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BMC Genomics

Open Access

Transcriptome sequencing of two wild barley (Hordeum spontaneum L.) ecotypes differentially adapted to drought stress reveals ecotype-specific transcripts

  • Girma Bedada1,
  • Anna Westerbergh1,
  • Thomas Müller3,
  • Eyal Galkin2,
  • Eyal Bdolach2,
  • Menachem Moshelion2,
  • Eyal Fridman2 and
  • Karl J Schmid1, 3Email author
BMC Genomics201415:995

https://doi.org/10.1186/1471-2164-15-995

Received: 9 June 2014

Accepted: 4 November 2014

Published: 19 November 2014

Abstract

Background

Wild barley is adapted to highly diverse environments throughout its geographical distribution range. Transcriptome sequencing of differentially adapted wild barley ecotypes from contrasting environments contributes to the identification of genes and genetic variation involved in abiotic stress tolerance and adaptation.

Results

Two differentially adapted wild barley ecotypes from desert (B1K2) and Mediterranean (B1K30) environments were analyzed for drought stress response under controlled conditions. The desert ecotype lost more water under both irrigation and drought, but exhibited higher relative water content (RWC) and better water use efficiency (WUE) than the coastal ecotype. We sequenced normalized cDNA libraries from drought-stressed leaves of both ecotypes with the 454 platform to identify drought-related transcripts. Over half million reads per ecotype were de novo assembled into 20,439 putative unique transcripts (PUTs) for B1K2, 21,494 for B1K30 and 28,720 for the joint assembly. Over 50% of PUTs of each ecotype were not shared with the other ecotype. Furthermore, 16% (3,245) of B1K2 and 17% (3,674) of B1K30 transcripts did not show orthologous sequence hits in the other wild barley ecotype and cultivated barley, and are candidates of ecotype-specific transcripts. Over 800 unique transcripts from each ecotype homologous to over 30 different stress-related genes were identified. We extracted 1,017 high quality SNPs that differentiated the two ecotypes. The genetic distance between the desert ecotype and cultivated barley was 1.9-fold higher than between the Mediterranean ecotype and cultivated barley. Moreover, the desert ecotype harbored a larger proportion of non-synonymous SNPs than the Mediterranean ecotype suggesting different demographic histories of these ecotypes.

Conclusions

The results indicate a strong physiological and genomic differentiation between the desert and Mediterranean wild barley ecotypes and a closer relationship of the Mediterranean to cultivated barley. A significant number of novel transcripts specific to wild barley were identified. The higher SNP density and larger proportion of SNPs with functional effects in the desert ecotype suggest different demographic histories and effects of natural selection in Mediterranean and desert wild barley. The data are a valuable genomic resource for an improved genome annotation, transcriptome studies of drought adaptation and a source of new genetic markers for future barley improvement.

Keywords

Hordeum spontaneum TranscriptomeDrought toleranceGenetic diversity

Background

Plants use different response and adaptive mechanisms to deal with abiotic water deficit stress [16]. These include (i) avoidance of dehydration by closing stomata to decrease water loss and to maintain turgor or osmotic pressure; (ii) drought escape by a rapid life cycle to cope with water deficit; and (iii) dehydration tolerance and maintenance of growth and development under low-water status by accumulating protective proteins, sugars, proline, antioxidants, by maintaining cell membrane stability, and by interrupting metabolic activity through dormancy. Abiotic stress response mechanisms in plants are linked to different physiological traits. Depending on their molecular and physiological attributes, plants control their stomatal aperture and water balance in very different ways with important consequences for their transpiration, biomass gain and survival. Accordingly, drought-tolerant plant ecotypes may have more flexible stomatal responses under drought conditions by sustaining longer periods of transpiration and CO2 assimilation. They may therefore outperform plants with more sensitive stomatal responses under conditions of mild to moderate drought.

Cultivated barley (Hordeum vulgare L.) is one of the most important global crops because it can be cultivated in highly diverse environments. Wild barley (Hordeum spontaneum L.) is the direct ancestor of cultivated barley and has a large geographical distribution ranging from deserts to highland climates [7]. Throughout its geographical distribution, wild barley is exposed to multiple environmental stresses such as drought, high temperatures, and high soil salinity. The correlation of genetic and environmental variation within its distribution range suggests the action of local adaptation along macro- and micro-environmental gradients [810]. It is therefore an excellent model system for studying plant adaptation and holds a high potential as a genetic resource for the breeding of stress tolerant varieties of cultivated barley [11, 12].

In barley, several traits characterize the differential response to drought stress, including grain carbon isotope discrimination [13], leaf relative water content [14], leaf osmotic potential and adjustment, and osmotic potential at full turgor [15, 16], water-soluble carbohydrate concentration [15] and chlorophyll parameters (e.g. fluorescence) [17]. These traits are correlated with morphological and phenological traits such as variation in seedling germination, flowering time, plant height and tillering, root growth and grain yield. Some of them differ between desert and Mediterranean wild barley ecotypes suggesting local adaptation [1820]. So far, over 100 quantitative trait loci (QTLs) were found to be associated with drought stress responsive traits in cultivated barley [1, 1316, 21, 22]. The complex nature of the trait and current shortcomings in QTL detection are responsible for the limited contribution of QTL studies to elucidate the genetic basis of drought tolerance and its utilization in plant breeding [2]. Modern genomic approaches may lead to a more efficient use of genetic resources like wild barley to utilize drought stress tolerance genes for crop improvement.

The recent sequencing of the barley genome greatly facilitates the mapping and utilization of useful genetic variation [23]. The current genome assembly is based on five cultivated barley cultivars and one wild barley accession of the Barley1K collection (B1K-04-12, hereafter called B1K4) [9], which resulted in 26,159 'high-confidence’ (HC) barley genes and a total estimate of 30,400 genes [23]. Levels of genome-wide variation were high in both wild and cultivated barley, but with an almost two-fold higher level of genetic variation in wild than in cultivated barley. This suggests that much untapped and potentially useful genetic variation is segregating in wild barley [24, 25]. Re-sequencing, transcriptome sequencing and sequencing of epigenomic variation contributes to the functional annotation of complex plant genomes and to the identification of genes and alleles responsible for adaptive evolution in contrasting environments [26, 27].

To identify genes expressed under drought stress in differentially adapted wild barley accessions, we first validated the different physiological adaptation to drought stress of two wild barley ecotypes from a collection of Hordeum spontaneum ecotypes (Barley1K) [9] and then performed 454 transcriptome sequencing of normalized cDNA libraries from drought-stressed leaves of these two accessions because transcriptome sequencing allow the identification of novel genes and improve the annotation of the barley genome. The two sequenced ecotypes included a desert ecotype (B1K-2-8, hereafter called B1K2) from the Negev desert, and a 'Mediterranean’ ecotype (B1K-30-09, hereafter called B1K30) from the cooler and moister North-Western region of Israel. Both genetic and phenotypic analyses strongly suggested that they are differentially adapted to heat and drought stress [20]. We identified numerous ecotype-specific genes, which may be involved in drought adaptation, and single nucleotide polymorphisms (SNPs) that can be used to differentiate between adapted and non-adapted B1K ecotypes.

Results

Physiological characterization of wild barley plants

The B1K2 and the B1K30 accessions from the Barley1K collection [9] were selected based on previous studies for their difference in physiological responses to drought. The physiological measurements were conducted in greenhouses and plants were kept under natural light conditions and semi-controlled temperature and humidity (Figure 1A-D, see Methods). Measured traits included mid-day whole-plant transpiration (E), weight gain and loss, which were normalized to the plant leaf area, water-use efficiency (WUE), and leaf relative content (RWC; see Methods). Unexpectedly, the desert ecotype lost more water than the Mediterranean ecotype cultivar under both well-irrigated (80% volumetric water content) and drought conditions (30% volumetric water content; Figure 1E). Despite its higher transpiration rate, the desert cultivar maintained a higher RWC than the Mediterranean ecotype under drought conditions (Figure 1F). Moreover, the ratio between the cumulative weight gain and cumulative transpiration of plants (Figure 1G) revealed that the desert ecotype has a higher WUE than the Mediterranean ecotype with WUE calculated as the ratio of RWC to transpiration rate, E (One-way ANOVA, p =0.02). To identify candidate genes reflecting the differential response to drought stress, leaf samples from two plants of each ecotype were sampled for RNA extraction at day 5 of the drought treatment (Methods).
Figure 1

Variation in transpiration, leaf RWC and WUE among wild barley ecotypes at different soil water contents. (A-D) Pictures of ecotypes B1K2 (A and B) and B1K30 (C and D) under well-irrigated condition or full field capacity - 80% SWC (A and C) and drought condition - 30% SWC (B and D). (E) Average mid-day whole-plant transpiration of B1K2 and B1K30 under normal (80% soil moisture content) and drought (30% soil moisture content) conditions. (F) Average mid-day leaf RWC of B1K2 and B1K30 under normal and drought conditions. (G) The WUE of each ecotype as determined by fitting a linear curve for the ratio between the plants’ cumulative weight gain and cumulative transpiration. The error bar and asterisk in (E) and (F) represent standard error (±SE) and significance differences (t test, p <0.05) among the ecotypes, respectively. Data (E and F) are means of four independent repetitions.

454 sequencing and de novoassembly of the wild barley transcriptome

Two cDNA libraries of the drought-stressed wild barley accessions were sequenced with 454 sequencing technology. A summary of the raw sequence data is presented in Table 1. Counts of raw reads and of the resulting unique transcripts were about the same in both libraries. After quality trimming, 99% of the reads had phred-like quality scores >20 and an average read length of 356 bp (B1K2), 313 bp (B1K30) and 338 (B1K). Almost 95% of both B1K2 and B1K30 were included in separate and joint de novo assemblies (Table 1). After clustering, we obtained three de novo assemblies with 20,439 putative unique transcripts (PUTs) for B1K2, 21,494 for B1K30 and 28,720 for B1K (joint assembly) that included isotigs and true singletons. Length distributions of the isotigs or PUTs in all three assemblies are given in Table 1, which shows that a joint assembly of the B1K2 and B1K30 libraries into a single assembly did not significantly increase average isotig length. There is also a strong positive correlation between the number of reads per isotig and the length of the isotig (Figure 2A), which affects the quality of subsequent functional and evolutionary analyses as presented below.
Table 1

Summary of wild barley 454 transcriptome sequencing, de novo assembly and annotation

Analysis

B1K2

B1K30

B1K

Reads:

   

 No. of raw reads (Mbp)

575,918 (223.40)

562,862 (193.91)

1,138,780 (417.31)

 No. of reads after trimming (Mbp)

559,019 (199.22)

549,791 (171.81)

1,097,384 (370.75)

 Mean trimmed reads length (bp)

356

313

338

 Range of trimmed reads (bp)

20 – 1,080

20 – 1,009

20 – 1,080

 No. of aligned reads (Mbp)

529,779 (191.06)

522,279 (164.34)

1,054,990 (356.35)

De novo assembly:

   

1No. of assembled reads (%)

529,297 (95)

522,093 (95)

1,035,947 (94)

2No. of isotigs (Mbp)

15,956

16,711

23,675

 N50 (bp)

915

914

910

 No of singletons

6,235

6,618

9,383

Unigenes (≥100 bp):

   

3All

20,503

21,553

28,828

4Clustered

20,439

21,494

28,720

 Mean size (bp)

619

562

609

 Size (Mbp)

12.658

12.090

17.491

 ≥ 500 bp (%)

9,732 (63)

8,633 (54)

12,785 (59)

 ≥ 1000 bp (%)

3,044 (20)

2,621 (16)

4,609 (21)

Annotation: number (%) of annotated transcripts

 InterPro

4,300 (21)

5,137 (24)

7,153 (25)

 Pfam

3,829 (19)

4,469 (21)

6,335 (22)

 NR

13,637 (67)

13,532 (63)

5na

 Swiss-Prot

8,143 (40)

7,906 (37)

na

 GO

7,293 (36)

7,069 (33)

na

 KGG

6,612 (32)

6,102 (28)

na

1assembled reads: fully and partially assembled; 2isotigs: groups of contigs and large contigs; 3all: clustered isotigs + singletons; and 4clustered: clustered isotigs + true-singletons (putative unique transcripts, PUTs). 5na: analysis not performed.

