The genomes of barley and wheat, two of the world’s most important crops, are very large and complex due to their high content of repetitive DNA. In order to obtain a whole-genome sequence sample, we performed two runs of 454 (GS20) sequencing on genomic DNA of barley cv. Morex, which yielded approximately 1% of a haploid genome equivalent. Almost 60% of the sequences comprised known transposable element (TE) families, and another 9% represented novel repetitive sequences. We also discovered high amounts of low-complexity DNA and non-genic low-copy DNA. We identified almost 2300 protein coding gene sequences and more than 660 putative conserved non-coding sequences. Comparison of the 454 reads with previously published genomic sequences suggested that TE families are distributed unequally along chromosomes. This was confirmed by in situ hybridizations of selected TEs. A comparison of these data for the barley genome with a large sample of publicly available wheat sequences showed that several TE families that are highly abundant in wheat are absent from the barley genome. This finding implies that the TE composition of their genomes differs dramatically, despite their very similar genome size and their close phylogenetic relationship.
The Triticeae include some of the world’s most important crops, such as barley and wheat. Their haploid genomes have a size of approximately 5700 Mbp and contain at least 80% repetitive DNA (Bennett and Smith, 1976). Despite their large genomes, the gene content of diploid Triticeae genomes is expected to be similar to that of the rice genome, which contains 30 192 genes according to the manually curated gene set of the rice annotation project (RAP-DB, http://rapdb.dna.affrc.go.jp). The repetitive fraction of the Triticeae genomes consists primarily of transposable elements (TEs). Over 200 TE families have been discovered in genomic sequences (mostly BACs) during the past few years and deposited in the database for Triticeae repeats (TREP, http://wheat.pw.usda.gov/ITMI/Repeats). Because of the large size and repetitiveness of their genomes, long genomic Triticeae sequences are rather rare. At the time this study was performed, only 23 genomic barley sequences longer than 20 kb, which total slightly more than 4.2 Mbp, had been deposited in GenBank. The available genomic barley sequences are possibly not representative for the entire genome of barley due to sampling, because most published sequences originated from map-based cloning projects, specifically targeting regions in the distal parts of chromosomes were most genes are located (Qi et al., 2004).
In contrast to barley, genomic resources for wheat have increased recently due to an international effort by the International Wheat Genome Sequencing Consortium to sequence the entire wheat genome. Currently, 359 large genomic sequences from wheat, with a cumulative size of more than 44 Mbp, are publicly available (approximately 0.6% of a haploid genome equivalent). The sample of wheat sequences is less biased because almost 250 BAC clones were selected randomly (Devos et al., 2005, J. Bennetzen, K. Devos (University of Georgia, Athens, GA) and P. SanMiguel (Purdue University, IN) personal communication). However, most of these sequences are not annotated, and knowledge on TE content and composition comes mostly from hand-annotated sequences that were generated in map-based cloning efforts. Thus, the overall composition of the Triticeae genomes has been extrapolated from limited datasets (Sabot et al., 2005), and no whole-genome survey has been performed so far. Nevertheless, a recent study, based on 36 bp sequence reads obtained on the Illumina Solexa Genome Analyzer 1 and mathematically defined repeats, concluded that knowledge of TE sequences and genome composition in Triticeae is fairly comprehensive, at least in the gene-rich regions (Wicker et al., 2008).
Wheat and barley diverged approximately 11.6 million years ago (Chalupska et al., 2008), and their close phylogenetic relationship is reflected in strong conservation of gene order and content, as well as in the composition of their TE fractions. Several high-copy TE families have been discovered in both barley and wheat, suggesting that similar dynamics shaped the genomes of both species. For example, elements of the BARE1 clade (BARE1 in barley and WIS and Angela in wheat) have been shown to contribute at least 10% to the barley genome (Vicient et al., 1999; Kalendar et al., 2000; Soleimani et al., 2006), and are expected to be present in similar amounts in the wheat genome.
The only other large plant genome for which repetitive DNA has been extensively studied is maize. In maize, it has been shown that the genome expanded mainly by amplification of LTR retrotransposons (SanMiguel and Bennetzen, 1998). These high-copy TE families were probably already present in the common ancestor of several maize species because the most abundant TE families were found in all species examined (Takahashi et al., 1999; Meyers et al., 2001). Most high-copy families were found more or less evenly distributed across the genome, although some (especially DNA transposons) were found to be somewhat enriched in distal chromosomal regions (Messing et al., 2004; Bruggmann et al., 2006). A similar pattern was found in the recently published sorghum genome (Paterson et al., 2009).
