B RIEFINGS IN BIOINF ORMATICS . VOL 10. NO 6. 609^ 618
doi:10.1093/bib/bbp039
De novo sequencing of plant genomes
using second-generation technologies
Michael Imelfort and David Edwards
Submitted: 4th May 2009; Received (in revised form) : 29th July 2009
Abstract
The ability to sequence the DNA of an organism has become one of the most important tools in modern biological
research. Until recently, the sequencing of even small model genomes required substantial funds and international
collaboration. The development of ‘second-generation’ sequencing technology has increased the throughput and
reduced the cost of sequence generation by several orders of magnitude. These new methods produce vast numbers
of relatively short reads, usually at the expense of read accuracy. Since the first commercial second-generation
sequencing system was produced by 454 Technologies and commercialised by Roche, several other companies including Illumina, Applied Biosystems, Helicos Biosciences and Pacific Biosciences have joined the competition. Because
of the relatively high error rate and lack of assembly tools, short-read sequence technology has mainly been applied
to the re-sequencing of genomes. However, some recent applications have focused on the de novo assembly of
these data. De novo assembly remains the greatest challenge for DNA sequencing and there are specific problems
for second generation sequencing which produces short reads with a high error rate. However, a number of different approaches for short-read assembly have been proposed and some have been implemented in working software.
In this review, we compare the current approaches for second-generation genome sequencing, explore the future
direction of this technology and the implications for plant genome research.
Keywords: genome sequencing; second generation; next generation; illumina; Roche 454; de novo assembly
INTRODUCTION TO CROP
GENOMES
Many crop genomes are large, complex and often
polyploid, making genome sequencing a major challenge. With the decreasing cost of second-generation
DNA sequencing technologies, genome size in itself
would not prevent the application of whole genome
sequencing approaches. However, the large size of
many crop genomes is predominantly due to the
amplification of repetitive elements as well as
whole or partial genome duplication. Estimates of
the dispersed repetitive fraction of the human
genome, range from 35% [1] to 45% [2]. One
study on the repetitive elements of the rice genus
found the percentage of repetitive elements in the
samples to range from 25% of reads in Oryza coarctata
(HHKK) to 66% of reads in Oryza officinalis (CC) [3].
A similar study for maize estimated that the repetitive
content ranges from 64 to 73% [4]. It is the resolution of repetitive sequences and the discrimination
of duplicated regions of genomes that provides the
greatest challenge for crop genome sequencing and
assembly.
OVERVIEW OF SEQUENCING
TECHNOLOGIES
Second-generation sequencing
The ability to sequence genomes is being advanced
by increasingly high throughput technology. This is
driven by what has been termed next or secondgeneration sequencing. Second-generation sequencing describes platforms that produce large amounts
(typically millions) of short DNA sequence reads of
Corresponding author. David Edwards, Australian Centre for Plant Functional Genomics, Institute for Molecular Biosciences and
School of Land, Crop and Food Sciences, University of Queensland, Brisbane, QLD 4072, Australia. Tel: þ61-7-3346-2615;
Fax: þ61-7-3346-2101; E-mail: [email protected]
Michael Imelfort is a PhD student at the University of Queensland funded by the Australian Centre for Plant Functional Genomics.
He is developing novel approaches to sequence complex genomes.
David Edwards is an Associate Professor at the University of Queensland. His research focuses on understanding the structure and
expression of complex genomes.
ß The Author 2009. Published by Oxford University Press. For Permissions, please email: [email protected]
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Imelfort and Edwards
length typically between 25 and 400 bp. While these
reads are shorter than the traditional Sanger sequence
reads, all current sequence reads are dwarfed by the
size of many crop genomes which frequently consist
of several thousand million base pairs of DNA.
The first approach to second-generation sequencing was pyrosequencing, and the first successful
pyrosequencing system was developed by 454 Life
Sciences and commercialised by Roche as the GS20.