Figure 2

Summary of assembled transcripts and homology among ecotypes. (A) Log-log plot comparing isotig length and numbers of reads per isotig. (B) Frequency distribution of isotig lengths. (C) A Venn diagram showing homology among ecotypes.

Reference-based mapping of 454 sequences and SNP identification

In addition to the de novo assembly, we also employed a reference-based mapping approach. With this method, 94% of the B1K2 reads could be mapped against the B1K30 unique transcripts (Table 2). Over 75% of the reads were mapped onto the cultivated barley full length cDNA (Hv. fl-cDNA) sequences and about 65% against the coding sequences (CDS) of the high-confidence (HC) cultivated barley genes. A substantial proportion of reads mapped to more than one target (15 to 47%; Table 2). Nearly all reads (98%) mapped against the Morex whole-genome shotgun sequence, although the number of reads mapped at several locations was two-fold higher than reads mapped only once.
Table 2

Summary of reference-based mapping of wild barley reads against cultivated barley sequences

Reference →

B1K30

Hv. fl-cDNA

Barley HC genes

Chr. Hv. WGS

Reads →

B1K2

B1K2

B1K30

B1K

B1K2

B1K30

B1K

B1K2

B1K30

B1K

1 Aligned reads (%):

All

93.6

77.5

76.6

77

67.6

63

65.3

80.0

79.4

79.7

1x

52.7

47.1

47.5

47.3

51.8

48.6

50.2

32.7

35.7

34.2

>1x

40.9

30.3

29.2

29.7

15.8

14.4

15.1

47.3

43.7

45.5

SNPs:

Raw SNPs

28,289

27,279

24,637

40,639

16,284

14,509

24,446

39,545

36,021

61,852

Filtered SNPs

1,017

1,067

1,717

9,092

1,184

1,081

5,036

4,053

4,372

15,330

2Gene

    

132

299

1,805

1,010

1,488

4,389

SNPs/gene

    

9

3.6

2.8

4.0

2.9

3.5

SNPs/kb

4.4

3.5

2

1.78

7.3

2.8

2

3.5

2.6

2.4

1Aligned reads (%): proportion of all aligned reads, reads aligned 1x and over 1x.

2Gene: number of genes with SNPs.

Mapping against chromosomal barley genome (Hordeum vulgare 030312 v2.16) was used for SNP effect annotation.

Identification of wild barley-specific transcripts

One goal of this study was to identify transcripts that are unique to wild barley. All PUTs were compared to each other with BLAST to identify the proportion of homologous PUTs shared among the two divergent wild barley ecotypes. The two divergent ecotypes shared about 9,546 (29%) of the PUTs from both ecotypes or 46% of B1K2 PUTs (Figure 2C). Hence, the majority of the transcripts were restricted to one of the two ecotypes. The mean length of shared transcripts among ecotypes (B1K2 = 800 bp and B1K30 = 741 bp) was significantly larger (p <0.001) than ecotype-specific transcripts (B1K2 = 461 bp and B1K30 = 420 bp). The proportion of shared PUTs increased to 69 and 84% for transcripts longer than 500 and 1,000 bp, respectively. This suggests either that ecotype-specific transcripts are shorter or that the power to identify orthologs in the other ecotype depends on transcript length.

We further compared the wild barley transcripts with cultivated barley genes using a reciprocal BLAST hit (RBH) approach to identify putative orthologs (Table 3). A total of 82% (16,781) of B1K2 and 81% (17,318) of B1K30 transcripts were orthologous to at least one of three cultivated barley sequence datasets. A proportion of 16% (3,245) of B1K2 and 17% (3,674) of B1K30 transcripts did not have a significant RBH in the other wild barley ecotype and the cultivated barley data. They can be considered as candidate ecotype-specific genes. Only a small proportion of 2% of transcripts matched the other ecotype but not cultivated barley. Among the combined B1K PUTs, 25% (7,102) did not match to cultivated barley. These results suggest that B1K2 and B1K30 are highly divergent ecotypes and that a significant proportion of wild barley transcripts are not homologous to currently known genes from the cultivated barley genome. Since this proportion may be influenced by variation in transcript length due to incompletely sequenced cDNAs (i.e., experimental error), we asked whether transcript length is correlated with the proportion of reciprocal BLAST hits to cultivated barley using PUTs from the joint assembly (B1K). The proportion of orthologous hits to cultivated barley increased with sequence length from 41.3% for sequences of 100–250 bp length to 82.5% with sequences ≥1,000 bp length (Additional file 1: Figure S2). The mean length of ecotype-specific PUTs was significantly shorter than of those occurring in both ecotypes (p <0.0001). The same pattern was observed among joint assembly PUTs with (695 bp average length) and without (347 bp) a hit to cultivated barley (p <0.0001). Therefore, sequence length variation needs to be considered in the identification of transcripts specific to wild barley. On the other hand, 73 out of 7,102 (1%) long wild barley (B1K) transcripts (≥1000 bp) currently have no orthologs in cultivated barley sequence data suggesting they are good candidates wild barley-specific genes.
Table 3

Homology within and among wild and domesticated barley sequences

Source

B1K2

B1K30

Hv*

No. of PUTs (%)

Transcript type

B1K2

x

x

3,245 (16)

B1K2-specific

x

413 (2)

wild barley-specific

x

7,648 (37)

B1K2- & Hv.-specific

9,133 (45)

in both wild & cultivated barley

B1K30

x

x

3,674 (17)

B1K30-specific

x

502 (2)

wild barley-specific

x

8,288 (39)

B1K30- & Hv.-specific

9,030 (42)

in both wild & cultivated barley

B1K

x

7,102 (25)

wild barley-specific

21,617 (75)

in both wild & cultivated barley

*Hv: RBH against sequence data from barley HC, fl-cDNA and HarvEST v1.83 assembly 36.

The orthologous analysis was based on RBH.

Conservation of unique transcripts in evolutionary distant plant species

To estimate how many wild barley transcripts are conserved in more distant plant species, we compared them against five fully sequenced and annotated plant genomes of variable evolutionary distance to barley: Brachypodium, rice, sorghum, maize and Arabidopsis. Based on the RBH approach with an e-value ≤1e-6, ≥75% identity and alignment length of ≥33 amino acids as cutoffs, the highest proportion of orthologous genes was observed between wild barley and Brachypodium, followed by rice, sorghum, maize and Arabidopsis, in decreasing order (Additional file 2: Table S1), which reflects the evolutionary distance of these species to barley. A total of 13% (19) long (≥1 kb) unique transcripts from the B1K assembly for which no ortholog was found in barley HC genes and fl-cDNA were homologous to other grass species. We also investigated which proportion of ecotype- and wild barley-specific transcripts are conserved across fully sequenced and annotated grass genomes. Based on RBH, 98% each of ecotype-specific (B1K2 = 3,191 and B1K30 = 3,606) and wild barley-specific (B1K =6,993) transcripts have no orthologs in Brachypodium, rice and sorghum. The results indicate that a significant proportion of putative unique ecotype- and wild barley-specific transcripts are not conserved in closely related grass species (Additional file 3: Table S2).

Annotation with GO terms and KEGG pathways

All wild barley transcripts were annotated based on homology searches in the non-redundant NCBI protein (NR), Swiss-Prot/Uniprot, KEGG and InterPro/Pfam databases using BLASTX. The results are summarized in Table 1 and Additional file 1: Figure S3A. Proportions of significant hits ranged from 40% (Swiss-Prot) to 67% (NR) for B1K2 PUTs, and from 37% (Swiss-Prot) to 63% (NR) for B1K30 PUTs. The majority of unique transcripts (87% of B1K2 and 88% of B1K30) longer than 500 bp showed significant BLASTX hits in the NR database, whereas the majority of PUTs without BLASTX hits in the NR database (81.6% of B1K2 and 87.2% of B1K30) were shorter than 500 bp (Additional file 1: Figure S3B). Therefore, transcript length affects the functional annotation by homology search because shorter transcripts may have a lower chance of a BLAST hit or are incomplete cDNAs without a protein-coding region.

We used BLAST2GO to assign Gene Ontology (GO) terms to protein-coding unique transcripts (Table 1 and Additional file 1: Figure S4). In both ecotypes, the majority of GO terms describe molecular function (36% of unique transcripts), followed by cellular component (34%) and biological process (29%). Among molecular function terms, the majority of transcripts was annotated with 'binding’ (GO: 0005488; 46%) and 'catalytic activity’ (GO: 0003824; 43%). Among biological process terms, most transcripts were annotated with the terms 'metabolic process’ (GO: 0008152; 41%), 'cellular process’ (GO: 0009987; 39%) and 'response to stimulus’ (GO: 0050896). The proportion of genes annotated with these terms was essentially identical for the B1K2 and B1K30 transcripts.

Identification of stress-related genes and transcription factors

To identify unique transcripts that are orthologous to previously annotated stress-related genes and transcription factors (TFs), we used a stringent BLASTX search measuring parameters combined with a RBH approach. Based on the list of candidate genes described in Materials and Methods, we found 43 stress-associated transcripts both in B1K2 and B1K30 unique transcripts, of which seven were found in B1K2, 18 only in B1K30 and 18 in both ecotypes (Additional file 3: Table S3). The majority of unique transcripts were homologous to heat shock proteins followed by aquaporin and ERD genes from different grasses; LEA and ABC transporter genes were also detected. To further identify wild barley putative transcripts involved in stress response, the literature was searched for functional genes, transcription factors and enzymes that regulate abiotic stress tolerance in different plants. Wild barley transcripts orthologous to these identified stress-responsive genes, transcription factors and enzymes were identified in the GO, KEGG and protein domain (Pfam) annotation results. We obtained 839 B1K2 and 881 B1K30 unique transcripts associated with more than 30 different stress-responsive genes (Additional file 2: Table S4). Most transcripts matched to zinc-finger domain containing genes (185 in B1K2 and 241 in B1K30), ABC transporters (104 and 93), and heat shock proteins (61 and 63). Likewise, 165 transcripts in B1K2 and 156 in B1K30 could be assigned to 14 different transcription factors responsible for abiotic stress tolerance.

