- Open Access
Mapping-by-sequencing accelerates forward genetics in barley
- Martin Mascher†1,
- Matthias Jost†1,
- Joel-Elias Kuon1,
- Axel Himmelbach1,
- Axel Aßfalg1,
- Sebastian Beier1,
- Uwe Scholz1,
- Andreas Graner1 and
- Nils Stein1Email author
© Mascher et al.; licensee BioMed Central Ltd. 2014
- Received: 21 February 2014
- Accepted: 10 June 2014
- Published: 10 June 2014
Mapping-by-sequencing has emerged as a powerful technique for genetic mapping in several plant and animal species. As this resequencing-based method requires a reference genome, its application to complex plant genomes with incomplete and fragmented sequence resources remains challenging. We perform exome sequencing of phenotypic bulks of a mapping population of barley segregating for a mutant phenotype that increases the rate of leaf initiation. Read depth analysis identifies a candidate gene, which is confirmed by the analysis of independent mutant alleles. Our method illustrates how the genomic resources of barley together with exome resequencing can underpin mapping-by-sequencing.
- Exome Sequencing
- Target Interval
- Exome Capture
- Wildtype Plant
- Mutant Pool
The recent profound transformation of molecular biology by next-generation sequencing (NGS) technologies  and the ready availability of reference genome sequences  has enriched the plant geneticist’s toolbox with what Schneeberger and Weigel named ‘fast-forward genetics’ . Combining classical bulked-segregant analysis  with aligning NGS read data to a reference genome has made gene cloning essentially a single-step computational procedure once a mapping population has been established . Within a few days’ time, mapping intervals can be delineated in silico and mined for likely candidate genes, deprecating marker saturation, and physical mapping of the target interval. Since its original implementation as ShoreMap in an F2 population of Arabidopsis thaliana, mapping-by-sequencing has been extended to other population types such as isogenic backcross populations [6, 7] as well as to other plant and animal species such as rice , maize , mouse, and zebrafish .
All successful attempts at mapping-by-sequencing in these species could take advantage of high-quality map-based reference sequences. A reference genome embeds almost all genes of a species in a genomic context, a crucial prerequisite for mapping-by-sequencing, as sequencing of phenotypic bulks provides only allele frequencies at variant positions, but no genotypic data that could be used to construct a genetic map de novo to infer marker order. How this order can be derived in the absence of a reference genome and how rapid NGS-based gene isolation may be implemented in species for which only draft genome assemblies are available is not obvious. Galvao et al. have proposed the collinear gene order in related species as a proxy for gene order in species without a reference genomes, but have also noted that this synteny-based approach may adversely affect mapping resolution. A novel bioinformatical procedure to find causal mutations by whole genome sequencing without using positional information has been applied to find causal variants in plant species with small genomes .
In addition to its importance for agriculture, barley (Hordeum vulgare L.) has been a model organism of genetics throughout the 20th century and boasts excellent resources for forward genetics. A large number of barley mutants had been created from the 1940s to the 1970s when mutation breeding programs flourished [13–16]. These mutant lines have been classified phenotypically and are nowadays maintained and distributed by seed banks. To further support the utilization of these resources in research and breeding, 881 original mutants have been backcrossed to cultivar (cv.) Bowman as a recurrent parent to obtain mutant alleles in a nearly isogenic background. Array-based genotyping of these introgression lines confirmed and broadly delimited introgression intervals . This legacy of half a century of meticulous research has been recently complemented by several mutant populations [18, 19] that were obtained in a systemic way via mutagenesis with ethyl methanesulfonate (EMS) to empower reverse genetics.
In this regard, the mutants of barley have been instrumental in confirming candidate genes discovered through mapping in bi-parental populations  or association panels . However, the full exploitation of the allelic diversity captured in these resources for basic research and crop improvement has been impeded by the lack of a reference genome sequence of barley. The major obstacles in assembling the barley genome are its sheer size (5 Gb) and its high content of repetitive DNA (80%), which pose a heavy sequencing load and put a challenge for current assembly algorithms . Boosted by the enormous increase in sequencing throughput, extensive sequence datasets have accumulated recently and have been integrated with a genome-wide physical map and high-density genetic maps . A large fraction of low-copy portion of the barley genome is now represented by contigs of a whole-genome shotgun assembly which are positioned with a resolution of approximately 3 cM . Moreover, an exome capture assay designed on the basis of the annotated sequence assembly has made approximately 60 Mb of mRNA-coding sequence accessible to cost-efficient high-throughput resequencing .
To date, the complex sequence framework of barley has not been used as a backbone for mapping-by-sequencing. Though the hopes are high, concerns remain that the fragmentary and incompletely ordered structure of the sequence assembly and the only partial representation of the gene complement may stall fast-forward genetics. Leveraging the physically and genetically anchored sequence assembly, exome sequencing and the extensive mutant collections available to the barley research community, we put mapping-by-sequencing to the test in barley and were able to rapidly identify a gene underlying the many-noded dwarf (mnd) phenotype.
Allele frequency mapping
Read depth analysis identifies a likely candidate gene
Deleted target regions within the genetic target interval (5H, 80 cM - 110 cM)
Morex WGS contig
HC confidence genes
Genetic position (POPSEQ)
5H, 96.6 cM
5H, 96.6 cM
5H, 99.9 cM
5H, 99.9 cM
MATE efflux family
5H, 99.9 cM
Tumor susceptibility 101 protein
5H, 107.1 cM
3’-5’ exoribonuclease CSL4
5H, 108.1 cM
DNA repair protein-like
5H, 109.4 cM
Mutant analysis confirms MLOC_64838.2 as HvMND
Sequence variation of MND in nearly isogenic lines of cv. Bowman  described as mnd
MND is a member of the CYP78A subfamily of cytochrome P450 enzymes
A physical map of the mndlocus
There may be concerns as to the general applicability of our strategy to other map-based cloning projects. The isolation of MND was facilitated by the facts that its homolog PLA1 in the model species rice is well characterized and that the phenotype of PLA1 knockout mutants mirrors mnd. If, moreover, MND had not been represented in the exome capture target space, no obvious candidate could have been pinpointed. In this case, the distribution of allele frequency confirmed by genetic mapping of markers developed from in silico variants would have only delimited a target interval to be subjected to further scrutiny. As was proposed earlier, the genome-wide physical map of barley should principally obviate the need of constructing local physical maps by map-based cloning to delimit candidate genes . BAC survey sequence data associated with the physical map of barley  can be used to associate marker sequences or candidate genes with physical contigs, whose minimum tiling paths  can then be sequenced. Thus it was our intention to test whether the information provided by the bulked-segregant sequencing experiment was sufficient to select a physical contig of the genome-wide physical map for delimitation of the target locus region and identification of a candidate gene.
