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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.

In this chapter, we introduce the main components of the Legume Information System ( https://legumeinfo.org ) and several associated resources. Additionally, we provide an example of their use by exploring a biological question: is there a common molecular basis, across legume species, that underlies the photoperiod-mediated transition from vegetative to reproductive development, that is, days to flowering? The Legume Information System (LIS) holds genetic and genomic data for a large number of crop and model legumes and provides a set of online bioinformatic tools designed to help biologists address questions and tasks related to legume biology. Such tasks include identifying the molecular basis of agronomic traits; identifying orthologs/syntelogs for known genes; determining gene expression patterns; accessing genomic datasets; identifying markers for breeding work; and identifying genetic similarities and differences among selected accessions. LIS integrates with other legume-focused informatics resources such as SoyBase ( https://soybase.org ), PeanutBase ( https://peanutbase.org ), and projects of the Legume Federation ( https://legumefederation.org ).

Efficient crop improvement depends on the application of accurate genetic information contained in diverse germplasm resources. Here we report a reference-grade genome of wild soybean accession W05, with a final assembled genome size of 1013.2 Mb and a contig N50 of 3.3 Mb. The analytical power of the W05 genome is demonstrated by several examples. First, we identify an inversion at the locus determining seed coat color during domestication. Second, a translocation event between chromosomes 11 and 13 of some genotypes is shown to interfere with the assignment of QTLs. Third, we find a region containing copy number variations of the Kunitz trypsin inhibitor (KTI) genes. Such findings illustrate the power of this assembly in the analysis of large structural variations in soybean germplasm collections. The wild soybean genome assembly has wide applications in comparative genomic and evolutionary studies, as well as in crop breeding and improvement programs., Wild relatives of crop plants are invaluable germplasm for genetic improvement. Here, Xie et al. report a reference-grade wild soybean genome and show that it can be used to identify structural variation and refine quantitative trait loci.

Background Large genotyping datasets have become commonplace due to efficient, cheap methods for SNP identification. Typical genotyping datasets may have thousands to millions of data points per accession, across tens to thousands of accessions. There is a need for tools to help rapidly explore such datasets, to assess characteristics such as overall differences between accessions and regional anomalies across the genome. Results We present GCViT (Genotype Comparison Visualization Tool), for visualizing and exploring large genotyping datasets. GCViT can be used to identify introgressions, conserved or divergent genomic regions, pedigrees, and other features for more detailed exploration. The program can be used online or as a local instance for whole genome visualization of resequencing or SNP array data. The program performs comparisons of variants among user-selected accessions to identify allele differences and similarities between accessions and a user-selected reference, providing visualizations through histogram, heatmap, or haplotype views. The resulting analyses and images can be exported in various formats. Conclusions GCViT provides methods for interactively visualizing SNP data on a whole genome scale, and can produce publication-ready figures. It can be used in online or local installations. GCViT enables users to confirm or identify genomics regions of interest associated with particular traits. GCViT is freely available at https://github.com/LegumeFederation/gcvit. The 1.0 version described here is available at 10.5281/zenodo.4008713.

In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and maturity, but can be inappropriate for ordinal traits. Generalized Linear Mixed Models have been developed for GP of ordinal response variables. However, neither approach addresses the most important questions for cultivar development and genetic improvement: How frequently are the ‘wrong’ genotypes retained, and how often are the ‘correct’ genotypes discarded? The research objective reported herein was to compare outcomes from four data modeling and six algorithmic modeling GP methods applied to IDC using decision metrics appropriate for variety development and genetic improvement projects. Appropriate metrics for decision making consist of specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. Data modeling methods for GP included ridge regression, logistic regression, penalized logistic regression, and Bayesian generalized linear regression. Algorithmic modeling methods include Random Forest, Gradient Boosting Machine, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Network. We found that a Support Vector Machine model provided the most specific decisions of correctly discarding IDC susceptible genotypes, while a Random Forest model resulted in the best decisions of retaining IDC tolerant genotypes, as well as the best outcomes when considering all decision metrics. Overall, the predictions from algorithmic modeling result in better decisions than from data modeling methods applied to soybean IDC.

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.

