Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the Agbiodata Consortium.

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as 'databases' throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL  https://www.agbiodata.org/databases.

Using Crop Databases to Explore Phenotypes: From QTL to Candidate Genes.

Seeds, especially those of certain grasses and legumes, provide the majority of the protein and carbohydrates for much of the world's population. Therefore, improvements in seed quality and yield are important drivers for the development of new crop varieties to feed a growing population. Quantitative Trait Loci (QTL) have been identified for many biologically interesting and agronomically important traits, including many seed quality traits. QTL can help explain the genetic architecture of the traits and can also be used to incorporate traits into new crop cultivars during breeding. Despite the important contributions that QTL have made to basic studies and plant breeding, knowing the exact gene(s) conditioning each QTL would greatly improve our ability to study the underlying genetics, biochemistry and regulatory networks. The data sets needed for identifying these genes are increasingly available and often housed in species- or clade-specific genetics and genomics databases. In this demonstration, we present a generalized walkthrough of how such databases can be used in these studies using SoyBase, the USDA soybean Genetics and Genomics Database, as an example.

Genetic variation among 481 diverse soybean accessions, inferred from genomic re-sequencing.

We report characteristics of soybean genetic diversity and structure from the resequencing of 481 diverse soybean accessions, comprising 52 wild (Glycine soja) selections and 429 cultivated (Glycine max) varieties (landraces and elites). This data was used to identify 7.8 million SNPs, to predict SNP effects relative to genic regions, and to identify the genetic structure, relationships, and linkage disequilibrium. We found evidence of distinct, mostly independent selection of lineages by particular geographic location. Among cultivated varieties, we identified numerous highly conserved regions, suggesting selection during domestication. Comparisons of these accessions against the whole U.S. germplasm genotyped with the SoySNP50K iSelect BeadChip revealed that over 95% of the re-sequenced accessions have a high similarity to their SoySNP50K counterparts. Probable errors in seed source or genotype tracking were also identified in approximately 5% of the accessions.

A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database.

SoyBase, a USDA genetic and genomics database, holds professionally curated soybean genetic and genomic data, which is integrated and made accessible to researchers and breeders. The site holds several reference genome assemblies, as well as genetic maps, thousands of mapped traits, expression and epigenetic data, pedigree information, and extensive variant and genotyping data sets. SoyBase displays include genetic, genomic, and epigenetic maps of the soybean genome. Gene expression data is presented in the genome viewer as heat maps and pictorial and tabular displays in gene report pages. Millions of sequence variants have been added, representing variations across various collections of cultivars. This variant data is explorable using new interactive tools to visualize the distribution of those variants across the genome, between selected accessions. SoyBase holds several reference-quality soybean genome assemblies, accessible via various query tools and browsers, including a new visualization system for exploring the soybean pan-genome. SoyBase also serves as a nexus of announcements pertinent to the greater soybean research community. The database also includes a soybean-specific anatomic and biochemical trait ontology. The database can be accessed at https://soybase.org.

Ten quick tips for sharing open genomic data.

As sequencing prices drop, genomic data accumulates-seemingly at a steadily increasing pace. Most genomic data potentially have value beyond the initial purpose-but only if shared with the scientific community. This, of course, is often easier said than done. Some of the challenges in sharing genomic data include data volume (raw file sizes and number of files), complexities, formats, nomenclatures, metadata descriptions, and the choice of a repository. In this paper, we describe 10 quick tips for sharing open genomic data.

AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture.

The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.

An ontology approach to comparative phenomics in plants.