Previous studies have also shown that the repetitive fractions of the Triticeae and other grass genomes are highly dynamic, with genomic DNA constantly being created through TE amplification and removed through deletions. This ‘genomic turnover’ results in a rapid reshuffling of intergenic sequences within a few million years, and virtually no TEs older than 4 or 5 million years have been identified (SanMiguel et al., 2002; Wicker et al., 2003; Swigonováet al., 2005; Wicker and Keller, 2007). Additionally, comparison of genomic sequences from various wheat species indicated that differences in TE activities lead to differential compositions of TE content in wheat genomes (Sabot et al., 2005), but no genome-wide study has been undertaken so far to compare the precise composition of their repetitive fraction.
The availability of new sequencing technologies such as 454 pyrosequencing (Margulies et al., 2005) has decreased costs of sequencing and allows rapid and cost-effective sampling of genomes (for review, see Mardis, 2008; Shendure and Ji, 2008). In addition to the reduced costs, 454 sequencing has the advantage that no genomic libraries have to be constructed, which could potentially discriminate against certain sequences. Indeed, sequence coverage with 454 reads was shown to be much more even than with Sanger reads derived form the classic shotgun approach (Wicker et al., 2006). As 454 sequencing is a young technology, only a few studies have been performed on entire plant genomes. 454 whole-genome shotgun sampling was recently used to characterize the repetitive fraction of the pea and soybean genomes (Macas et al., 2007; Swaminathan et al., 2007). These studies provided a large amount of data on repetitive sequences in two genomes that had been poorly characterized up to that point.
In this study, we extended that approach by comparing the outcome of a whole-genome snapshot from barley with a set of well-characterized genomic sequences. This led to conclusions on the relative abundance of TEs in the barley genome, and provided a broad insight into the gene space of barley. Based on 454 reads and publicly available sequences, we found an uneven distribution of certain TE families along chromosomes, a finding that was confirmed by in situ hybridizations. Additionally, a comparison with genomic sequences from wheat revealed strong differences in the TE compositions of the two genomes despite their very similar size.
Two runs of GS20 sequencing provide approximately 1% of a haploid barley genome equivalent
The two GS20 runs produced a total of 571 509 reads, with a mean size of 103 bp. They total 58.91 Mb, which corresponds to slightly more than 1% of the haploid barley genome (assuming a size of 5700 Mbp; Bennett and Smith, 1976). We refer to the 454 sequence library produced as a ‘whole-genome snapshot’. To determine the sequence composition of the genome snapshot, the individual short reads were subjected to various analyses, including a series of BLAST searches against various databases.
The first step was the identification of sequence reads that are composed primarily of simple sequence repeats (SSRs) and other low-complexity sequences by identifying sequences with under-represented bases (see Experimental procedures). We identified a total of 11 876 reads (2.08% of the snapshot) that largely consisted of low-complexity motifs (e.g. large microsatellites). The contributions of chloroplast and mitochondrial DNA to the 454 snapshot were 4.80 and 0.15%, respectively. In addition to the genomic DNA that is contained in the organelles, nuclear genomes frequently contain insertions of fragments of organellar DNA (Leister, 2005). Chloroplast and mitochondrial DNA insertions constitute approximately 0.18–0.24% of the rice genome (International Rice Genome Sequencing Project, 2005). If barley contains similar amounts of organellar insertions in its nuclear genome, the mitochondrial sequences could indeed represent genomic insertions, while the higher numbers of chloroplast sequences are more likely to originate from chloroplasts that were included during DNA preparation.