Next, we assembled the MTPs of these three physical contigs (Figure 6a) by sequencing 38 BACs (Figure 6b; Additional file 7: Table S5) on the Illumina HiSeq2000. Single BACs were assembled to ‘phase-1’ quality, that is, unordered contig sequences. All-against-all BLAST searches of BAC assemblies confirmed the contiguity of contigs 46058 and 45097 as well as the overlap between them. Contig_1020 did not overlap with either of them. Markers M4 and M5 were located on a contiguous sequence scaffold, which enabled to us to estimate an approximate ratio between physical and genetic distance at the MND locus of approximately 740 kb per cM.
In the following step, gene models (Figure 6c) were predicted on repeat masked BAC assemblies by using an ab initio method and through alignment of gene models defined on the Morex WGS assembly . Overall, 98 non-redundant gene models were defined on the BAC sequences. Twenty-five genes were found by both methods, 35 were only predicted ab initio and likely represent pseudogenes. Thirty-eight genes were included only in the IBSC annotation, the majority (23 genes) of them classified as low confidence transcripts, which are also putative pseudogenes or gene fragments. Gene order was largely collinear to Brachypodium with some minor rearrangements (Figure 6c). Synteny enabled us to orient contig_1020 relative to the other two contigs.
Additional fingerprinted (FP) BAC contigs at the mnd locus
5H, 97 cM
5H, 96 cM
BAC end sequenceb
Morex WGS contigs
Barley high-confidence gene
Orthologous Brachypodium gene
In summary, at the genetic resolution provided by 100 F2 plants, we were not able to obtain in one step a single physical sequence scaffold of overlapping BAC clones from the MND locus between the two closest flanking markers. However, the remaining gap may be closed by sequencing the MTP of the two additional FP contigs identified based on conserved synteny information to Brachypodium. Furthermore, increasing the genetic resolution significantly to several thousand meioses, as often required in barley, may allow to resolve recombinations between marker M4 and the MND gene, which would result in landing with flanking markers on a single BAC contig scaffold provided by the physical map of barley. Thus, in spite of the advanced genomic resources that are now available for barley, an iterative process involving more than one round of MTP sequencing and overlap analysis may still be required to obtain a contiguous physical map of a candidate locus.
We have implemented mapping-by-sequencing in barley. Through sequencing two small phenotypic bulks from an F2 mapping population of 100 individuals segregating for the mnd phenotype, we were able to identify in a single sequencing experiment the deletion of a cytochrome P450 gene of the CYP78A subfamily as a likely candidate for the causal mutation. Resequencing of this candidate in other mnd mutants from several independent sources revealed a partial as well as complete deletion alleles, truncated protein products, splice site mutations and single amino acid substitutions, in summary confirming our candidate as the MND gene.
Previous mapping-by-sequencing experiments have mainly targeted EMS mutants. In rice, mapping-by-sequencing has been combined with local de novo assembly to clone a resistance gene missing from the reference genome, that is, the mutant harbored an insertion relative to the reference . Our results show that mapping-by-sequencing can also easily be adapted to deletion mutants obtained by X-ray or fast neutron mutagenesis, the major adjustment to the analysis procedure being the inspection of read depth instead of SNP effects on coding sequence. As we mined our sequence data, we prioritized large (≥150 bp) deletions. It may be necessary to relax this criterion as the spectrum of radiation-induced mutations also includes deletions of various sizes and even single base substitutions . Of note, we could make use of an existing WGS assembly of one parent of our mapping population . Otherwise, we would have used the assembly of cv. Morex as a reference for read mapping and sequenced one parent to determine its genomic background relative to Morex, similar to the procedure described in . In the present study, we genotyped the individuals of our mapping population using single-marker assays developed from SNP detected in the exome sequencing data. Although these data confirmed and refined the target interval determined through mapping-by-sequencing, additional genotypic data of a mapping population are in general not necessary supplements to a mapping-by-sequencing experiment. In the present study, even a broadly defined interval of 30 cM (5H, 80 to 110 cM) harbored only six deleted capture targets overlapping with high-confidence genes. Completely forgoing genetic mapping, however, for instance by only comparing read depth in sequencing for one mutant and one wildtype individuals, does not seem advisable as it would be challenging to prioritize candidates without any additional positional information.
A simulation study  has recently highlighted pool size, sequencing depth, and recombination frequency as key determinants of mapping resolution in mapping-by-sequencing experiments. As we targeted a deletion mutant located in a highly recombinogenic subtelomeric region, even a small pool of mutant plants selected from a population of 100 plants, delimited a mapping interval small enough to clearly prioritize a single deleted region. By contrast, genes located in the genetic centromeres of barley chromosomes, where meiotic recombination is severely suppressed, are notoriously difficult to clone [34–36] and further research should investigate whether sequencing-based methods can make the rarely recombining regions accessible to positional cloning.
Sequencing depth was difficult to control in our study, as we employed exome capture to reduce the genomic complexity of DNA samples prior to sequencing. For the time being, we consider complexity reduction as a necessary evil to perform cost-efficient resequencing experiments in the large genomes of barley (5 Gb) or related Triticeae such as wheat (17 Gb) and rye (7 Gb). For instance, sequencing both pools to 20× whole genome coverage would have required six lanes of a Hiseq2000, while we used only one for exome sequencing. As the capture target comprises only approximately 60 Mb of the barley gene space and has been estimated to capture approximately 75% of the sequence of high-confidence exons reliably , exome sequencing always incurs the risk of missing the target gene (or those parts of its sequence that contain the causal mutation). Even so, the analysis of allele frequency distribution in phenotypic bulks would always afford a sufficient number of markers to delineate genetically a target interval, which may then be analyzed in further details. If, for example, MND had not been in the capture space, we would still have been able to identify BAC contigs with closely flanking and co-segregating markers. Increasing the size of the mapping population may then have further reduced the target interval. We have not made further efforts to close gaps in the physical map between the two closest flanking markers, since the International Barley Genome Sequencing Consortium is currently sequencing the MTP of all chromosomes, so respective sequence assemblies of all BAC contigs will become available in the near future.