BACKGROUND: Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and details tailored to investigators with different research objectives and backgrounds. Although phenotype comparisons across datasets have long been possible on a small scale, comprehensive queries and analyses that span a broad set of reference species, research disciplines, and knowledge domains continue to be severely limited by the absence of a common semantic framework. RESULTS: We developed a workflow to curate and standardize existing phenotype datasets for six plant species, encompassing both model species and crop plants with established genetic resources. Our effort focused on mutant phenotypes associated with genes of known sequence in Arabidopsis thaliana (L.) Heynh. (Arabidopsis), Zea mays L. subsp. mays (maize), Medicago truncatula Gaertn. (barrel medic or Medicago), Oryza sativa L. (rice), Glycine max (L.) Merr. (soybean), and Solanum lycopersicum L. (tomato). We applied the same ontologies, annotation standards, formats, and best practices across all six species, thereby ensuring that the shared dataset could be used for cross-species querying and semantic similarity analyses. Curated phenotypes were first converted into a common format using taxonomically broad ontologies such as the Plant Ontology, Gene Ontology, and Phenotype and Trait Ontology. We then compared ontology-based phenotypic descriptions with an existing classification system for plant phenotypes and evaluated our semantic similarity dataset for its ability to enhance predictions of gene families, protein functions, and shared metabolic pathways that underlie informative plant phenotypes. CONCLUSIONS: The use of ontologies, annotation standards, shared formats, and best practices for cross-taxon phenotype data analyses represents a novel approach to plant phenomics that enhances the utility of model genetic organisms and can be readily applied to species with fewer genetic resources and less well-characterized genomes. In addition, these tools should enhance future efforts to explore the relationships among phenotypic similarity, gene function, and sequence similarity in plants, and to make genotype-to-phenotype predictions relevant to plant biology, crop improvement, and potentially even human health.

Transcriptome analyses and virus induced gene silencing identify genes in the Rpp4-mediated Asian soybean rust resistance pathway.

Rpp4 (Resistance to Phakopsora pachyrhizi 4) confers resistance to Phakopsora pachyrhizi Sydow, the causal agent of Asian soybean rust (ASR). By combining expression profiling and virus induced gene silencing (VIGS), we are developing a genetic framework for Rpp4-mediated resistance. We measured gene expression in mock-inoculated and P. pachyrhizi-infected leaves of resistant soybean accession PI459025B (Rpp4) and the susceptible cultivar (Williams 82) across a 12-day time course. Unexpectedly, two biphasic responses were identified. In the incompatible reaction, genes induced at 12h after infection (hai) were not differentially expressed at 24 hai, but were induced at 72 hai. In contrast, genes repressed at 12 hai were not differentially expressed from 24 to 144 hai, but were repressed 216 hai and later. To differentiate between basal and resistance-gene (R-gene) mediated defence responses, we compared gene expression in Rpp4-silenced and empty vector-treated PI459025B plants 14 days after infection (dai) with P. pachyrhizi. This identified genes, including transcription factors, whose differential expression is dependent upon Rpp4. To identify differentially expressed genes conserved across multiple P. pachyrhizi resistance pathways, Rpp4 expression datasets were compared with microarray data previously generated for Rpp2 and Rpp3-mediated defence responses. Fourteen transcription factors common to all resistant and susceptible responses were identified, as well as fourteen transcription factors unique to R-gene-mediated resistance responses. These genes are targets for future P. pachyrhizi resistance research.

Biphasic gene expression changes elicited by Phakopsora pachyrhizi in soybean correlate with fungal penetration and haustoria formation.

Inoculation of soybean (Glycine max) plants with Phakopsora pachyrhizi, the causal organism of Asian soybean rust, elicits a biphasic response characterized by a burst of differential gene expression in the first 12 h. A quiescent period occurs from 24 to 48 h after inoculation, in which P. pachyrhizi continues to develop but does not elicit strong host responses, followed by a second phase of intense gene expression. To correlate soybean responses with P. pachyrhizi growth and development, we inoculated the soybean cultivar Ankur (accession PI462312), which carries the Rpp3 resistance gene, with avirulent and virulent isolates of P. pachyrhizi. The avirulent isolate Hawaii 94-1 elicits hypersensitive cell death that limits fungal growth on Ankur and results in an incompatible response, while the virulent isolate Taiwan 80-2 grows extensively, sporulates profusely, and produces a compatible reaction. Inoculated leaves were collected over a 288-h time course for microarray analysis of soybean gene expression and microscopic analysis of P. pachyrhizi growth and development. The first burst in gene expression correlated with appressorium formation and penetration of epidermal cells, while the second burst of gene expression changes followed the onset of haustoria formation in both compatible and incompatible interactions. The proliferation of haustoria coincided with the inhibition of P. pachyrhizi growth in the incompatible interaction or the beginning of accelerated growth in the compatible interaction. The temporal relationships between P. pachyrhizi growth and host responses provide an important context in which to view interacting gene networks that mediate the outcomes of their interactions.