Fourteen transposable element families constitute 50% of the barley genome
To identify known repetitive elements, all individual 454 reads were used in BLASTN searches against the TREP database. The 342 476 reads (59.92% of the total snapshot) that produced significant hits with E-values below 10E-6 were sorted according to the TE family to which they belong. In total, 248 previously known TE families are represented in the genome snapshot. The contribution of each TE family to the total snapshot was calculated in order to estimate their overall abundance in the barley genome (Table 1). Fourteen TE families contributed 50.31% of the sequences (Table 1), while the remaining 234 families contributed another 9.61% (not shown). Twelve of the top 14 TE families are LTR retrotransposons, ten of which belong to the Gypsy superfamily and two to the Copia superfamily. As expected, the Copia element BARE1 tops the list, contributing more than 12% to the total genome, confirming previous estimates (Vicient et al., 1999; Kalendar et al., 2000; Soleimani et al., 2006). The second and third most abundant are the two Gypsy families Sabrina and WHAM, which contribute almost 8.5 and 5.5% to the total sequences, respectively. Only two DNA (class 2) transposon families were found in the top 14 TE families (Balduin and Caspar). Both belong to the CACTA superfamily and rank 7th and 8th, contributing 2.32 and 2.1% to the total snapshot, respectively.
Table 1. Families of TEs and their contribution to 454 reads
Number of 454 reads
aMatch at the DNA level in TREP.
bNo match at the DNA level.
Other known familiesa
Novel TE familiesb
As TREP and other repeat databases do not contain all of the probably thousands of TE families in the barley genome, new families (i.e. families that do not show enough homology to well characterised TE families at the DNA level) were identified by a BLASTX search against PTREP, the protein division of TREP. This approach has the limitation that only coding regions of TEs can be identified; non-coding parts or regions encoding highly divergent proteins cannot be detected. The BLASTX search against PTREP yielded hits to only 0.77% of the leftover 454 sequences. An additional 0.15% of TE protein-encoding sequences were identified during the search for genes (see below), bringing the total TE content of the 454 snapshot to 60.77%. The low number of newly identified TE families indicates that the TREP database contained most of the abundant Triticeae TE families (Table 1).
The 454 snapshot contains coding sequences of almost 2000 genes and many putative conserved non-coding sequences
Potential gene coding sequences were identified by BLASTX of the 454 reads against a database containing all 66 710 predicted rice proteins from the Institute for Genomic Research (TIGR) rice genome version 5 (http://rice.plantbiology.msu.edu) and the dataset of the rice annotation project database (RAP-DB), which contains 30 192 proteins. The TIGR dataset includes at least 20 000 TE-related sequences, whereas the TE content in the RAP-DB set is expected to be minimal. We used both datasets to allow identification of putative genes as well as additional TE-related sequences. The BLAST searches against TIGR identified 2445 potential coding sequences, while the searches against RAP-DB identified 2399. Interestingly, only 1777 reads were identified by both BLAST searches. The 668 reads that were identified only in the TIGR dataset were considered to be TE-related and added to the repetitive fraction.
A cross-BLAST of the RAP-DB reads against the TIGR database identified an additional 252 reads that were designated as retrotransposons or transposons in the TIGR dataset. These were also added to the repetitive fraction, giving a final gene count at 2147 (2399−252=2147). To discover additional putative genic sequences, those 454 reads that had not yet been characterized as repeats or genes were used in BLASTN searches against the entire rice genome. The search yielded 845 sequences that produced significant alignments (> 50 bp, E-value < 10E-6). Because all 454 reads with homology to rice coding sequences or proteins had been filtered out, these sequences represented potential conserved non-coding sequences (CNS) or non-annotated genes. Interestingly, the vast majority (770) of these reads were low-copy, with only 1–5 copies in the rice genome, and another 50 reads had 6–20 copies. Only 25 had more than 20 copies and were added to the repetitive dataset.
To test whether the 820 candidate CNS with 20 or fewer copies could still encode proteins, we screened the alignments of barley and rice sequences for base mismatches that are separated from each other by multiples of three. In protein coding sequences, the third position of the codon is more likely to differ, due to the degeneration of the genetic code. We found that 158 sequence alignments were significantly enriched (P < 0.05) for such mismatch spacings. These sequences were considered to be protein encoding and added to the total gene count (2147), bringing it to 2305 (Figure 1). The other 662 reads were classified as putative CNS (Figure 1). Additionally, all 687 putative CNS were used in BLASTN searches against a collection of 560 small nucleolar, small nuclear and non-coding RNA sequences from plants. Only four of them produced BLASTN hits that were significant. Forty-one of these CNS are conserved in Arabidopsis.