This was capable of sequencing over 20 million base
pairs in just over 4 h [5]. The GS20 was replaced
during 2007 by the GS FLX model, capable of producing over 100 million base pairs of sequence in
a similar amount of time. More recently, this technology has advanced to produce more than 400 Mbp
with the introduction of the Titanium chemistry.
Two alternative ultra high throughput sequencing
systems now compete with the Roche GS FLX;
the SOLiD system from Applied Biosystems (AB);
and Solexa Genome Analyser technology, which
is now commercialised by Illumina. For a summary
of the current (June 2009) data produced by each
of these technologies, see Table 1.
The Solexa Genome Analyzer (currently the
GAIIx) system uses reversible terminator chemistry
to generate up to 50 000 million bases of usable data
per run. Sequencing templates are immobilised on
a flow cell surface, and solid phase amplification
creates clusters of identical copies of each DNA
molecule. Sequencing then uses four proprietary
fluorescently labelled nucleotides to sequence the
millions of clusters on the flow cell surface. These
nucleotides possess a reversible termination property,
allowing each cycle of the sequencing reaction to
occur simultaneously in the presence of the four
nucleotides. Illumina sequencing has been developed
predominantly for re-sequencing, with more than
10-fold coverage usually ensuring high confidence
in the determination of genetic differences. In addition, each raw read base has an assigned quality
score, assisting assembly and sequence comparisons.
The GAII is used for transcriptome profiling
(RNA Seq) and Chromatin ImmunoPrecipitation
sequencing (ChIP Seq), and the relatively low
error rate of this system supports de novo sequencing
applications. This technology has now been applied
for the sequencing of several crop species including
cucumber [6] and Brassica rapa.
The AB SOLiD System (currently version 3)
enables parallel sequencing of clonally amplified
DNA fragments linked to beads. The method is
based on sequential ligation with dye labelled oligonucleotides and can generate more than 20 gigabases
of mappable data per run. The system can be used
for tag-based applications such as gene expression
and Chromatin ImmunoPrecipitation sequencing,
where a large number of reads are required and
where the high throughput provides greater sensitivity for the detection of lowly expressed genes. The
AB SOLiD system is predominantly used for
re-sequencing where comparison to a reference
and high sequence redundancy enables the identification and removal of erroneous sequence reads.
In contrast to the short-read sequencers, the
Roche 454 FLX system produces read lengths on
average 300–500 bp and is capable of producing
over 400 Mbp of sequence with a single-read accuracy of >99.5%. Amplification and sequencing
is performed in a highly parallelised picoliter
format. Emulsion PCR enables the amplification
of a DNA fragment immobilised on a bead from
a single fragment to 10 million identical copies,
generating sufficient DNA for the subsequent
sequencing reaction. Sequencing involves the
sequential flow of both nucleotides and enzymes
over the beads, which converts chemicals generated
during nucleotide incorporation into a chemiluminescent signal that can be detected by a CCD
camera. The light signal is quantified to determine
the number of nucleotides incorporated during the
extension of the DNA sequence. Newbler software
has been developed by 454 life sciences specifically
to assemble this type of data and it has been
successfully applied for the assembly of a bacterial
genome [7].
Third-generation sequencing
Table 1: Data produced by current DNA sequencing
technologies.
Sanger GSFLX AB SOLiD Illumina GAII
Nucleotides per run 70 kbp
Read length
750 bp
400 Mbp 20 Gbp
400 bp 50 bp
50 Gbp
75 bp
There has been a dramatic increase in secondgeneration sequencing capability over the last 2–3
years and it is clear that we are still in the early
stages of what can be achieved. It is expected that
the Illumina and AB systems will be producing up
to 100 Gbp of data per run by the end of 2009 due
to an increase in read length and number of reads.
