al., 1996;
Deamer and Akeson, 2000). It was proved that
a single adenine nucleotide at a specific location
can be identified by the characteristic reductions
of ionic current in the α-hemolysin nanopore
(Ashkenasy et al., 2005). However, the technique
still cannot discriminate each base because the
polynucleotide translocation rate is too high. By
using an exonuclease enzyme to cleave individual
nucleotides from DNA and covalent attachment
of an adapter molecule to the protein nanopore,
continuous detection of unlabelled individual
nucleotide has been achieved (Clarke et al., 2009).
The first commercial nanopore sequencer for
sequencing single DNA molecules is MinION
released by Oxford Nanopore Technologies. It
is an inexpensive portable device connecting to
a PC or laptop by USB and capable of producing
reads of up to 10 kb (van Dijk et al., 2014; Brown
and Clarke, 2016). The initial ASIC (applicationspecific integrated circuit) chip designed for
MinION Mk1 flow cell has 512 individual channels
enabling to sequence at ~70 bp per second. A new
3000-channel ASIC was developed for the new
released MinION Mk1B (with an expected increase
to 500 bp per second) and PromethION, an
ultra-high-throughput platform possessing 48 individual flow cells with running at 500 bp per second
(Goodwin et al., 2016; https://nanoporetech.
com/products/minion). Very recently the protocol
for direct RNA sequencing by the MinIon device
has been developed. It is currently the only platform
available for directly sequencing the original RNA
strands without cDNA synthesis and PCR reaction. Although the direct RNA sequencing method
was firstly reported by Helicos in 2009, it depends
on the synthetic copies of the native RNA strands
through the SBS reaction. The RNA modifications
cannot be detected by this approach (Garalde et al.,
2016).
Without performing PCR and fluorescent labelling steps before sequencing, the Nanopore system
can reduce costs and increase sequencing speeds
(Clarke et al., 2009; Laver et al., 2015). In addition,
except for exonuclease, it is not required to use
polymerase and ligase, making Oxford Nanopore
less temperature sensitive than other platforms (Liu
et al., 2012). Because sequence quality is high in
the long reads sequenced by the Nanopore system,
Next-generation Sequencing Technologies in Orchid Biology
it benefits to de novo sequencing, long-range haplotype mapping and the high-resolution analysis
of chromosomal structure variation (Clarke et al.,
2009; Laver et al., 2015).
An overview of current of orchid
genome project
EST and BAC
Genomics studies for the orchids are just in their
infancy. A survey of the literature revealed that
genome size data for Orchidaceae are comparatively
rare, representing just 327 species (Leitch et al.,
2009). Nevertheless, they reveal that Orchidaceae
are currently the most variable angiosperm family
with genome sizes ranging 168-fold (1C = 0.33–
55.4 pg). Large scale sequence analysis of orchid
genomes was first revealed by bacterial artificial
chromosome (BAC) end sequences analysis in
Phalaenopsis orchid (Hsu et al., 2011). This work
offers the first insights into the composition of
the Phalaenopsis genome in terms of GC content,
transposable elements present, protein-encoding
regions, simple sequence repeats, and potential
microsynteny between Phalaenopsis and other plant
species (Hsu et al., 2011). In addition to the nuclear
genome, the entire chloroplast genome of Phalaenopsis orchid is also sequenced. The chloroplast
genome of P. aphrodite subsp. formosana is about
150 kb, which encode 110 different known genes,
including 74 protein-coding genes, four rRNA
genes, 30 tRNA genes and two conserved reading
frames of unknown function (Chang et al., 2006).
Furthermore, the transcripts of 74 protein-coding
genes from the chloroplast genome of P. aphrodite
subsp. formosana were used to study extensively the
pattern of RNA editing in chloroplasts. A total of
44 editing sites are identified in the 24 transcripts
of P. aphrodite chloroplast genes, and all are of the
C-to-U conversion type (Zeng et al., 2007). On
the basis of the above information, the chloroplast
genome of several orchids were sequenced, including Oncidium Gower Ramsey, P. equestris, Erycina
pusilla, seven species in Cymbidium, Dendrobium
officinale and Cypripedium macranthos (Wu et al.,
2010; Jheng et al., 2012; Pan et al., 2012; Yang et al.,
2013; Luo et al., 2014). Further plastome sequencing of orchids will be necessary to clarify the
diversity of chloroplast genomes and to improve
Curr. Issues Mol. Biol. Vol. 27
our understanding of the relationships within the
Orchidaceae.
Large-scale EST sequencing provides a gateway
into the genome of organisms owing to the massive
information buried in the genome-scale expression
data. Before NGS technology has been developed,
the most popular sequencing method has been
the Sanger method applied to the EST sequencing
project. A subtractive EST library was constructed
from the pseudobulb of O. Gower Ramsey, and
1080 subtractive ESTs were obtained. Most ESTs
were annotated as being involved in carbohydrate
metabolism, in mannose, pectin and starch biosynthesis, transportation, and stress-related and
regulatory function (Tan et al., 2005). To study gene
expression in Phalaenopsis reproductive organs, a
cDNA library was constructed from mature flower
buds of P. equestris; 5593 ESTs were sequenced and
assembled into 3688 unigenes (Tsai et al., 2006).
In addition, a cDNA library has been constructed
from scented P. bellina flower buds with the column
removed; 2359 ESTs were sequenced and assembled into 1187 unigenes (Hsiao et al., 2006). The
set of floral scent-producing enzymes in the biosynthetic pathway from glyceraldehyde-3-phosphate
to geraniol and linalool is recognized through these
ESTs and distinguished by comparing their expression patterns in P. bellina and a scentless species,
P. equestris (Hsiao et al., 2006). A similar strategy
was adopted for Vanda Mimi Palmer principally to
mine any potential fragrance-related EST-SSRs as
markers in the identification of fragrant vandaceous
orchids endemic to Malaysia (Teh et al., 2011).
Orchid transcriptomes generated by
NGS technologies
The sudden rise of relatively low-cost and rapid
NGS technologies is dramatically advancing
our ability to comprehensively interrogate the
nucleic-acid-based information in a cell at unparalleled resolution and depth (Delseny et al., 2010).
The technologies were rapidly adopted for orchid
transcriptome analysis (Table 3.2). 206,960 ESTs
were released from the pool containing P. equestris,
P. aphrodite, and P. bellina and a total of 50,908
contig sequences were from six different tissues
of O. Gower Ramsey (Chang et al., 2011) by 454
technology respectively to expansively cover the
Phalaenopsis and Oncidium orchid transcriptome
and facilitate identifying sets of genes involved
|
59
60
|
Yeh et al.
