Article

Nature 463, 763-768 (11 February 2010) | doi:10.1038/nature08747; Received 29 August 2009; Accepted 9 December 2009

Genome sequencing and analysis of the model grass Brachypodium distachyon

The International Brachypodium Initiative

  1. USDA-ARS Western Regional Research Center, Albany, California 94710, USA.
  2. USDA-ARS Plant Science Research Unit and University of Minnesota, St Paul, Minnesota 55108, USA.
  3. Oregon State University, Corvallis, Oregon 97331-4501, USA.
  4. HudsonAlpha Institute, Huntsville, Alabama 35806, USA.
  5. US DOE Joint Genome Institute, Walnut Creek, California 94598, USA.
  6. University of California Berkeley, Berkeley, California 94720, USA.
  7. John Innes Centre, Norwich NR4 7UJ, UK.
  8. University of California Davis, Davis, California 95616, USA.
  9. University of Silesia, 40-032 Katowice, Poland.
  10. Iowa State University, Ames, Iowa 50011, USA.
  11. Washington State University, Pullman, Washington 99163, USA.
  12. University of Florida, Gainsville, Florida 32611, USA.
  13. Rutgers University, Piscataway, New Jersey 08855-0759, USA.
  14. University of Massachusetts, Amherst, Massachusetts 01003-9292, USA.
  15. USDA-ARS Vegetable Crops Research Unit, Horticulture Department, University of Wisconsin, Madison, Wisconsin 53706, USA.
  16. Helmholtz Zentrum München, D-85764 Neuherberg, Germany.
  17. Technical University München, 80333 München, Germany.
  18. Cornell University, Ithaca, New York 14853, USA.
  19. Boyce Thompson Institute for Plant Research, Ithaca, New York 14853-1801, USA.
  20. University of Zurich, 8008 Zurich, Switzerland.
  21. MTT Agrifood Research and University of Helsinki, FIN-00014 Helsinki, Finland.
  22. Federal University of Pelotas, Pelotas, 96001-970, RS, Brazil.
  23. Michigan State University, East Lansing, Michigan 48824, USA.
  24. China Agricultural University, Beijing 10094, China.
  25. Purdue University, West Lafayette, Indiana 47907, USA.
  26. The University of Texas, Arlington, Arlington, Texas 76019, USA.
  27. Institut National de la Recherché Agronomique UMR 1095, 63100 Clermont-Ferrand, France.
  28. University of California San Diego, La Jolla, California 92093, USA.
  29. National Centre for Genome Resources, Santa Fe, New Mexico 87505, USA.
  30. University of Delaware, Newark, Delaware 19716, USA.
  31. Joint Bioenergy Institute, Emeryville, California 94720, USA.
  32. University of Copenhagen, Frederiksberg DK-1871, Denmark.
  33. USDA-ARS Appalachian Fruit Research Station, Kearneysville, West Virginia 25430, USA.
  34. VIB Department of Plant Systems Biology, VIB and Department of Plant Biotechnology and Genetics, Ghent University, Technologiepark 927, 9052 Gent, Belgium.
  35. Institut de Biologie Moléculaire des Plantes du CNRS, Strasbourg 67084, France.
  36. BioEnergy Science Center and Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6422, USA.
  37. University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.
  38. The Ohio State University, Columbus, Ohio 43210, USA.
  39. Institut Jean-Pierre Bourgin, UMR1318, Institut National de la Recherche Agronomique, 78026 Versailles cedex, France.
  40. Université de Picardie, Amiens 80039, France.
  41. Plant Gene Expression Center, University of California Berkeley, Albany, California 94710, USA.
  42. Illinois State University and DOE Great Lakes Bioenergy Research Center, Normal, Illinois 61790, USA.
  43. Sabanci University, Istanbul 34956, Turkey.
  44. Unité de Recherche en Génomique Végétale: URGV (INRA-CNRS-UEVE), Evry 91057, France.
  45. USDA-ARS/Donald Danforth Plant Science Center, St Louis, Missouri 63130, USA.
  46. Present address: The School of Plant Molecular Systems Biotechnology, Kyung Hee University, Yongin 446-701, Korea.
  47. A list of participants and their affiliations appears at the end of the paper.