Mapping-by-sequencing is robust enough to tolerate some experimental error, as even a single heterozygote in the mutant pool did not prevent us from detecting the deletion of HvMND. An alternative to pooled sequencing of phenotypic bulks, which confounds the identity of individual samples, is genotyping-by-sequencing (GBS) of an entire mapping population. GBS couples digestion with restriction enzymes to reduce the complexity of DNA samples with barcoded high-throughput sequencing for cost-effective multiplexed genome-wide genotyping [37, 38]. As GBS, in contrast to exome capture, produces only short sequence tags and no contiguous gene sequences, the causal polymorphism is likely to be missed. For instance, absence of GBS tags in genes is no evidence for a deletion, but may simply be caused by the absence of suitable restriction sites. Consequently, GBS would necessitate follow-up experiments before a candidate can be determined with any confidence. For instance, GBS may be supplemented with whole-genome or exome sequencing of the parents of the mapping population to obtain a variation database for the design of single marker assays for further fine-mapping, or the target interval delineated by GBS may be mined for candidate genes based on an educated guess assisted by the information provided by the annotated reference assembly. A better balance between complexity reduction and multiplexing might be achievable with barcoded exome capture of an entire mapping population or selected individuals of phenotypic bulks. However, the number of samples to be processed with a single commercial exome capture kit is currently limited to 24 due to technical restrictions. A possible solution could be to combine deep multiplexing protocols  with exome sequencing.
A recapitulatory word of caution may not be amiss at this point. The immediate success of a mapping-by-sequencing experiment, that is, pinpointing a candidate in a single step, can be hindered by many factors. Beyond an intrinsic dependence of genetic mapping on recombination rate and the degree of polymorphism between the parents of the mapping population, sequence-based methods are contingent on genomic resources. In barley, further complexity is added both by incomplete reference sequence information and incomplete resequencing data as a result of complexity reduction and we caution researchers adopting our strategy that they may not meet with success in as straightforward a manner as we did.
In the present study, the identification of a candidate for MND was facilitated by the previous characterization of a homolog in rice and the advantageous ratio between physical and genetic distance at the target locus (<1 Mb per cM). Nevertheless, we believe our result to be a showcase for what mapping-by-sequencing can achieve in the context of the current genomic framework of barley despite of its fragmentary structure. The contigs of the whole genome shotgun assembly serve, as far as read mapping is concerned, as effective surrogates for the pseudomolecules of a high-quality reference genome, because the low-copy portion of the barley gene space is reasonably well represented by them. Physical and genetic maps - occasionally assisted by synteny to the model grasses - localize these contigs with sufficient density and resolution to order the majority of sequence variants discovered through exome capture. The functional gene annotation - though mainly based on sequence similarity - is accurate enough to identify the correct gene family of MND.
MND and its rice homolog PLA1 are part of the CYP78A family of cytochrome P450 enzymes, which have been proposed to generate a novel mobile signaling compound involved in the regulation of organ size and cell proliferation of vegetative and reproductive tissue in plants . The reactions catalyzed by CYP78A genes and the regulatory pathways governing their activity are largely unknown . In vitro results indicated that CYP78A enzymes catalyze the hydroxylation of fatty acids [41, 42]. Members of the CYP78A family may act in the same physiological pathway as ALTERED MERISTEM PROGRAM 1 (AMP1), a glutamate carboxypeptidase, whose Arabidopsis mutants show pleiotropic phenotypes such as a shortened plastochron, aberrant meristem programs, and early flowering . A homolog of AMP1 in rice, PLASTOCHRON3, was also cloned as a plastochron mutant . Whereas both CYP78A and AMP1 mutants of Arabidopsis and rice also exhibit an altered seed size [45–47], we did not see any effect on seed size in mnd plants (data not shown).
Phylogenetic analyses have shown that CYP78A enzymes have evolved differently in the Poaceae relative to rice and maize and suggested that MND may have taken over the functions of a lost ortholog of rice PLA1 and Arabidopsis CYP78A7. This supports the hypothesis that several CYP78A enzymes act in the same physiological pathway and may catalyze similar biochemical reactions . Resolving the unknowns about the substrate(s) of CYP78A enzymes and their upstream regulators  seems an attractive research goal insomuch, as the potentially beneficial effects of these genes on important agricultural traits such as the size of seeds and fruits [47, 48], the balance between endosperm and embryo  and growth stature  might make them valuable breeding targets if adverse effects like increased tillering can be kept to a minimum.
In conclusion, we have demonstrated the feasibility of mapping-by-sequencing in barley by combining reduced representation sequencing, computational analyses contextualized by comprehensive genomic resources, and mining the extensive mutant collections of barley. Similar approaches may be adopted by other map-based cloning projects in barley and in related species with large genomes, if a comparable genomic infrastructure is available for them.
Plant material and phenotyping
The mnd mutant was obtained from the genebank of IPK Gatersleben (accession: MHOR474). This mutant had been induced by X-ray mutagenesis of barley cv. Saale . An F2 population was developed by crossing the mutant to cv. Barke. One hundred F2 plants were grown to full maturation under greenhouse conditions in 2012 (18°C / 16°C day / night temperature). Natural light as well as additional sodium lamps were used for illumination. Twenty F3 offspring plants of each F2 individual were grown in 2013 to corroborate phenotypic scores. One half of the F3 plants were grown in pots under greenhouse conditions, the other half were grown in a nursery under field-like conditions. Plants were visually phenotyped for the number of internodes, spike length (five spikes per plant), tiller number and plant height (height of the main tiller). Plants with more than five internodes at full maturity were classified as carriers of the mnd allele. Bowman nearly-isogenic lines described as mnd were obtained from the James Hutton Institute (Dundee, UK). Additionally, 37 accessions, phenotypically classified as mnd, were ordered from the Nordic gene bank (NordGen, Alnarp, Sweden) and cultivated under greenhouse conditions.
Preparation of genomic DNA
Plant material was harvested of young seedlings at three-leaf stage and DNA was extracted according to a modified cetyl-trimethylammonium bromide-based (CTAB) protocol of . Volumes of reagents were adjusted to 1.2 mL to accommodate a 96-well plate format.
DNA from 18 mutant and 30 wildtype plants was combined into two pools. Exome capture and sequencing was performed according to the protocol of .