Table 3.2 Characteristics of findings in the literature for the application of next-generation sequencing to orchid
transcriptomes
Sequencing
platform
Subfamily
Species
Tissue
Apostasioideae
Apostasia
shenzhenica
Illumina/Solexa Mature flower buds
Apostasioideae
Neuwiedia
malipoensis
Illumina/Solexa Mature flower buds
Vanilloideae
Vanilla shenzhenica
Illumina/Solexa Mature flower buds
Vanilloideae
Galeola faberi
Illumina/Solexa Mature flower buds
Cypripedioideae Paphiopedilum
armeniacum
Illumina/Solexa Mature flower buds
Cypripedioideae Cypripedium
singchii
Illumina/Solexa Mature flower buds
Orchidoideae
Illumina/Solexa Mature flower buds
Habenaria delavayi
Orchidoideae
Hemipilia forrestii
Illumina/Solexa Mature flower buds
Epidendroideae
Phalaenopsis
equestris
Illumina/Solexa Mature flower buds
Study aim
Reference
Study of floral
development and
evolutionary trends of
orchid flowers
Tsai et al.,
2013
Epidendroideae
Cymbidium sinense
Illumina/Solexa Mature flower buds
Vanilloideae
Vanilla planifolia
Illumina/Solexa; Pod tissues, seeds
Roche/454
Study of biosynthetic
routes to flavour
components
Rao et al.,
2014
Cypripedioideae Paphiopedilum
concolor
Illumina Hiseq
2000
Identify the genes that
control root growth
and development
Li et al.,
2015b
Orchidoideae
Ophrys species
Roche/454;
Flowers, labellums,
Illumina/Solexa leaves, flower organ
from open flowers
and buds
Identify genes
responding for
pollinator attraction
Sedeek et
al., 2013
Orchidoideae
Orchis italica
Illumina/Solexa Inflorescences
(MiSeq)
The roles of small
RNAs on the flower
development
Aceto et
al., 2014
Orchidoideae
Orchis italica
Illumina Hiseq
2500
Florets of
inflorescence before
anthesis
Analysing transcripts
potentially involved in
flower development
Paolo et
al., 2014
Orchidoideae
Serapias vomeracea Roche/454
Protocorms
Investigate the
Perotto et
molecular bases of
al., 2014
the orchid response to
mycorrhizal invasion
Orchidoideae
Anoectochilus
roxburghii (Wall.)
Lindl.
Illumina HiSeq
4000
Dry seeds, seeds
from asymbiotic
or symbiotic
germination
Study of seed
germination process
Liu et al.,
2015
Orchidoideae
Gastrodia elata
Blume
Illumina Hiseq
2000
Vegetative tissues,
corms, juvenile
tubers
Address the gene
regulation mechanism
in gastrodin
biosynthesis
Tsai et al.,
2016
Epidendroideae
Phalaenopsis
aphrodite
Sanger: EST
Protocorms
Gene discovery and
genomic annotation
Epidendroideae
Phalaenopsis
equestris
Sanger: EST
Mature flower buds
Fu et al.,
2011;
Hsiao et
al., 2011
Epidendroideae
Phalaenopsis bellina Sanger: EST
Mature flower buds
without column
Curr. Issues Mol. Biol. Vol. 27
Roots
Next-generation Sequencing Technologies in Orchid Biology
Table 3.2 Continued
Sequencing
platform
Subfamily
Species
Tissue
Study aim
Reference
Epidendroideae
Roche/454
Phalaenopsis
aphrodite;
Phalaenopsis
equestris;
Phalaenopsis bellina
Mixed tissues
Gene discovery and
genomic annotation
Fu et al.,
2011;
Hsiao et
al., 2011
Epidendroideae
Phalaenopsis
equestris
Illumina/Solexa Leaves
Epidendroideae
Phalaenopsis
aphrodite
Roche/454;
Leaves, stems,
Illumina/Solexa roots, young
inflorescences,
stalks, flower buds,
flowers, germinating
seeds
Investigate expressed
genes involved in
many biological
processes of orchids
Su et al.,
2011
Epidendroideae
Phalaenopsis
aphrodite
Illumina/Solexa Leaves, stalks,
flower buds
Study the roles of
small RNAs on the
regulation of flowering
An et al.,
2011;
An and
Chan,
2012
Epidendroideae
Phalaenopsis
aphrodite
Illumina/Solexa Leaves, roots,
flowers, germinating
seeds, young
inflorescences
Identify speciesand tissue-specific
miRNAs
Chao et
al., 2014
Epidendroideae
Phalaenopsis
Brother Spring
Dancer ‘KHM190’
Illumina Hiseq
2000
Study regulation
Petals, sepals or
labellums from flower of floral- organ
development
buds of wild-type
and peloric petal
mutant plants
Epidendroideae
Phalaenopsis sp.
Illumina Hiseq
2000
Explants
Examine Phalaenopsis Xu et al.,
leaf explant browning 2015
Epidendroideae
Oncidium ‘Gower
Ramsey’
Roche/454
Leaves,
pseudobulbs, young
inflorescences,
inflorescences,
flower buds, mature
flowers
Identify genes
associated with
flowering time
Epidendroideae
Oncidium ‘Gower
Ramsey’
Illumina/Solexa Roots with or without Study the roles of
fungus
small RNAs on the
interaction between
root and the fungus
Epidendroideae
Erycina pusilla
Illumina/Solexa Roots, leaves,
peduncles, flowers,
capsules
Investigate
photoperioddependent flowering
genes
Chou et
al., 2013
Epidendroideae
Erycina pusilla
Illumina/Solexa Roots, leaves,
peduncles, flowers,
capsules
Study the roles of
small RNAs on the
regulation of flowering
Lin et al.,
2013
Epidendroideae
Cymbidium
ensifolium ‘Tiegusu’
Illumina HiSeq
2000
Flower buds, mature
flower
Identify genes
associated with floral
development
Li et al.,
2013
Epidendroideae
Cymbidium sinense
‘Qi Jian Bai Mo’
Illumina HiSeq
2000
Identify genes
Plants in vegetative
associated with floral
phase/floral
differentiation phase/ development
reproductive phase
Zhang et
al., 2013
Epidendroideae
Cymbidium
hybridum ‘Golden
Boy’
Illumina HiSeq
2000
Roots with or without Study of orchidfungus
mycorrhizal fungi
interactions
Zhao et
al., 2014
Curr. Issues Mol. Biol. Vol. 27
Huang et
al., 2015
Chang et
al., 2011
Ye et al.,
2014
|
61
62
|
Yeh et al.
Table 3.2 Continued
Subfamily
Species
Epidendroideae
Cymbidium
ensifolium ‘Tiegusu’
Epidendroideae
Sequencing
platform
Study aim
Reference
Illumina/Solexa Flower bud
Identify miRNAs
related to floral
development
Li et al.,
2015c
Cymbidium
ensifolium ‘tianesu’
Roche/454
Sepals, petals,
labellums,
gynostemia from
flower buds and
mature flowers
Reveal genes
associated with floral
organ differentiation
Yang and
Zhu, 2015
Epidendroideae
Cymbidium sinense
‘Dharma’
Roche/454
Roots, leaves,
pseudobulbs,
flowers
Analyse molecular
Zhu et al.,
mechanism underlying 2015
leaf-colour variations
Epidendroideae
Cymbidium sinense; Illumina HiSeq
2000
Cymbidium
atropurpureum;
Cymbidium mannii
Leaves
Explore the evolution
and molecular
regulation of CAM
plants
Zhang et
al., 2016c
Epidendroideae
Dendrobium
officinale
Roche/454
Stems
Study of alkaloid
biosynthesis
Guo et
al., 2013
Epidendroideae
Dendrobium
officinale
Illumina HiSeq
2000
Juvenile and adult
plants
Identify genes
associated with
polysaccharide
synthesis
Zhang et
al., 2016b
Epidendroideae
Dendrobium
officinale
Illumina HiSeq
2500
Flower, roots, leaves, Study of the
Meng et
stems
regulatory networks
al., 2016
of the production and
accumulation of the
medicinal constituents
in a broad range of biological processes (Hsiao et
al., 2011a; Chang et al., 2011). A total of 121,917
unique transcripts were obtained from the Ophrys
species by using 454 pyrosequencing and Illumina
(Solexa) technologies to identify genes responding
for pollinator attraction (Sedeek et al., 2013). To
study the genes involved in alkaloid biosynthetic
pathway and polysaccharide biosynthesis in Dendrobium officinale, an important traditional Chinese
herb, 454 pyrosequencing and Illumina technology
was respectively applied to generate plentiful ESTs
(Guo et al., 2013; Zhang et al., 2016b). To provide
a general resource for studying on the pod development of Vanilla plantifolia, one of the most valued
flavour species for its flavour qualities and is therefore widely cultivated and used for the production
of food additives, the combined 454/Illumina
RNA-seq platforms produced high quality de novo
transcriptome assembly for this non-model crop
species (Rao et al., 2014). In addition, to improve
the horticultural value of Phalaenopsis and Cymbidium, transcriptome derived from browning leaf
Curr. Issues Mol. Biol. Vol. 27
Tissue
of Phalaenopsis explant (sequencing by Illumina
HiSeq 2000), and variable colour of Cymbidium leaf
(sequencing by 454 pyrosequencing) were investigated (Xu et al., 2015; Zhu et al., 2015). Orchids are
unique among plants in that mycorrhizal symbioses
with soil fungi are required throughout the life history stages, from seed germination to adulthood.