The barley genome contains large amounts of low or moderately repetitive DNA
At this point in the study, 391 092 of the 571 509 reads had been classified as either an SSR, repeat, gene or CNS. The remaining 180 417 sequences (31.6%) were used for an assembly (see Experimental procedures). This resulted in 51 972 reads being assembled into 16 040 contigs. We considered all 51 972 reads as repetitive, because the 1% genome coverage that the 454 snapshot provides makes it very unlikely that many low-copy sequences would be covered multiple times. The remaining 128 445 reads that were considered singletons were low- or single-copy sequences. A total of 15 727 contigs were shorter than 200 bp, 246 were 200–300 bp in size, and only 67 were longer than 300 bp. Five of these large contigs were derived from ribosomal DNA (rDNA).
Most of the large contigs had a relatively high coverage (i.e. they were assembled from a large number of reads). The coverage of the largest 100 contigs ranged from 10- to 180-fold. A 180-fold coverage translates into an approximate copy number of 18 000 (because of the 1% coverage of the genome with the 454 reads). The 16 040 contigs mostly represent sequences that we could not characterize any further. Only 52 of them showed homology to TE sequences. However, the mere fact that the 51 970 reads could be assembled into contigs indicates that they are repetitive. Thus, they were added to the set of repetitive sequences, bringing the total repeat content of the snapshot to 69.15% (Figure 1).
As the assembly of the 454 reads was relatively stringent, we wished to investigate whether some of the resulting contigs may actually belong to the same repeat family (i.e. share an 80% sequence identity). We found 607 contigs with significant homology to others (see Experimental procedures). These 607 contigs could be clustered into 117 supercontigs with a mean size of 295 bp. The longest contig was 1083 bp. Only one of them showed homology to a Gypsy retrotransposon.
Some repeats are unequally distributed across the barley genome
For a previous study, we selected a set of nine large genomic sequences from barley, totalling more than 2 Mbp, and manually annotated them carefully to identify as many TE sequences as possible (Wicker et al., 2008). These nine sequences (hereafter be referred to as the ‘reference set’) originate from distal chromosomal (i.e. gene-rich) regions. Because 454 sequencing provides very even coverage (Wicker et al., 2006), we wished to test whether we could identify TE sequences that are unevenly distributed along chromosome arms.
Thus, we compared the relative abundance of the top 14 TE families in the 454 reads with their occurrence in the reference set. As expected, the abundant TEs in both the 454 snapshot and the reference set were well-known high-copy TE families such as BARE1, Sabrina and WHAM (Figure 2), although Sabrina and WHAM were somewhat under-represented in the reference set. A surprising exception is BAGY2, which is the 4th most abundant TE family in the 454 snapshot, but was found only once in the reference set, corresponding to an almost tenfold under-representation (Figure 2). The family WHAM was also approximately twofold under-represented in the reference set. In contrast, Caspar and Jeli were more than three- and twofold over-represented, respectively.
To verify these results, we derived probes for fluorescence in situ hybridizations (FISH) from BAGY2, Caspar and Jeli, as these elements were either under- or over-represented in the reference set. Jeli-specific signals were found scattered throughout the chromosomes, indicating a sporadic but more or less even distribution (Figure 3a). In contrast, Caspar-specific signals were highly enriched in the sub-telomeric regions, and BAGY2 signals were found throughout all chromosomes, except in sub-telomeric regions (Figure 3b,c). In addition to the previously described TE sequences, five probes derived from large contigs of the assembly (see above) were also used for in situ hybridizations. These five were chosen because of their high copy number and because they could not be classified as any known repeat type. Four of the five probes (10010, 100156, 10027 and 06796) resulted in relatively uniform labelling of the chromosomes (Figure S1). The signals for probe 100156 were less abundant in sub-telomeric regions. Probe 00112 produced the most intense signals in the centromeric regions of one chromosome pair (Figure 3d). Additional hybridization with an SSR marker identified the centromeric signal as specific for chromosome 3H (data not shown).