Correspondence to: Correspondence and requests for materials should be addressed to J.P.V. (Email: john.vogel@ars.usda.gov) or D.F.G. (Email: david.garvin@ars.usda.gov) or T.C.M. (Email: tmockler@cgrb.oregonstate.edu) or M.W.B. (Email: michael.bevan@bbsrc.ac.uk).

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Three subfamilies of grasses, the Ehrhartoideae, Panicoideae and Pooideae, provide the bulk of human nutrition and are poised to become major sources of renewable energy. Here we describe the genome sequence of the wild grass Brachypodium distachyon (Brachypodium), which is, to our knowledge, the first member of the Pooideae subfamily to be sequenced. Comparison of the Brachypodium, rice and sorghum genomes shows a precise history of genome evolution across a broad diversity of the grasses, and establishes a template for analysis of the large genomes of economically important pooid grasses such as wheat. The high-quality genome sequence, coupled with ease of cultivation and transformation, small size and rapid life cycle, will help Brachypodium reach its potential as an important model system for developing new energy and food crops.

Grasses provide the bulk of human nutrition, and highly productive grasses are promising sources of sustainable energy1. The grass family (Poaceae) comprises over 600 genera and more than 10,000 species that dominate many ecological and agricultural systems2, 3. So far, genomic efforts have largely focused on two economically important grass subfamilies, the Ehrhartoideae (rice) and the Panicoideae (maize, sorghum, sugarcane and millets). The rice4 and sorghum5 genome sequences and a detailed physical map of maize6 showed extensive conservation of gene order5, 7 and both ancient and relatively recent polyploidization.

Most cool season cereal, forage and turf grasses belong to the Pooideae subfamily, which is also the largest grass subfamily. The genomes of many pooids are characterized by daunting size and complexity. For example, the bread wheat genome is approximately 17,000megabases (Mb) and contains three independent genomes8. This has prohibited genome-scale comparisons spanning the three most economically important grass subfamilies.

Brachypodium, a member of the Pooideae subfamily, is a wild annual grass endemic to the Mediterranean and Middle East9 that has promise as a model system. This has led to the development of highly efficient transformation10, 11, germplasm collections12, 13, 14, genetic markers14, a genetic linkage map15, bacterial artificial chromosome (BAC) libraries16, 17, physical maps18 (M.F., unpublished observations), mutant collections (http://brachypodium.pw.usda.gov, http://www.brachytag.org), microarrays and databases (http://www.brachybase.org, http://www.phytozome.net, http://www.modelcrop.org, http://mips.helmholtz-muenchen.de/plant/index.jsp) that are facilitating the use of Brachypodium by the research community. The genome sequence described here will allow Brachypodium to act as a powerful functional genomics resource for the grasses. It is also an important advance in grass structural genomics, permitting, for the first time, whole-genome comparisons between members of the three most economically important grass subfamilies.

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Genome sequence assembly and annotation

The diploid inbred line Bd21 (ref. 19) was sequenced using whole-genome shotgun sequencing (Supplementary Table 1). The ten largest scaffolds contained 99.6% of all sequenced nucleotides (Supplementary Table 2). Comparison of these ten scaffolds with a genetic map (Supplementary Fig. 1) detected two false joins and created a further seven joins to produce five pseudomolecules that spanned 272Mb (Supplementary Table 3), within the range measured by flow cytometry20, 21. The assembly was confirmed by cytogenetic analysis (Supplementary Fig. 2) and alignment with two physical maps and sequenced BACs (Supplementary Data). More than 98% of expressed sequence tags (ESTs) mapped to the sequence assembly, consistent with a near-complete genome (Supplementary Table 4 and Supplementary Fig. 3). Compared to other grasses, the Brachypodium genome is very compact, with retrotransposons concentrated at the centromeres and syntenic breakpoints (Fig. 1). DNA transposons and derivatives are broadly distributed and primarily associated with gene-rich regions.

Figure 1: Chromosomal distribution of the main Brachypodium genome features.
Figure 1 : Chromosomal distribution of the main Brachypodium genome features. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.com

The abundance and distribution of the following genome elements are shown: complete LTR retroelements (cLTRs); solo-LTRs (sLTRs); potentially autonomous DNA transposons that are not miniature inverted-repeat transposable elements (MITEs) (DNA-TEs); MITEs; gene exons (CDS); gene introns and satellite tandem arrays (STA). Graphs are from 0 to 100 per cent base-pair (%bp) coverage of the respective window. The heat map tracks have different ranges and different maximum (max) pseudocolour levels: STA (0–55, scaled to max 10)%bp; cLTRs (0–36, scaled to max 20)%bp; sLTRs (0–4) %bp; DNA-TEs (0–20)%bp; MITEs (0–22)%bp; CDS (exons) (0–22.3)%bp. The triangles identify syntenic breakpoints.