Since response of drought tolerance is regulated by TFs, we used the RBH approach to find orthologous of known barley TFs among wild barley PUTs. We identified 203 (out of 780) known barley TFs orthologous to 165 B1K2 and 155 B1K30 unique transcripts and 312 TFs from Arabidopsis thaliana and five grasses (Brachypodium, rice, sorghum, maize and wheat) that were homologous to 165 B1K2 and 170 B1K30 unique transcripts (Additional file 3: Table S3).

Gene prediction based on barley HC genes

To evaluate the use of wild barley transcriptome sequences for improving the cultivated barley genome annotation, we first investigated how many barley HC genes were fully and partially covered by our unique transcripts. We achieved this by predicting the extent of coding sequence (CDS) covered by the unique transcripts using BLASTX (Additional file 2: Table S5). 62% of B1K PUTs tagged 45% of barley HC genes, and 5% of B1K PUTs homologous to barley HC genes fully covered HC genes. The majority of shorter HC genes (i.e., less than 1500 bp) were well covered by PUTs while the longer HC genes were less covered (Figure 3A and 3B). A higher proportion of longer than shorter HC genes has a hit with wild barley PUTs (Figure 3B).
Figure 3

Log-log plot of correlation among B1K PUTs and barley genes. (A) Correlation among the length of B1K PUTs and Hv. HC CDS. (B) Correlation among the length of B1K PUTs CDS predicted based on BLASTX and Hv. HC CDS. (C) Correlation among the length of B1K PUTs CDS predicted by OrfPredictor and Hv. HC CDS. The color bars showed the proportion of B1K PUTs length aligned to the orthologous HC CDS (A), the proportion of HC CDS covered by aligned PUTs (B), and the length difference (bp) among CDS of PUTs and HC genes.

In addition, we used OrfPredictor to predict CDS (Additional file 2: Table S5). Over 99% of the unique transcripts from all three assemblies consist CDS longer than 33 nucleotides with an average predicted CDS length of 350 bp (B1K); 19% were longer than 500 bp and 4% were longer than 1 kb. 3.3% (584) of the predicted CDS from B1K PUTs had the same CDS length as their orthologous HC CDS, while 2.6% (456) were longer than their orthologs (Figure 3C and Additional file 2: Table S5). We also analysed the proportion of novel transcripts annotated with CDS. Almost all (98%) of them were annotated with CDS ≥33 bp and 85% with CDS ≥200 bp (Additional file 2: Table S5). The results indicate that ab initio prediction of CDS identifies a substantial number of transcripts that differ from annotated orthologous barley genes and may contribute to an improved annotation (Additional file 1: Figure S5A-D).

SNP discovery in transcriptome data

We searched for SNPs in the transcriptome sequences to differentiate the two wild barley ecotypes. As a first step, we compared three SNP calling tools (Bowtie-2, BWA-SW, and GSMapper, see Methods) by analyzing B1K2 reads aligned onto Hv. fl-cDNA. After a very stringent filtering, a total of 1,939 SNPs were identified, of which 53% (1,032 SNPs) were unique to a single tool, while 47% (907 SNPs) were identified by more than one tool (Figure 4A). SNP numbers differed ten-fold between tools, and only 5.1% (98 SNPs) were identified by all three tools (Figure 4A). Under the assumption that SNPs identified by at least two tools are likely true SNPs, Bowtie-2 appears to be the most accurate SNP caller. We identified 1,017 high quality SNPs from 28,289 raw SNPs between the two wild barley ecotypes (B1K2 and B1K30) with Bowtie-2 and stringent SNP filtering criteria. This corresponds to a frequency of 4.4 SNPs per 1 kb (Table 2). SNP counts identified by using the cultivated barley sequences as reference are shown in Table 2. The B1K set produced a proportionally larger number of filtered SNPs than the B1K2 and B1K30 libraries alone, which reflect the sum of both accessions as well as the higher coverage per nucleotide position in the combined dataset (Figure 4B, Additional file 1: Figure S6 and Additional file 1: Figure S7). We also compared how SNPs identified from desert B1K2 and Mediterranean B1K30 ecotypes overlap with SNPs discovered from wild barley ecotype B1K4 , which belongs to the same Barley1K collection and was shotgun sequenced at low coverage [23]. A quarter of SNPs identified in each ecotype (9,775 in B1K2 and 8,682 in B1K30) overlapped with SNPs identified in B1K4 (Additional file 2: Table S6) using a different sequencing approach. This overlap suggests that a significant proportion of SNPs in the transcriptome sequence data are correctly inferred.
Figure 4

SNPs identified from the two wild barley ecotypes. (A) A Venn diagram showing the number of SNPs identified from assembly made by three different reads mapping programs and their combinations. SNPs are identified after stringent filtering (sequence depth ≥8x and a minimum of four reads supporting the reference and variant SNPs) and SNPs in the intersection of two or more ellipse belong to the SNPs called from mapping made by two or more programs. (B) Circos diagram showing the frequency of stringently filtered SNPs per kb (depth ≥8x from which minimum of four reads each supporting reference and variant SNPs). (C) Circos diagram showing the frequency of wild barley SNPs density per kb, filtered SNPs per kb (depth ≥8x from which minimum of two reads each supporting reference and variant SNPs) and estimated recombination rates (4N e r) per kb. Histograms are showing SNP frequencies among wild and cultivated barley. Recombination rates are estimated among wild barley. For the display, the maximum SNP frequencies are set at 20 (B, SNPs/kb), 10 (B, nsSNPs/kb), 30 (C, SNPs/kb), 20 (C, SNPs density/kb) and 4 (C, 4N e r/kb) – these maximum values are higher than the 3rd quartiles of the respective data.

SNP annotation

In the B1K set, 15,330 SNPs were identified from 61,852 raw SNPs after filtering in the B1K set. The top three SNP categories were SNPs in 3′ untranslated regions (3′-UTR; 48%), synonymous SNP (sSNP; 29%), and non-synonymous SNP (nsSNP, 14%). Other (9%) type of SNPs included intronic and intergenic SNPs, possibly because of unprocessed mRNAs in the libraries (Table 4). The high proportion of 3′-UTR SNPs likely resulted from abias towards the 3′-end of transcripts due to the cDNA library construction method [28]. In both ecotypes, the proportion of sSNP in genic and CDS/exon regions was over 2-fold higher than the proportion of nsSNP (Table 4). The average number of nsSNP in the genic regions was by 2-fold higher in the desert ecotype B1K2 than in the Mediterranean ecotype B1K30. Similarly, other SNP types with strong functional effects (start codon loss, stop codon gain, splice site acceptor and splice site donor) were 2-fold more frequent in B1K2 than in B1K30 (Wilcoxon test, p = 0.004). The average proportion of nsSNP/sSNP ratios was higher in stress-related genes of both ecotypes (B1K2 = 0.38:0.62 and B1K3 = 0.45:0.55) than the average proportion across all genes (0.32:0.68 in both ecotypes). Some of these genes belong to highly conserved protein families such as heat shock/chaperone, aquaporin, bZIP, Myb, Chlorophyll a-b binding and photosystem II (PSII) chlorophyll apoproteins suggesting that these gene classes be involved in adaptive evolution. The chromosomal distribution of nsSNPs is shown in Figure 4B, the distribution and proportion of sSNP and nsSNP in selected barley genes is further summarized in Additional file 1: Figure S8, and 30 barley genes with the largest numbers of putatively function-affecting SNPs are summarized in Additional file 2: Table S7.
Table 4

Summary of SNP effects in wild barley 454 transcripts

SNP effect

B1K2

B1K30

B1K

B1K2

B1K30

B1K

B1K2

B1K30

B1K

In all regions (#)

In genic region (%)

Mean per genic region

Synonymous

1,514

1,225

3,943

45.49

32.63

28.81

2.411

1.284

1.298

Non-synonymous

699

564

1,919

21.00

15.02

14.02

1.113

0.591

0.632

3′ UTR

816

1,546

6,570

24.52

41.18

48.01

1.299

1.621

2.163

5′ UTR

154

149

458

4.63

3.97

3.35

0.245

0.156

0.151

Non-synonymous start

1

2

2

0.03

0.05

0.01

0.002

0.002

0.001

Start lost

0

1

1

0.00

0.03

0.01

 

0.001

0.000

Stop gained

8

14

27

0.24

0.37

0.20

0.013

0.015

0.009

1Splice site acceptor

2

3

10

0.06

0.08

0.07

0.003

0.003

0.003

2Splice site donor

3

6

13

0.09

0.16

0.09

0.005

0.006

0.004

Start gained

26

31

95

0.78

0.83

0.69

0.041

0.032

0.031

Intron

105

213

648

3.16

5.67

4.73

   

3Upstream

753

1,041

3,445

   

0.746

0.70

0.785

4Downstream

1,504

1,942

7,381

   

1.489

1.31

1.682

Intergenic

1,173

1,272

3,854

   

0.167

0.223

0.213

Summary – SNPs with/in:

    

5High/moderate impact

712

588

1,970

   

1.134

0.616

0.648

6Gene region

5,585

6,737

24,512

      

7Genic region

3,328

3,754

13,686

      

8CDS/exon regions

3,192

3,501

12,920

      

9Gene with genic SNPs

628

954

3,039

      

1Splice site acceptor: variant hits a splice acceptor site (two bases before exon start, except for the first exon).

2Splice site donor: variant hits a Splice donor site (two bases after coding exon end, except for the last exon).

3Upstream: variant hits Upstream of a gene (within length of 5Kb).

4Downstream: variant hits Downstream of a gene (within length of 5Kb).

5High/Moderate SNPs effect: nsSNP CDS + Spice acceptor + splice donor + start lost + stop gained.

6Gene region: Upstream +5 UTR + Exon + Intron +3 UTR + Downstream.

7Genic region: 5 UTR + Exon + Intron +3 UTR.

8CDS/exon region: 5 UTR + Exon +3 UTR.

9Number of genes with SNPs affecting the UTRs, exon and intron.

SNP effects are based on mapping of 454 NR reads against chromosomal barley genome (Hv. 030312 v2.16).

Estimation of the population recombination parameter

We estimated the population recombination parameter ρ =4N e r from three wild barley ecotypes (B1K2, B1K30, B1K4) using 7,440 SNPs, and found an average value of 0.089 ρ/kb ±1.02 (SD). It varies between chromosomes with the highest and lowest rates in chromosome 1 (0.169) and 5 (0.062), respectively. The recombination rates on the remaining chromosomes were 0.073 (2), 0.084 (3), 0.083 (4), 0.082 (6) and 0.067 (7). Nonetheless, the recombination rate per kb does not differ between chromosomes (one-way ANOVA, p =0.178). High recombination rates were observed in telomeric regions of each chromosome (Figure 4C). There is no significant correlation (Pearson’s r = -0.03, p =0.12) between SNP density and the population recombination parameter (Additional file 1: Figure S9).