To understand the molecular mechanism of orchid
seed germination and the symbiotic orchid–fungus
relationship, 454 and Illumina were adopted to
explore transcriptomes derived from Serapias
vomeracea (Perotto et al., 2014), C. hybridium
(Zhao et al., 2014), Anoectochilus roxburghii (Liu et
al., 2015), and Gastrodia elata (Tsai et al., 2016). In
Orchidaceae, about 40% species adopt crassulacean
acid metabolism (CAM) to fix carbon dioxide
suggesting the Orchidaceae is the largest CAM
clade (Silvera et al., 2009). To illuminate the origin
and evolution of CAM pathway, transcriptomes
derived from leaves of CAM orchids P. equestris, D.
terminale and C. mannii were sampled at different
time interval and sequencing by Illumine HiSeq
Next-generation Sequencing Technologies in Orchid Biology
2000 (Deng et al., 2016; Zhang et al., 2016c). To
study the development of spectacular orchid flower
morphology, developing floral transcriptomes
originating from Cymbidium (Li et al., 2013; Yang
et al., 2015; Zhang et al., 2013), Orchis (De Paolo
et al., 2014), and Phalaenopsis (Huang et al., 2015)
were applied to identify genes associated with
floral development. Recently, root transcriptome
from Paphiopedilum concolor was also produced to
explore genes involved in orchid root development
(Li et al., 2015b). The accumulated transcribed
sequences could be directly used to develop
microarray platform, and be the resource for phylogenetic analysis. For example, an oligomicroarray
containing 14,732 unigenes based on the information of expressed sequence tags derived from
Phalaenopsis orchids was developed and applied
to compare transcriptome among different types
of floral organs including sepal, petal and labellum
(Hsiao et al., 2013). 315 single-copy orthologous
genes extracted from the transcriptomes of species covering five subfamilies of Orchidaceae were
applied to reconstruct a more robust phylogeny of
orchids, and the results indicated that this method
is more efficient and reliable than methods based
on a few gene markers for phylogenic analyses,
especially for the holomycotrophic species or
those whose DNA sequences have been difficult to
amplify (Deng et al., 2015).
Next-generation sequencing technologies are
not only applied to characterize orchid transcriptomes but also used to systematically analyse small
RNAs in orchids (Table 3.2). The roles of small
RNAs were studied on the regulation of flowering
in P. aphrodite and E. pusilla (An et al., 2011; An and
Chan, 2012; Lin et al., 2013), flower development
in Orchis italica (Aceto et al., 2014; De Paolo et al.,
2014) and Cymbidium ensifolium (Li et al., 2015c),
and interaction between the fungus Piriformospora
indica and the root of an Oncidium hybrid orchid
(Ye et al., 2014). Later, comprehensive collection
of small RNAs derived from P. aphrodite (Chao
et al., 2014), and D. officinale (Meng et al., 2016)
were performed. These efforts provide valuable
information about the composition, expression
and function of small RNAs and will aid functional
genomics studies of orchids.
Recently, OrchidBase has collected the transcriptome sequences from 11 Phalaenopsis
cDNA libraries and flower tissue of 10 species
Curr. Issues Mol. Biol. Vol. 27
distributed in five subfamilies of Orchidaceae (Fu
et al., 2011; Tsai et al., 2013; Niu et al., 2016).
The EST sequences collected in OrchidBase were
obtained through both deep sequencing with ABI
3730, Roche 454 and Illumina/Solexa. OrchidBase is freely available at http://orchidbase.itps.
ncku.edu.tw/ and provides researchers with a
high-quality genetic resource for data mining and
efficient experimental studies of orchid biology
and biotechnology. Another orchid transcriptomic
database, Orchidstra (http://orchidstra.abrc.
sinica.edu.tw), was constructed from the 233,924
unique contigs of the transcriptome sequences of
P. aphrodite by use of a Roche 454 and Illumina/
Solexa platform, and the genes of tissue-specific
expression were categorized by profiling analysis
with RNA-seq (Su et al., 2011). Oncidium cDNA
libraries for six different organs, including leaves,
pseudobulbs, young inflorescences, inflorescences,
flower buds and mature flowers, were generated
from 50,908 contig sequences by use of the Roche
454 platform and were constructed into the OncidiumOrchidGenomeBase (http://predictor.nchu.
edu.tw/oogb/) (Chang et al., 2011). All this EST
information will be very useful for gene annotation
in genomic sequencing, specificity of orchids, and
organization of the orchid genome. The plentiful
collection of ESTs and BESs in Phalaenopsis makes
them reasonable candidates for orchid wholegenome sequencing. The two native Phalaenopsis
species in Taiwan, P. equestris and P. aphrodite subsp.
formosana, are usually used as parents for breeding and have a relatively small genome size of
3.37 pg/2C and 2.80 pg/2C, respectively. The basic
studies and genomics information collected have
laid the groundwork for P. equestris to serve as a
model orchid plant for whole-genome sequencing.
Orchid genome project
With the quick development and lower cost of NGS,
whole genome sequencing of non-model species,
like orchids, can be realized. The first milestone is
sequencing the tropical epiphytic orchid Phalaenopsis equestris, a frequently used parent species for
orchid breeding (Cai et al., 2015). The P. equestris
genome is sequenced via a whole-genome shotgun
strategy (Illumina technology) and the genome
size is estimated to be 1.16 Gb contains with 29,431
predicted protein-coding genes. Analysing the P.
equestris genome showed that repetitive DNAs,
|
63
64
|
Yeh et al.
mostly transposable elements (TEs), account for
the majority of the genome, at 62%. The authors
find evidence for an orchid-specific paleopolyploidy event that preceded the radiation of most
orchid clades. This species is also the first wholegenome-sequenced CAM plant and a gene family
(α carbonic anhydrase) involved in CAM pathway
is found having an obvious expansion which suggests that gene duplication might have contributed
to the evolution of CAM photosynthesis in P. equestris. In addition, genes located at the heterozygous
regions might relate to self-incompatibility. Genes
in type II MADS-box clades, including the E-class,
C/D-class, B-class AP3 and AGL6 clades, are found
contained more genes than other species. These
expanded clades are involved in orchid floral organs
which can support the unique evolutionary routes
of these floral organ identity genes associated with
the unique labellum and gynostemium innovation
in orchids. Furthermore, the Phalaenopsis genome
sequence was applied to identify MYB genes controlling floral pigmentation patterning (Hsu et al.,
2015), and TCP genes involved in ovule development (Lin et al., 2016).