Diploid wheat has a very different genome composition than barley
Analogous to the barley reference set of genomic sequences, we prepared a set of manually annotated genomic sequences from diploid wheat Triticum monococcum (Wicker et al., 2008). Its repetitive fraction was compared to the data from the barley 454 genome snapshot. Because barley and wheat diverged only approximately 11.6 million years ago (Chalupska et al., 2008), they contain some very similar TE families. For example, the TE families WHAM, Sabrina and BARE1 from barley are 75–80% identical at the DNA level with their wheat homologs. It should be noted here that, mainly for historical reasons, the wheat homologues of BARE1 are called WIS and Angela, although their strong sequence homology would justify classification of all three into just one family. Thus, BARE1, WIS and Angela were treated as the same family for this study (BARE1). As in barley, BARE1 is the most abundant TE family, contributing 16.71% to the T. monococcum reference sequences (Figure 2). The second and third most abundant families are Sabrina and WHAM, which occur at similar frequencies in the T. monococcum reference set as in the barley sequences. In total, 11 TE families contribute 50% of the T. monococcum sequences (data not shown). Interestingly, these include five TE families (Fatima, Jorge, Daniela, Erika and Claudia) that were practically absent from the 454 barley snapshot, as they only produced between 51 (Jorge) and 354 (Daniela) significant BLASTN hits in the barley 454 reads. Fatima, Daniela and Erika are LTR retrotransposons of the Gypsy superfamily, and Claudia is a Copia element. Jorge is a class 2 transposon of the CACTA superfamily.
It is possible that the absence of BLAST hits for these five TE families is simply caused by sequence divergence. However, if one assumes that these five TE families diverged at a similar pace as WHAM, Sabrina and BARE1, one would expect that the elements from barley would be 75–80% identical to those from wheat and therefore should be unambiguously detected. To measure the influence of sequence divergence on the number of sequences hit, we used BARE1, Sabrina and WHAM sequences from both barley and wheat in BLASTN searches against the 454 barley snapshot. These three TE families are abundant in both species. For all three families, the elements from barley produced approximately twice as many hits to the 454 snapshot than those from wheat. For example, a Sabrina element from barley showed significant homology to 28 048 of the 454 sequences, while the one from wheat had significant homology with only approximately half as many (13 798). Taking into account this reduction in identification sensitivity, the number of Fatima, Jorge, Daniela, Erika and Claudia type TEs in the barley snapshot should be approximately twice the number of BLASTN hits (approximately 100-700 BLASTIN hits to 454 reads). However, this is still a negligible amount.
There was also the possibility that the reference sequence set from T. monococcum is biased and contains a high number of Fatima, Jorge, Daniela, Erika and Claudia elements by pure chance. Thus, we broadened the sample by determining their contribution to all publicly available large genomic sequences (i.e. larger than 20 kb) from wheat. The majority of the 359 sequences from wheat (Triticum) species that are available from the National Center for Biotechnology Information (NCBI) are randomly picked BAC clones, and should therefore sample the genome relatively evenly (J. Bennetzen, K. Devos (University of Georgia, Athens, GA) and P. SanMiguel (Purdue University, IN) personal communication). The overall contribution to the complete sequence set and to the reference set was estimated by measuring the cumulative size of all BLASTN alignments of the five TE families in both sequence sets. This is a very rough approach and not comparable in quality to hand annotation. For example, divergent parts of TEs are almost certainly not recognized, but can be easily identified by hand annotation. Nevertheless, as shown in Table S1, the five TE families contributed similar amounts to both the reference set and the entire public dataset of wheat sequences. Thus, the high frequency of the five TE families in the reference set is not merely coincidental, as they are present at similar frequencies in the mostly randomly selected wheat BACs.
Our analysis of 454 reads that cover approximately 1% of a haploid barley genome equivalent provided a new and unbiased view of the composition of Triticeae genomes. The repetitive fraction of Triticeae genomes is probably better characterized than for any other plant genome. We have used these resources for comparison and integration of biological information on the nature of the Triticeae genomes, and to evaluate the current knowledge on their genome composition. Our analysis therefore provided additional aspects to similar 454 based whole-genome surveys performed for pea and soybean (Macas et al., 2007; Swaminathan et al., 2007). In this study, we used this approach for a grass genome, and demonstrated that, by massively parallel DNA sequencing, the repeat composition of large and highly repetitive genomes can be investigated at a high resolution. The data provided a quantitative assessment of all major TE families in the barley genome, and we obtained information on the differential distribution of TE families along chromosomes. Additionally, we identified strong differences in the composition of TE families between barley and wheat.