Phenotypic and genomic analyses of a fast neutron mutant population resource in soybean.

Mutagenized populations have become indispensable resources for introducing variation and studying gene function in plant genomics research. In this study, fast neutron (FN) radiation was used to induce deletion mutations in the soybean (Glycine max) genome. Approximately 120,000 soybean seeds were exposed to FN radiation doses of up to 32 Gray units to develop over 23,000 independent M2 lines. Here, we demonstrate the utility of this population for phenotypic screening and associated genomic characterization of striking and agronomically important traits. Plant variation was cataloged for seed composition, maturity, morphology, pigmentation, and nodulation traits. Mutants that showed significant increases or decreases in seed protein and oil content across multiple generations and environments were identified. The application of comparative genomic hybridization (CGH) to lesion-induced mutants for deletion mapping was validated on a midoleate x-ray mutant, M23, with a known FAD2-1A (for fatty acid desaturase) gene deletion. Using CGH, a subset of mutants was characterized, revealing deletion regions and candidate genes associated with phenotypes of interest. Exome resequencing and sequencing of PCR products confirmed FN-induced deletions detected by CGH. Beyond characterization of soybean FN mutants, this study demonstrates the utility of CGH, exome sequence capture, and next-generation sequencing approaches for analyses of mutant plant genomes. We present this FN mutant soybean population as a valuable public resource for future genetic screens and functional genomics research.

Gene expression patterns are correlated with genomic and genic structure in soybean.

Studies have indicated that exon and intron size and intergenic distance are correlated with gene expression levels and expression breadth. Previous reports on these correlations in plants and animals have been conflicting. In this study, next-generation sequence data, which has been shown to be more sensitive than previous expression profiling technologies, were generated and analyzed from 14 tissues. Our results revealed a novel dichotomy. At the low expression level, an increase in expression breadth correlated with an increase in transcript size because of an increase in the number of exons and introns. No significant changes in intron or exon sizes were noted. Conversely, genes expressed at the intermediate to high expression levels displayed a decrease in transcript size as their expression breadth increased. This was due to smaller exons, with no significant change in the number of exons. Taking advantage of the known gene space of soybean, we evaluated the positioning of genes and found significant clustering of similarly expressed genes. Identifying the correlations between the physical parameters of individual genes could lead to uncovering the role of regulation owing to nucleotide composition, which might have potential impacts in discerning the role of the noncoding regions.

RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome.

BACKGROUND: Next generation sequencing is transforming our understanding of transcriptomes. It can determine the expression level of transcripts with a dynamic range of over six orders of magnitude from multiple tissues, developmental stages or conditions. Patterns of gene expression provide insight into functions of genes with unknown annotation. RESULTS: The RNA Seq-Atlas presented here provides a record of high-resolution gene expression in a set of fourteen diverse tissues. Hierarchical clustering of transcriptional profiles for these tissues suggests three clades with similar profiles: aerial, underground and seed tissues. We also investigate the relationship between gene structure and gene expression and find a correlation between gene length and expression. Additionally, we find dramatic tissue-specific gene expression of both the most highly-expressed genes and the genes specific to legumes in seed development and nodule tissues. Analysis of the gene expression profiles of over 2,000 genes with preferential gene expression in seed suggests there are more than 177 genes with functional roles that are involved in the economically important seed filling process. Finally, the Seq-atlas also provides a means of evaluating existing gene model annotations for the Glycine max genome. CONCLUSIONS: This RNA-Seq atlas extends the analyses of previous gene expression atlases performed using Affymetrix GeneChip technology and provides an example of new methods to accommodate the increase in transcriptome data obtained from next generation sequencing. Data contained within this RNA-Seq atlas of Glycine max can be explored at http://www.soybase.org/soyseq.

Applications and methods utilizing the Simple Semantic Web Architecture and Protocol (SSWAP) for bioinformatics resource discovery and disparate data and service integration.