Having both ornamental value and a broad range
of therapeutic effects, Dendrobium officinale is the
other Orchidaceae plant which was sequenced by
combining the second-generation Illumina Hiseq
2000 and third-generation PacBio sequencing technologies (Yan et al., 2015). The assembled genome
of D. officinale has a predicted 35,567 proteincoding genes. The number of predicted genes in
D. officinale is higher than that in Phalaenopis. For
example, the number of B-class MADS-box genes
presented in D. officinale is much higher than that in
Phalaenopsis. In Phalaenopsis, there are four members in B-class AP3-like subfamily, and one member
in B-class PI-like subfamily. In contrast, there are
19 AP3-like genes and five PI-like genes presented
in the Dendrobium genome. It is possible that the
plants used for the whole genome sequencing
are not native species, but hybrids. Later, another
Dendrobium species, Dendrobium catenatum (鐵皮
石斛), was whole genome sequenced by Illumina
HiSeq 2000 platform (Zhang et al., 2016a). The
predicted 28,910 protein-coding genes are comparable with those of Phalaenopsis, and a whole
genome duplication event could be share with Phalaenopsis. The expansion of many resistance-related
genes in Dendrobium suggests a powerful immune
Curr. Issues Mol. Biol. Vol. 27
system responsible for adaptation to a wide range
of ecological niches. In addition, extensive duplication of genes involved in glucomannan synthase
activities is likely related to the synthesis of medicinal polysaccharides. Expansion of MADS-box gene
clades ANR1, StMADS11, and MIKC*, involved in
the regulation of development and growth, suggests
that these expansions are associated with the astonishing diversity of plant architecture in the genus
Dendrobium (Zhang et al., 2016a).
These complete genome sequences of Orchidaceae species will facilitate future research on
the diversity and evolution of orchid plants. The
genome sequences will also be an important
resource for genetic engineering, such as molecular
marker-assisted breeding and the production of
transgenic plants, which are necessary to increase
the efficiency of orchid breeding and aid orchid
horticulture research.
Future perspective
About 2500 years ago Confucius wrote ‘Lan
(orchid) that grows in the deep valleys never
withholds its fragrances even without being appreciated’. Then, 300 years ago, Charles Darwin, in a
letter to Joseph Hooker, wrote ‘I never was more
interested in any subject in my life, than in this
of Orchids’. Because of the unique reproduction
strategy in orchids, their origin has been a recurring
question in botany and evolutionary biology since
the nineteenth century. The study of orchid biology
by using NGS technologies, although still young,
has already offered new and exciting perspectives
on this intriguing plant family. Recent advances in
sequencing technologies and functional genomics
methodologies have allowed studies on orchid biology to become a standard scientific research topic
accessible to many investigators, which has in turn
resulted in many exciting new discoveries. With the
whole genome sequences of P. equestris, D. catenatum, and D. officinale available, the genetic blueprint
of orchids provides a basic understanding of the
genetic basis of orchids. Furthermore, the genome
sequences of the primitive orchid Apostasia and one
of the most popular aromatic orchids, Vanilla, will
be available soon. The efforts by many scientists to
use a plethora of genome information and genomics tools will lead to a promising understanding of
the biological, physiological, molecular and genetic
Next-generation Sequencing Technologies in Orchid Biology
mechanisms of orchids in years to come. In addition, we will have access to a greater portion of their
genetic diversity, thus allowing orchid breeders
to associate this diversity with phenotypic traits
and to continue to engineer new varieties better
adapted to a changing environment. Clearly a new
era of orchid biology is now open because of the
sequencing revolution.
Acknowledgements
This work was supported by Grants
103-2313-B-006-001-MY3,
104-2321-B-006025-, and 105-2321-B-006-026- from Ministry of
Science and Technology, Taiwan, and was supported
by the 948 Program of State Forestry Administration, China (No. 2011-4-53), Development Special
Fund of Biological Industry of Shenzhen Municipality (No. JC201005310692A), Development
Funds for Emerging Industries of Strategic Importance of Shenzhen (No. JCYJ20140402093332029,
No. NYSW20140331010039), Fundamental
Research Project of Shenzhen Municipality (No.
JCYJ20150403150235943), and Forestry Science and Technology Innovation Fund Project of
Guangdong Province (No. 2016KJCX025).
References
Aceto, S., Sica, M., De Paolo, S., D’Argenio, V., Cantiello,
P., Salvatore, F., and Gaudio, L. (2014). The analysis
of the inflorescence miRNome of the orchid Orchis
italica reveals a DEF-like MADS-box gene as a new
miRNA target. PLOS ONE 9, e97839. http://dx.doi.
org/10.1371/journal.pone.0097839
An, F.M., and Chan, M.T. (2012). Transcriptome-wide
characterization of miRNA-directed and non-miRNAdirected endonucleolytic cleavage using Degradome
analysis under low ambient temperature in Phalaenopsis
aphrodite subsp. formosana. Plant Cell Physiol. 53,
1737–1750. http://dx.doi.org/10.1093/pcp/pcs118
An, F.M., Hsiao, S.R., and Chan, M.T. (2011).
Sequencing-based approaches reveal low ambient
temperature-responsive and tissue-specific microRNAs
in Phalaenopsis orchid. PLOS ONE 6, e18937. http://
dx.doi.org/0.1371/journal.pone.0018937
(The reference is the same as The Arabidopsis Genome
Initiative. (2000). Analysis of the genome sequence of
the flowering plant Arabidopsis thaliana. Nature 408,
796–815. http://dx.doi.org/10.1038/35048692 which
listed in Page 4)Arditti, J. (1992). Fundamentals of
Orchid Biology. John Wiley, New York, USA.
Ashkenasy, N., Sánchez-Quesada, J., Bayley, H., and Ghadiri,
M.R. (2005). Recognizing a single base in an individual
DNA strand: a step toward DNA sequencing in
nanopores. Angew. Chem. Int. Ed. Engl. 44, 1401–1404.
http://dx.doi.org/10.1002/anie.200462114
Curr. Issues Mol. Biol. Vol. 27
Atwood, J.T. (1986). The size of Orchidaceae and the
systematic distribution of epiphytic orchids. Selbyana 9,
171–186.
Baker, M. (2012). De novo genome assembly: what every
biologist should know. Nature Met. 9, 333–337. http://
dx.doi.org/10.1038/nmeth.1935
Brown, C.G., and Clarke, J. (2016). Nanopore development
at Oxford Nanopore. Nat. Biotechnol. 34, 810–811.
http://dx.doi.org/10.1038/nbt.3622
Buermans, H.P., and den Dunnen, J.T. (2014). Next
generation sequencing technology: Advances and
applications. Biochim. Biophys. Acta 1842, 1932–1941.
Cai, J., Liu, X., Vanneste, K., Proost, S., Tsai, W.C., Liu, K.W.,
Chen, L.J., He, Y., Xu, Q., Bian, C., et al. (2015). The
genome sequence of the orchid Phalaenopsis equestris.
Nature Genet. 47, 65–72. http://dx.doi.org/10.1038/
ng.3149
Chaitankar, V., Karakülah, G., Ratnapriya, R., Giuste, F.O.,
Brooks, M.J., and Swaroop, A. (2016). Next generation
sequencing technology and genomewide data analysis:
Perspectives for retinal research. Prog. Retin. Eye. Res.