The 454 snapshot sampled many genes and conserved non-coding sequences
The 454 sampling of the barley genome provided 2305 reads that contained protein coding sequences of genes. The high number of reads produced by 454 sequencing provided an excellent sample depth, as we were able to sample 7.4% of all barley genes (assuming a total gene number of 31 000), although the actual number of bases sequenced amounted to only approximately 1% of a haploid genome equivalent. Nevertheless, the actual number of protein coding sequences in our 454 snapshot might be even higher than 2305. This assumption is based on determination of the sampling depth: the mean size of coding sequences in the rice RAP-DB dataset is 1723 bp. Therefore, the 31 000 rice genes correspond to approximately 54 Mbp of actual coding sequence. If barley has the same number and mean size of genes, only approximately 0.95% of its 5700 Mbp genome will actually be protein coding sequences. Thus, we would expect 5430 protein coding gene sequences (0.95%) instead of 2305. This lower than expected gene content may be due to several reasons. First, although rice is the species closest to barley for which the genome has been sequenced, the two plants diverged approximately 50 million years ago (Paterson et al., 2004). Fast-evolving genes may have diverged to a degree that hampers their homology recognition by BLAST searches using relatively short 454 reads. Second, many coding sequences might also have been missed because they were only partially covered by the reads and thus did not produce significant BLAST hits.
It is intriguing that we identified more than 660 sequences that do not seem to have coding capacity but still remained conserved during the roughly 50 million years since barley and rice diverged (Paterson et al., 2004). Interestingly, most of these CNS have very low copy numbers, indicating that they do not simply represent conserved parts of transposable elements. Forty-one of them were also conserved in Arabidopsis. This indicates that most CNS probably diverge more quickly than gene sequences. Nevertheless, their conservation in various plant species implies a biological function. Additionally, a few studies have shown that CNS can be found in colinear positions in different species (Bossolini et al., 2007; Pourkheirandish et al., 2007), further supporting the possibility of a conserved function.
The repetitive landscape of the barley genome is extremely diverse and not homogeneous
The 454 snapshot provided very precise estimates for the abundance of various TE families in the barley genome. The results clearly showed that a few TE families have reached such enormous copy numbers that they completely dominate the repetitive fraction while dozens of other previously described TE families contribute only a few per cent to the total genome. As expected, the ‘usual suspects’BARE1, Sabrina and WHAM clearly topped the list. All three have long been known to constitute large proportions of the barley genome, but actual copy number estimates were only available for BARE1 prior to this study (Vicient et al., 1999; Kalendar et al., 2000; Soleimani et al., 2006), while the abundance of all other TE families so far had to be inferred from very limited datasets (Sabot et al., 2005). Interestingly, two class 2 transposons (Caspar and Balduin) were found in the top ten most abundant elements, contributing more than 4.5% to the total genomic sequences studied. This is in contrast to the situation in maize, where all known CACTA families combined constitute only approximately 1% of the genome (Messing et al., 2004).
The known repetitive elements accounted for approximately 60% of the 454 reads, raising questions about the nature of the other up to 39% that are not genes, CNS, rDNA or organellar DNA. The fact that we could assemble over 16 000 contigs from these uncharacterized reads indicated that that there may be thousands of TE families populating the barley genome at low or moderate copy numbers. Different TE families often share no detectable sequence homology at the DNA level, and can only be detected based on their coding sequences (e.g. by BLASTX searches). However, TEs usually contain considerable stretches of non-coding DNA (e.g. LTRs or terminal inverted repeats (TIRs)) that cannot be detected by homology search. We assume that many of these 16 000 sequence contigs actually represented non-coding portions of TEs.
More than 22% of the 454 reads remained completely uncharacterized. It is expected that many of them are repetitive, as at a 1% genome coverage, sequences represented by less than 100 copies per genome are very likely to remain singletons. Thus, the situation in barley might not be fundamentally different from that in smaller genomes such as rice. In rice, dozens of LTR retrotransposon families have been described, but only relatively few reached very high copy numbers. Most are present at low or moderate copy numbers (Vitte et al., 2007). The difference between large and small plant genomes might therefore be merely quantitative, as only a few high-copy TE families actually determine genome size.
However, a recent study indicated that a relatively large percentage of the barley genome might simply be non-coding low-copy DNA (Wicker et al., 2008). Based on the current data, the possibility cannot be excluded that some of the uncharacterized reads are simply sequencing artefacts. More extensive studies with larger datasets will be needed to answer this question.