Read mapping and allele frequency visualization
Reads (2 × 100 bp) of the mutant and wildtype pools were mapped against the whole-genome shotgun assembly of barley cv. Barke  with BWA  version 0.6.2 (commands ‘aln’ and ‘sampe’). Single-sample SNP calling was performed for each pool with SAMtools version 0.1.18 . Allele frequencies in both pools were calculated as the number of reads supporting the mutant allele divided by the number of reads at a SNP positions with a custom AWK script (Additional file 8: Text S1) and visualized along the integrated physical and genetic map of barley  using standard functions of the R statistical environment . For visualization, allele frequencies at SNP positions with at least 30-fold coverage in both pools were averaged in 1 cM bins. SNPs with allele frequencies ≥80% in both pools were not considered. Only bins with at least 30 SNPs were considered. The genetic positions of sequence contigs of cv. Barke were downloaded from MIPS PlantsDB [53, 54].
Read depth analysis
For coverage analysis, reads were mapped with BWA-MEM 0.7.4 against the WGS assembly of barley cv. Morex as gene models and exome capture targets are only defined on the Morex assembly [23, 25]. Read depth was calculated with ‘samtools depth’ . Regions longer than 150 bp that satisfied one of the following conditions were identified using custom AWK scripts and bedtools : (1) at least 5× average read depth in the wildtype pool and no read coverage in the mutant; (2) the ratio (coverage_mutant/ coverage_wildtype) was at least 4 and the coverage in the mutant pool was ≤2 and ≥5 in the wildtype pool. Condition (2) was chosen to tolerate a small proportion of mis-phenotyped wildtype plants in the mutant pool. The functional annotation of genes located on WGS contigs harboring such regions and the genetic positions of these contigs [23, 24] were inspected. Functional annotations were downloaded from . The POPSEQ positions of Morex WGS contigs were retrieved from . The longest putatively deleted region (349 bp) located on a gene-bearing contig (morex_contig_49382 with MLOC_64838.2 annotated as ‘Cytochrome P450’) was assigned to the long arm of chromosome 5H, approximately 95 to 96 cM in the iSelect map  and coincided with the peaks of contrasting SNP allele frequency. MLOC_64838.2 was selected as the primary candidate for further validation. Expression data for MND and other CYP78A genes in barley was retrieved from .
Marker development, marker analysis, and genetic mapping
SNPs derived from the exome-capture experiment were converted into CAPS markers (Additional file 3: Table S2) using SNP2CAPS software . Restriction digests were performed according to manufacturer guidelines on a thermocycler. DNA fragments were separated on a 1.5% agrarose gel for genotyping. JoinMap version 4.0 (Kyazma B.V., Wageningen, The Netherlands) with Kosambi mapping function was used to construct a linkage map based on genotyping and phenotypic data.
PCR amplification and Sanger sequencing
Polymerase chain reaction (PCR) was performed on GeneAmp PCR System 9700 (Applied Biosystems, Carlsbad, CA, USA). A standardized touch down (TD-) PCR profile was used for all PCR analyses containing two cycling steps: initial denaturation for 15 min at 95°C, followed by 10 cycles of denaturation at 95°C / 30 s; annealing at 60°C / 30 s (decreasing by 0.5°C per cycle) followed by extension at 72°C / 60 s); then 35 cycles denaturation at 95°C / 30 s, annealing at 55°C / 30 s, and extension at 72°C / 60 s followed by a final extension step at 72°C / 7 min. PCR products were resolved by agarose gel electrophoresis using 1.5% agarose gel (Invitrogen GmbH, Darmstadt, Germany) strength and 1×TBE buffer. A list of primers used to amplify neighboring genes of MND as inferred by synteny to B. distachyon is given in Additional file 3: Table S6.
PCR amplicons were purified with NucleoFast 96 ultra-filtration plates (MACHEREY-NAGEL GmbH & Co. KG, Düren, Germany) and sequenced using BigDye® Terminator v3.1 Ready Reaction Cycle Sequencing Kit (Applied Biosystems, Carlsbad, CA, USA) on the 3730 × l DNA Analyzer (Applied Biosystems, Carlsbad, CA, USA). Obtained sequence reads were analysis was done with ‘Sequencher 4’ software (Genecodes Corporation, USA).
Identification of mutant alleles
We screened a TILLING population of 10,279 EMS-treated plants of cv. Barke  to identify mutant alleles of HvMND. Two Primer combinations were used to amplify the full ORF (HvMND_EX1_F/R1 and HvMND_Ex2_F/R1; Additional file 3: Table S7) by using PCR with heteroduplex step as described in . PCR products were digested with dsDNA Cleavage Kit and analyzed using Mutation Discovery Kit and Gel - dsDNA reagent kit on the AdvanCETM FS96 system according to manufacturer’s guidelines (Advanced Analytical, IA, USA).
Three oligo combinations (HvMND_F/R1, HvMND_F/R2, HvMND_F/R3) spanning the ORF plus intron were used to resequence the gene in independent mnd accessions (Additional file 3: Table S7). Identified SNPs were confirmed by Sanger sequencing (see above). Functional characterization of SNPs was performed using PARSESNP software .
BAC sequencing, assembly, and sequence analysis
A BAC harboring MLOC_64838.2 (HVVMRXALLhB0080C03, FP_contig_45097) was identified by screening a custom re-arrayed BAC library representing all clones of the minimum-tiling path of the genome-wide physical map of barley  by amplifying a single gene fragment (HvMND_F/R4, see Additional file 3: Table S7). Contig_46058 was identified as harboring flanking markers based on sequence analysis using available BAC sequences . Thirty-eight BACs from these contigs were shotgun-sequenced on the Illumina HiSeq2000 and assembled with CLC assembly cell version 4.0.6 , or on the 454 platform and assembled with MIRA . In addition to MTP clones, we selected additional clones at the ends of FP contigs for sequencing to corroborate potential overlaps between BAC contigs. We also included six previously sequenced BACs  in the analysis (Additional file 7: Table S5). Overlap between BACs was detected by an all-against-all alignment with megablast  considering only BLAST hits longer than 2 kb and 99.5% sequence identity. BAC sequence contigs were subjected to k-mer-based repeat masking using the Kmasker pipeline . Structural gene annotation of repeat-masked contigs was performed with Augustus  using the maize model. Predicted protein sequences were functionally annotated with the AHRD pipeline  which parses the description of BLASTP hits against the TAIR , Uniprot/trEMBL, and Uniprot/SwissProt  databases. Genes annotated as unknown proteins or transposable elements were excluded from further analysis. Gene-bearing Morex WGS contigs were aligned against the BAC assembly with megablast  considering only hits longer than 500 bp and a minimum sequence identity of 99.5% to assign IBSC gene models  to BACs. Transcript sequences of Augustus models and IBSC genes were clustered with CAP3  to collapse gene models on overlapping BAC clones and to link ab initio models to genes in the IBSC annotation.