55, 1–31.
Chang, C.C., Lin, H.C., Lin, I.P., Chow, T.Y., Chen,
H.H., Chen, W.H., Cheng, C.H., Lin, C.Y., Liu, S.M.,
Chang, C.C., et al. (2006). The chloroplast genome of
Phalaenopsis aphrodite (Orchidaceae): comparative
analysis of evolutionary rate with that of grasses and its
phylogenetic implications. Mol. Biol. Evol. 23, 279–291.
http://dx.doi.org/10.1093/molbev/msj029
Chang, Y.Y., Chu, Y.W., Chen, C.W., Leu, W.M., Hsu,
H.F., and Yang, C.H. (2011). Characterization of
Oncidium ‘Gower Ramsey’ transcriptomes using 454
GS-FLX pyrosequencing and their application to the
identification of genes associated with flowering time.
Plant. Cell. Physiol. 52, 1532–1545. http://dx.doi.
org/10.1093/pcp/pcr101
Chao, Y.T., Su, C.L., Jean, W.H., Chen, W.C., Chang, Y.C., and
Shih, M.C. (2014). Identification and characterization
of the microRNA transcriptome of a moth orchid
Phalaenopsis aphrodite. Plant. Mol. Biol. 84, 529–548.
http://dx.doi.org/10.1007/s11103-013-0150-0
Chou, M.L., Shih, M.C., Chan, M.T., Liao, S.Y., Hsu, C.T.,
Haung, Y.T., Chen, J.J., Liao, D.C., Wu, F.H., and
Lin, C.S. (2013). Global transcriptome analysis and
identification of a CONSTANS-like gene family in the
orchid Erycina pusilla. Planta 237, 1425–1441. http://
dx.doi.org/10.1007/s00425-013-1850-z
Clarke, J., Wu, H.C., Jayasinghe, L., Patel, A., Reid, S., and
Bayley, H. (2009). Continuous base identification
for single-molecule nanopore DNA sequencing. Nat.
Nanotechnol. 4, 265–270. http://dx.doi.org/10.1038/
nnano.2009.12
Cozzolino, S., and Widmer, A. (2005). Orchid diversity: an
evolutionary consequence of deception? Trends Ecol.
Evol. 20, 487–494.
Crane, P.R., Friis, E.M., and Pedersen, K.R. (1995). The
origin and early diversification of angiosperms. Nature
374, 27–33. http://dx.doi.org/10.1038/374027a0
De Paolo, S., Salvemini, M., Gaudio, L., and Aceto, S. (2014).
De novo transcriptome assembly from inflorescence
of Orchis italica: analysis of coding and non-coding
transcripts. PLOS ONE 9, e102155. http://dx.doi.
org/10.1371/journal.pone.0102155
|
65
66
|
Yeh et al.
Deamer, D.W., and Akeson, M. (2000). Nanopores and
nucleic acids: prospects for ultrarapid sequencing.
Trends Biotechnol. 18, 147–151.
Delseny, M., Han, B., and Hsing, Y.I.e. (2010). High
throughput DNA sequencing: The new sequencing
revolution. Plant. Sci. 179, 407–422. http://dx.doi.
org/10.1016/j.plantsci.2010.07.019
Deng, H., Zhang, G.Q., Lin, M., Wang, Y., and Liu, Z.J.
(2015). Mining from transcriptomes: 315 single-copy
orthologous genes concatenated for the phylogenetic
analyses of Orchidaceae. Ecol. Evol. 5, 3800–3807.
http://dx.doi.org/10.1002/ece3.1642
Deng, H., Zhang, L.S., Zhang, G.Q., Zheng, B.Q., Liu, Z.J.,
and Wang, Y. (2016). Evolutionary history of PEPC
genes in green plants: Implications for the evolution of
CAM in orchids. Mol. Phylogenet. Evol. 94, 559–564.
http://dx.doi.org/10.1016/j.ympev.2015.10.007
Dressler, R.L. (1981). The orchids: Natural history and
classification. Cambridge, Massachusetts, USA: Harvard
University Press.
Egan, A.N., Schlueter, J., and Spooner, D.M. (2012).
Applications of next-generation sequencing in plant
biology. Am. J. Bot. 99, 175–185. http://dx.doi.
org/10.3732/ajb.1200020
Eid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G., Peluso,
P., Rank, D., Baybayan, P., Bettman, B., et al. (2009).
Real-time DNA sequencing from single polymerase
molecules. Science 323, 133–138. http://dx.doi.
org/10.1126/science.1162986
Fu, C.H., Chen, Y.W., Hsiao, Y.Y., Pan, Z.J., Liu, Z.J., Huang,
Y.M., Tsai, W.C., and Chen, H.H. (2011). OrchidBase:
a collection of sequences of the transcriptome derived
from orchids. Plant. Cell. Physiol. 52, 238–243. http://
dx.doi.org/10.1093/pcp/pcq201
Garalde, D.R., Snell, E.A., Jachimowicz, D., Heron, A.J.,
Bruce, M., Lloyd, J., Warland, A., Pantic, N., Admassu,
T., Ciccone, J.,et al. (2016). Highly parallel direct RNA
sequencing on an array of nanopores. http://dx.doi.
org/10.1101/068809.
Goodwin, S., McPherson, J.D., and McCombie, W.R.
(2016). Coming of age: ten years of next-generation
sequencing technologies. Nat. Rev. Genet. 17, 333–351.
http://dx.doi.org/10.1038/nrg.2016.49
Górniak, M., Paun, O., and Chase, M.W. (2010).
Phylogenetic relationships within Orchidaceae based
on a low-copy nuclear coding gene, Xdh: Congruence
with organellar and nuclear ribosomal DNA results.
Mol. Phylogenet. Evol. 56, 784–795. http://dx.doi.
org/10.1016/j.ympev.2010.03.003
Guo, X., Li, Y., Li, C., Luo, H., Wang, L., Qian, J., Luo, X.,
Xiang, L., Song, J., Sun, C., et al. (2013). Analysis of the
Dendrobium officinale transcriptome reveals putative
alkaloid biosynthetic genes and genetic markers.
Gene 527, 131–138. http://dx.doi.org/10.1016/j.
gene.2013.05.073
Harris, T.D., Buzby, P.R., Babcock, H., Beer, E., Bowers,
J., Braslavsky, I., Causey, M., Colonell, J., Dimeo, J.,
Efcavitch, J.W., et al. (2008). Single-molecule DNA
sequencing of a viral genome. Science 320, 106–109.
http://dx.doi.org/10.1126/science.1150427
Hsiao, Y.Y., Chen, Y.W., Huang, S.C., Pan, Z.J., Fu, C.H.,
Chen, W.H., Tsai, W.C., and Chen, H.H. (2011a). Gene
discovery using next-generation pyrosequencing to
Curr. Issues Mol. Biol. Vol. 27
develop ESTs for Phalaenopsis orchids. BMC Genomics
12, 360. http://dx.doi.org/10.1186/1471-2164-12-360
Hsiao, Y.Y., Pan, Z.J., Hsu, C.C., Yang, Y.P., Hsu, Y.C.,
Chuang, Y.C., Shih, H.H., Chen, W.H., Tsai, W.C., and
Chen, H.H. (2011b). Research on orchid biology and
biotechnology. Plant. Cell. Physiol. 52, 1467–1486.
http://dx.doi.org/10.1093/pcp/pcr100
Hsiao, Y.Y., Huang, T.H., Fu, C.H., Huang, S.C., Chen, Y.J.,
Huang, Y.M., Chen, W.H., Tsai, W.C., and Chen, H.H.