Discussion

Phenotypic response to drought stress in wild barley

Wild barley ecotypes from the divergent Mediterranean and desert climates are phenotypically different in several quantitative traits [18, 20]. Recent work has corroborated this finding by genomic and phenotypic analyses [8, 9, 20, 29]. Furthermore, drought stress tolerance in barley likely appears to be linked to WUE because wild barley genotypes from contrasting environments and with different WUE differed in their level of drought stress tolerance [30]. This study led to the discovery of the barley dehydration-responsive Hsdr4 gene, which is more strongly expressed in drought-tolerant than sensitive genotypes. The physiological responses in the present study are consistent with these results and contribute to the list of ecologically relevant phenotypic differences between Mediterranean and desert barley that may reflect differential adaptation. Although the desert ecotype showed a higher absolute water loss, its WUE and RWC were significantly larger under drought conditions. In contrast to our results, Eppel et al.[29] observed a similar decrease of WUE under drought stress in both Mediterranean and desert wild barley ecotypes, whereas other physiological traits such as Photosystem II yield and non-photochemical quenching (NPQ) differed significantly between both ecotypes. Currently, no studies are available that investigated the relationship between plant fitness in dry environments and WUE or RWC in wild barley. WUE was positively correlated with differential fitness in Boechera stricta[31], suggesting a contribution to fitness, but this was not observed in the desert annual Helianthus anomalus[32]. Taken together, these contrasting results highlight the complex nature of the trait. Future studies will have to investigate the genetic relationship between fitness and physiological traits in differentially adapted wild barley using, for example, segregating populations derived from crosses between differentially adapted parental lines or accessions.

Sequencing and assembly of wild barley transcriptome

The sequencing of normalized cDNA libraries resulted in more than half million reads per library of which almost 95% of high-quality trimmed reads could be de novo assembled into putative unique contigs. This high proportion of assembled reads is similar to other studies utilizing 454 transcriptome sequencing in species with complex genomes such as Douglas-fir [33], and significantly higher than in other studies [34, 35]. The unique transcripts length increased with the number of assembled reads per transcripts, in agreement with a previous study [33]. The present study with an average contig length of 609 bp and a N50 value of 910 bp in the B1K assembly is significantly larger than in a previous 454 transcriptome analysis of cultivated barley with a mean contig size of 505 bp and N50 values of 531 bp [36], or in a similar survey in two grass species of the genus Spartina[34]. The numbers of assembled unique transcripts in each ecotype are quite close to the 26,159 barley HC genes, and the number of putative unique transcripts from the combined B1K assembly is even higher. The full-length transcript data from cultivated barley (Hv. fl-cDNA) originate from complete genome sequencing efforts [37, 38] that involved several sequencing runs using different sequencing approaches and were therefore not comparable with transcripts obtained in a single 454 run. In the present study the libraries were made from leaf tissue only. We therefore expect that a significant proportion of barley genes may not have been captured, since only 72-84% of barley HC genes were expressed in more than one developmental stage or tissue [23].

Because no reference genome for H. spontaneum was available, we used the Hv. fl-cDNA, Hv. HC CDS and the currently released barley genome as references for read mapping and SNP discovery. When mapped against the full-length cDNA, over three-fourth (76%) of both B1K2 and B1K30 non-redundant reads were mapped; whereas only around two-third were mapped against Hv. HC genes, and about 80% onto chromosomal genomic sequences. Within wild barley, the majority (93%) of B1K2 non-redundant reads were mapped onto the B1K30 transcriptome from de novo assembly. A substantial proportion of reads were mapped to many sites of the reference sequences: 41% reads in mapping among ecotypes and up to 47% reads in mapping of ecotype onto WGS. Similar results have been reported for chickpea (47-60%) [39] and maize (52%) [40]. The presence of paralogous genes and isoforms from splice variants, and quality of the reference sequences could contribute to the observed duplicate mapping [3941] and some 454 reads mapping to multiple sites may represent transcribed transposable elements.

Identification of new barley genes by transcriptome sequencing

Since the cDNA libraries were normalized, a significant proportion of rare transcripts should have been sequenced with higher probability than non-normalized libraries and contributed to the discovery of novel transcripts and genetic variants [28, 42]. The homology searches against all three current barley databases, barley HC genes, Hv. fl-cDNA and HarvEST, supported this expectation because a substantial proportion of the unique transcripts (25% or 7,102 in the B1K set) has not been annotated as genes in cultivated barley, although 98% of the reads were mapped onto the cultivated barley genome. Furthermore, the majority of new transcripts from both ecotypes did not show significant BLAST hits in closely related grass species (98% without homology in Brachypodium, rice and sorghum) and protein databases (87% without homology in NR, Swiss-Prot and InterPro). On the other hand, 85% of new or novel transcripts were annotated with a coding sequence length longer than 100 bp. Although these results look unexpected, similar results were reported in cultivated barley [36] with 40% new contigs, as well as in wheat [35], the grass Spartina[43] and the zebra finch [42]. Since all of these studies employed 454 sequencing of normalized cDNA libraries, it appears to be an efficient approach for the discovery of new genes. The total number of barley genes was estimated to be around 30,400, and 26,159 (86%) of them are reported as high-confidence (HC) genes [23]. The discrepancy between the high proportion of reads mapped to the H. vulgare genome and the large number of novel transcripts specific to wild barley found in our data may be explained as follows: (i) The novel transcripts represent genes that are present in wild but not in cultivated barley, or (ii) are present but have not yet been identified in cultivated barley because the 'Morex’ WGS data, to which 98% the raw 454 reads could be mapped, still represent a draft genome assembly; (iii) the novel transcripts may be derived from genes with structural variants and alternative splicing. The local alignment approach used to map 98% of the reads against the WGS data 'soft-trimmed’ the read end, which leads to a high proportion of aligned reads by removing non-matching ends. It has been shown before that such trimmed reads are associated with structural variants and alternative splicing, both of which are highly prevalent in barley [23, 25]; (iv) Novel transcripts without any homology may represent poorly conserved non-coding RNAs (the 'dark matter’ of genome) [44] or untranslated regions (UTRs) of the genome because they originated from incompletely transcribed or sequenced mRNAs and may be too short to generate significant BLAST hits.

Based on the RBH analysis, our assemblies represent 41-47% orthologs of the current set of 26,159 barley HC genes. A comparison of our GO annotation with the barley Affymetrix GO terms [45] showed a similar distribution suggesting that our transcripts well represent wild barley genes. Known barley genes not present in the transcriptome data may have been lost or not sampled during library preparation or sequencing. Alternatively, they may not be expressed in wild barley leaf under drought stress, or may show low expression or expression at different developmental stages or plant tissues, since only 72-84% of the HC genes were expressed in all developmental stages or tissue samples [23].

The unique transcripts generated from the differentially adapted wild barley ecotypes contribute to an improved annotation of the barley genome. This is because many PUTs identified in this study are not orthologous to barley HC genes and/or fl-cDNA, but some of these non-orthologous transcripts are conserved in grasses and other plant species. A significant number of CDS in our transcript set are longer than their orthologous barley HC genes, indicating that some of the annotated barley genes are not complete or differentially spliced.

A substantial proportion of PUTs are not shared between the two wild barley ecotypes. They may (i) represent genes whose transcripts were lost during cDNA normalization or library preparation, (ii) presence/absence polymorphisms, i.e., ecotype-specific or non-shared transcripts that reflect genome divergence due to differential loss or gain of transcripts, as has been documented in maize [4648], or (iii) differential expression of genes in both accessions in response to the drought treatment. Since we sequenced normalized cDNA libraries and did not include untreated plants, our data do not allow us to differentiate between presence/absence polymorphisms and differential expression. Genome re-sequencing using targeted exome capture and RNAseq of treated and untreated plants will reveal why transcripts are not shared between the two ecotypes.

Identification of candidate stress-related genes and transcription factors

Studies in different model organisms such as rice and Arabidopsis thaliana uncovered numerous genes encoding transcription factors, signal transduction and transporter proteins with well characterized roles in drought stress regulation [49]. Transcriptome sequences generated from ecotypes adapted to different environments can be used to identify homologs of known stress-related genes in non-model organisms. Using RBH- and keywords-based searches, we identified more than 800 genes with known stress-related genes from other species in each accession. Genes encoding zinc-finger proteins, ABC transporters, heat shock proteins and transcription factors (e.g., MYB, MAPK, bHLH, bZIP, NAC, WRKY) were the most abundant types of stress-related genes. As an example, the ABC subfamily G (ABCG) transporter significantly contributes to leaf water retention in wild barley and rice [50]. Some of these transcription factors, for instance, bZIP, bHLH and MYB are found to be regulating different stress responses in plants [51, 52]. As discussed above, the presence of different homologs to known stress-related genes in the two ecotypes does not necessarily mean that they are involved in drought-response or indicative of the genetic divergence between the two ecotypes, but are putative candidates for further evolutionary or functional investigations. For instance, the barley dehydration-responsive Hsdr4 gene, which encodes a Rho-GTPase-activating protein and is highly expressed in drought tolerant relative to sensitive genotypes under drought stress [30, 53] is also present in the PUTs identified in this study. In previous studies, SNPs were only found in the intron and promoter regions of tolerant and sensitive genotypes, but the comparison of PUTs and Hsdr4 identified six new SNPs in the coding region.

SNP discovery in transcriptome sequences

Despite the great power of current sequencing technologies, SNP identification is still challenging and affected by several factors [54]. We found ten-fold different numbers of SNPs between SNP callers indicating that SNP calling strongly depends the particular algorithm [33]. Early SNP calling methods such as GSMapper that use fixed cutoff rules tend to underestimate SNP numbers when compared to probabilistic methods as implemented in Bowtie [54]. Probabilistic methods also depend on parameter settings because numbers of SNPs identified with Bowtie 2 differed by 2.5-fold between local and end-to-end methods. One solution to this is the use of several tools because SNPs discovered by two or more programs are more likely true SNPs than those predicted by one only [39] and SNPs called at higher coverage most likely represent true SNPs [55].