BLASTP searches  against databases of barley , A. thaliana, rice , maize, B. distachyon, Ae. tauschii, and T. urartu proteins were performed to identify CYP78A homologs of MND in these species. A phylogenetic tree was generated with MEGA5  following the protocol of . The evolutionary history was inferred by using the Maximum Likelihood method based on the JTT matrix-based model . The bootstrap consensus tree inferred from 1,000 replicates  was taken to represent the evolutionary history of the taxa analyzed. Branches corresponding to partitions reproduced in less than 50% bootstrap replicates were collapsed. Initial trees for the heuristic search were obtained by applying the Neighbor-Joining method to a matrix of pairwise distances estimated using a JTT model. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 1.5089)). The analysis involved 38 amino acid sequences. All positions with less than 80% site coverage were eliminated. That is, fewer than 20% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 411 positions in the final dataset.
Illumina exome sequencing data of two phenotypic pools and BAC sequencing raw data have been deposited at EMBL-ENA as accessions PRJEB5319 (exome capture) and PRJEB5363 (BACs). BAC assemblies are available from GenBank (for accession number see Additional file 7: Table S5). Sanger resequencing data is available at EMBL-ENA (accessions: HG965223 - HG965231).
We gratefully acknowledge the skillful technical assistance of Mary Ziems, Manuela Knauft, Jacqueline Pohl, Jelena Perovic, and Heike Ernst. We thank Doreen Stengel for sequence data submission. We greatly acknowledge Arnis Druka, James Hutton Institute, Dundee for providing seeds of the BW introgression lines and Nordic Genetic Resource Center, Alnarp, Sweden for proving seeds of the mnd accessions hosted at NordGen. Our research was supported by the German Federal Ministry of Research and Education (BMBF) in frame of the NuGGET project (grant #0315957A to NS und US).
- Huang X, Han B: Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol. 2013, 65: 531-551.PubMedView ArticleGoogle Scholar
- Michael TP, Jackson S: The first 50 plant genomes. Plant Genome. 2013, 6: doi:10.3835/plantgenome2013.03.0001inGoogle Scholar
- Schneeberger K, Weigel D: Fast-forward genetics enabled by new sequencing technologies. Trends Plant Sci. 2011, 16: 282-288. 10.1016/j.tplants.2011.02.006.PubMedView ArticleGoogle Scholar
- Michelmore RW, Paran I, Kesseli RV: Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proc Natl Acad Sci U S A. 1991, 88: 9828-9832. 10.1073/pnas.88.21.9828.PubMedPubMed CentralView ArticleGoogle Scholar
- Schneeberger K, Ossowski S, Lanz C, Juul T, Petersen AH, Nielsen KL, Jorgensen JE, Weigel D, Andersen SU: SHOREmap: simultaneous mapping and mutation identification by deep sequencing. Nat Methods. 2009, 6: 550-551. 10.1038/nmeth0809-550.PubMedView ArticleGoogle Scholar
- Lindner H, Raissig MT, Sailer C, Shimosato-Asano H, Bruggmann R, Grossniklaus U: SNP-Ratio Mapping (SRM): identifying lethal alleles and mutations in complex genetic backgrounds by next-generation sequencing. Genetics. 2012, 191: 1381-1386. 10.1534/genetics.112.141341.PubMedPubMed CentralView ArticleGoogle Scholar
- Hartwig B, James GV, Konrad K, Schneeberger K, Turck F: Fast isogenic mapping-by-sequencing of ethyl methanesulfonate-induced mutant bulks. Plant Physiol. 2012, 160: 591-600. 10.1104/pp.112.200311.PubMedPubMed CentralView ArticleGoogle Scholar
- Abe A, Kosugi S, Yoshida K, Natsume S, Takagi H, Kanzaki H, Matsumura H, Yoshida K, Mitsuoka C, Tamiru M, Innan H, Cano L, Kamoun S, Terauchi R: Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol. 2012, 30: 174-178. 10.1038/nbt.2095.PubMedView ArticleGoogle Scholar
- Liu S, Yeh CT, Tang HM, Nettleton D, Schnable PS: Gene mapping via bulked segregant RNA-Seq (BSR-Seq). PLoS One. 2012, 7: e36406-10.1371/journal.pone.0036406.PubMedPubMed CentralView ArticleGoogle Scholar
- Leshchiner I, Alexa K, Kelsey P, Adzhubei I, Austin-Tse CA, Cooney JD, Anderson H, King MJ, Stottmann RW, Garnaas MK, Ha S, Drummond IA, Paw B, North TE, Beier DR, Goessling W, Sunyaev SR: Mutation mapping and identification by whole-genome sequencing. Genome Res. 2012, 22: 1541-1548. 10.1101/gr.135541.111.PubMedPubMed CentralView ArticleGoogle Scholar
- Galvao VC, Nordstrom KJ, Lanz C, Sulz P, Mathieu J, Pose D, Schmid M, Weigel D, Schneeberger K: Synteny-based mapping-by-sequencing enabled by targeted enrichment. Plant J. 2012, 71: 517-526.PubMedGoogle Scholar
- Nordstrom KJ, Albani MC, James GV, Gutjahr C, Hartwig B, Turck F, Paszkowski U, Coupland G, Schneeberger K: Mutation identification by direct comparison of whole-genome sequencing data from mutant and wild-type individuals using k-mers. Nat Biotechnol. 2013, 31: 325-330. 10.1038/nbt.2515.PubMedView ArticleGoogle Scholar
- Scholz F, Lehmann CO: Die Gaterslebener Mutanten der Saatgerste in Beziehung zur Formenmannigfaltigkeit der Art Hordeum vulgare L. III. Die Kulturpflanze. 1961, 9: 230-272. 10.1007/BF02095754.View ArticleGoogle Scholar