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We analysed small RNA populations from inflorescence tissues with deep Illumina sequencing, and mapped them onto the genome sequence (Fig. 2a, Supplementary Fig. 4 and Supplementary Table 5). Small RNA reads were most dense in regions of high repeat density, similar to the distribution reported in Arabidopsis22. We identified 413 and 198 21- and 24-nucleotide phased short interfering RNA (siRNA) loci, respectively. Using the same algorithm, the only phased loci identified in Arabidopsis were five of the eight trans-acting siRNA loci, and none was 24-nucelotide phased. The biological functions of these clusters of Brachypodium phased siRNAs, which account for a significant number of small RNAs that map outside repeat regions, are not known at present.

Figure 2: Transcript and gene identification and distribution among three grass subfamilies.
Figure 2 : Transcript and gene identification and distribution among three grass subfamilies. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.com

a, Genome-wide distribution of small RNA loci and transcripts in the Brachypodium genome. Brachypodium chromosomes (1–5) are shown at the top. Total small RNA reads (black lines) and total small RNA loci (red lines) are shown on the top panel. Histograms plot 21-nucleotide (nt) (blue) or 24-nucleotide (red) small RNA reads normalized for repeated matches to the genome. The phased loci histograms plot the position and phase-score of 21-nucleotide (blue) and 24-nucleotide (red) phased small RNA loci. Repeat-normalized RNA-seq read histograms plot the abundance of reads matching RNA transcripts (green), normalized for ambiguous matches to the genome. b, Transcript coverage over gene features. Perfect match 32-base oligonucleotide Illumina reads were mapped to the Brachypodium v1.0 annotation features using HashMatch (http://mocklerlab-tools.cgrb.oregonstate.edu/). Plots of Illumina coverage were calculated as the percentage of bases along the length of the sequence feature supported by Illumina reads for the indicated gene model features. The bottom and top of the box represent the 25th and 75th quartiles, respectively. The white line is the median and the red diamonds denote the mean. SJS, splice junction site. c, Venn diagram showing the distribution of shared gene families between representatives of Ehrhartoideae (rice RAP2), Panicoideae (sorghum v1.4) and Pooideae (Brachypodium v1.0, and Triticum aestivum and Hordeum vulgare TCs (transcript consensus)/EST sequences). Paralogous gene families were collapsed in these data sets.

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A total of 25,532 protein-coding gene loci was predicted in the v1.0 annotation (Supplementary Information and Supplementary Table 6). This is in the same range as rice (RAP2, 28,236)23 and sorghum (v1.4, 27,640)5, suggesting similar gene numbers across a broad diversity of grasses. Gene models were evaluated using ~10.2gigabases (Gb) of Illumina RNA-seq data (Supplementary Fig. 5)24. Overall, 92.7% of predicted coding sequences (CDS) were supported by Illumina data (Fig. 2b), demonstrating the high accuracy of the Brachypodium gene predictions. These gene models are available from several databases (such as http://www.brachybase.org, http://www.phytozome.net, http://www.modelcrop.org and http://mips.org).

Between 77 and 84% of gene families (defined according to Supplementary Fig. 6) are shared among the three grass subfamilies represented by Brachypodium, rice and sorghum, reflecting a relatively recent common origin (Fig. 2c). Grass-specific genes include transmembrane receptor protein kinases, glycosyltransferases, peroxidases and P450 proteins (Supplementary Table 7B). The Pooideae-specific gene set contains only 265 gene families (Supplementary Table 7C) comprising 811 genes (1,400 including singletons). Genes enriched in grasses were significantly more likely to be contained in tandem arrays than random genes, demonstrating a prominent role for tandem gene expansion in the evolution of grass-specific genes (Supplementary Fig. 7 and Supplementary Table 8).