We identified over one thousand SNPs in wild barley, resulting in 4.4 SNPs per kb at high coverage. This is similar to a recent survey of wild barley collected across Israel (one SNP per 239 bp, θ w =0.00418) [10]. We also identified over one thousand SNPs by mapping against the cultivated barley sequences (Hv. fl-cDNA and Hv. HC genes). A quarter of SNPs from each ecotype are shared with SNPs identified from wild barley ecotype B1K4 used for barley genome sequencing [23], which independently confirms a significant proportion of discovered SNPs. Based on the comparisons of each ecotype against all three cultivated barley datasets, the SNP density of the desert ecotype B1K2 against cultivated barley is 1.9-fold higher than of the Mediterranean ecotype B1K30, which was calculated as the mean of the SNP densities of 1.8-fold to fl-cDNA, 2.6-fold to HC and 1.4-fold to the chromosomal genome sequences. There is more genetic variation in the desert barley and a higher genetic similarity between Mediterranean wild barley and cultivated barley suggesting that (i) the cultivated barley domestication occurred in the northern part of Israel, (ii) there is gene flow among cultivated and Mediterranean ecotypes, and/or (iii) the desert ecotypes diverged more because of the accumulation of adaptive and linked neutral variation through adaptive evolution to the desert conditions. Based on the proportion of shared SNPs, the B1K4 ecotype from Ein Prat appears more similar to the desert ecotype B1K2 than to B1K30.

These differences are supported by the analysis of SNP types. The higher absolute frequency of nsSNPs and of SNPs with functional effects, and the higher ratio of nsSNPs to sSNPs in the desert than Mediterranean ecotype reflects the larger genetic distance of the desert ecotype to cultivated barley. The higher number of functional polymorphisms in the desert ecotype may result from of a smaller effective population size causing a higher frequency of slightly deleterious amino acid polymorphisms due to reduced purifying selection, or a stronger and more frequent genome-wide positive selection as a consequence of adaptation to a stressful environment.

It should be noted that the observed distances are likely biased because of the SNP calling problems, the small sample size and the sequencing of a subset of genes captured by the stress-induced library. On the other hand, the larger genetic distance between the desert ecotype to cultivated barley than between the Mediterranean ecotype to cultivated barley is consistent with the phenotypic differentiation of the desert and Mediterranean wild barley types to cultivated barley for several quantitative traits [20]. SNP density at telomeric chromosomal regions is higher than in other regions in agreement with genome sequencing surveys of both cultivated and wild barley [23]. It may result from a higher gene density [23] or increased recombination rates in telomeric regions (Figure 4C). SNP density within wild barley is 2.2-fold greater than among wild (B1K) and cultivated barley. However, it is similar to the SNP density predicted in cultivated barley from the assembly of public barley EST sequences, one SNP per 240 bp (i.e., 4.1 SNPs/kb) [56]. This estimate may be biased by using public EST sequences originating from several genotypes for SNP identification.

Conclusion

Physiological analysis of drought stressed desert and Mediterranean wild barley ecotypes suggested the existence of genomic differences and the resulting 454 transcriptome sequencing led to the discovery of novel transcripts in both ecotypes. The desert ecotype has a relatively higher SNP density and more SNPs with significant effects on protein coding genes than the Mediterranean ecotype. Based on functional and evolutionary conservation, several stress-related candidate transcripts and transcription factors were identified. The data generated in this study are valuable genomic resources for further improvement of the barley transcriptome and genome annotation. Using this resource, exome capture arrays can be designed to investigate presence/absence polymorphisms of putative wild-barley and ecotype-specific genes as well as to analyze genetic diversity in different groups of genes. The markers generated in the putatively stress-related genes can be used for the genetic analysis of drought adaptation and provide an avenue for the introgression of useful genetic variation into cultivated barley breeding populations.

Methods

Plant material

The two wild barley ecotypes used for this study were single seed descendents from the original Barley1K (B1K) wild barley collection [9]. Seeds from the desert ecotype (B1K2) were collected from Yerucham in the Negev desert (34.865265 E, 30.933476 N) and the Mediterranean ecotype (B1K30) from Nahal Oren ('Evolution Canyon’) near Haifa in Northern Israel (34.975673 E, 32.715772 N). The mean annual rainfall at B1K2 and B1K30 locations are 112 and 623 mm, while the water content in the soil are 0.543% and 10.379%, respectively [9].

Physiological measurements

Sowing and cultivation of plants followed a previously described protocol [20] with few modifications. Seeds were planted in soil in planting trays and kept in the dark for 10 days at 4°C before they were incubated in a phytotron for 21 days at 16°C/10°C and short day conditions (9 h light; thereafter, seedlings were transplanted to 4 liter pots, which were irrigated twice per day in long day conditions (16 h light) at 22°C/16°C. The pots were filled with a commercial potting soil (Matza Gan; Shaham, Givat-Ada, Israel) and each pot contained one plant. The physiological measurement was conducted in greenhouses located at the Faculty of Agriculture, Food and Environment in Rehovot, Israel during December 2012 and January 2013. In the greenhouses, plants were kept under natural light conditions and vents and/or cooled moist air were used to ensure that the maximum temperature in the greenhouse did not exceed 35°C. The temperature and relative humidity were in the range of 18-35°C and 20-35% during the experiment.

After the plants were kept in the greenhouse for two weeks and when the nodes started to appear, the phenotypic measurements were started and further conducted for two months. Mid-day whole-plant transpiration (E) was calculated based on the difference between two weight readings, 90 min apart, taken between 11:00 and 13:00. The weight loss of each plant was normalized to the plant leaf area, which was taken using a leaf scanner (LI-COR 3100 Area Meter, LI-COR, USA). The daily plant weight (biomass) gain was determined based on the difference between the pot weights on the morning of two consecutive days, when the drainage from the pot (following an thorough-irrigation event) had finished. Agronomic WUE, defined as the ratio between the plant weight gain and the amount of water transpired. The WUE of each line was determined by fitting a linear curve for the cumulative plant weight gain during the well-irrigated stage vs. cumulative water transpiration (calculated daily based on the difference between pot weights before dawn and in the evening). Soil relative volumetric water content (SWC) was measured using the EC-5 soil moisture sensor combined with the 'ProCheck’ interface reader (Decagon Devices, Pullman, WA. USA). The SWC under well-irrigated and drought conditions were 80% and 30%, respectively. Under full field capacity, the SWC is equal to 80%, and hence 30% of SWC is equivalent to ~38% field capacity. The 30% SWC was reached within 10–15 days after the experiment was started and the flag leaves were fully expanded at this time. Leaf relative water content (RWC) was collected at 12:00 – 14:00 on days when the water content in the pots reached the specified soil water content levels. Measurement data were collected from a single leaf from each plant. RWC was evaluated using the following protocol: leaf fresh weight (FW) was immediately recorded, then leaves were soaked for 8 h in 5 mM CaCl solution at room temperature in the dark to record the turgid weight (TW). Total dry weight (DW) was recorded after drying these leaves at 70°C to a constant weight. Relative water content (RWC) was calculated as (FW - DW/TW - DW) × 100.

Preparation of total RNA and cDNA for transcriptome sequencing

Total RNA was extracted with the TriZol RNA extraction kit from leaves of single B1K2 and B1K30 plants at the fifth day of water deficit period (Additional file 1: Figure S1). The total RNAs extracted from two stressed samples of both B1K2 and B1K30 ecotypes were quality checked and pooled together for ds cDNA synthesis and cDNA normalization. The quality test, ds cDNA synthesis and cDNA normalization processes were carried out by Evrogen (Evrogen Lab, http://www.evrogen.com) using the following steps. The ds cDNA synthesis of pooled samples was performed with the SMART method [57]. First strand cDNA synthesis was performed using 5 μl reaction mixture containing 0.3 μg of pooled total RNA, 10 pmol SMART Oligo II oligonucleotide (5′-AAGCAGTGGTATCAACGCAGAGTACGCrGrGrG-3′) and 10 pmol CDS-T22 primer (5′-AGCAGTGGTATCAACGCAGAGTTTTTGTTTTTTTCTTTTTTTTTTVN-3′). The ds cDNA synthesis was performed by long-distance PCR [58]. Finally, amplified cDNA PCR products were purified using QIAquick PCR Purification Kit (QIAGEN, CA) and concentrated by ethanol precipitation. The synthesized cDNA libraries were normalized using duplex-specific nuclease (DSN) normalization method [59] with the Evrogen Trimmer kit [60]. Finally, the normalized cDNA libraries were first diluted by adding 30 μl milliQ water and amplified in in 50 μl PCR reaction containing 1 μl diluted cDNA, 1 x Advantage 2 reaction buffer (Clontech), 200 μM dNTPs, 0.3 μM SMART PCR primer and 1 x Advantage 2 Polymerize mix (Clontech). A total of 18 PCR cycles each involving 95°C for 7 seconds, 65°C for 20 seconds; 72°C for 3 minutes was carried out.

454 transcriptome sequencing

The 454 GS FLX Titanium library construction, emPCR and sequencing were performed by Macrogen (Macrogen Inc., Seoul, Korea). After fragmentation of normalized cDNA libraries using nebulization, the quality of the libraries was checked with an Agilent 2100 Bioanalyzer. The libraries were sequenced on one full-plate (half-plate for each sample) using a 454 GS FLX Sequencer and Titanium Chemistry protocol.

De novoassembly of transcriptome reads

A de novo assembly of 454 transcriptome sequence raw reads was carried out using Newbler v2.6 [61]. The overall summary of the workflow used for the de novo assembly of the transcriptome reads is indicated Additional file 1: Figure S1. As separate de novo assembly (of B1K2 and B1K30) was performed using the following parameters: -cdna (to assemble transcripts), -ml 90% (minimum overlap read length, default 40 bp), -urt (to extend contigs using the ends of single read) and -v (a fasta file database for trimming of the contaminants: adapters, primers and poly-A/T). The trimming database was developed based on contaminant information obtained from trial assemblies and contains a list of all used adapters and primers with their isoforms identified in the trial assemblies. To trim out poly-A/T, we also included a list of poly-As that contains >10 A in the trimming database. For the combined de novo assembly of B1K2 and B1K30 reads (B1K assembly), we used the same parameters as for the de novo assembly except for the following settings -ml 95%, -mi 95 (minimum overlap% identity for pairwise alignment, default 90) and -minlen 30 (minimum length of reads for assembly, default 20 bp). The assembled isotigs (which are equivalent to the transcripts) and trimmed singletons were further cleaned by SeqClean [62] using the following two databases: database of adapters and primers sequences used for cDNA library preparation, and database of vector sequences [63] to screen for contaminant vectors.

Construction of a nonredundant barley full-length cDNA set

We downloaded 28,620 Hordeum vulgare full-length cDNAs (Hv. fl-cDNA) from the NCBI database: accession numbers AK248134 to AK253139 for 5,006 fl-cDNA [38] and AK353559 to AK377171 for 23,614 Hv. fl-cDNAs [37]. These 28,620 Hv. fl-cDNA sequences were clustered into 23,356 nonredundant (NR) sequences with a minimum of 100 bp length using CD-HIT-EST [64] with parameters: -c 0.98 and -n 9. The clustered NR Hv. fl-cDNAs were used as a reference for reference-based transcriptome mapping and for homologous gene search.