Some TE families are distributed unevenly across the genome
A surprising finding was that two TE families (BAGY2 and WHAM) were clearly under-represented, while two others (Caspar and Jeli) were over-represented in the reference sequence set. This indicated that the previously published sequences are not representative of the composition of the entire barley genome, and that TE composition varies along the chromosomes. Previous studies in maize found that most high-copy TE families are more or less randomly distributed across the maize genome (Meyers et al., 2001; Bruggmann et al., 2006). However, some TE families are distributed unevenly, especially DNA transposons, which were found to be enriched in gene-rich regions (Bureau and Wessler, 1994; Bruggmann et al., 2006). In the recently published genome of sorghum, retrotransposons were found to have a tendency to cluster in proximal regions, while DNA transposons are enriched in distal chromosomal regions (Paterson et al., 2009).
In this respect, our results showthat the situation in barley is similar to that in other grass genomes. However, our targeted approach of comparing whole-genome 454 reads with previously published sequences allowed efficient identification of TEs with an unusual distribution, despite the lack of a complete genome sequence. We were therefore able to specifically design probes for in situ hybridizations that allowed us to demonstrate that some repetitive sequences are very unevenly distributed along chromosomes. Our data showed that major high-copy TE families such as Caspar tend to be found in distal (i.e. gene-rich) regions. The converse situation was found with BAGY2 and probe 00112, which were distributed along the length of all chromosomes except the sub-telomeric and peri-centromeric regions. Interestingly, probe 00112 exhibited in addition to genome wide dispersion a pattern of chromosome-specific accumulation. Although we tested only a relatively small number of repetitive sequences in situ, three of them showed very distinct chromosomal distribution. These results suggest that there may be a large number of repetitive sequences with insertion site preferences. Thus, complex models for the interaction of TEs with their host genome may be necessary to explain our observations.
Parallel evolution of dynamic genome size equilibrium in barley and wheat
The common perception is that barley and wheat have very similar composition of their repetitive fractions because they have very similar diploid genome sizes and diverged only approximately 11 million years ago (Chalupska et al., 2008). Indeed, we found that some high-copy TE families such as Sabrina, WHAM and the BARE1/Angela/WIS clade contribute similar proportions to the genomes of barley and wheat. This is reminiscent of the finding that the same major high-copy retrotransposon families are found in all maize species studied (Takahashi et al., 1999; Meyers et al., 2001).
A highly interesting and novel aspect of our study is that several TE families that are highly abundant in the available genomic sequences of wheat are virtually absent in barley. Apparently, several TE families (e.g. Fatima, Jorge, Daniela, Erika and Claudia) have become very successful in wheat during the past 11 million years, but have become practically extinct in barley. We were able to observe this phenomenon because the high resolution of the 454 genome snapshot allowed us to consider TE families other than the main high-copy TE families, and we speculate that a similar situation may also be found when maize species are compared using 454 sequencing.
Our findings add to the increase/decrease model evidence that genome size can remain in dynamic equilibrium for several million years (approximately 11 million years in the case of barley and wheat) even though the TE composition of the genome has undergone drastic changes. This suggests the existence of regulatory forces that keep genome size constant over long periods of time. For example, one can speculate that the physical size of chromosomes has to stay within a relatively narrow range as to allow anytime pairing and recombination between individuals of a population. Thus, intergenic regions may be turned over rapidly as described in the increase/decrease model as long as overall genome size does not change too much. Drastic changes in genome size (for example, as observed between different grass species) might therefore be restricted to actual species formation events. Future studies targeting the genomes of Triticeae and other grass species for whole-genome snapshots are necessary to further elucidate this phenomenon.
The data presented show the potential of new high-throughput sequencing technology to survey entire complex genomes and to allow broad insights into many aspects of genome evolution and organization. Using larger sample sizes and upgraded sequence technologies [e.g. the Roche/454 GS-FLX Titanium (http://www.roche.com), which produces reads of approximately 400 bp], more detailed quantitative and qualitative analyses will be possible in the near future. The data also demonstrated that the gene space can be effectively sampled with modest financial effort even in very large and complex genomes. Improved sequencing technology and decreasing costs will allow us to expand this type of study to several different Triticeae species. The larger amount of data will provide a greater level of detail in assessing of gene content, the number of CNS and the overall repeat make-up of the various genomes. Such studies will make it possible to develop detailed models of the mechanisms and dynamics of genome evolution in Triticeae and other plants.