To validate and improve the v1.0 gene models, we manually annotated 2,755 gene models from 97 diverse gene families (Supplementary Tables 9–11) relevant to bioenergy and food crop improvement. We annotated 866 genes involved in cell wall biosynthesis/modification and 948 transcription factors from 16 families25. Only 13% of the gene models required modification and very few pseudogenes were identified, demonstrating the accuracy of the v1.0 annotation. Phylogenetic trees for 62 gene families were constructed using genes from rice, Arabidopsis, sorghum and poplar. In nearly all cases, Brachypodium genes had a similar distribution to rice and sorghum, demonstrating that Brachypodium is suitably generic for grass functional genomics research (Supplementary Figs 8 and 9). Analysis of the predicted secretome identified substantial differences in the distribution of cell wall metabolism genes between dicots and grasses (Supplementary Tables 12, 13 and Supplementary Fig. 10), consistent with their different cell walls26. Signal peptide probability curves also suggested that start codons were accurately predicted (Supplementary Fig. 11).

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Maintaining a small grass genome size

Exhaustive analysis of transposable elements (Supplementary Information and Supplementary Table 14) showed retrotransposon sequences comprise 21.4% of the genome, compared to 26% in rice, 54% in sorghum, and more than 80% in wheat27. Thirteen retroelement sets were younger than 20,000 years, showing a recent activation compared to rice28 (Supplementary Fig. 12), and a further 53 retroelement sets were less than 0.1million years (Myr) old. A minimum of 17.4Mb has been lost by long terminal repeat (LTR)–LTR recombination, demonstrating that retroelement expansion is countered by removal through recombination. In contrast, retroelements persist for very long periods of time in the closely related Triticeae28.

DNA transposons comprise 4.77% of the Brachypodium genome, within the range found in other grass genomes5, 29. Transcriptome data and structural analysis suggest that many non-autonomous Mariner DTT and Harbinger elements recruit transposases from other families. Two CACTA DTC families (M and N) carried five non-element genes, and the Harbinger U family has amplified a NBS-LRR gene family (Supplementary Figs 13 and 14), adding it to the group of transposable elements implicated in gene mobility30, 31. Centromeric regions were characterized by low gene density, characteristic repeats and retroelement clusters (Supplementary Fig. 15). Other repeat classes are described in Supplementary Table 15. Conserved non-coding sequences are described in Supplementary Fig. 16.

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Whole-genome comparison of three diverse grass genomes

The evolutionary relationships between Brachypodium, sorghum, rice and wheat were assessed by measuring the mean synonymous substitution rates (Ks) of orthologous gene pairs (Supplementary Information, Supplementary Fig. 17 and Supplementary Table 16), from which divergence times of Brachypodium from wheat 32–39 Myr ago, rice 40–53Myr ago, and sorghum 45–60Myr ago (Fig. 3a) were estimated. The Ks of orthologous gene pairs in the intragenomic Brachypodium duplications (Fig. 3b) suggests duplication 56–72Myr ago, before the diversification of the grasses. This is consistent with previous evolutionary histories inferred from a small number of genes3, 32, 33, 34.

Figure 3: Brachypodium genome evolution and synteny between grass subfamilies.
Figure 3 : Brachypodium genome evolution and synteny between grass subfamilies. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.com

a, The distribution maxima of mean synonymous substitution rates (Ks) of Brachypodium, rice, sorghum and wheat orthologous gene pairs (Supplementary Table 16) were used to define the divergence times of these species and the age of interchromosomal duplications in Brachypodium. WGD, whole-genome duplication. The numbers refer to the predicted divergence times measured as Myr ago by the NG or ML methods. b, Diagram showing the six major interchromosomal Brachypodium duplications, defined by 723 paralogous relationships, as coloured bands linking the five chromosomes. c, Identification of chromosome relationships between the Brachypodium, rice and sorghum genomes. Orthologous relationships between the 25,532 protein-coding Brachypodium genes, 7,216 sorghum orthologues (12 syntenic blocks), and 8,533 rice orthologues (12 syntenic blocks) were defined. Sets of collinear orthologous relationships are represented by a coloured band according to each Brachypodium chromosome (blue, chromosome (chr.) 1; yellow, chr.2; violet, chr.3; red, chr.4; green, chr.5). The white region in each Brachypodium chromosome represents the centromeric region. d, Orthologous gene relationships between Brachypodium and barley and Ae. tauschii were identified using genetically mapped ESTs. 2,516 orthologous relationships defined 12 syntenic blocks. These are shown as coloured bands. e, Orthologous gene relationships between Brachypodium and hexaploid bread wheat defined by 5,003 ESTs mapped to wheat deletion bins. Each set of orthologous relationships is represented by a band that is evenly spread across each deletion interval on the wheat chromosomes.