Reference-based assembly of transcriptome reads

To identify SNPs within wild barley ecotypes and among wild and cultivated barley, high quality and clustered reads of B1K2 and B1K30 reads were mapped against NR Hv. fl-cDNAs, and B1K2 reads against PUTs of B1K30 sequences using Bowtie-2 v2-2.0.0-beta6 [65]. The overall summary of the workflow used for the reference-based mapping of the transcriptome reads is indicated in Additional file 1: Figure S1. B1K2 and B1K30 reads used for mapping were selected as follows. Initially, de novo assemblies were performed on B1K2 and B1K30 reads using Newbler v2.6 [66] (used parameters: -cdna, -urt, -mi 95, -ml 95% and -minlen 45). From the assembly output files, assembled (fully and partially) and singleton reads were identified and extracted using Roche 454’s fnafile utility. The extracted reads were further clustered into nonredundant reads using USEARCH/UCLUST program v5.1 [67]. Clustered reads were then cleaned using SeqClean program for contaminant vectors [63] and adapters and primers. Finally the cleaned and nonredundant reads were mapped to the indexed reference using Bowtie-2, using the '–local’ alignment mode. The same parameters were also used to map B1K2 high quality reads onto B1K30 sequences (NR isotigs and true singletons). In addition to Bowtie-2, we also used other two different assembly/SNP calling tools: BWA-SW implemented in BWA (Burrow-Wheeler Aligner) v0.5.9 [68] using –z 100 (Z-best heuristics, default =1) to increase the accuracy of the alignment, and GSMapper (Newbler v2.6) from Roche 454 [66]. The B1K reads were also mapped onto the filtered chromosomal barley reference genome, Hordeum vulgare 030312 v2.16 from ENSEMBL release 16 and barley HC genes CDS (version: MIPS 23 March 2012) [23] using Bowtie-2.

SNP identification and annotation

To select the best SNP identification approach, we first compared the three SNP calling tools: Bowtie-2, BWA-SW and GSMapper using B1K2 reads mapped against Hv. fl-cDNA. After selecting Bowtie 2 for mapping, SNP identification was performed using SAMtools v0.1.18 [69] as follows. From sorted BAM files of B1K2 and B1K30 assemblies, raw SNPs were called by mpileup utility of SAMtools using the following parameters: -D (output per-sample read depth), -g (compute genotype likelihoods and output them in the binary call format [BCF]), -u (same as -g but uncompressed BCF), and –I (without INDEL calling). Called SNPs were further filtered by varfilter utility of bcftools using parameter -D (maximum read depth) of 1000 and a minimum depth of two reads to generate raw SNPs. From raw SNPs, high quality SNPs were further identified based on very stringent filtering criterion that a minimum of eight reads in which four reads each had to support both reference and variant nucleotides (alleles). This SNPs filtering approach was applied to discover high quality SNPs from mapping of B1K2 NR reads against B1K30 transcripts and the NR reads of both ecotypes against barley fl-cDNA and HC genes. For SNP annotation, we however used less stringent filtering criterion to reduce the loss of information, and hence SNPs with ≥8x coverage and a minimum of two reads supporting each allele (reference and variant) were used. Using this approach, SNPs discovered from mapping of B1K2, B1K30 and B1K reads against chromosomal barley reference genome were annotated using snpEff v3.1 [70].

Estimation of the population recombination parameter, 4N e r

The population recombination parameter, 4N e r across barley chromosomes was estimated using a Bayesian reversible-jump MCMC scheme of the rhomap program included in the LDhat package v2.1 [71] using the following parameters: cross-over model, 1 million iterations, 1 million burn-ins and 100,000 samples. We used filtered SNP data (minimum depth =8x) from our transcriptome sequences (B1K2 and B1K30 SNPs) and wild barley (B1K4) genome sequence [23] to estimate the recombination rates.

Identification of homologous genes

We compared each of our assemblies (B1K2, B1K30 and B1K) against NR Hv. fl-cDNA and 70,144 barley ESTs unigenes (HarvEST v1.83 assembly 36, http://harvest.ucr.edu/) using MegaBLAST with the following parameters: -p 95 -r 1 -q -2 -G 1 -E 2 and an E-value of <10-10. Blast-hits with an alignment length of ≥50 bp were filtered from the MegaBLAST tabular outputs and reciprocal blast-hits (RBHs) or were identified using PERL scripts [72]. We applied the same approaches to identify homology for our unique transcripts and to identify how many are annotated in the 26,159 HC genes CDS (version: MIPS_23Mar12) [21]. Homologous genes were identified in annotated proteins from other fully sequenced grass species: Brachypodium distachyon[73] (Brachypodium Genome v1.2, ftp://ftpmips.helmholtz-muenchen.de/plants/brachypodium/v1.2/), Oryza sativa[74] (MSU6, ftp://ftp.ensemblgenomes.org/pub/plants/release-11/fasta/oryza_sativa/pep/), Sorghum bicolor[75] (Sbi1_4, filtered model, http://genome.jgi-psf.org/Sorbi1/Sorbi1.download.ftp.html), Zea mays[76] (ZMB73_4a.53, filtered model, http://www.maizesequence.org/index.html) and Arabidopsis thaliana[77] (TAIR10, ftp://ftp.arabidopsis.org/home/tair/Proteins/). B1K2, B1K30 and B1K sequences were compared against all databases using BLASTX and outputs were filtered for hits with ≥75% similarity and an alignment length of ≥33 amino acids. RBH were identified from filtered hits using PERL scripts [72].

Identification of stress- and other trait-related genes from 454 transcripts

Confirmed and putative stress related genes such as aquaporin, dehydrin, ABC (ATP-binding cassette subfamily G, ABCG or Eibi), ERD (Early Response to Dehydration stresses), LEA (Late Embryogenesis Abundance), HSP (Heat Shock Protein) in barley, wheat, sorghum, maize, rice and Arabidopsis were searched in the literature and downloaded from the Uniprot and NCBI databases. 481 protein sequences of these stress-related genes were compared with B1K2 and B1K30 sequences using BLASTX with an E-value of 10-6. Hits with similarity ≥75% and an alignment length of ≥33 amino acids were further filtered from the BLASTX tabular outputs, and RBH were identified from the filtered outputs using PERL scripts. Similarly, to identify wild barley transcripts that were homologous to already identified rice trait genes, we downloaded the list of all genes from Oryzabase [78] from http://www.shigen.nig.ac.jp/rice/oryzabase/. Rice trait genes homologous to our 454 sequences were then identified from RBH (similarity ≥75% and an alignment length of ≥33 amino acids) results to rice genome by their IDs.

Transcription factors identification from 454 transcripts

Protein sequences of barley, Brachypodium distachyon, rice (O. sativa Japonica), sorghum, maize, wheat (Triticum aestivum) and Arabidopsis thaliana transcription factors were downloaded from Peking University Plant Transcription factors Database [79] at http://planttfdb.cbi.pku.edu.cn/. Sequences were separated into two groups (barley and the rest) and the RBH approach was used for the identification of putatively orthologous wild barley transcription factors using BLASTX and E-value of 1e-20 and further filtering for hits with similarity ≥85% and an alignment length of ≥33 amino acids.

Functional annotation of putative unique transcripts

The functional annotation and classification of clustered wild barley was carried out with Blast2GO v2.5 [80]. The unique transcripts were compared against the NCBI non-redundant (NR) and Swiss-Prot protein databases using BLASTX with an E-value of 1e-06, and GO terms were mapped to the obtained hits. The mapped GO terms were initially annotated with the default annotation threshold (55) and un-annotated sequences were further annotated at a threshold of 45. GO annotations were further enriched and refined using the two methods implemented in Blast2GO, Augment Annotation by Annex (ANNEX) [81] and GO-Slims [82] methods. After the GO annotations were refined by GO-slim for plant, GO terms were functionally categorized into biological process, molecular functions and cellular components at level 2. The InterPro scan annotation was also performed using Blast2GO program to identify protein family and domain associated with wild barley transcripts.

Gene prediction

We estimated the number of barley HC genes captured by our assembled transcripts using three different homology-based search approaches: one-to-many, RBH and BLASTX. For BLASTX hit search, we used BLASTX parser pipeline (http://www.atgc.org/SeqsExtractor/) to extract Hv. HC CDS homologous CDS from our transcripts. Furthermore, OrfPredictor [83] was used to predict the CDS from the de novo assembled unique transcripts.

Availability of supporting data

Raw 454 sequence data were submitted to the NCBI short sequence read archive (Accession number: SRX319401 and SRX319413). Putative unique transcripts, SNP calls and annotation are available from LabArchives (DOI:10.6070/H4R20ZB1).

Abbreviations

B1K2 (B1K-2-8): 

Desert wild barley ecotype

B1K30 (B1K-30-9): 

Mediterranean/costal wild barley ecotype

B1K4 (B1K-04-12): 

Wild barley ecotype from Ein Prat, Israel

B1K: 

de novo assembly from B1K2 and B1K30 reads

Hv. Fl-cDNA: 

Barley full length cDNA

Hv. HC: 

Barley high-confidence

SNP: 

Single nucleotide polymorphism

WGS: 

Whole genome sequence

RBH: 

Reciprocal (bi-directional) blast hit

SD: 

Standard deviation.

Declarations

Acknowledgements

This work was funded by FORMAS project grant 2008–1220 and by the Rehovot-Hohenheim partnership program.

Authors’ Affiliations

(1)
Department of Plant Biology, Uppsala BioCenter, Linnean Centre of Plant Biology in Uppsala, Swedish University of Agricultural Sciences (SLU)
(2)
Institute of Plant Science and Genetics, The Hebrew University of Jerusalem
(3)
Institute for Plant Breeding, Seed Science and Population Genetics, University of Hohenheim