De novo sequencing of plant genomes
In addition, several companies plan to bring to
market what is termed third-generation sequencing,
taking DNA sequence production to a further level
of scale and dramatically reducing costs. The first
of these technologies to come to market uses single
molecule sequencing, commercialised by Helicos
Biosciences (Cambridge, MA, USA). Termed
‘True Single-Molecule Sequencing’, this method
has been used to sequence the genome of the virus
M13 [8]. Pacific biosciences have developed a single
molecule real time sequencing system that promises
to produce several Gbp of relatively long reads
(>1 kbp) [9]. Other companies such as VisiGen
Biotechnologies (Houston, TX, USA, http://www
.visigenbio.com); Complete Genomics (Mountain
View, CA, USA, www.completegenomics.com/);
NABsys (Providence, Rhode Island, USA,
www.nabsys.com); ZS Genetics (North Reading,
MA, USA, www.zsgenetics.com) and Oxford
Nanopore Technologies (Oxford, UK, www.
nanoporetech.com) are also working on thirdgeneration sequencing systems and it is likely that
more advanced sequencing will be available in the
relatively near future. These changes will revolutionise genome sequencing and as data production
becomes facile, the challenge increasingly becomes
the ability to analyse this data and in particular,
the ability to associate variation in sequences with
heritable traits.
DE NOVO GENOME ASSEMBLY
APPROACHES
Assembling the data
One challenge to sequencing crop genomes is
the vast difference in scale between the size of the
genomes and the lengths of the reads produced
by the different sequencing methods. While there
may be a 10–500 difference in scale between the
short reads produced by second-generation sequencing and modern Sanger sequencing, this is still
dwarfed by the difference between Sanger read
length and the lengths of complete chromosomes.
Human chromosomes vary between 47 and 245 million nucleotides in length, around 50 000–250 000
larger than average Sanger reads, and it was not considered feasible to sequence and de novo assemble the
human genome using Sanger alone due to the complexity of assembly. It is therefore counter intuitive
to consider sequencing more complex genomes
using shorter reads. However, the ability to produce
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vast quantities of paired read sequence data gives
significant power over traditional Sanger data,
enabling the adoption of novel strategies to sequence
complex plant genomes.
The first sequence fragment assembly algorithms
were developed beginning in the late 1970s [10, 11].
Early sequencing efforts focussed on creating multiple alignments of the reads to produce a layout
assembly of the data. Finally, a consensus sequence
could be read from the alignment and the DNA
sequence was inferred from this consensus. This
approach is referred to as the overlap-layout-consensus approach and culminated in a variety of software
applications such as CAP3 [12] and Phrap (Phil
Green, Phrap Documentation, www.phrap.org).
From the early to mid 1990’s, research began to
focus on formalising, benchmarking and classifying
fragment assembly algorithm approaches. Two
papers [13, 14] formalised the approach of placing
sequence reads or fragments of reads in a directed
graph. In this graph, each node represents a read
or fragment and any two nodes are joined by an
edge if there is an overlap on the left-hand side of
one read with the right-hand-side of another. The
resulting DNA sequence is inferred by walking
along the edges of the graph. These two papers
have formed the foundation for modern secondgeneration sequence assembly approaches. Idury
and Waterman introduced the concept of Eulerian
tours of a graph of equal length sequence fragments,
while Myers formalised the concept of a fragment
assembly string graph, where the read fragments
need not be of the same length. These ideas were
implemented in software such as Euler [15], and
all modern sequence fragment assemblers are extensions of these concepts and in many cases they use
some combination of the two.
Problems of assembling complex
genomes
One challenge of genome sequencing lies in the fact
that only a small portion of the genome encodes
genes, and that these genes are surrounded by repetitive DNA that are often difficult to characterise.
Repeats can cause ambiguity with fragment assembly
and thus pose the greatest challenge when assembling
genomic data.
The most robust method to overcome the problems of repeats when assembling shotgun reads is
to increase the read length to a point that every
repeat is spanned by at least one read. However,
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Imelfort and Edwards
Figure 1: Repetitive regions (X) in genomes confound sequence assembly using the overlap consensus approach
where the reads do not span the repeats. The use of paired reads that span the repetitive regions provide a reference for the spatial relationship between contigs and permit the ordering and orientation of unique sequences.
increasing sequence read length has proven technically problematic and most second-generation
sequencing technologies compromise read length
to deliver an increased number of reads.