- Ehrenberg L, Lundqvist U, Osterman S, Sparrman B: On the mutagenic action of alkanesulfonic esters in barley. Hereditas. 1966, 56: 277-305.View ArticleGoogle Scholar
- Gustafsson Å, Hagberg A, Lundqvist U, Persson G: A proposed system of symbols for the collection of barley mutants at Svalöv. Hereditas. 1969, 62: 409-414.View ArticleGoogle Scholar
- Gustafsson Å, Hagberg A, Persson G, Wiklund K: Induced mutations and barley improvement. Theor Appl Genet. 1971, 41: 239-248.PubMedView ArticleGoogle Scholar
- Druka A, Franckowiak J, Lundqvist U, Bonar N, Alexander J, Houston K, Radovic S, Shahinnia F, Vendramin V, Morgante M, Stein N, Waugh R: Genetic dissection of barley morphology and development. Plant Physiol. 2011, 155: 617-627. 10.1104/pp.110.166249.PubMedPubMed CentralView ArticleGoogle Scholar
- Gottwald S, Bauer P, Komatsuda T, Lundqvist U, Stein N: TILLING in the two-rowed barley cultivar ‘Barke’ reveals preferred sites of functional diversity in the gene HvHox1. BMC Res Notes. 2009, 2: 258-10.1186/1756-0500-2-258.PubMedPubMed CentralView ArticleGoogle Scholar
- Caldwell DG, McCallum N, Shaw P, Muehlbauer GJ, Marshall DF, Waugh R: A structured mutant population for forward and reverse genetics in Barley (Hordeum vulgare L.). Plant J. 2004, 40: 143-150. 10.1111/j.1365-313X.2004.02190.x.PubMedView ArticleGoogle Scholar
- Koppolu R, Anwar N, Sakuma S, Tagiri A, Lundqvist U, Pourkheirandish M, Rutten T, Seiler C, Himmelbach A, Ariyadasa R, Youssef HM, Stein N, Sreenivasulu N, Komatsuda T, Schnurbusch T: Six-rowed spike4 (Vrs4) controls spikelet determinacy and row-type in barley. Proc Natl Acad Sci U S A. 2013, 110: 13198-13203. 10.1073/pnas.1221950110.PubMedPubMed CentralView ArticleGoogle Scholar
- Comadran J, Kilian B, Russell J, Ramsay L, Stein N, Ganal M, Shaw P, Bayer M, Thomas W, Marshall D, Hedley P, Tondelli A, Pecchioni N, Francia E, Korzun V, Walther A, Waugh R: Natural variation in a homolog of Antirrhinum CENTRORADIALIS contributed to spring growth habit and environmental adaptation in cultivated barley. Nat Genet. 2012, 44: 1388-1392. 10.1038/ng.2447.PubMedView ArticleGoogle Scholar
- Schulte D, Close TJ, Graner A, Langridge P, Matsumoto T, Muehlbauer G, Sato K, Schulman AH, Waugh R, Wise RP, Stein N: The international barley sequencing consortium–at the threshold of efficient access to the barley genome. Plant Physiol. 2009, 149: 142-147. 10.1104/pp.108.128967.PubMedPubMed CentralView ArticleGoogle Scholar
- International Barley Genome Sequencing Consortium T: A physical, genetic and functional sequence assembly of the barley genome. Nature. 2012, 491: 711-716.Google Scholar
- Mascher M, Muehlbauer GJ, Rokhsar DS, Chapman J, Schmutz J, Barry K, Munoz-Amatriain M, Close TJ, Wise RP, Schulman AH, Himmelbach A, Mayer KFX, Scholz U, Poland JA, Stein N, Waugh R: Anchoring and ordering NGS contig assemblies by population sequencing (POPSEQ). Plant J. 2013, 76: 718-727. 10.1111/tpj.12319.PubMedPubMed CentralView ArticleGoogle Scholar
- 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: 494-505. 10.1111/tpj.12294.PubMedPubMed CentralView ArticleGoogle Scholar
- Nelson SL, Giver CR, Grosovsky AJ: Spectrum of X-ray-induced mutations in the human hprt gene. Carcinogenesis. 1994, 15: 495-502. 10.1093/carcin/15.3.495.PubMedView ArticleGoogle Scholar
- Miyoshi K, Ahn BO, Kawakatsu T, Ito Y, Itoh J, Nagato Y, Kurata N: PLASTOCHRON1, a timekeeper of leaf initiation in rice, encodes cytochrome P450. Proc Natl Acad Sci U S A. 2004, 101: 875-880. 10.1073/pnas.2636936100.PubMedPubMed CentralView ArticleGoogle Scholar
- Mayer KF, Martis M, Hedley PE, Simkova H, Liu H, Morris JA, Steuernagel B, Taudien S, Roessner S, Gundlach H, Kubaláková M, Suchánková P, Murat F, Felder M, Nussbaumer T, Graner A, Salse J, Endo T, Sakai H, Tanaka T, Itoh T, Sato K, Platzer M, Matsumoto T, Scholz U, Dolezel J, Waugh R, Stein N: Unlocking the barley genome by chromosomal and comparative genomics. Plant Cell. 2011, 23: 1249-1263. 10.1105/tpc.110.082537.PubMedPubMed CentralView ArticleGoogle Scholar
- Ariyadasa R, Mascher M, Nussbaumer T, Schulte D, Frenkel Z, Poursarebani N, Zhou R, Steuernagel B, Gundlach H, Taudien S, Felder M, Platzer M, Himmelbach A, Schmutzer T, Hedley PE, Muehlbauer GJ, Scholz U, Korol A, Mayer KF, Waugh R, Langridge P, Graner A, Stein N: A sequence-ready physical map of barley anchored genetically by two million SNPs. Plant Physiol. 2013, 164: 412-423.PubMedPubMed CentralView ArticleGoogle Scholar
- Takagi H, Uemura A, Yaegashi H, Tamiru M, Abe A, Mitsuoka C, Utsushi H, Natsume S, Kanzaki H, Matsumura H, Saitoh H, Yoshida K, Cano LM, Kamoun S, Terauchi R: MutMap-Gap: whole-genome resequencing of mutant F2 progeny bulk combined with de novo assembly of gap regions identifies the rice blast resistance gene Pii. New Phytol. 2013, 200: 276-283. 10.1111/nph.12369.PubMedView ArticleGoogle Scholar
- Masumura K, Kuniya K, Kurobe T, Fukuoka M, Yatagai F, Nohmi T: Heavy-ion-induced mutations in the gpt delta transgenic mouse: comparison of mutation spectra induced by heavy-ion, X-ray, and gamma-ray radiation. Environ Mol Mutagen. 2002, 40: 207-215. 10.1002/em.10108.PubMedView ArticleGoogle Scholar