References

  1. Langridge P, Paltridge N, Fincher G: Functional genomics of abiotic stress tolerance in cereals. Brief Funct Genomics Proteomics. 2006, 4 (4): 343-354.View ArticleGoogle Scholar
  2. Fleury D, Jefferies S, Kuchel H, Langridge P: Genetic and genomic tools to improve drought tolerance in wheat. J Exp Bot. 2010, 61 (12): 3211-3222.PubMedView ArticleGoogle Scholar
  3. Verslues PE, Juenger TE: Drought, metabolites, and Arabidopsis natural variation: a promising combination for understanding adaptation to water-limited environments. Curr Opin Plant Biol. 2011, 14 (3): 240-245.PubMedView ArticleGoogle Scholar
  4. Chaves MM, Maroco JP, Pereira JS: Understanding plant responses to drought - from genes to the whole plant. Funct Plant Biol. 2003, 30 (3): 239-264.View ArticleGoogle Scholar
  5. Barnabas B, Jager K, Feher A: The effect of drought and heat stress on reproductive processes in cereals. Plant Cell Environ. 2008, 31 (1): 11-38.PubMedGoogle Scholar
  6. Blum A: Drought resistance and its improvement. Plant Breeding for Water-Limited Environments. 2011, New York: Springer New York, 53-152.View ArticleGoogle Scholar
  7. Harlan JR, Zohary D: Distribution of wild wheats and barley. Science. 1966, 153 (3740): 1074-1080.PubMedView ArticleGoogle Scholar
  8. Hubner S, Gunther T, Flavell A, Fridman E, Graner A, Korol A, Schmid KJ: Islands and streams: clusters and gene flow in wild barley populations from the Levant. Mol Ecol. 2012, 21 (5): 1115-1129.PubMedView ArticleGoogle Scholar
  9. Hubner S, Hoffken M, Oren E, Haseneyer G, Stein N, Graner A, Schmid K, Fridman E: Strong correlation of wild barley (Hordeum spontaneum) population structure with temperature and precipitation variation. Mol Ecol. 2009, 18 (7): 1523-1536.PubMedView ArticleGoogle Scholar
  10. Bedada G, Westerbergh A, Nevo E, Korol A, Schmid KJ: DNA sequence variation of wild barley Hordeum spontaneum (L.) across environmental gradients in Israel. Heredity (Edinb). 2014, 112 (6): 646-655.View ArticleGoogle Scholar
  11. Nevo E: Genome evolution of wild cereal diversity and prospects for crop improvement. Plant Genetic Resour Charact Util. 2006, 4: 1-ISSN 1479-2621(print)|1479-1263x(electronic)View ArticleGoogle Scholar
  12. Ellis RP, Forster BP, Robinson D, Handley LL, Gordon DC, Russell JR, Powell W: Wild barley: a source of genes for crop improvement in the 21st century?. J Exp Bot. 2000, 51 (342): 9-17.PubMedView ArticleGoogle Scholar
  13. Teulat B, Merah O, Sirault X, Borries C, Waugh R, This D: QTLs for grain carbon isotope discrimination in field-grown barley. Theor Appl Genet. 2002, 106 (1): 118-126.PubMedGoogle Scholar
  14. Teulat B, Zoumarou-Wallis N, Rotter B, Ben Salem M, Bahri H, This D: QTL for relative water content in field-grown barley and their stability across Mediterranean environments. Theor Appl Genet. 2003, 108 (1): 181-188.PubMedView ArticleGoogle Scholar
  15. Diab AA, Teulat-Merah B, This D, Ozturk NZ, Benscher D, Sorrells ME: Identification of drought-inducible genes and differentially expressed sequence tags in barley. Theor Appl Genet. 2004, 109 (7): 1417-1425.PubMedView ArticleGoogle Scholar
  16. Teulat B, Borries C, This D: New QTLs identified for plant water status, water-soluble carbohydrate and osmotic adjustment in a barley population grown in a growth-chamber under two water regimes. Theor Appl Genet. 2001, 103 (1): 161-170.View ArticleGoogle Scholar
  17. Guo PG, Baum M, Varshney RK, Graner A, Grando S, Ceccarelli S: QTLs for chlorophyll and chlorophyll fluorescence parameters in barley under post-flowering drought. Euphytica. 2008, 163 (2): 203-214.View ArticleGoogle Scholar
  18. Volis S, Mendlinger S, Ward D: Differentiation in populations of Hordeum spontaneum Koch along a gradient of environmental productivity and predictability: plasticity in response to water and nutrient stress. Biol J Linn Soc. 2002, 75 (3): 301-312.View ArticleGoogle Scholar
  19. Volis S, Mendlinger S, Ward D: Adaptive traits of wild barley plants of Mediterranean and desert origin. Oecologia. 2002, 133 (2): 131-138.View ArticleGoogle Scholar
  20. Hubner S, Bdolach E, Ein-Gedy S, Schmid KJ, Korol A, Fridman E: Phenotypic landscapes: phenological patterns in wild and cultivated barley. J Evol Biol. 2013, 26 (1): 163-174.PubMedView ArticleGoogle Scholar
  21. von Korff M, Grando S, Del Greco A, This D, Baum M, Ceccarelli S: Quantitative trait loci associated with adaptation to Mediterranean dryland conditions in barley. Theor Appl Genet. 2008, 117 (5): 653-669.PubMedView ArticleGoogle Scholar
  22. Varshney RK, Langridge P, Graner A: Application of genomics to molecular breeding of wheat and barley. Adv Genet. 2007, 58: 121-+.PubMedView ArticleGoogle Scholar
  23. Mayer KF, Waugh R, Brown JW, Schulman A, Langridge P, Platzer M, Fincher GB, Muehlbauer GJ, Sato K, Close TJ, Wise RP, Stein N, IBGS Consortium: A physical, genetic and functional sequence assembly of the barley genome. Nature. 2012, 491 (7426): 711-716.PubMedGoogle Scholar
  24. Mascher M, Richmond TA, Gerhardt DJ, Himmelbach A, Clissold L, Sampath D, Ayling S, Steuernagel B, Pfeifer M, D’Ascenzo M, Akhunov ED, Hedley PE, Gonzales AM, Morrell PL, Kilian B, Blattner FR, Scholz U, Mayer KF, Flavell AJ, Muehlbauer GJ, Waugh R, Jeddeloh JA, Stein N: Barley whole exome capture: a tool for genomic research in the genus Hordeum and beyond. Plant J. 2013, 76 (3): 494-505.PubMed CentralPubMedView ArticleGoogle Scholar
  25. Munoz-Amatriain M, Eichten SR, Wicker T, Richmond TA, Mascher M, Steuernagel B, Scholz U, Ariyadasa R, Spannagl M, Nussbaumer T, Mayer KF, Taudien S, Platzer M, Jeddeloh JA, Springer NM, Muehlbauer GJ, Stein N: Distribution, functional impact, and origin mechanisms of copy number variation in the barley genome. Genome Biol. 2013, 14 (6): R58-PubMed CentralPubMedView ArticleGoogle Scholar
  26. Morrell PL, Buckler ES, Ross-Ibarra J: Crop genomics: advances and applications. Nat Rev Genet. 2011, 13 (2): 85-96.PubMedGoogle Scholar
  27. Stapley J, Reger J, Feulner PG, Smadja C, Galindo J, Ekblom R, Bennison C, Ball AD, Beckerman AP, Slate J: Adaptation genomics: the next generation. Trends Ecol Evol. 2010, 25 (12): 705-712.PubMedView ArticleGoogle Scholar
  28. Ekblom R, Galindo J: Applications of next generation sequencing in molecular ecology of non-model organisms. Heredity. 2011, 107 (1): 1-15.PubMed CentralPubMedView ArticleGoogle Scholar
  29. Eppel A, Keren N, Salomon E, Volis S, Rachmilevitch S: The response of Hordeum spontaneum desert ecotype to drought and excessive light intensity is characterized by induction of O2 dependent photochemical activity and anthocyanin accumulation. Plant Sci. 2013, 201–202: 74-80.PubMedView ArticleGoogle Scholar
  30. Suprunova T, Krugman T, Distelfeld A, Fahima T, Nevo E, Korol A: Identification of a novel gene (Hsdr4) involved in water-stress tolerance in wild barley. Plant Mol Biol. 2007, 64 (1–2): 17-34.PubMedView ArticleGoogle Scholar
  31. Knight CA, Vogel H, Kroymann J, Shumate A, Witsenboer H, Mitchell-Olds T: Expression profiling and local adaptation of Boechera holboellii populations for water use efficiency across a naturally occurring water stress gradient. Mol Ecol. 2006, 15 (5): 1229-1237.PubMedView ArticleGoogle Scholar
  32. Brouillette LC, Mason CM, Shirk RY, Donovan LA: Adaptive differentiation of traits related to resource use in a desert annual along a resource gradient. New Phytol. 2014, 201 (4): 1316-1327.PubMedView ArticleGoogle Scholar
  33. Müller T, Ensminger I, Schmid KJ: A catalogue of putative unique transcripts from Douglas-fir (Pseudotsuga menziesii) based on 454 transcriptome sequencing of genetically diverse, drought stressed seedlings. BMC Genomics. 2012, 13 (1): 673-PubMed CentralPubMedView ArticleGoogle Scholar
  34. Haseneyer G, Schmutzer T, Seidel M, Zhou R, Mascher M, Schon CC, Taudien S, Scholz U, Stein N, Mayer KF, Bauer E: From RNA-seq to large-scale genotyping - genomics resources for rye (Secale cereale L.). BMC Plant Biol. 2011, 11: 131-PubMed CentralPubMedView ArticleGoogle Scholar
  35. Cantu D, Pearce SP, Distelfeld A, Christiansen MW, Uauy C, Akhunov E, Fahima T, Dubcovsky J: Effect of the down-regulation of the high Grain Protein Content (GPC) genes on the wheat transcriptome during monocarpic senescence. BMC Genomics. 2011, 12: 492-PubMed CentralPubMedView ArticleGoogle Scholar
  36. Thiel J, Hollmann J, Rutten T, Weber H, Scholz U, Weschke W: 454 Transcriptome sequencing suggests a role for two-component signalling in cellularization and differentiation of barley endosperm transfer cells. Plos One. 2012, 7 (7): e41867-PubMed CentralPubMedView ArticleGoogle Scholar
  37. Matsumoto T, Tanaka T, Sakai H, Amano N, Kanamori H, Kurita K, Kikuta A, Kamiya K, Yamamoto M, Ikawa H, Fujii N, Hori K, Itoh T, Sato K: Comprehensive sequence analysis of 24,783 barley full-length cDNAs derived from 12 clone libraries. Plant Physiol. 2011, 156 (1): 20-28.PubMed CentralPubMedView ArticleGoogle Scholar
  38. Sato K, Shin-I T, Seki M, Shinozaki K, Yoshida H, Takeda K, Yamazaki Y, Conte M, Kohara Y: Development of 5006 full-length CDNAs in barley: a tool for accessing cereal genomics resources. DNA Res. 2009, 16 (2): 81-89.PubMed CentralPubMedView ArticleGoogle Scholar
  39. Azam S, Thakur V, Ruperao P, Shah T, Balaji J, Amindala B, Farmer AD, Studholme DJ, May GD, Edwards D, Jones JD, Varshney RK: Coverage-based consensus calling (CbCC) of short sequence reads and comparison of CbCC results to identify SNPs in chickpea (Cicer arietinum; Fabaceae), a crop species without a reference genome. Am J Bot. 2012, 99 (2): 186-192.PubMedView ArticleGoogle Scholar
  40. Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CN: RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2010, 26 (4): 493-500.PubMed CentralPubMedView ArticleGoogle Scholar