(2013). Transcriptomic analysis of floral organs from
Phalaenopsis orchid by using oligonucleotide microarray.
Gene 518, 91–100. http://dx.doi.org/10.1016/j.
gene.2012.11.069
Hsiao, Y.Y., Tsai, W.C., Kuoh, C.S., Huang, T.H., Wang, H.C.,
Leu, Y.L., Wu, T.S., Chen, W.H., and Chen, H.H. (2006).
Comparison of transcripts in Phalaenopsis bellina and
Phalaenopsis equestris (Orchidaceae) flowers to deduce
the monoterpene biosynthesis pathway. BMC Plant
Biol. 6, 18. http://dx.doi.org/10.1186/1471-2229-6-14
Illumina. (2016). An introduction to next-generation
sequencing technology. (www.illumina.com/content/
dam/illumina-marketing/documents/products/
illumina_sequencing_introduction.pdf).
International Human Genome Sequencing Consortium.
(2004). Finishing the euchromatic sequence of the
human genome. Nature 431, 931–945. http://dx.doi.
org/10.1016/j.prrv.2009.04.003
International Rice Genome Sequencing Project. (2005).
The map-based sequence of the rice genome. Nature
436, 793–800. http://dx.doi.org/10.1038/nature03895
Jheng, C.F., Chen, T.C., Lin, J.Y., Chen, T.C., Wu, W.L.,
and Chang, C.C. (2012). The comparative chloroplast
genomic analysis of photosynthetic orchids and
developing DNA markers to distinguish Phalaenopsis
orchids. Plant Sci. 190, 62–73. http://dx.doi.
org/10.1016/j.plantsci.2012.04.001
Kasianowicz, J.J., Brandin, E., Branton, D., and Deamer, D.W.
(1996). Characterization of individual polynucleotide
molecules using a membrane channel. Proc. Natl. Acad.
Sci. U.S.A. 93, 13770–13773.
Laver, T., Harrison, J., O’Neill, P.A., Moore, K., Farbos, A.,
Paszkiewicz, K., and Studholme, D.J. (2015). Assessing
the performance of the Oxford Nanopore Technologies
MinION. Biomol. Detect. Quantif. 3, 1–8. http://
dx.doi.org/10.1016/j.bdq.2015.02.001
Leitch, I.J., Kahandawala, I., Suda, J., Hanson, L., Ingrouille,
M.J., Chase, M.W., and Fay, M.F. (2009). Genome size
diversity in orchids: consequences and evolution. Ann.
Bot. 104, 469–481. http://dx.doi.org/10.1093/aob/
mcp003
Levene, M.J., Korlach, J., Turner, S.W., Foquet, M.,
Craighead, H.G., and Webb, W.W. (2003). Zeromode waveguides for single-molecule analysis at high
concentrations. Science 299, 682–686. http://dx.doi.
org/10.1126/science.1079700
Li, R., Hsieh, C.L., Young, A., Zhang, Z., Ren, X., and Zhao,
Z. (2015a). Illumina Synthetic Long Read Sequencing
Allows Recovery of Missing Sequences even in the
‘Finished’ C. elegans Genome. Sci. Rep. 5, 10814. http://
dx.doi.org/10.1038/srep10814
Li, D.M., Zhao, C.Y., Liu, X.R., Liu, X.F., Lin, Y.J., Liu, J.W.,
Chen, H.M., and Lǚ, F.B. (2015b). De novo assembly
and characterization of the root transcriptome and
Next-generation Sequencing Technologies in Orchid Biology
development of simple sequence repeat markers in
Paphiopedilum concolor. Genet. Mol. Res. 14, 6189–
6201. http://dx.doi.org/10.4238/2015.June.9.5
Li, X., Jin, F., Jin, L., Jackson, A., Ma, X., Shu, X., Wu, D.,
and Jin, G. (2015c). Characterization and comparative
profiling of the small RNA transcriptomes in two phases
of flowering in Cymbidium ensifolium. BMC Genomics
16, 622. http://dx.doi.org/10.1186/s12864-0151764-1
Li, X., Luo, J., Yan, T., Xiang, L., Jin, F., Qin, D., Sun, C.,
and Xie, M. (2013). Deep sequencing-based analysis of
the Cymbidium ensifolium floral transcriptome. PLOS
ONE 8, e85480. http://dx.doi.org/10.1371/journal.
pone.0085480
Lin, C.S., Chen, J.J., Huang, Y.T., Hsu, C.T., Lu, H.C.,
Chou, M.L., Chen, L.C., Ou, C.I., Liao, D.C., Yeh, Y.Y.,
et al. (2013). Catalog of Erycina pusilla miRNA and
categorization of reproductive phase-related miRNAs
and their target gene families. Plant. Mol. Biol. 82, 193–
204. http://dx.doi.org/10.1007/s11103-013-0055-y
Lin, Y.F., Chen, Y.Y., Hsiao, Y.Y., Shen, C.Y., Hsu, J.L.,
Yeh, C.M., Mitsuda, N., Ohme-Takagi, M., Liu, Z.J.,
and Tsai, W.C. (2016). Genome-wide identification
and characterization of TCP genes involved in ovule
development of Phalaenopsis equestris. J. Exp. Bot. 67,
5051–5066. http://dx.doi.org/10.1093/jxb/erw273
Liu, L., Li, Y., Li, S., Hu, N., He, Y., Pong, R., Lin, D., Lu,
L., and Law, M. (2012). Comparison of next-generation
sequencing systems. J. Biomed. Biotechnol. 2012,
251364. http://dx.doi.org/10.1155/2012/251364
Liu, S.S., Chen, J., Li, S.C., Zeng, X., Meng, Z.X., and Guo,
S.X. (2015). Comparative transcriptome analysis of
genes involved in GA-GID1-DELLA regulatory module
in symbiotic and asymbiotic seed germination of
Anoectochilus roxburghii (Wall.) Lindl. (Orchidaceae).
Int. J. Mol. Sci. 16, 30190–30203. http://dx.doi.
org/10.3390/ijms161226224
Luo, J., Hou, B.W., Niu, Z.T., Liu, W., Xue, Q.Y., and Ding,
X.Y. (2014). Comparative chloroplast genomes of
photosynthetic orchids: insights into evolution of the
orchidaceae and development of molecular markers
for phylogenetic applications. PLOS ONE 9, e99016.
http://dx.doi.org/10.1371/journal.pone.0099016
McCarthy, A. (2010). Third-generation DNA sequencing:
pacific biosciences’ single molecule real time technology.
Chem. Biol. 17, 675–676. http://dx.doi.org/10.1016/j.
chembiol.2010.07.004
Margulies, M., Egholm, M., Altman, W.E., Attiya, S., Bader,
J.S., Bemben, L.A., Berka, J., Braverman, M.S., Chen,
Y.J., Chen, Z., et al. (2005). Genome sequencing in
microfabricated high-density picolitre reactors. Nature
437, 376–380.