- Laitinen RA, Schneeberger K, Jelly NS, Ossowski S, Weigel D: Identification of a spontaneous frame shift mutation in a nonreference Arabidopsis accession using whole genome sequencing. Plant Physiol. 2010, 153: 652-654. 10.1104/pp.110.156448.PubMedPubMed CentralView ArticleGoogle Scholar
- James GV, Patel V, Nordstrom KJ, Klasen JR, Salome PA, Weigel D, Schneeberger K: User guide for mapping-by-sequencing in Arabidopsis. Genome Biol. 2013, 14: R61-10.1186/gb-2013-14-6-r61.PubMedPubMed CentralView ArticleGoogle Scholar
- Okagaki RJ, Cho S, Kruger WM, Xu WW, Heinen S, Muehlbauer GJ: The barley UNICULM2 gene resides in a centromeric region and may be associated with signaling and stress responses. Funct Integr Genomics. 2013, 13: 33-41. 10.1007/s10142-012-0299-7.PubMedView ArticleGoogle Scholar
- Shahinnia F, Druka A, Franckowiak J, Morgante M, Waugh R, Stein N: High resolution mapping of Dense spike-ar (dsp.ar) to the genetic centromere of barley chromosome 7H. Theor Appl Genet. 2012, 124: 373-384. 10.1007/s00122-011-1712-7.PubMedView ArticleGoogle Scholar
- Acevedo-Garcia J, Collins NC, Ahmadinejad N, Ma L, Houben A, Bednarek P, Benjdia M, Freialdenhoven A, Altmuller J, Nurnberg P, Reinhardt R, Schulze-Lefert P, Panstruga R: Fine mapping and chromosome walking towards the Ror1 locus in barley (Hordeum vulgare L.). Theor Appl Genet. 2013, 126: 2969-2982. 10.1007/s00122-013-2186-6.PubMedView ArticleGoogle Scholar
- Poland JA, Brown PJ, Sorrells ME, Jannink JL: Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One. 2012, 7: e32253-10.1371/journal.pone.0032253.PubMedPubMed CentralView ArticleGoogle Scholar
- Mascher M, Wu S, Amand PS, Stein N, Poland J: Application of genotyping-by-sequencing on semiconductor sequencing platforms: a comparison of genetic and reference-based marker ordering in barley. PLoS One. 2013, 8: e76925-10.1371/journal.pone.0076925.PubMedPubMed CentralView ArticleGoogle Scholar
- Meyer M, Kircher M: Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb Protoc. 2010, 2010: pdb prot5448-10.1101/pdb.prot5448.PubMedView ArticleGoogle Scholar
- Mizutani M, Ohta D: Diversification of P450 genes during land plant evolution. Annu Rev Plant Biol. 2010, 61: 291-315. 10.1146/annurev-arplant-042809-112305.PubMedView ArticleGoogle Scholar
- Imaishi H, Matsuo S, Swai E, Ohkawa H: CYP78A1 preferentially expressed in developing inflorescences of Zea mays encoded a cytochrome P450-dependent lauric acid 12-monooxygenase. Biosci Biotechnol Biochem. 2000, 64: 1696-1701. 10.1271/bbb.64.1696.PubMedView ArticleGoogle Scholar
- Kai K, Hashidzume H, Yoshimura K, Suzuki H, Sakurai N, Shibata D, Ohta D: Metabolomics for the characterization of cytochromes P450-dependent fatty acid hydroxylation reactions in Arabidopsis. Plant Biotechnol. 2009, 26: 175-182. 10.5511/plantbiotechnology.26.175.View ArticleGoogle Scholar
- Helliwell CA, Chin-Atkins AN, Wilson IW, Chapple R, Dennis ES, Chaudhury A: The Arabidopsis AMP1 gene encodes a putative glutamate carboxypeptidase. Plant Cell. 2001, 13: 2115-2125. 10.1105/tpc.13.9.2115.PubMedPubMed CentralView ArticleGoogle Scholar
- Kawakatsu T, Taramino G, Itoh J, Allen J, Sato Y, Hong SK, Yule R, Nagasawa N, Kojima M, Kusaba M, Sakakibara H, Sakai H, Nagato Y: PLASTOCHRON3/GOLIATH encodes a glutamate carboxypeptidase required for proper development in rice. Plant J. 2009, 58: 1028-1040. 10.1111/j.1365-313X.2009.03841.x.PubMedView ArticleGoogle Scholar
- Nagasawa N, Hibara KI, Heppard EP, Vander Velden KA, Luck S, Beatty M, Nagato Y, Sakai H: GIANT EMBRYO encodes CYP78A13, required for proper size balance between embryo and endosperm in rice. Plant J. 2013, 75: 592-605. 10.1111/tpj.12223.PubMedView ArticleGoogle Scholar
- Fang WJ, Wang ZB, Cui RF, Li J, Li YH: Maternal control of seed size by EOD3/CYP78A6 in Arabidopsis thaliana. Plant J. 2012, 70: 929-939. 10.1111/j.1365-313X.2012.04907.x.PubMedView ArticleGoogle Scholar
- Adamski NM, Anastasiou E, Eriksson S, O’Neill CM, Lenhard M: Local maternal control of seed size by KLUH/CYP78A5-dependent growth signaling. Proc Nat Acad Sci U S A. 2009, 106: 20115-20120. 10.1073/pnas.0907024106.View ArticleGoogle Scholar
- Chakrabarti M, Zhang N, Sauvage C, Munos S, Blanca J, Canizares J, Diez MJ, Schneider R, Mazourek M, McClead J, Causse M, van der Knaap E: A cytochrome P450 regulates a domestication trait in cultivated tomato. Proc Natl Acad Sci U S A. 2013, 110: 17125-17130. 10.1073/pnas.1307313110.PubMedPubMed CentralView ArticleGoogle Scholar
- Doyle JJ: Isolation of plant DNA from fresh tissue. Focus. 1990, 12: 13-15.Google Scholar
- Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25: 1754-1760. 10.1093/bioinformatics/btp324.PubMedPubMed CentralView ArticleGoogle Scholar
- Li H: A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011, 27: 2987-2993. 10.1093/bioinformatics/btr509.PubMedPubMed CentralView ArticleGoogle Scholar
- The R project. [http://www.r-project.org]
- Nussbaumer T, Martis MM, Roessner SK, Pfeifer M, Bader KC, Sharma S, Gundlach H, Spannagl M: MIPS PlantsDB: a database framework for comparative plant genome research. Nucleic Acids Res. 2013, 41: D1144-D1151. 10.1093/nar/gks1153.PubMedPubMed CentralView ArticleGoogle Scholar