  41. Costa V, Angelini C, De Feis I, Ciccodicola A: Uncovering the complexity of transcriptomes with RNA-Seq. J Biomed Biotechnol. 2010, 2010: 853916-PubMed CentralPubMedView ArticleGoogle Scholar
  42. Ekblom R, Slate J, Horsburgh GJ, Birkhead T, Burke T: Comparison between Normalised and Unnormalised 454-Sequencing Libraries for Small-Scale RNA-Seq Studies. Comp Funct Genomics. 2012, 2012: 8-View ArticleGoogle Scholar
  43. Ferreira De Carvalho J, Poulain J, Da Silva C, Wincker P, Michon-Coudouel S, Dheilly A, Naquin D, Boutte J, Salmon A, Ainouche M: Transcriptome de novo assembly from next-generation sequencing and comparative analyses in the hexaploid salt marsh species Spartina maritima and Spartina alterniflora (Poaceae). Hered (Edinb). 2013, 110 (2): 181-193.View ArticleGoogle Scholar
  44. Blaxter M: Revealing the dark matter of the genome. Science. 2010, 330 (6012): 1758-1759.PubMedView ArticleGoogle Scholar
  45. Close TJ, Wanamaker SI, Caldo RA, Turner SM, Ashlock DA, Dickerson JA, Wing RA, Muehlbauer GJ, Kleinhofs A, Wise RP: A new resource for cereal genomics: 22 K barley GeneChip comes of age. Plant Physiol. 2004, 134 (3): 960-968.PubMed CentralPubMedView ArticleGoogle Scholar
  46. Morgante M, Brunner S, Pea G, Fengler K, Zuccolo A, Rafalski A: Gene duplication and exon shuffling by helitron-like transposons generate intraspecies diversity in maize. Nat Genet. 2005, 37 (9): 997-1002.PubMedView ArticleGoogle Scholar
  47. Wang Q, Dooner HK: Remarkable variation in maize genome structure inferred from haplotype diversity at the bz locus. Proc Natl Acad Sci U S A. 2006, 103 (47): 17644-17649.PubMed CentralPubMedView ArticleGoogle Scholar
  48. Morgante M, De Paoli E, Radovic S: Transposable elements and the plant pan-genomes. Curr Opin Plant Biol. 2007, 10 (2): 149-155.PubMedView ArticleGoogle Scholar
  49. Bartels D, Sunkar R: Drought and Salt Tolerance in Plants. Crit Rev Plant Sci. 2005, 24 (1): 23-58.View ArticleGoogle Scholar
  50. Chen GX, Komatsuda T, Ma JF, Nawrath C, Pourkheirandish M, Tagiri A, Hu YG, Sameri M, Li XR, Zhao X, Liu YB, Li C, Ma XY, Wang AD, Nair S, Wang N, Miyao A, Sakuma S, Yamaji N, Zheng XT, Nevo E: An ATP-binding cassette subfamily G full transporter is essential for the retention of leaf water in both wild barley and rice. Proc Natl Acad Sci U S A. 2011, 108 (30): 12354-12359.PubMed CentralPubMedView ArticleGoogle Scholar
  51. Schutze K, Harter K, Chaban C: Post-translational regulation of plant bZIP factors. Trends Plant Sci. 2008, 13 (5): 247-255.PubMedView ArticleGoogle Scholar
  52. Feller A, Machemer K, Braun EL, Grotewold E: Evolutionary and comparative analysis of MYB and bHLH plant transcription factors. Plant J. 2011, 66 (1): 94-116.PubMedView ArticleGoogle Scholar
  53. de Mezer M, Turska-Taraska A, Kaczmarek Z, Glowacka K, Swarcewicz B, Rorat T: Differential physiological and molecular response of barley genotypes to water deficit. Plant Physiol Biochem PPB / Soc Francaise Physiol Vegetale. 2014, 80C: 234-248.View ArticleGoogle Scholar
  54. Nielsen R, Paul JS, Albrechtsen A, Song YS: Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet. 2011, 12 (6): 443-451.PubMed CentralPubMedView ArticleGoogle Scholar
  55. Quinn EM, Cormican P, Kenny EM, Hill M, Anney R, Gill M, Corvin AP, Morris DW: Development of strategies for SNP detection in RNA-Seq data: application to lymphoblastoid cell lines and evaluation using 1000 genomes data. Plos One. 2013, 8 (3): e58815-PubMed CentralPubMedView ArticleGoogle Scholar
  56. Duran C, Appleby N, Vardy M, Imelfort M, Edwards D, Batley J: Single nucleotide polymorphism discovery in barley using autoSNPdb. Plant Biotechnol J. 2009, 7 (4): 326-333.PubMedView ArticleGoogle Scholar
  57. Zhu YY, Machleder EM, Chenchik A, Li R, Siebert PD: Reverse transcriptase template switching: A SMART (TM) approach for full-length cDNA library construction. Biotechniques. 2001, 30 (4): 892-897.PubMedGoogle Scholar
  58. Barnes WM: PCR amplification of up to 35-kb DNA with high fidelity and high yield from lambda bacteriophage templates. Proc Natl Acad Sci U S A. 1994, 91 (6): 2216-2220.PubMed CentralPubMedView ArticleGoogle Scholar
  59. Zhulidov PA, Bogdanova EA, Shcheglov AS, Vagner LL, Khaspekov GL, Kozhemyako VB, Matz MV, Meleshkevitch E, Moroz LL, Lukyanov SA, Shagin DA: Simple cDNA normalization using kamchatka crab duplex‒specific nuclease. Nucleic Acids Res. 2004, 32 (3): e37-PubMed CentralPubMedView ArticleGoogle Scholar
  60. Shagin DA, Rebrikov DV, Kozhemyako VB, Altshuler IM, Shcheglov AS, Zhulidov PA, Bogdanova EA, Staroverov DB, Rasskazov VA, Lukyanov S: A novel method for SNP detection using a New duplex-specific nuclease from crab hepatopancreas. Genome Res. 2002, 12 (12): 1935-1942.PubMed CentralPubMedView ArticleGoogle Scholar
  61. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen ZT, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W, Helgesen S, Ho CH, Irzyk GP, Jando SC, Alenquer MLI, Jarvie TP, Jirage KB, Kim JB, Knight JR, Lanza JR, Leamon JH, Lefkowitz SM, Lei M, Li J, et al: Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005, 437 (7057): 376-380.PubMed CentralPubMedGoogle Scholar
  62. SeqClean. ftp://occams.dfci.harvard.edu/pub/bio/tgi/software/,
  63. The UniVec database. ftp://ftp.ncbi.nih.gov/pub/UniVec/,
  64. Li WZ, Godzik A: Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006, 22 (13): 1658-1659.PubMedView ArticleGoogle Scholar
  65. Langmead B, Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012, 9 (4): 357-359.PubMed CentralPubMedView ArticleGoogle Scholar
  66. 454 Life Science: Genome Sequencer FLX System Software Manual, Version 2.3. 2009Google Scholar
  67. Edgar RC: Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010, 26 (19): 2460-2461.PubMedView ArticleGoogle Scholar
  68. Li H, Durbin R: Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010, 26 (5): 589-595.PubMed CentralPubMedView ArticleGoogle Scholar
  69. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Proc GPD: The sequence alignment/Map format and SAMtools. Bioinformatics. 2009, 25 (16): 2078-2079.PubMed CentralPubMedView ArticleGoogle Scholar
  70. Cingolani P, Platts A, le Wang L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM: A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012, 6 (2): 80-92.PubMed CentralPubMedView ArticleGoogle Scholar
  71. Auton A, McVean G: Recombination rate estimation in the presence of hotspots. Genome Res. 2007, 17 (8): 1219-1227.PubMed CentralPubMedView ArticleGoogle Scholar
  72. PERL scripts for RBH analysis. http://sysbio.harvard.edu/csb/resources/computational/scriptome/unix/Protocols/Sequences.html,
  73. Vogel JP, Garvin DF, Mockler TC, Schmutz J, Rokhsar D, Bevan MW, Barry K, Lucas S, Harmon-Smith M, Lail K, Tice H, Grimwood J, McKenzie N, Huo NX, Gu YQ, Lazo GR, Anderson OD, You FM, Luo MC, Dvorak J, Wright J, Febrer M, Idziak D, Hasterok R, Lindquist E, Wang M, Fox SE, Priest HD, Filichkin SA, Givan SA, et al: Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature. 2010, 463 (7282): 763-768.View ArticleGoogle Scholar
  74. Matsumoto T, Wu JZ, Kanamori H, Katayose Y, Fujisawa M, Namiki N, Mizuno H, Yamamoto K, Antonio BA, Baba T, Sakata K, Nagamura Y, Aoki H, Arikawa K, Arita K, Bito T, Chiden Y, Fujitsuka N, Fukunaka R, Hamada M, Harada C, Hayashi A, Hijishita S, Honda M, Hosokawa S, Ichikawa Y, Idonuma A, Iijima M, Ikeda M, Ikeno M, et al: The map-based sequence of the rice genome. Nature. 2005, 436 (7052): 793-800.View ArticleGoogle Scholar
  75. Paterson AH, Bowers JE, Bruggmann R, Dubchak I, Grimwood J, Gundlach H, Haberer G, Hellsten U, Mitros T, Poliakov A, Schmutz J, Spannagl M, Tang HB, Wang XY, Wicker T, Bharti AK, Chapman J, Feltus FA, Gowik U, Grigoriev IV, Lyons E, Maher CA, Martis M, Narechania A, Otillar RP, Penning BW, Salamov AA, Wang Y, Zhang LF, Carpita NC, et al: The Sorghum bicolor genome and the diversification of grasses. Nature. 2009, 457 (7229): 551-556.PubMedView ArticleGoogle Scholar
  76. Lawrence CJ, Schaeffer ML, Seigfried TE, Campbell DA, Harper LC: MaizeGDB’s new data types, resources and activities. Nucleic Acids Res. 2007, 35: D895-D900.PubMed CentralPubMedView ArticleGoogle Scholar
  77. Swarbreck D, Wilks C, Lamesch P, Berardini TZ, Garcia-Hernandez M, Foerster H, Li D, Meyer T, Muller R, Ploetz L, Radenbaugh A, Singh S, Swing V, Tissier C, Zhang P, Huala E: The Arabidopsis Information Resource (TAIR): gene structure and function annotation. Nucleic Acids Res. 2008, 36: D1009-D1014.PubMed CentralPubMedView ArticleGoogle Scholar
  78. Kurata N, Nonomura KI, Harushima Y: Rice genome organization: The centromere and genome interactions. Ann Bot. 2002, 90 (4): 427-435.PubMed CentralPubMedView ArticleGoogle Scholar
  79. Zhang H, Jin JP, Tang LA, Zhao Y, Gu XC, Gao G, Luo JC: PlantTFDB 2.0: update and improvement of the comprehensive plant transcription factor database. Nucleic Acids Res. 2011, 39: D1114-D1117.PubMed CentralPubMedView ArticleGoogle Scholar
  80. Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21 (18): 3674-3676.PubMedView ArticleGoogle Scholar
  81. Myhre S, Tveit H, Mollestad T, Laegreid A: Additional Gene Ontology structure for improved biological reasoning. Bioinformatics. 2006, 22 (16): 2020-2027.PubMedView ArticleGoogle Scholar
  82. Camon E, Magrane M, Barrell DVL, Dimmer E, Maslen J, Binns D, Harte N, Lopez R, Apweiler R: The gene ontology annotation (GOA) database: sharing knowledge in uniprot with gene ontology. Nucleic Acids Res. 2004, 32: D262-D266.PubMed CentralPubMedView ArticleGoogle Scholar
  83. Min XJ, Butler G, Storms R, Tsang A: OrfPredictor: predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res. 2005, 33 (Web Server issue): W677-W680.PubMed CentralPubMedView ArticleGoogle Scholar

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