Meng, Y., Yu, D., Xue, J., Lu, J., Feng, S., Shen, C., and
Wang, H. (2016). A transcriptome-wide, organ-specific
regulatory map of Dendrobium officinale, an important
traditional Chinese orchid herb. Sci. Rep. 6, 18864.
http://dx.doi.org/10.1038/srep18864
Niu, S.C., Xu, Q., Zhang, G.Q., Zhang, Y.Q., Tsai, W.C., Hsu,
J.L., Liang, C.K., Luo, Y.B., and Liu, Z.J. (2016). De
novo transcriptome assembly databases for the butterfly
orchid Phalaenopsis equestris. Sci. Data 3, 160083.
http://dx.doi.org/10.1038/sdata.2016.83
Curr. Issues Mol. Biol. Vol. 27
Otero, J.T., and Flanagan, N.S. (2006). Orchid diversity –
beyond deception. Trends Ecol. Evol. 21, 64–65.
Ozsolak, F., Platt, A.R., Jones, D.R., Reifenberger, J.G., Sass,
L.E., McInerney, P., Thompson, J.F., Bowers, J., Jarosz,
M., and Milos, P.M. (2009). Direct RNA sequencing.
Nature 461, 814–818. http://dx.doi.org/10.1038/
nature08390
Ozsolak, F. (2016). Attomole-level Genomics with Singlemolecule Direct DNA, cDNA and RNA Sequencing
Technologies. Curr. Issues. Mol. Biol. 18, 43–48.
Pan, I.C., Liao, D.C., Wu, F.H., Daniell, H., Singh, N.D.,
Chang, C., Shih, M.C., Chan, M.T., and Lin, C.S. (2012).
Complete chloroplast genome sequence of an orchid
model plant candidate: Erycina pusilla apply in tropical
Oncidium breeding. PLOS ONE 7, e34738. http://
dx.doi.org/10.1371/journal.pone.0034738
Peakall, R. (2007). Speciation in the Orchidaceae:
confronting the challenges. Mol. Ecol. 16, 2834–2837.
Perotto, S., Rodda, M., Benetti, A., Sillo, F., Ercole, E., Rodda,
M., Girlanda, M., Murat, C., and Balestrini, R. (2014).
Gene expression in mycorrhizal orchid protocorms
suggests a friendly plant-fungus relationship. Planta 239,
1337–1349. http://dx.doi.org/10.1007/s00425-0142062-x
Pushkarev, D., Neff, N.F., and Quake, S.R. (2009).
Single-molecule sequencing of an individual human
genome. Nat. Biotechnol. 27, 847–850. http://dx.doi.
org/10.1038/nbt.1561
Ramírez, S.R., Gravendeel, B., Singer, R.B., Marshall,
C.R., and Pierce, N.E. (2007). Dating the origin of the
Orchidaceae from a fossil orchid with its pollinator.
Nature 448, 1042–1045.
Rao, X., Krom, N., Tang, Y., Widiez, T., Havkin-Frenkel,
D., Belanger, F.C., Dixon, R.A., and Chen, F. (2014). A
deep transcriptomic analysis of pod development in the
vanilla orchid (Vanilla planifolia). BMC Genomics 15,
964. http://dx.doi.org/10.1186/1471-2164-15-964
Rothberg, J.M., Hinz, W., Rearick, T.M., Schultz, J., Mileski,
W., Davey, M., Leamon, J.H., Johnson, K., Milgrew, M.J.,
Edwards, M., et al. (2011). An integrated semiconductor
device enabling non-optical genome sequencing. Nature
475, 348–352. http://dx.doi.org/10.1038/nature10242
Sanger, F., Air, G.M., Barrell, B.G., Brown, N.L., Coulson,
A.R., Fiddes, J.C., Hutchison III, C.A., Slocombe,
P.M., and Smith, M. (1977a). Nucleotide sequence of
bacteriophage phi X174 DNA. Nature 265, 687–695.
http://dx.doi.org/10.1038/265687a0
Sanger, F., Nicklen, S., and Coulson, A.R. (1977b). DNA
sequencing with chain-terminating inhibitors. Proc.
Natl. Acad. Sci. U.S.A. 74, 5463–5467.
Schiestl, F.P., Peakall, R., Mant, J.G., Ibarra, F., Schulz, C.,
Franke, S., and Francke, W. (2003). The chemistry of
sexual deception in an orchid-wasp pollination system.
Science 302, 437–438. http://dx.doi.org/10.1126/
science.1087835
Sedeek, K.E., Qi, W., Schauer, M.A., Gupta, A.K., Poveda,
L., Xu, S., Liu, Z.J., Grossniklaus, U., Schiestl, F.P., and
Schlüter, P.M. (2013). Transcriptome and proteome
data reveal candidate genes for pollinator attraction
in sexually deceptive orchids. PLOS ONE 8, e64621.
http://dx.doi.org/10.1371/journal.pone.0064621
Shendure, J., Porreca, G.J., Reppas, N.B., Lin, X.,
McCutcheon, J.P., Rosenbaum, A.M., Wang, M.D.,
|
67
68
|
Yeh et al.
Zhang, K., Mitra, R.D., and Church, G.M. (2005).
Accurate multiplex polony sequencing of an evolved
bacterial genome. Science 309, 1728–1732.
Silvera, K., Santiago, L.S., Cushman, J.C., and Winter, K.
(2009). Crassulacean acid metabolism and epiphytism
linked to adaptive radiations in the Orchidaceae. Plant
Physiol. 149, 1838–1847. http://dx.doi.org/10.1104/
pp.108.132555
Su, C.L., Chao, Y.T., Alex Chang, Y.C., Chen, W.C., Chen,
C.Y., Lee, A.Y., Hwa, K.T., and Shih, M.C. (2011). De
novo assembly of expressed transcripts and global
analysis of the Phalaenopsis aphrodite transcriptome.
Plant. Cell. Physiol. 52, 1501–1514. http://dx.doi.
org/10.1093/pcp/pcr097
Tan, J., Wang, H.L., and Yeh, K.W. (2005). Analysis of
organ-specific, expressed genes in Oncidium orchid by
subtractive expressed sequence tags library. Biotechnol.
Lett 27, 1517–1528. http://dx.doi.org/10.1007/
s10529-005-1468-8
Teh, S.L., Chan, W.S., Abdullah, J.O., and Namasivayam,
P. (2011). Development of expressed sequence tag
resources for Vanda Mimi Palmer and data mining for
EST-SSR. Mol. Biol. Rep. 38, 3903–3909. http://dx.doi.
org/10.1007/s11033-010-0506-3
The Arabidopsis Genome Initiative. (2000). Analysis of the
genome sequence of the flowering plant Arabidopsis
thaliana. Nature 408, 796–815. http://dx.doi.
org/10.1038/35048692
Thompson, J.F., and Steinmann, K.E. (2010). Single
molecule sequencing with a HeliScope genetic analysis
system. Curr. Protoc. Mol. Biol. Chapter 7, Unit7.10.
http://dx.doi.org/10.1002/0471142727.mb0710s92
Tremblay, R.L., Ackerman, J.D., Zimmerman, J.K., and
Calvo, R.N. (2005). Variation in sexual reproduction in
orchids and its evolutionary consequence: a spasmodic
journey to diversification. Biol. J. Linn. Soc. 84, 1–54.
http://dx.doi.org/10.1111/j.1095-8312.2004.00400.x
Tsai, C.C., Wu, K.M., Chiang, T.Y., Huang, C.Y., Chou,
C.H., Li, S.J., and Chiang, Y.C. (2016). Comparative
transcriptome analysis of Gastrodia elata (Orchidaceae)
in response to fungus symbiosis to identify gastrodin
biosynthesis-related genes. BMC Genomics 17, 212.
http://dx.doi.org/10.1186/s12864-016-2508-6
Tsai, W.C., Fu, C.H., Hsiao, Y.Y., Huang, Y.M., Chen, L.J.,
Wang, M., Liu, Z.J., and Chen, H.H. (2013). OrchidBase
2.0: comprehensive collection of Orchidaceae floral
transcriptomes. Plant. Cell. Physiol. 54, e7. http://
dx.doi.org/10.1093/pcp/pcs187
Tsai, W.C., Hsiao, Y.Y., Lee, S.H., Tung, C.W., Wang, D.P.,
Wang, H.C., Chen, W.H., and Chen, H.H. (2006).