We report reference-quality genome assemblies and annotations for two accessions of soybean (Glycine max) and for one accession of Glycine soja, the closest wild relative of G.max. The G.max assemblies provided are for widely used US cultivars: the northern line Williams82 (Wm82) and the southern line Lee. The Wm82 assembly improves the prior published assembly, and the Lee and G.soja assemblies are new for these accessions. Comparisons among the three accessions show generally high structural conservation, but nucleotide difference of 1.7single-nucleotide polymorphisms (snps) per kb between Wm82 and Lee, and 4.7snpsperkb between these lines and G.soja. snp distributions and comparisons with genotypes of the Lee and Wm82 parents highlight patterns of introgression and haplotype structure. Comparisons against the US germplasm collection show placement of the sequenced accessions relative to global soybean diversity. Analysis of a pan-gene collection shows generally high conservation, with variation occurring primarily in genomically clustered gene families. We found approximately 40-42 inversions per chromosome between either Lee or Wm82v4 and G.soja, and approximately 32 inversions per chromosome between Wm82 and Lee. We also investigated five domestication loci. For each locus, we found two different alleles with functional differences between G.soja and the two domesticated accessions. The genome assemblies for multiple cultivated accessions and for the closest wild ancestor of soybean provides a valuable set of resources for identifying causal variants that underlie traits for the domestication and improvement of soybean, serving as a basis for future research and crop improvement efforts for this important crop species.

Legume Information System (LIS), at http://legumeinfo.org, is a genomic data portal (GDP) for the legume family. LIS provides access to genetic and genomic information for major crop and model legumes. With more than two-dozen domesticated legume species, there are numerous specialists working on particular species, and also numerous GDPs for these species. LIS has been redesigned in the last three years both to better integrate data sets across the crop and model legumes, and to better accommodate specialized GDPs that serve particular legume species. To integrate data sets, LIS provides genome and map viewers, holds synteny mappings among all sequenced legume species and provides a set of gene families to allow traversal among orthologous and paralogous sequences across the legumes. To better accommodate other specialized GDPs, LIS uses open-source GMOD components where possible, and advocates use of common data templates, formats, schemas and interfaces so that data collected by one legume research community are accessible across all legume GDPs, through similar interfaces and using common APIs. This federated model for the legumes is managed as part of the 'Legume Federation' project (accessible via http://legumefederation.org), which can be thought of as an umbrella project encompassing LIS and other legume GDPs.

© The Author(s) 2018. Published by Oxford University Press. 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.

Peanut or groundnut (Arachis hypogaea), an allotetraploid species, recently became the focus of a global genomics initiative with the goal of developing genome-wide molecular resources to facilitate breeding. Publicly available reference genome sequence of peanut’s diploid progenitors (peanutbase.org) is enabling expansion of genotyping tools for genetic mapping of quantitative trait loci (QTL) for disease resistance and reproductive traits. Gene models supported by transcriptome data from cultivated peanut and its wild relatives empower the association of allelic variants with putative function, establishing a foundation for gene-based selection. This review summarizes recent advances and emerging impacts.

Preferential accumulation of transposable elements (TEs), particularly long terminal repeat retrotransposons (LTR-RTs), in recombination-suppressed pericentromeric regions seems to be a general pattern of TE distribution in flowering plants. However, whether such a pattern was formed primarily by preferential TE insertions into pericentromeric regions or by selection against TE insertions into euchromatin remains obscure. We recently investigated TE insertions in 31 resequenced wild and cultivated soybean (Glycine max) genomes and detected 34,154 unique nonreference TE insertions mappable to the reference genome. Our data revealed consistent distribution patterns of the nonreference LTR-RT insertions and those present in the reference genome, whereas the distribution patterns of the nonreference DNA TE insertions and the accumulated ones were significantly different. The densities of the nonreference LTR-RT insertions were found to negatively correlate with the rates of local genetic recombination, but no significant correlation between the densities of nonreference DNA TE insertions and the rates of local genetic recombination was detected. These observations suggest that distinct insertional preferences were primary factors that resulted in different levels of effectiveness of purifying selection, perhaps as an effect of local genomic features, such as recombination rates and gene densities that reshaped the distribution patterns of LTR-RTs and DNA TEs in soybean.