Modifications of second-generation sequencing
methods attempt to overcome the problem of short
reads by producing read pairs, where two reads are
produced with a known orientation and approximate distance between them, increasing the ‘effective’ read length. If the distance between the paired
ends is large enough, repeats will be spanned by pairs
of reads removing ambiguity from the assembly
(Figure 1). It is expected that the ability to produce
increased quantities of paired read sequence data,
combined with custom bioinformatics tools, will
provide the foundation to sequence large and complex plant genomes.
Current second-generation de novo
sequence assembly tools
There have been a number of sequence assembly
tools published in recent years. Successive tools
have increasingly improved in terms of both the
quality of the output assemblies as well as robustness
in the assembly of more complex genomes [16].
There has been a general trend of improving elements of the algorithm underlying the Eulerian
approach.
SSAKE
SSAKE was one of the first short-read assemblers
released [17]. When SSAKE is run, all reads that
can be joined using the method are returned as contigs and all reads not incorporated are returned in
a separate file. The user has the option to set the
size of the minimum overlap allowable between
reads, and SSAKE can be set to stop whenever
there are two or more candidates for extension
(strict mode). When SSAKE is not run in strict
mode, extension can still proceed in the face of multiple candidates. SSAKE does not incorporate data
from paired ends and thus has no way to identify
and differentiate between repetitive regions. Indeed
in non-strict mode, SSAKE will effectively collapse
multiple repetitive regions into one contig, and
in strict mode any reads at the boundary of a repetitive region will break the contig and errors in
the dataset will also break contigs.
VCAKE
VCAKE was released in 2007 and is an extension
of SSAKE that can handle erroneous reads [18].
VCAKE begins exactly as SSAKE but handles the
cases where there are no candidates or multiple candidates for extension differently. In the case where
there is no read to extend with, VCAKE will allow
a read with at most one mismatch after the first 11
bases to be used in the extension. In the case where
there are multiple candidates, VCAKE employs a
majority vote method based on read depth at the
base in question to decide which read to extend
with. In the case that the second most occurring
read is present more than a set number of times,
extension is terminated, as it is likely that the
read is at the border of a repetitive region. In tests
between VCAKE and SSAKE, SSAKE and VCAKE
perform equally well on simple viral sequences but
VCAKE appears to perform much better on more
complicated sequences [18]. VCAKE does not make
use of paired end data and so also cannot resolve
ambiguities caused by repetitive regions.
SHARCGS
SHARGCS builds on the methods used in
SSAKE and VCAKE [19]. SHARGCS employs
De novo sequencing of plant genomes
a three-phase algorithm to assemble the data, including filtering, naı̈ve assembly and refinement. Filtering
comprises two separate operations. First, any reads
not appearing a minimum number of times are
removed, as are any reads which do not meet a
minimum quality standard. When there are different
quality values for a base on reads with the same
orientation, the maximum is taken as the quality
value for that base, and when the reads have reversed
orientations, the sum of the quality values is taken.
A second filtering step removes all reads that do
not overlap with at least one read on either side.
The default behaviour for SHARCGS produces
three assemblies with different filtering levels.
SHARCGS then attempts to merge contigs from
different rounds of assembly to produce a more accurate set of contigs which are returned to the user
with quality values. As with both SSAKE and
VCAKE, SHARCGS does not include paired end
data in the algorithm and as a result has an increased
probability of collapsing repetitive regions.
Edena
Edena is the first short-read assembly algorithm to
be released that uses the traditional overlap-layoutconsensus approach [20]. Edena does not include
an error correction phase before assembly, which
leads to the formation of an overly complex
sequence graph; however, it does include an error
correction phase which cleans the graph before
assembly begins. The first phase of the algorithm
removes duplicate reads, keeping only the original
read and the number of times it has been seen.