BACKGROUND: Scientific data integration and computational service discovery are challenges for the bioinformatic community. This process is made more difficult by the separate and independent construction of biological databases, which makes the exchange of data between information resources difficult and labor intensive. A recently described semantic web protocol, the Simple Semantic Web Architecture and Protocol (SSWAP; pronounced "swap") offers the ability to describe data and services in a semantically meaningful way. We report how three major information resources (Gramene, SoyBase and the Legume Information System [LIS]) used SSWAP to semantically describe selected data and web services. METHODS: We selected high-priority Quantitative Trait Locus (QTL), genomic mapping, trait, phenotypic, and sequence data and associated services such as BLAST for publication, data retrieval, and service invocation via semantic web services. Data and services were mapped to concepts and categories as implemented in legacy and de novo community ontologies. We used SSWAP to express these offerings in OWL Web Ontology Language (OWL), Resource Description Framework (RDF) and eXtensible Markup Language (XML) documents, which are appropriate for their semantic discovery and retrieval. We implemented SSWAP services to respond to web queries and return data. These services are registered with the SSWAP Discovery Server and are available for semantic discovery at http://sswap.info. RESULTS: A total of ten services delivering QTL information from Gramene were created. From SoyBase, we created six services delivering information about soybean QTLs, and seven services delivering genetic locus information. For LIS we constructed three services, two of which allow the retrieval of DNA and RNA FASTA sequences with the third service providing nucleic acid sequence comparison capability (BLAST). CONCLUSIONS: The need for semantic integration technologies has preceded available solutions. We report the feasibility of mapping high priority data from local, independent, idiosyncratic data schemas to common shared concepts as implemented in web-accessible ontologies. These mappings are then amenable for use in semantic web services. Our implementation of approximately two dozen services means that biological data at three large information resources (Gramene, SoyBase, and LIS) is available for programmatic access, semantic searching, and enhanced interaction between the separate missions of these resources.

SoyTEdb: a comprehensive database of transposable elements in the soybean genome.

BACKGROUND: Transposable elements are the most abundant components of all characterized genomes of higher eukaryotes. It has been documented that these elements not only contribute to the shaping and reshaping of their host genomes, but also play significant roles in regulating gene expression, altering gene function, and creating new genes. Thus, complete identification of transposable elements in sequenced genomes and construction of comprehensive transposable element databases are essential for accurate annotation of genes and other genomic components, for investigation of potential functional interaction between transposable elements and genes, and for study of genome evolution. The recent availability of the soybean genome sequence has provided an unprecedented opportunity for discovery, and structural and functional characterization of transposable elements in this economically important legume crop. DESCRIPTION: Using a combination of structure-based and homology-based approaches, a total of 32,552 retrotransposons (Class I) and 6,029 DNA transposons (Class II) with clear boundaries and insertion sites were structurally annotated and clearly categorized, and a soybean transposable element database, SoyTEdb, was established. These transposable elements have been anchored in and integrated with the soybean physical map and genetic map, and are browsable and visualizable at any scale along the 20 soybean chromosomes, along with predicted genes and other sequence annotations. BLAST search and other infrastracture tools were implemented to facilitate annotation of transposable elements or fragments from soybean and other related legume species. The majority (> 95%) of these elements (particularly a few hundred low-copy-number families) are first described in this study. CONCLUSION: SoyTEdb provides resources and information related to transposable elements in the soybean genome, representing the most comprehensive and the largest manually curated transposable element database for any individual plant genome completely sequenced to date. Transposable elements previously identified in legumes, the third largest family of flowering plants, are relatively scarce. Thus this database will facilitate structural, evolutionary, functional, and epigenetic analyses of transposable elements in soybean and other legume species.

SoyBase, the USDA-ARS soybean genetics and genomics database.

SoyBase, the USDA-ARS soybean genetic database, is a comprehensive repository for professionally curated genetics, genomics and related data resources for soybean. SoyBase contains the most current genetic, physical and genomic sequence maps integrated with qualitative and quantitative traits. The quantitative trait loci (QTL) represent more than 18 years of QTL mapping of more than 90 unique traits. SoyBase also contains the well-annotated 'Williams 82' genomic sequence and associated data mining tools. The genetic and sequence views of the soybean chromosomes and the extensive data on traits and phenotypes are extensively interlinked. This allows entry to the database using almost any kind of available information, such as genetic map symbols, soybean gene names or phenotypic traits. SoyBase is the repository for controlled vocabularies for soybean growth, development and trait terms, which are also linked to the more general plant ontologies. SoyBase can be accessed at http://soybase.org.