For species with potential as new crops, rapid improvement may be facilitated by new genomic methods. Apios (Apios americana Medik.), once a staple food source of Native American Indians, produces protein-rich tubers, tolerates a wide range of soils, and symbiotically fixes nitrogen. We report the first high-quality de novo transcriptome assembly, an expression atlas, and a set of 58,154 SNP and 39,609 gene expression markers (GEMs) for characterization of a breeding collection. Both SNPs and GEMs identify six genotypic clusters in the collection. Transcripts mapped to the Phaseolus vulgaris genome–another phaseoloid legume with the same chromosome number–provide provisional genetic locations for 46,852 SNPs. Linkage disequilibrium decays within 10 kb (based on the provisional genetic locations), consistent with outcrossing reproduction. SNPs and GEMs identify more than 21 marker-trait associations for at least 11 traits. This study demonstrates a holistic approach for mining plant collections to accelerate crop improvement.

Large-scale genomic data for peanut have only become available in the last few years, with the advent of low-cost sequencing technologies. To make the data accessible to researchers and to integrate across diverse types of data, the International Peanut Genomics Consortium funded the development of PeanutBase, at http://peanutbase.org . This website provides access to genetic maps and markers, locations of quantitative trait loci (QTLs), genome sequences, gene locations and sequences, gene families and correspondences with genes in other species, and descriptions of traits and growth characteristics. The website also provides tools for exploration and analysis, including sequence of genomic and genic sequences, and keyword searches of genes, gene families, and QTL studies. These resources should facilitate breeding advancements in peanut, helping improve crop productivity and there are a variety of resources for peanut research around the web, ranging from tools for basic plant biology to information for growers and various sectors of the peanut industry to resources for plant breeders. Many of these resources are listed and/or maintained at http://peanutbase.org/community .

Chantier qualité GA; International audience; Legumes (Fabaceae or Leguminosae) are unique among cultivated plants for their ability to carry out endosymbiotic nitrogen fixation with rhizobial bacteria, a process that takes place in a specialized structure known as the nodule. Legumes belong to one of the two main groups of eurosids, the Fabidae, which includes most species capable of endosymbiotic nitrogen fixation1. Legumes comprise several evolutionary lineages derived from a common ancestor 60 million years ago (Myr ago). Papilionoids are the largest clade, dating nearly to the origin of legumes and containing most cultivated species2. Medicago truncatula is a long-established model for the study of legume biology. Here we describe the draft sequence of the M. truncatula euchromatin based on a recently completed BAC assembly supplemented with Illumina shotgun sequence, together capturing ~94% of all M. truncatula genes. A whole-genome duplication (WGD) approximately 58 Myr ago had a major role in shaping the M. truncatula genome and thereby contributed to the evolution of endosymbiotic nitrogen fixation. Subsequent to the WGD, the M. truncatula genome experienced higher levels of rearrangement than two other sequenced legumes, Glycine max and Lotus japonicus. M. truncatula is a close relative of alfalfa (Medicago sativa), a widely cultivated crop with limited genomics tools and complex autotetraploid genetics. As such, the M. truncatula genome sequence provides significant opportunities to expand alfalfa’s genomic toolbox.

With the advent of high-throughput sequencing, the availability of genomic sequence for comparative genomics is increasing exponentially. Numerous completed plant genome sequences enable characterization of patterns of the retention and evolution of genes within gene families due to multiple polyploidy events, gene loss and fractionation, and differential evolutionary pressures over time and across different gene families. In this report, we trace the changes that have occurred in 12 surviving homoeologous genomic regions from three rounds of polyploidy that contributed to the current Glycine max genome: a genome triplication before the origin of the rosids (~130 to 240 million years ago), a genome duplication early in the legumes (~58 million years ago), and a duplication in the Glycine lineage (~13 million years ago). Patterns of gene retention following the genome triplication event generally support predictions of the Gene Balance Hypothesis. Finally, we find that genes in networks with a high level of connectivity are more strongly conserved than those with low connectivity and that the enrichment of these highly connected genes in the 12 highly conserved homoeologous segments may in part explain their retention over more than 100 million years and repeated polyploidy events.

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.