Next, it uses the reads to construct an overlap
graph where the reads are represented as nodes.
Two nodes are joined by an edge if there is an overlap between them. The resulting graph contains
erroneous edges that need to be removed. First
Edena removes transitive edges using the method
described by Myers [14]. Following this, dead end
paths of consecutive nodes shorter than 10 reads,
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that are attached to the main body of the graph on
only one side are removed. Finally Edena removes
P-bubbles. P-bubbles occur when two subsequences are almost identical. This causes the
graph to first fork and then rejoin, giving the appearance of a bubble in the graph. Where this is caused
by single nucleotide polymorphisms (SNPs) between
repetitive regions, each side of the bubble would
have a similar topology and copy number. Where
a P-bubble is caused by an error, one side of the
bubble would have sparse topology and lower
copy number (Figure 2). Edena removes the side
of the bubble with the lowest copy number/sparsest
topology. Once the graph has been cleaned, an
assembly is produced and the resulting contigs
returned to the user. Edena does not make use of
paired end data.
Velvet
Velvet [21] uses an Idury/Waterman/Pevzner
model [13, 15] to make an initial graph. Like
Edena, Velvet does not include an initial error correction phase but instead uses a series of error correction algorithms analogous to Edena to clean up
the graph. Unlike Edena, Velvet uses an Euler-like
strategy, which while efficient in terms of memory
use, appears to complicate P-bubble removal. Edena
removes P-bubbles by investigating topology and
read depth and removing erroneous reads in their
entirety. In an Euler-like algorithm, kmers can
belong to both perfect and imperfect reads and
there is a non-trivial amount of housekeeping to
be done when removing individual kmers. Velvet
includes an algorithm called Tour Bus that traverses
the graph looking for P-bubbles, and uses a combination of copy number and topographical information to remove the erroneous edges. Velvet then
removes all remaining low copy number edges.
Velvet has recently been extended to include the
use of paired reads using the Pebble and
BreadCrumb algorithms.
Figure 2: An example of a P-bubble most likely caused by an error. The reads making up the lower sequence have a
low copy number and the overlaps are short. This phenomenon can also be caused by low copy number repeats.
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Imelfort and Edwards
EulerSR
The latest addition to the EULER family of algorithms [15] is EULER SR [22] which has been optimised to assemble short reads. The original EULER
algorithm was designed as an implementation of the
Idury and Waterman algorithm but included a novel
method for error correction. Short reads are broken
down into even shorter k-tuples. Pevzner describes
a k-tuple as ‘weak’ if it appears less than a set number
of times and ‘solid’ otherwise. For any read which
contains weak k-tuples, the algorithm tries to find
the minimum number of base changes which will
make these k-tuples solid. If that number is less
than an amount defined by the user, then the
changes are made, otherwise the sequence is discarded. Pevzner shows that this method corrects
over 86% of errors for the dataset he analysed [15].
After the reads have been corrected, a de Bruijn
graph is constructed and a set of contigs produced
and returned to the user. Like Velvet, Euler can
make use of paired read data to assist in the assembly.
AllPaths
ALLPATHS [23] begins by correcting errors using
an EULER like method, and then makes a large
set of assemblies, referred to as unipaths.
ALLPATHS then uses paired read information to
sort unipaths into localised groups. For each localised
group of unipaths, the number of paths is trimmed
until, ideally only one path remains. This method
reduces the complexity in the overall graph by
making local optimisations. The results from the
local optimisations are stitched together to produce
one long sequence graph. The final phase of the
algorithm attempts to fix errors that may have been
incorporated in earlier stages. Unlike most other
assemblers, AllPaths returns the assembly as a graph,
complete with ambiguities which can arise from limitations of the data set and also from polymorphism
in diploid genomes. This graph can be used in
further downstream analysis.