Preparation and sequencing of the 454 sequencing library was essentially performed according to the manufacturer’s instructions (GS20 shotgun DNA library preparation kit/sequencing kit, Roche Diagnostics, http://www.roche.com). Approximately 4 μg of barley cv. Morex whole-genome DNA were fragmented for 1 min at 300 kPa N2 using nebulizers to give a mean fragment length of 700 bp (determined using a DNA-1000 LabChip, Agilent, http://www.agilent.com/). The library was quantified using an RNA-1000 LabChip (Agilent) and a Quant-IT RiboGreen RNA assay (Invitrogen, http://www.invitrogen.com/). The reads analysed in this study were generated on a GS20 by two runs (two segments each) using 70 × 75 picotiter plates and four out of eight segments of a 40 × 75 picotitre plate.
All analyses were performed on LINUX systems. The BLAST program was obtained from NCBI (http://www.ncbi.nlm.nih.gov/). For classification of 454 reads, local BLAST databases were created, including datasets obtained from the TIGR rice genome version 5 (rice.plantbiology.msu.edu), RAP-DB (rapdb.dna.affrc.go.jp), TREP (wheat.pw.usda.gov/ITMI/Repeats/) and organellar DNA from plants and animals. BLAST searches of individual 454 reads were performed using a custom Perl script that immediately evaluated the BLAST report and deleted it again to save disc space. Sequences with hits were collected in one file, those without hits in another. The ‘no hit’ file was then used for BLAST searches against other databases. Throughout this study, we considered BLAST hits with E-values < 10E-6 as significant, except for the CNS search where we used the more stringent criterion of hits > 50 bp.
Because low-complexity sequences (e.g. microsatellites) are inherently non-random patterns, they were identified using a Perl program that finds sequences with under-represented bases. Under-represented was defined as a base frequency of less than 0.07 in a stretch of 100 bp. In a random sequence, the chance of a particular base (A, C, T, G) occurring six or fewer times is less than one in a million.
BLASTN searches for known TE families were performed against the complete TREP database (totalTREP), which was chosen because it contains several copies of known high-copy elements instead of consensus sequences or single representatives as in the non-redundant TREP database (nrTREP). The reason for this was that members of TE families show a certain degree of sequence variation. The TE family definition described by Wicker et al. (2007a) was used.
Assembly of 454 sequences
Assembly of the subset of 454 reads that could not be classified as either genic or repetitive sequences was performed using MIRA version 2.9.25 (Chevreux et al., 1999, http://chevreux.org/projects_mira.html) using default parameters for 454 data. Assemblies were performed with sequence text files only without using quality data (‘notraceinfo’ option).
Contigs that belong to the same repeat family (i.e. share 80% sequence identity) but were not similar enough to be assembled using MIRA were identified in an all-versus-all BLASTN search. Contigs with more than five significant hits to other contigs were selected for clustering using CLUSTAL W with default settings. The aligned multiple sequence file was screened using a custom Perl script for groups of sequences that share regions of more than 50 bp and 80% sequence identity. These groups were then used to construct consensus sequences. One additional round of ‘all-versus-all’ comparison with DOTTER (http://www.sonnhammer.sbc.su.se/dotter.html) was performed to identify further overlapping supercontigs, which were then combined by hand into single supercontigs.
Probe preparation and fluorescence in situ hybridization (FISH)
Primer pairs (Table S2) were designed based on assembled 454 contigs and used to amplify the corresponding genomic fragments. PCR products were cloned and sequenced. Clones with the highest similarity to the original 454-derived sequences were selected for FISH and labelled with digoxigenin-11-dUTP by nick translation. Preparation of mitotic chromosomes and subsequent in situ hybridization were performed as previously described (Houben et al., 2006). Hybridization was detected by incubation with conjugated fluorescence anti-DIG antibodies. The fluorescent signals were recorded using a cooled sensitive CCD camera, and pseudo-coloured using Adobe Photoshop software (http://www.abode.com/).
The complete set of 454 sequences has been deposited at GenBank (Short Read Archive SRA008654). The dataset can also be obtained from the authors via FTP upon request.
This work was supported by grant number 0314000A from the Federal Ministry of Education and Research of Germany to N.S., and by grant numbers 3100A0-122242/1 to T.W. and 3100-105620 to B.K. from the Swiss National Science Foundation. T.W., B.K. and N.S. are cooperation partners in the EU COST action FA0604 ‘Tritigen’. We thank K. Kumke and O. Weiß for excellent technical support.