This year marks the essential completion of the genome sequences of soybean ( Glycine max ), barrel medic ( Medicago truncatula ), and birdsfoot trefoil ( Lotus japonicus ). The impact of these assembled, annotated genomes will be enormous. Birdsfoot trefoil and barrel medic, both forage crops, are

Cultivated peanut (Arachis hypogaea) is an allotetraploid with closely related subgenomes of a total size of ∼2.7 Gb. This makes the assembly of chromosomal pseudomolecules very challenging. As a foundation to understanding the genome of cultivated peanut, we report the genome sequences of its diploid ancestors (Arachis duranensis and Arachis ipaensis). We show that these genomes are similar to cultivated peanut's A and B subgenomes and use them to identify candidate disease resistance genes, to guide tetraploid transcript assemblies and to detect genetic exchange between cultivated peanut's subgenomes. On the basis of remarkably high DNA identity of the A. ipaensis genome and the B subgenome of cultivated peanut and biogeographic evidence, we conclude that A. ipaensis may be a direct descendant of the same population that contributed the B subgenome to cultivated peanut.

In the eight years since phylogenomics was introduced as the intersection of genomics and phylogenetics, the field has provided fundamental insights into gene function, genome history and organismal relationships. The utility of phylogenomics is growing with the increase in the number and diversity of taxa for which whole genome and large transcriptome sequence sets are being generated. We assert that the synergy between genomic and phylogenetic perspectives in comparative biology would be enhanced by the development and refinement of minimal reporting standards for phylogenetic analyses. Encouraged by the development of the Minimum Information About a Microarray Experiment (MIAME) standard, we propose a similar roadmap for the development of a Minimal Information About a Phylogenetic Analysis (MIAPA) standard. Key in the successful development and implementation of such a standard will be broad participation by developers of phylogenetic analysis software, phylogenetic database developers, practitioners of phylogenomics, and journal editors.

An international consortium is sequencing the euchromatic genespace of Medicago truncatula. Extensive bioinformatic and database resources support the marker-anchored bacterial artificial chromosome (BAC) sequencing strategy. Existing physical and genetic maps and deep BAC-end sequencing help to guide the sequencing effort, while EST databases provide essential resources for genome annotation as well as transcriptome characterization and microarray design. Finished BAC sequences are joined into overlapping sequence assemblies and undergo an automated annotation process that integrates ab initio predictions with EST, protein, and other recognizable features. Because of the sequencing project's international and collaborative nature, data production, storage, and visualization tools are broadly distributed. This paper describes databases and Web resources for the project, which provide support for physical and genetic maps, genome sequence assembly, gene prediction, and integration of EST data. A central project Web site at medicago.org/genome provides access to genome viewers and other resources project-wide, including an Ensembl implementation at medicago.org, physical map and marker resources at mtgenome.ucdavis.edu, and genome viewers at the University of Oklahoma (www.genome.ou.edu), the Institute for Genomic Research (www.tigr.org), and Munich Information for Protein Sequences Center (mips.gsf.de).

The primary model legumes to date have been Medicago truncatula and Lotus japonicus. Both species are tractable both genetically and in the greenhouse, and for both, substantial sets of tools and resources for molecular genetic research have been assembled. As sequencing costs have declined, however, additional legume genomes have been sequenced, and the funding available to crops such as soybean has enabled these to be developed to the status of genetic models in their own right. This chapter, therefore, describes a broader set of model species in the legumes, and discusses similarities and differences between the genomes sequenced to date, as well as computational resources available for various legume species. Genome structural characteristics in, for example, Medicago truncatula and Glycine max, can have large impacts on the kinds of functional genomic research that may be carried out in these species. Both of these genomes have substantial redundancy for many gene families, but the nature of the redundancy is different in the two genomes-with the redundancy typically being in the form of local gene duplications in Medicago, and in whole-genome-duplication-derived duplications in Glycine. Similar considerations (about gene environments and genome structure) will likely need to be taken into account for any model or crop species.