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Paralogous relationships among Brachypodium chromosomes showed six major chromosomal duplications covering 92.1% of the genome (Fig. 3b), representing ancestral whole-genome duplication35. Using the rice and sorghum genome sequences, genetic maps of barley36 and Aegilops tauschii (the D genome donor of hexaploid wheat)37, and bin-mapped wheat ESTs38, 39, 21,045 orthologous relationships between Brachypodium, rice, sorghum and Triticeae were identified (Supplementary Information). These identified 59 blocks of collinear genes covering 99.2% of the Brachypodium genome (Fig. 3c–e). The orthologous relationships are consistent with an evolutionary model that shaped five Brachypodium chromosomes from a five-chromosome ancestral genome by a 12-chromosome intermediate involving seven major chromosome fusions39 (Supplementary Fig. 18). These collinear blocks of orthologous genes provide a robust and precise sequence framework for understanding grass genome evolution and aiding the assembly of sequences from other pooid grasses. We identified 14 major syntenic disruptions between Brachypodium and rice/sorghum that can be explained by nested insertions of entire chromosomes into centromeric regions (Fig. 4a, b)2, 37, 40. Similar nested insertions in sorghum37 and barley (Fig. 4c, d) were also identified. Centromeric repeats and peaks in retroelements at the junctions of chromosome insertions are footprints of these insertion events (Supplementary Fig. 15C and Fig. 1), as is higher gene density at the former distal regions of the inserted chromosomes (Fig. 1). Notably, the reduction in chromosome number in Brachypodium and wheat occurred independently because none of the chromosome fusions are shared by Brachypodium and the Triticeae37 (Supplementary Fig. 18).

Figure 4: A recurring pattern of nested chromosome fusions in grasses.
Figure 4 : A recurring pattern of nested chromosome fusions in grasses. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.com

a, The five Brachypodium chromosomes are coloured according to homology with rice chromosomes (Os1–Os12). Chromosomes descended from an ancestral chromosome (A4–A11) through whole-genome duplication are shown in shades of the same colour. Gene density is indicated as a red line above the chromosome maps. Major discontinuities in gene density identify syntenic breakpoints, which are marked by a diamond. White diamonds identify fusion points containing remnant centromeric repeats. b, A pattern of nested insertions of whole chromosomes into centromeric regions explains the observed syntenic break points. Bd5 has not undergone chromosome fusion. c, Examples of nested chromosome insertions in sorghum (Sb) chromosomes 1 and 2. d, Examples of nested chromosome insertions in barley (H chromosomes) inferred from genetic maps. Nested insertions were not identified in other chromosomes, possibly owing to the low resolution of genetic maps.

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Comparisons of evolutionary rates between Brachypodium, sorghum, rice and Ae. tauschii demonstrated a substantially higher rate of genome change in Ae. tauschii (Supplementary Table 17). This may be due to retroelement activity that increases syntenic disruptions, as proposed for chromosome 5S later41. Among seven relatively large gene families, four were highly syntenic and two (NBS-LRR and F-box) were almost never found in syntenic order when compared to rice and sorghum (Supplementary Table 18), consistent with the rapid diversification of the NBS-LRR and F-box gene families42.

The short arm of chromosome 5 (Bd5S) has a gene density roughly half of the rest of the genome, high LTR retrotransposon density, the youngest intact Gypsy elements and the lowest solo LTR density. Thus, unlike the rest of the Brachypodium genome, Bd5S is gaining retrotransposons by replication and losing fewer by recombination. Syntenic regions of rice (Os4S) and sorghum (Sb6S) demonstrate maintenance of this high repeat content for ~50–70Myr (Supplementary Fig. 19)43. Bd5S, Os4S and Sb6S also have the lowest proportion of collinear genes (Fig. 4a and Supplementary Fig. 19). We propose that the chromosome ancestral to Bd5S reached a tipping point in which high retrotransposon density had deleterious effects on genes.