- Anchoring information of barley WGS assemblies. [ftp://ftpmips.helmholtz-muenchen.de/plants/barley/public_data/anchoring/wgs_anc/WGS_ANC.TXT]Google Scholar
- Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010, 26: 841-842. 10.1093/bioinformatics/btq033.PubMedPubMed CentralView ArticleGoogle Scholar
- GFF file of barley HC genes. [ftp://ftpmips.helmholtz-muenchen.de/plants/barley/public_data/genes/barley_HighConf_genes_MIPS_23Mar12_HumReadDesc.txt]Google Scholar
- Barley POPSEQ results. [ftp://ftp.ipk-gatersleben.de/barley-popseq/]Google Scholar
- Barley gene expression data. [ftp://ftpmips.helmholtz-muenchen.de/plants/barley/public_data/expression/]Google Scholar
- Thiel T, Kota R, Grosse I, Stein N, Graner A: SNP2CAPS: a SNP and INDEL analysis tool for CAPS marker development. Nucleic Acids Res. 2004, 32: e5-10.1093/nar/gnh006.PubMedPubMed CentralView ArticleGoogle Scholar
- Taylor NE, Greene EA: PARSESNP: A tool for the analysis of nucleotide polymorphisms. Nucleic Acids Res. 2003, 31: 3808-3811. 10.1093/nar/gkg574.PubMedPubMed CentralView ArticleGoogle Scholar
- CLC bio website. [http://www.clcbio.com]
- Chevreux B, Wetter T, Suhai S: Genome Sequence Assembly Using Trace Signals and Additional Sequence Information. Computer Science and Biology: Proceedings of the German Conference on Bioinformatics (GCB). 1999, 99: 45-56.Google Scholar
- Zhang Z, Schwartz S, Wagner L, Miller W: A greedy algorithm for aligning DNA sequences. J Comput Biol. 2000, 7: 203-214. 10.1089/10665270050081478.PubMedView ArticleGoogle Scholar
- Schmutzer T, Ma L, Pousarebani N, Bull F, Stein N, Houben A, Scholz U: Kmasker - A Tool for in silico Prediction of Single-Copy FISH Probes for the Large-Genome Species Hordeum vulgare. Cytogenet Genome Res. 2014, 142: 66-78. 10.1159/000356460.PubMedView ArticleGoogle Scholar
- Stanke M, Steinkamp R, Waack S, Morgenstern B: AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res. 2004, 32: W309-W312. 10.1093/nar/gkh379.PubMedPubMed CentralView ArticleGoogle Scholar
- Source code of AHRD. [https://github.com/groupschoof/AHRD]
- Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R, Muller R, Dreher K, Alexander DL, Garcia-Hernandez M, Karthikeyan AS, Lee CH, Nelson WD, Ploetz L, Singh S, Wensel A, Huala E: The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 2012, 40: D1202-D1210. 10.1093/nar/gkr1090.PubMedPubMed CentralView ArticleGoogle Scholar
- UniProt C: Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Res. 2013, 41: D43-D47.View ArticleGoogle Scholar
- Huang X, Madan A: CAP3: A DNA sequence assembly program. Genome Res. 1999, 9: 868-877. 10.1101/gr.9.9.868.PubMedPubMed CentralView ArticleGoogle Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215: 403-410. 10.1016/S0022-2836(05)80360-2.PubMedView ArticleGoogle Scholar
- Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA, 1000 Genomes Project Consortium: An integrated map of genetic variation from 1,092 human genomes. Nature. 2012, 491: 56-65. 10.1038/nature11632.PubMedView ArticleGoogle Scholar
- 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.PubMedPubMed CentralView ArticleGoogle Scholar
- Ouyang S, Zhu W, Hamilton J, Lin H, Campbell M, Childs K, Thibaud-Nissen F, Malek RL, Lee Y, Zheng L, Orvis J, Haas B, Wortman J, Buell CR: The TIGR Rice Genome Annotation Resource: improvements and new features. Nucleic Acids Res. 2007, 35: D883-D887. 10.1093/nar/gkl976.PubMedPubMed CentralView ArticleGoogle Scholar
- International Brachypodium Initiative T: Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature. 2010, 463: 763-768. 10.1038/nature08747.View ArticleGoogle Scholar
- Jia J, Zhao S, Kong X, Li Y, Zhao G, He W, Appels R, Pfeifer M, Tao Y, Zhang X, Jing R, Zhang C, Ma Y, Gao L, Gao C, Spannagl M, Mayer KF, Li D, Pan S, Zheng F, Hu Q, Xia X, Li J, Liang Q, Chen J, Wicker T, Gou C, Kuang H, He G, Luo Y, et al: Aegilops tauschii draft genome sequence reveals a gene repertoire for wheat adaptation. Nature. 2013, 496: 91-95. 10.1038/nature12028.PubMedView ArticleGoogle Scholar
- Ling HQ, Zhao S, Liu D, Wang J, Sun H, Zhang C, Fan H, Li D, Dong L, Tao Y, Gao C, Wu H, Li Y, Cui Y, Guo X, Zheng S, Wang B, Yu K, Liang Q, Yang W, Lou X, Chen J, Feng M, Jian J, Zhang X, Luo G, Jiang Y, Liu J, Wang Z, Sha Y, et al: Draft genome of the wheat A-genome progenitor Triticum urartu. Nature. 2013, 496: 87-90. 10.1038/nature11997.PubMedView ArticleGoogle Scholar
- Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011, 28: 2731-2739. 10.1093/molbev/msr121.PubMedPubMed CentralView ArticleGoogle Scholar
- Hall BG: Building phylogenetic trees from molecular data with MEGA. Mol Biol Evol. 2013, 30: 1229-1235. 10.1093/molbev/mst012.PubMedView ArticleGoogle Scholar
- Jones DT, Taylor WR, Thornton JM: The rapid generation of mutation data matrices from protein sequences. Comput Appl Biosci. 1992, 8: 275-282.PubMedGoogle Scholar
- Felsenstein J: Confidence-limits on phylogenies - an approach using the bootstrap. Evolution. 1985, 39: 783-791. 10.2307/2408678.View ArticleGoogle Scholar
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