The primary model legumes to date have been Medicago truncatula and Lotus japonicus. Both species are tractable both genetically and in the greenhouse, and for both, substantial sets of tools and resources for molecular genetic research have been assembled. As sequencing costs have declined, however, additional legume genomes have been sequenced, and the funding available to crops such as soybean has enabled these to be developed to the status of genetic models in their own right. This chapter, therefore, describes a broader set of model species in the legumes, and discusses similarities and differences between the genomes sequenced to date, as well as computational resources available for various legume species. Genome structural characteristics in, for example, Medicago truncatula and Glycine max, can have large impacts on the kinds of functional genomic research that may be carried out in these species. Both of these genomes have substantial redundancy for many gene families, but the nature of the redundancy is different in the two genomes-with the redundancy typically being in the form of local gene duplications in Medicago, and in whole-genome-duplication-derived duplications in Glycine. Similar considerations (about gene environments and genome structure) will likely need to be taken into account for any model or crop species.

Background The nutritional and economic value of many crops is effectively a function of seed protein and oil content. Insight into the genetic and molecular control mechanisms involved in the deposition of these constituents in the developing seed is needed to guide crop improvement. A quantitative trait locus (QTL) on Linkage Group I (LG I) of soybean (Glycine max (L.) Merrill) has a striking effect on seed protein content. Results A soybean near-isogenic line (NIL) pair contrasting in seed protein and differing in an introgressed genomic segment containing the LG I protein QTL was used as a resource to demarcate the QTL region and to study variation in transcript abundance in developing seed. The LG I QTL region was delineated to less than 8.4 Mbp of genomic sequence on chromosome 20. Using Affymetrix® Soy GeneChip and high-throughput Illumina® whole transcriptome sequencing platforms, 13 genes displaying significant seed transcript accumulation differences between NILs were identified that mapped to the 8.4 Mbp LG I protein QTL region. Conclusions This study identifies gene candidates at the LG I protein QTL for potential involvement in the regulation of protein content in the soybean seed. The results demonstrate the power of complementary approaches to characterize contrasting NILs and provide genome-wide transcriptome insight towards understanding seed biology and the soybean genome.

Expanding legume research beyond the model members of the subfamily Papilionoideae (papilionoids) is necessary if we wish to capture more of the diversity of the enormous, economically important legume family. Chamaecrista fasciculata is emerging as a nonpapilionoid model, belonging to the

Soybean (Glycine max) is one of the most important crop plants for seed protein and oil content, and for its capacity to fix atmospheric nitrogen through symbioses with soil-borne microorganisms. We sequenced the 1.1-gigabase genome by a whole-genome shotgun approach and integrated it with physical and high-density genetic maps to create achromosome-scale draft sequence assembly. We predict 46,430 protein-coding genes, 70percent more than Arabidopsis and similar to the poplar genome which, like soybean, is an ancient polyploid (palaeopolyploid). About 78percent of the predicted genes occur in chromosome ends, which comprise less than one-half of the genome but account for nearly all of the genetic recombination. Genome duplications occurred at approximately 59 and 13 million years ago, resulting in a highly duplicated genome with nearly 75percent of the genes present in multiple copies. The two duplication events were followed by genediversification and loss, and numerous chromosome rearrangements. An accurate soybean genome sequence will facilitate the identification of the genetic basis of many soybean traits, and accelerate the creation of improved soybean varieties.

Background Most agriculturally important legumes fall within two sub-clades of the Papilionoid legumes: the Phaseoloids and Galegoids, which diverged about 50 Mya. The Phaseoloids are mostly tropical and include crops such as common bean and soybean. The Galegoids are mostly temperate and include clover, fava bean and the model legumes Lotus and Medicago (both with substantially sequenced genomes). In contrast, peanut (Arachis hypogaea) falls in the Dalbergioid clade which is more basal in its divergence within the Papilionoids. The aim of this work was to integrate the genetic map of Arachis with Lotus and Medicago and improve our understanding of the Arachis genome and legume genomes in general. To do this we placed on the Arachis map, comparative anchor markers defined using a previously described bioinformatics pipeline. Also we investigated the possible role of transposons in the patterns of synteny that were observed. Results The Arachis genetic map was substantially aligned with Lotus and Medicago with most synteny blocks presenting a single main affinity to each genome. This indicates that the last common whole genome duplication within the Papilionoid legumes predated the divergence of Arachis from the Galegoids and Phaseoloids sufficiently that the common ancestral genome was substantially diploidized. The Arachis and model legume genomes comparison made here, together with a previously published comparison of Lotus and Medicago allowed all possible Arachis-Lotus-Medicago species by species comparisons to be made and genome syntenies observed. Distinct conserved synteny blocks and non-conserved regions were present in all genome comparisons, implying that certain legume genomic regions are consistently more stable during evolution than others. We found that in Medicago and possibly also in Lotus, retrotransposons tend to be more frequent in the variable regions. Furthermore, while these variable regions generally have lower densities of single copy genes than the more conserved regions, some harbor high densities of the fast evolving disease resistance genes. Conclusion We suggest that gene space in Papilionoids may be divided into two broadly defined components: more conserved regions which tend to have low retrotransposon densities and are relatively stable during evolution; and variable regions that tend to have high retrotransposon densities, and whose frequent restructuring may fuel the evolution of some gene families.