ABySS
ABySS is the newest next-generation short-read
assembler and the first to natively embrace parallel
architectures [24]. The algorithm behind ABySS
is almost identical to that used in other popular
Eulerian assemblers. The distributed elements of
ABySS use the Message Passing Interface (MPI) protocol for communication between nodes.
Summary
Currently, the most popular tools for genome assembly using short read data are Velvet, ABySS and
EulerSR. These programs offer different advantages;
for example Velvet can use a mixture of both short
reads and traditional long (Sanger) reads while
ABySS has been designed to run on a distributed
system. Generally, the older algorithms that do not
use paired end data will not be capable of accurately
assembling the genomes of organisms more complex
than bacteria. There continue to be advances in
the current published algorithms, and additional
approaches are under development. We expect that
this field of research will continue to advance to face
the challenge of assembling currently intractable
large and complex genomes such as wheat and
barley.
Sequencing approaches
Sequencing technology has undergone a major revolution since the first Eukaryotic genomes were
sequenced using traditional Sanger methods. The
ability to produce gigabases of DNA sequence data
has allowed researchers to consider sequencing genomes which were previously considered intractable.
The approaches to genome sequencing have also
changed, moving from laborious BAC by BAC to
whole genome shotgun (WGS) methods. The most
appropriate genome sequencing approach depends
on the genome size and complexity as well as the
technology available at the time.
Genome sequencing using bacterial artificial
chromosomes
The most robust method for genome sequencing
is known as BAC by BAC sequencing. The idea
behind this method is to cut a large genome into
smaller overlapping sections (usually 100–150 kbp
in length). The DNA fragments are maintained as
bacterial artificial chromosomes (BACs) enabling
the production of sufficient quantities of DNA for
sequencing. The next step is usually to order these
fragments using physical and genetic mapping, and
then sequence a minimal tiling path of these fragments that represents the complete genome. BAC
sequencing can use traditional Sanger, 454 pyrosequencing or Illumina GAII short read sequencing, or
potentially combinations of these methods. Assembly
of the sequence reads to produce the representation
of the BAC fragment could use a range of assemblers
specific to the data type. Examples include Phrap,
De novo sequencing of plant genomes
CAP3 [12], Newbler, or Velvet [21]. Several additional assemblers are available or under development.
The benefit of BAC by BAC genome sequencing is
the reduction of complexity, as only relatively short
regions are assembled at one time, and this approach
is still favoured for genomes which contain a large
number of repetitive elements. However, the cost
of producing and mapping the BAC library as well
as the significant cost associated with the sequencing
of large numbers of BACs makes BAC by BAC
sequencing approaches increasingly unfavourable,
and several genome sequencing projects which
started with the BAC by BAC approach have now
moved to WGS methods.
WGS
An alternative approach to BAC by BAC sequencing
is the WGS method. This approach avoids the costly
process of generating a BAC library and minimum
tiling path by randomly cutting the entire genome
into many smaller reads which are then individually
sequenced. Computational algorithms order and
assemble the vast number of reads to resolve the
complete genome sequence. There are often time
and cost savings of using WGS over a BAC by
BAC approach; however these benefits are offset
by the difficulty in assembling the reads. With large
and complex cereal genomes, WGS remains unfeasible, as the majority of the reads will represent repetitive elements in the genome. However, the ability
to produce increasing volumes of data along with
the development of advanced algorithms for the
assembly of this data is making WGS increasingly
popular.
Alternative approaches
While the BAC by BAC approach remains expensive and WGS is difficult for complex genomes,
alternative methods have been developed to reduce
the genome complexity for sequencing. Some methods attempt to sequence a portion of the genome.
These include restriction analysis, where genomic
DNA is digested with restriction endonucleases and
fractionated to remove repeat abundant fractions
[25]. An alternative method for reduced complexity
sequencing involves the isolation of specific chromosomes or portions of chromosomes by chromosome
sorting [26–28]. Other methods enrich for gene rich
regions of the genome. These include methyl filtration, where genomic sequences are enriched for
hypomethylated low copy regions [29, 30], and
CoT based cloning [31–33] which uses DNA
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renaturation kinetics to enrich for low abundance
sequences. These methods all reduce the complexity
of the sample being sequenced and provide an alternative to BAC by BAC and WGS approaches.