Expression analysis of the ESTs derived from the flower
buds of Phalaenopsis equestris. Plant Sci. 170, 426–432.
http://dx.doi.org/10.1016/j.plantsci.2005.08.029
Tsai, W.C., Pan, Z.J., Su, Y.Y., and Liu, Z.J. (2014). New
insight into the regulation of floral morphogenesis.
Int. Rev. Cell. Mol. Biol. 311, 157–182. http://dx.doi.
org/10.1016/B978-0-12-800179-0.00003-9
Valouev, A., Ichikawa, J., Tonthat, T., Stuart, J., Ranade, S.,
Peckham, H., Zeng, K., Malek, J.A., Costa, G., McKernan,
K., et al. (2008). A high-resolution, nucleosome position
map of C. elegans reveals a lack of universal sequencedictated positioning. Genome Res. 18, 1051–1063.
http://dx.doi.org/10.1101/gr.076463.108
Curr. Issues Mol. Biol. Vol. 27
van Dijk, E.L., Auger, H., Jaszczyszyn, Y., and Thermes,
C. (2014). Ten years of next-generation sequencing
technology. Trends Genet. 30, 418–426. http://dx.doi.
org/10.1016/j.tig.2014.07.001
Voelkerding, K.V., Dames, S.A., and Durtschi, J.D. (2009).
Next-generation sequencing: from basic research to
diagnostics. Clin. Chem. 55, 641–658. http://dx.doi.
org/10.1373/clinchem.2008.112789
Wu, F.H., Chan, M.T., Liao, D.C., Hsu, C.T., Lee, Y.W.,
Daniell, H., Duvall, M.R., and Lin, C.S. (2010). Complete
chloroplast genome of Oncidium Gower Ramsey and
evaluation of molecular markers for identification and
breeding in Oncidiinae. BMC Plant Biol. 10, 68. http://
dx.doi.org/10.1186/1471-2229-10-68
Xu, C., Zeng, B., Huang, J., Huang, W., and Liu, Y. (2015).
Genome-wide transcriptome and expression profile
analysis of Phalaenopsis during explant browning.
PLOS ONE 10, e0123356. http://dx.doi.org/10.1371/
journal.pone.0123356
Yan, L., Wang, X., Liu, H., Tian, Y., Lian, J., Yang, R., Hao,
S., Wang, X., Yang, S., Li, Q., et al. (2015). The genome
of Dendrobium officinale illuminates the biology of
the important traditional chinese orchid herb. Mol.
Plant 8, 922–934. http://dx.doi.org/10.1016/j.
molp.2014.12.011
Yang, F., and Zhu, G. (2015). Digital gene expression
analysis based on de novo transcriptome assembly reveals
new genes associated with floral organ differentiation
of the orchid plant Cymbidium ensifolium. PLOS ONE
10, e0142434. http://dx.doi.org/10.1371/journal.
pone.0142434
Yang, J.B., Tang, M., Li, H.T., Zhang, Z.R., and Li, D.Z.
(2013). Complete chloroplast genome of the genus
Cymbidium: lights into the species identification,
phylogenetic implications and population genetic
analyses. BMC Evol. Biol. 13, 84. http://dx.doi.
org/10.1186/1471-2148-13-84
Ye, W., Shen, C.H., Lin, Y., Chen, P.J., Xu, X., Oelmüller,
R., Yeh, K.W., and Lai, Z. (2014). Growth promotionrelated miRNAs in Oncidium orchid roots colonized
by the endophytic fungus Piriformospora indica. PLOS
ONE 9, e84920. http://dx.doi.org/10.1371/journal.
pone.0084920
Yu, H., and Goh, C.J. (2001). Molecular genetics of
reproductive biology in orchids. Plant Physiol. 127,
1390–1393.
Zeng, W.H., Liao, S.C., and Chang, C.C. (2007).
Identification of RNA editing sites in chloroplast
transcripts of Phalaenopsis aphrodite and comparative
analysis with those of other seed plants. Plant. Cell.
Physiol. 48, 362–368.
Zhang, G.Q., Xu, Q., Bian, C., Tsai, W.C., Yeh, C.M., Liu,
K.W., Yoshida, K., Zhang, L.S., Chang, S.B., Chen, F., et
al. (2016a). The Dendrobium catenatum Lindl. genome
sequence provides insights into polysaccharide synthase,
floral development and adaptive evolution. Sci. Rep. 6,
19029. http://dx.doi.org/10.1038/srep19029
Zhang, J., He, C., Wu, K., Teixeira da Silva, J.A., Zeng,
S., Zhang, X., Yu, Z., Xia, H., and Duan, J. (2016b).
Transcriptome analysis of Dendrobium officinale and its
application to the identification of genes associated with
polysaccharide synthesis. Front. Plant Sci. 7, 5. http://
dx.doi.org/10.3389/fpls.2016.00005
Next-generation Sequencing Technologies in Orchid Biology
Zhang, J., Wu, K., Zeng, S., Teixeira da Silva, J.A., Zhao, X.,
Tian, C.E., Xia, H., and Duan, J. (2013). Transcriptome
analysis of Cymbidium sinense and its application
to the identification of genes associated with floral
development. BMC Genomics 14, 279. http://dx.doi.
org/10.1186/1471-2164-14-279
Zhang, L., Chen, F., Zhang, G.Q., Zhang, Y.Q., Niu, S.,
Xiong, J.S., Lin, Z., Cheng, Z.M., and Liu, Z.J. (2016c).
Origin and mechanism of crassulacean acid metabolism
in orchids as implied by comparative transcriptomics
and genomics of the carbon fixation pathway. Plant J. 86,
175–185. http://dx.doi.org/10.1111/tpj.13159
Curr. Issues Mol. Biol. Vol. 27
Zhao, X., Zhang, J., Chen, C., Yang, J., Zhu, H., Liu, M.,
and Lv, F. (2014). Deep sequencing-based comparative
transcriptional profiles of Cymbidium hybridum roots
in response to mycorrhizal and non-mycorrhizal
beneficial fungi. BMC Genomics 15, 747. http://dx.doi.
org/10.1186/1471-2164-15-747
Zhu, G., Yang, F., Shi, S., Li, D., Wang, Z., Liu, H., Huang, D.,
and Wang, C. (2015). Transcriptome characterization of
Cymbidium sinense ‘Dharma’ using 454 pyrosequencing
and its application in the identification of genes
associated with leaf color variation. PLOS ONE
10, e0128592. http://dx.doi.org/10.1371/journal.
pone.0128592
|
69
Keep reading this paper — and 50 million others — with a free Academia account
Used by leading Academics
Renate Scheibe
Universität Osnabrück
John Leslie
Kansas State University
Mónica Moraes R.
UNIVERSIDAD MAYOR de SAN ANDRES UMSA
Jorge Jhoncon
Universidad Nacional de Educación "Enrique Guzmán y Valle