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Discussion

As the first genome sequence of a pooid grass, the Brachypodium genome aids genome analysis and gene identification in the large and complex genomes of wheat and barley, two other pooid grasses that are among the world’s most important crops. The very high quality of the Brachypodium genome sequence, in combination with those from two other grass subfamilies, enabled reconstruction of chromosome evolution across a broad diversity of grasses. This analysis contributes to our understanding of grass diversification by explaining how the varying chromosome numbers found in the major grass subfamilies derive from an ancestral set of five chromosomes by nested insertions of whole chromosomes into centromeres. The relatively small genome of Brachypodium contains many active retroelement families, but recombination between these keeps genome expansion in check. The short arm of chromosome 5 deviates from the rest of the genome by exhibiting a trend towards genome expansion through increased retroelement numbers and disruption of gene order more typical of the larger genomes of closely related grasses.

Grass crop improvement for sustainable fuel44 and food45 production requires a substantial increase in research in species such as Miscanthus, switchgrass, wheat and cool season forage grasses. These considerations have led to the rapid adoption of Brachypodium as an experimental system for grass research. The similarities in gene content and gene family structure between Brachypodium, rice and sorghum support the value of Brachypodium as a functional genomics model for all grasses. The Brachypodium genome sequence analysis reported here is therefore an important advance towards securing sustainable supplies of food, feed and fuel from new generations of grass crops.

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Methods Summary

Genome sequencing and assembly

Sanger sequencing was used to generate paired-end reads from 3kb, 8kb, fosmid (35kb) and BAC (100kb) clones to generate 9.4× coverage (Supplementary Table 1). The final assembly of 83 scaffolds covers 271.9Mb (Supplementary Table 3). Sequence scaffolds were aligned to a genetic map to create pseudomolecules covering each chromosome (Supplementary Figs 1 and 2).

Protein-coding gene annotation

Gene models were derived from weighted consensus prediction from several ab initio gene finders, optimal spliced alignments of ESTs and transcript assemblies, and protein homology. Illumina transcriptome sequence was aligned to predicted genome features to validate exons, splice sites and alternatively spliced transcripts.

Repeats analysis

The MIPS ANGELA pipeline was used to integrate analyses from expert groups. LTR-STRUCT and LTR-HARVEST46 were used for de novo retroelement searches.

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Supplementary Information

Supplementary information accompanies this paper.

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Acknowledgements

We acknowledge the contributions of the late M. Gale, who identified the importance of conserved gene order in grass genomes. This work was mainly supported by the US Department of Energy Joint Genome Institute Community Sequencing Program project with J.P.V., D.F.G., T.C.M. and M.W.B., a BBSRC grant to M.W.B., an EU Contract Agronomics grant to M.W.B. and K.F.X.M., and GABI Barlex grant to K.F.X.M. Illumina transcriptome sequencing was supported by a DOE Plant Feedstock Genomics for Bioenergy grant and an Oregon State Agricultural Research Foundation grant to T.C.M.; small RNA research was supported by the DOE Plant Feedstock Genomics for Bioenergy grants to P.J.G. and T.C.M.; annotation was supported by a DOE Plant Feedstocks for Genomics Bioenergy grant to J.P.V. A full list of support and acknowledgements is in the Supplementary Information.

Author Contributions See list of consortium authors below.

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Author Information

The whole-genome shotgun sequence of Brachypodium distachyon has been deposited at DDBJ/EMBL/GenBank under the accession ADDN00000000. (The version described in this manuscript is the first version, accession ADDN01000000). EST sequences have been deposited with dbEST (accessions 67946317–68053959) and GenBank (accessions GT758162–GT865804). The short read archive accession for RNA-seq data is SRA010177.

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The International Brachypodium Initiative

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Principal investigators

John P. Vogel1, David F. Garvin2, Todd C. Mockler3, Jeremy Schmutz4, Dan Rokhsar5,6 & Michael W. Bevan7
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DNA sequencing and assembly

Kerrie Barry5, Susan Lucas5, Miranda Harmon-Smith5, Kathleen Lail5, Hope Tice5, Jeremy Schmutz (Leader)4, Jane Grimwood4, Neil McKenzie7 & Michael W. Bevan7
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Pseudomolecule assembly and BAC end sequencing

Naxin Huo1, Yong Q. Gu1, Gerard R. Lazo1, Olin D. Anderson1, John P. Vogel (Leader)1, Frank M. You8, Ming-Cheng Luo8, Jan Dvorak8, Jonathan Wright7, Melanie Febrer7, Michael W. Bevan7, Dominika Idziak9, Robert Hasterok9 & David F. Garvin2
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Transcriptome sequencing and analysis