SSWAP: A Simple Semantic Web Architecture and Protocol for semantic web services.

BACKGROUND: SSWAP (Simple Semantic Web Architecture and Protocol; pronounced "swap") is an architecture, protocol, and platform for using reasoning to semantically integrate heterogeneous disparate data and services on the web. SSWAP was developed as a hybrid semantic web services technology to overcome limitations found in both pure web service technologies and pure semantic web technologies. RESULTS: There are currently over 2400 resources published in SSWAP. Approximately two dozen are custom-written services for QTL (Quantitative Trait Loci) and mapping data for legumes and grasses (grains). The remaining are wrappers to Nucleic Acids Research Database and Web Server entries. As an architecture, SSWAP establishes how clients (users of data, services, and ontologies), providers (suppliers of data, services, and ontologies), and discovery servers (semantic search engines) interact to allow for the description, querying, discovery, invocation, and response of semantic web services. As a protocol, SSWAP provides the vocabulary and semantics to allow clients, providers, and discovery servers to engage in semantic web services. The protocol is based on the W3C-sanctioned first-order description logic language OWL DL. As an open source platform, a discovery server running at http://sswap.info (as in to "swap info") uses the description logic reasoner Pellet to integrate semantic resources. The platform hosts an interactive guide to the protocol at http://sswap.info/protocol.jsp, developer tools at http://sswap.info/developer.jsp, and a portal to third-party ontologies at http://sswapmeet.sswap.info (a "swap meet"). CONCLUSION: SSWAP addresses the three basic requirements of a semantic web services architecture (i.e., a common syntax, shared semantic, and semantic discovery) while addressing three technology limitations common in distributed service systems: i.e., i) the fatal mutability of traditional interfaces, ii) the rigidity and fragility of static subsumption hierarchies, and iii) the confounding of content, structure, and presentation. SSWAP is novel by establishing the concept of a canonical yet mutable OWL DL graph that allows data and service providers to describe their resources, to allow discovery servers to offer semantically rich search engines, to allow clients to discover and invoke those resources, and to allow providers to respond with semantically tagged data. SSWAP allows for a mix-and-match of terms from both new and legacy third-party ontologies in these graphs.

Integrating microarray analysis and the soybean genome to understand the soybeans iron deficiency response.

BACKGROUND: Soybeans grown in the upper Midwestern United States often suffer from iron deficiency chlorosis, which results in yield loss at the end of the season. To better understand the effect of iron availability on soybean yield, we identified genes in two near isogenic lines with changes in expression patterns when plants were grown in iron sufficient and iron deficient conditions. RESULTS: Transcriptional profiles of soybean (Glycine max, L. Merr) near isogenic lines Clark (PI548553, iron efficient) and IsoClark (PI547430, iron inefficient) grown under Fe-sufficient and Fe-limited conditions were analyzed and compared using the Affymetrix GeneChip Soybean Genome Array. There were 835 candidate genes in the Clark (PI548553) genotype and 200 candidate genes in the IsoClark (PI547430) genotype putatively involved in soybean's iron stress response. Of these candidate genes, fifty-eight genes in the Clark genotype were identified with a genetic location within known iron efficiency QTL and 21 in the IsoClark genotype. The arrays also identified 170 single feature polymorphisms (SFPs) specific to either Clark or IsoClark. A sliding window analysis of the microarray data and the 7X genome assembly coupled with an iterative model of the data showed the candidate genes are clustered in the genome. An analysis of 5' untranslated regions in the promoter of candidate genes identified 11 conserved motifs in 248 differentially expressed genes, all from the Clark genotype, representing 129 clusters identified earlier, confirming the cluster analysis results. CONCLUSION: These analyses have identified the first genes with expression patterns that are affected by iron stress and are located within QTL specific to iron deficiency stress. The genetic location and promoter motif analysis results support the hypothesis that the differentially expressed genes are co-regulated. The combined results of all analyses lead us to postulate iron inefficiency in soybean is a result of a mutation in a transcription factor(s), which controls the expression of genes required in inducing an iron stress response.

Microsatellite discovery from BAC end sequences and genetic mapping to anchor the soybean physical and genetic maps.