Two model legumes, Medicago truncatula ( Mt ) and Lotus japonicus ( Lj ), are currently targets of large-scale genome sequencing projects. As a result, legumes are one of few plant families with extensive genome sequence in multiple species. The prospect of integrating genome information from Mt and

Genomic data sets offer opportunities for students to generate original findings without expensive laboratory equipment, extending the benefits of undergraduate research to the classroom ( 1 – 4 ). Yet, working with data sets online can frustrate biology students who may miss connections to fascinating biological questions. Integrating organismal and molecular biology with bioinformatics can enhance student learning. We have developed a program in genomics, adapted for a plant ( Chamaecrista fasciculata ) and an animal ( Aiptasia pallida ), designed to support student learning with a Web-based guide ( ) (see the first chart).

The Fabaceae, the third largest family of plants and the source of many crops, has been the target of many genomic studies. Currently, only the grasses surpass the legumes for the number of publicly available expressed sequence tags (ESTs). The quantity of sequences from diverse plants enables the use of computational approaches to identify novel genes in specific taxa. We used BLAST algorithms to compare unigene sets from Medicago truncatula, Lotus japonicus, and soybean (Glycine max and Glycine soja) to nonlegume unigene sets, to GenBank's nonredundant and EST databases, and to the genomic sequences of rice (Oryza sativa) and Arabidopsis. As a working definition, putatively legume-specific genes had no sequence homology, below a specified threshold, to publicly available sequences of nonlegumes. Using this approach, 2,525 legume-specific EST contigs were identified, of which less than three percent had clear homology to previously characterized legume genes. As a first step toward predicting function, related sequences were clustered to build motifs that could be searched against protein databases. Three families of interest were more deeply characterized: F-box related proteins, Pro-rich proteins, and Cys cluster proteins (CCPs). Of particular interest were the >300 CCPs, primarily from nodules or seeds, with predicted similarity to defensins. Motif searching also identified several previously unknown CCP-like open reading frames in Arabidopsis. Evolutionary analyses of the genomic sequences of several CCPs in M. truncatula suggest that this family has evolved by local duplications and divergent selection.

A comprehensive transcriptome assembly of chickpea has been developed using 134.95 million Illumina single-end reads, 7.12 million single-end FLX/454 reads and 139,214 Sanger expressed sequence tags (ESTs) from >17 genotypes. This hybrid transcriptome assembly, referred to as Cicer arietinum Transcriptome Assembly version 2 (CaTA v2, available at http://data.comparative-legumes.org/ transcriptomes/cicar/lista-cicar-201201), comprising 46,369 transcript assembly contigs (TACs) has an N50 length of 1,726 bp and a maximum contig size of 15,644 bp. Putative functions were determined for 32,869 (70.8%) of the TACs and gene ontology assignments were determined for 21,471 (46.3%). The new transcriptome assembly was compared with the previously available chickpea transcriptome assemblies as well as to the chickpea genome. Comparative analysis of CaTA v2 against transcriptomes of three legumes - Medicago, soybean and common bean, resulted in 27,771 TACs common to all three legumes indicating strong conservation of genes across legumes. CaTA v2 was also used for identification of simple sequence repeats (SSRs) and intron spanning regions (ISRs) for developing molecular markers. ISRs were identified by aligning TACs to the Medicago genome, and their putative mapping positions at chromosomal level were identified using transcript map of chickpea. Primer pairs were designed for 4,990 ISRs, each representing a single contig for which predicted positions are inferred and distributed across eight linkage groups. A subset of randomly selected ISRs representing all eight chickpea linkage groups were validated on five chickpea genotypes and showed 20% polymorphism with average polymorphic information content (PIC) of 0.27. In summary, the hybrid transcriptome assembly developed and novel markers identified can be used for a variety of applications such as gene discovery, marker-trait association, diversity analysis etc., to advance genetics research and breeding applications in chickpea and other related legumes.