Re-sequencing
The process of whole genome re-sequencing
involves aligning a set of reads to a reference
genome that is similar to the subject being
sequenced. Re-sequencing has proved to be a valuable tool for studying genetic variation and with
the re-sequencing of the genomes of James Watson
and Craig Venter [34, 35], the task of re-sequencing
has largely been conquered and large scale
re-sequencing of human populations is now underway [36]. Although re-sequencing can be very
useful, the quality of the re-sequenced genome
is highly dependant on the quality and relatedness
of the reference sequence.
EXAMPLES OF PLANT GENOME
SEQUENCING PROJECTS
Arabidopsis [37] and rice [38, 39] were the first plant
genomes to be sequenced, applying standard Sanger
sequencing of tiled genomic fragments maintained
in BAC vectors. Plant genome sequencing projects
are rapidly changing pace with the new technology
and researchers are quickly adopting secondgeneration sequencing to gain insight into their
favourite genome. Roche 454 technology is being
used to sequence the 430 Mbp genome of Theobroma
cacao [40], while a combination of Sanger and
Roche 454 Sanger sequencing and 454 (4 versus
12 coverage, respectively) is being used to interrogate the apple genome [41, 42]. A similar approach is
being applied to develop a draft consensus sequence
for the 504 Mbp grape genome [43] where a combination of 6.5 Sanger paired read sequences and
4.2 unpaired Roche 454 reads were assembled
into 2093 metacontigs (ordered and aligned scaffolds
of contiguous sequence with some gaps) representing
an estimated 94.6% of the genome. This study also
identified 1.7 million SNPs which were mapped
to chromosomes. Illumina Solexa and Roche 454
sequencing has also been used to characterise the
genomes of cotton [44]. Roche 454 sequencing
has been used to survey the genome of Miscanthus
[45], while Sanger, Illumina Solexa and Roche 454
sequencing is being used to interrogate the banana
genome [46].
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Imelfort and Edwards
Ossowski et al. [47] reported the re-sequencing
of reference Arabidopsis accession Col-0 and two
divergent strains, Bur-0 and Tsu-1 using Illumina
Solexa technology to produce between 15 and
25 coverage. As well as finding over 2000 potential
errors in the reference genome sequence, they identified more than 800 000 unique SNPs and almost
80 000 1–3 bp indels. Longer indels are more
difficult to identify from short read data without
the use of paired reads, however, they did identify
more than 3.4 Mbp of the divergent accessions
that is dissimilar to the Col-0 reference. The resequencing of rice and Medicago truncatula has also
been undertaken using the Illumina Solexa for
SNP discovery [48, 49]. As increasing numbers of
reference genomes become available, it is expected
that whole genome re-sequencing of crop genomes
will become common, providing insights into crop
genome structure and diversity. While Brassica shares
extensive synteny with Arabidopsis thaliana, there is
not enough similarity at the microsynteny level
to approach Brassica genome sequencing as a resequencing of Arabidopsis. The Multinational
Brassica Genome Project (MBGP) steering committee selected B. rapa as the first Brassica species to
be fully sequenced, and established a BAC by BAC
approach, though recently, llumina GAII sequencing
has been applied to generate 70 coverage of this
genome in a collaboration with the Beijing Genome
Institute in Shenzhen (BGI) and the Institute of
Vegetables and Flowers (IVF) in Beijing (X Wang,
pers. comm.). It is expected that the combination
of the BAC sequence data and the assembled contigs
from the Illumina GAII data will lead to the
release of a high quality genome sequence during
2009. The sequencing of Brassica rapa follows from
the success of sequencing the cucumber genome,
where a hybrid strategy consisting of 4 Sanger,
68 Illumina Solexa, as well as 20 and 10 coverage from end sequenced fosmid and BAC libraries
were used to assemble more than 96% of the cucumber genome [6].