Whole-genome sequencing of the soybean (Glycine max (L.) Merr. 'Williams 82') has made it important to integrate its physical and genetic maps. To facilitate this integration of maps, we screened 3290 microsatellites (SSRs) identified from BAC end sequences of clones comprising the 'Williams 82' physical map. SSRs were screened against 3 mapping populations. We found the AAT and ACT motifs produced the greatest frequency of length polymorphisms, ranging from 17.2% to 32.3% and from 11.8% to 33.3%, respectively. Other useful motifs include the dinucleotide repeats AG, AT, and AG, with frequency of length polymorphisms ranging from 11.2% to 18.4% (AT), 12.4% to 20.6% (AG), and 11.3% to 16.4% (GT). Repeat lengths less than 16 bp were generally less useful than repeat lengths of 40-60 bp. Two hundred and sixty-five SSRs were genetically mapped in at least one population. Of the 265 mapped SSRs, 60 came from BAC singletons not yet placed into contigs of the physical map. One hundred and ten originated in BACs located in contigs for which no genetic map location was previously known. Ninety-five SSRs came from BACs within contigs for which one or more other BACs had already been mapped. For these fingerprinted contigs (FPC) a high percentage of the mapped markers showed inconsistent map locations. A strategy is introduced by which physical and genetic map inconsistencies can be resolved using the preliminary 4x assembly of the whole genome sequence of soybean.

Gene duplication and paleopolyploidy in soybean and the implications for whole genome sequencing.

BACKGROUND: Soybean, Glycine max (L.) Merr., is a well documented paleopolyploid. What remains relatively under characterized is the level of sequence identity in retained homeologous regions of the genome. Recently, the Department of Energy Joint Genome Institute and United States Department of Agriculture jointly announced the sequencing of the soybean genome. One of the initial concerns is to what extent sequence identity in homeologous regions would have on whole genome shotgun sequence assembly. RESULTS: Seventeen BACs representing approximately 2.03 Mb were sequenced as representative potential homeologous regions from the soybean genome. Genetic mapping of each BAC shows that 11 of the 20 chromosomes are represented. Sequence comparisons between homeologous BACs shows that the soybean genome is a mosaic of retained paleopolyploid regions. Some regions appear to be highly conserved while other regions have diverged significantly. Large-scale "batch" reassembly of all 17 BACs combined showed that even the most homeologous BACs with upwards of 95% sequence identity resolve into their respective homeologous sequences. Potential assembly errors were generated by tandemly duplicated pentatricopeptide repeat containing genes and long simple sequence repeats. Analysis of a whole-genome shotgun assembly of 80,000 randomly chosen JGI-DOE sequence traces reveals some new soybean-specific repeat sequences. CONCLUSION: This analysis investigated both the structure of the paleopolyploid soybean genome and the potential effects retained homeology will have on assembling the whole genome shotgun sequence. Based upon these results, homeologous regions similar to those characterized here will not cause major assembly issues.

Distinct biphasic mRNA changes in response to Asian soybean rust infection.

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is now established in all major soybean-producing countries. Currently, there is little information about the molecular basis of ASR-soybean interactions, which will be needed to assist future efforts to develop effective resistance. Toward this end, abundance changes of soybean mRNAs were measured over a 7-day ASR infection time course in mock-inoculated and infected leaves of a soybean accession (PI230970) carrying the Rpp2 resistance gene and a susceptible genotype (Embrapa-48). The expression profiles of differentially expressed genes (ASR-infected compared with the mock-inoculated control) revealed a biphasic response to ASR in each genotype. Within the first 12 h after inoculation (hai), which corresponds to fungal germination and penetration of the epidermal cells, differential gene expression changes were evident in both genotypes. mRNA expression of these genes mostly returned to levels found in mock-inoculated plants by 24 hai. In the susceptible genotype, gene expression remained unaffected by rust infection until 96 hai, a time period when rapid fungal growth began. In contrast, gene expression in the resistant genotype diverged from the mock-inoculated control earlier, at 72 h, demonstrating that Rpp2-mediated defenses were initiated prior to this time. These data suggest that ASR initially induces a nonspecific response that is transient or is suppressed when early steps in colonization are completed in both soybean genotypes. The race-specific resistance phenotype of Rpp2 is manifested in massive gene expression changes after the initial response prior to the onset of rapid fungal growth that occurs in the susceptible genotype.