Erika Lindquist5, Mei Wang5, Samuel E. Fox3, Henry D. Priest3, Sergei A. Filichkin3, Scott A. Givan3, Douglas W. Bryant3, Jeff H. Chang3, Todd C. Mockler (Leader)3, Haiyan Wu10,24, Wei Wu10, An-Ping Hsia10, Patrick S. Schnable10,24, Anantharaman Kalyanaraman11, Brad Barbazuk12, Todd P. Michael13, Samuel P. Hazen14, Jennifer N. Bragg1, Debbie Laudencia-Chingcuanco1, John P. Vogel1, David F. Garvin2, Yiqun Weng15, Neil McKenzie7 & Michael W. Bevan7
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Gene analysis and annotation

Georg Haberer16, Manuel Spannagl16, Klaus Mayer (Leader)16, Thomas Rattei17, Therese Mitros6, Dan Rokhsar6, Sang-Jik Lee18, Jocelyn K. C. Rose18, Lukas A. Mueller19 & Thomas L. York19
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Repeats analysis

Thomas Wicker (Leader)20, Jan P. Buchmann20, Jaakko Tanskanen21, Alan H. Schulman (Leader)21, Heidrun Gundlach16, Jonathan Wright7, Michael Bevan7, Antonio Costa de Oliveira22, Luciano da C. Maia22, William Belknap1, Yong Q. Gu1, Ning Jiang23, Jinsheng Lai24, Liucun Zhu25, Jianxin Ma25, Cheng Sun26 & Ellen Pritham26
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Comparative genomics

Jerome Salse (Leader)27, Florent Murat27, Michael Abrouk27, Georg Haberer16, Manuel Spannagl16, Klaus Mayer16, Remy Bruggmann13, Joachim Messing13, Frank M. You8, Ming-Cheng Luo8 & Jan Dvorak8
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Small RNA analysis

Noah Fahlgren3, Samuel E. Fox3, Christopher M. Sullivan3, Todd C. Mockler3, James C. Carrington3, Elisabeth J. Chapman3,28, Greg D. May29, Jixian Zhai30, Matthias Ganssmann30, Sai Guna Ranjan Gurazada30, Marcelo German30, Blake C. Meyers30 & Pamela J. Green (Leader)30
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Manual annotation and gene family analysis

Jennifer N. Bragg1, Ludmila Tyler1,6, Jiajie Wu1,8, Yong Q. Gu1, Gerard R. Lazo1, Debbie Laudencia-Chingcuanco1, James Thomson1, John P. Vogel (Leader)1, Samuel P. Hazen14, Shan Chen14, Henrik V. Scheller31, Jesper Harholt32, Peter Ulvskov32, Samuel E. Fox3, Sergei A. Filichkin3, Noah Fahlgren3, Jeffrey A. Kimbrel3, Jeff H. Chang3, Christopher M. Sullivan3, Elisabeth J. Chapman3,27, James C. Carrington3, Todd C. Mockler3, Laura E. Bartley8,31, Peijian Cao8,31, Ki-Hong Jung8,31,46, Manoj K Sharma8,31, Miguel Vega-Sanchez8,31, Pamela Ronald8,31, Christopher D. Dardick33, Stefanie De Bodt34, Wim Verelst34, Dirk Inzé34, Maren Heese35, Arp Schnittger35, Xiaohan Yang36, Udaya C. Kalluri36, Gerald A. Tuskan36, Zhihua Hua37, Richard D. Vierstra37, David F. Garvin3, Yu Cui24, Shuhong Ouyang24, Qixin Sun24, Zhiyong Liu24, Alper Yilmaz38, Erich Grotewold38, Richard Sibout39, Kian Hematy39, Gregory Mouille39, Herman Höfte39, Todd Michael13, Jérome Pelloux40, Devin O’Connor41, James Schnable41, Scott Rowe41, Frank Harmon41, Cynthia L. Cass42, John C. Sedbrook42, Mary E. Byrne7, Sean Walsh7, Janet Higgins7, Michael Bevan7, Pinghua Li19, Thomas Brutnell19, Turgay Unver43, Hikmet Budak43, Harry Belcram44, Mathieu Charles44, Boulos Chalhoub44 & Ivan Baxter45

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