The large cereal genomes remain elusive, but the
advances in technology are starting to make de novo
sequencing of these genomes feasible. Roche 454
has been applied for the sequencing of complex
BACs from barley [50, 51] and this has been complemented by repeat characterisation using WGS
Illumina Solexa data [52]. However, the size of the
wheat genome is much larger (17 000 Mbp) than
related cereal genomes such as barley (Hordeum
vulgare, 5000 Mbp), rye (Secale cereale, 9100 Mbp)
and oat (Avena sativa, 11 000 Mbp). The size and
hexaploid nature of the wheat genome creates significant problems in elucidating its genome sequence
and it may take several years before sequencing technology is fast and cheap enough to readily determine
the whole genome sequence. A pilot project led
by the French National Institute for Agricultural
Research (INRA) was initiated in 2004. A total of
68 000 BAC clones of a 3B chromosome specific
BAC library [53] have been fingerprinted and
there are plans to sequence a minimal tiling path
of BACs for this chromosome using Roche 454
technology. Expectations are that by the end of
2010, the majority of the gene-rich regions of hexaploid wheat will have been sequenced. However,
these expectations are likely to be modified by the
rapid changes in sequencing technology and the
ever-reducing cost of producing sequence data.
CONCLUSIONS, FUTURE
DIRECTIONS AND CHALLENGES
The advances in genome sequencing technology
have provided a means to sequence increasingly
large and complex genomes. Data generation is
now no longer limiting complex genome sequencing, and the sequencing and re-sequencing of previously intractable plant genomes is now becoming
feasible. The ability to analyse and assemble this data
is currently limited by a lack of dedicated bioinformatics tools that are designed to cope with the repetitive nature of many crop genomes. We have also
seen that with the release of each new assembly
tool we see an improvement, even for static datasets,
in assembly statistics. These include amongst others:
an increase in contig sizes, a decrease in the total
number of contigs and a decrease in the number of
assembly errors. While it has not been formally
proven that complex repeats can always be resolved
from the data currently produced simply using novel
algorithms, newer algorithms have continually
demonstrated improvements in the overall quality
of assemblies and this is largely due to the ever
increasing sophistication in their methods of handling repeats. However sophisticated the current
methods for handling repeats are now, they still
represent a nascent stage in the evolution of these
algorithms. There are many directions this improvement can take and we are confident that the greatest
increases in the ability to accurately resolve repeat
De novo sequencing of plant genomes
structures in crop genome assemblies will stem from
improvements made to the assembly algorithms.
Improvements in data quality and quantity will of
course also assist in producing better assemblies. As
improved algorithms are developed, it may be
expected that the sequencing of large and complex
genomes will become commonplace, and following
the research path of mammalian genomics, focus will
move to the detection of sequence variation and the
association of this variation with important agronomic traits, with downstream applications for crop
improvement.
7.
8.
9.
10.
11.
FUNDING
Grains Research and Development Corporation
(Project DAN00117); Australian Research Council
(Projects LP0882095, LP0883462, LP0989200);
Australian Genome Research Facility (AGRF); the
Queensland Cyber Infrastructure Foundation
(QCIF); the Australian Partnership for Advanced
Computing (APAC); and Queensland Facility for
Advanced Bioinformatics (QFAB).
12.
13.
14.
15.
16.
17.
Key Points
The ability to produce genome sequence data continues to
increase at an unprecedented rate.
The size of many plant genomes combined with the large
number of repetitive elements and polyploidy creates a challenge
for traditional genome sequencing methods.
Current sequencing and assembly approaches cannot produce
a finished genome for large and complex cereal genomes.
Novel approaches will be required to enable the sequencing
of complex plant genomes using second or third-generation
sequencing data.
18.
19.
20.
21.
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