Background The ubiquitous LysM motif recognizes peptidoglycan, chitooligosaccharides (chitin) and, presumably, other structurally-related oligosaccharides. LysM-containing proteins were first shown to be involved in bacterial cell wall degradation and, more recently, were implicated in perceiving chitin (one of the established pathogen-associated molecular patterns) and lipo-chitin (nodulation factors) in flowering plants. However, the majority of LysM genes in plants remain functionally uncharacterized and the evolutionary history of complex LysM genes remains elusive. Results We show that LysM-containing proteins display a wide range of complex domain architectures. However, only a simple core architecture is conserved across kingdoms. Each individual kingdom appears to have evolved a distinct array of domain architectures. We show that early plant lineages acquired four characteristic architectures and progressively lost several primitive architectures. We report plant LysM phylogenies and associated gene, protein and genomic features, and infer the relative timing of duplications of LYK genes. Conclusion We report a domain architecture catalogue of LysM proteins across all kingdoms. The unique pattern of LysM protein domain architectures indicates the presence of distinctive evolutionary paths in individual kingdoms. We describe a comparative and evolutionary genomics study of LysM genes in plant kingdom. One of the two groups of tandemly arrayed plant LYK genes likely resulted from an ancient genome duplication followed by local genomic rearrangement, while the origin of the other groups of tandemly arrayed LYK genes remains obscure. Given the fact that no animal LysM motif-containing genes have been functionally characterized, this study provides clues to functional characterization of plant LysM genes and is also informative with regard to evolutionary and functional studies of animal LysM genes.

The DiagHunter and GenoPix2D applications work together to enable genomic comparisons and exploration at both genome-wide and single-gene scales. DiagHunter identifies homologous regions (synteny blocks) within or between genomes. DiagHunter works efficiently with diverse, large datasets to predict extended and interrupted synteny blocks and to generate graphical and text output quickly. GenoPix2D allows interactive display of synteny blocks and other genomic features, as well as querying by annotation and by sequence similarity.

Like many other crops, the cultivated peanut (Arachis hypogaea L.) is of hybrid origin and has a polyploid genome that contains essentially complete sets of chromosomes from two ancestral species. Here we report the genome sequence of peanut and show that after its polyploid origin, the genome has evolved through mobile-element activity, deletions and by the flow of genetic information between corresponding ancestral chromosomes (that is, homeologous recombination). Uniformity of patterns of homeologous recombination at the ends of chromosomes favors a single origin for cultivated peanut and its wild counterpart A. monticola. However, through much of the genome, homeologous recombination has created diversity. Using new polyploid hybrids made from the ancestral species, we show how this can generate phenotypic changes such as spontaneous changes in the color of the flowers. We suggest that diversity generated by these genetic mechanisms helped to favor the domestication of the polyploid A. hypogaea over other diploid Arachis species cultivated by humans. Fil: Bertioli, David J.. University of Georgia; Estados Unidos Fil: Jenkins, Jerry. Hudsonalpha Institute For Biotechnology; Estados Unidos Fil: Clevenger, Josh. University of Georgia; Estados Unidos Fil: Dudchenko, Olga. The Center for Genome Architecture; Estados Unidos Fil: Gao, Dongying. University of Georgia; Estados Unidos Fil: Seijo, José Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; Argentina Fil: Leal Bertioli, Soraya C.M.. Universidad Nacional del Nordeste; Argentina Fil: Ren, Longhui. University of Georgia; Estados Unidos Fil: Farmer, Andrew D.. University of Georgia; Estados Unidos Fil: Pandey, Manish K.. Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); India Fil: Samoluk, Sergio Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; Argentina Fil: Abernathy, Brian. University of Georgia; Estados Unidos Fil: Agarwal, Gaurav. University of Georgia; Estados Unidos Fil: Ballén Taborda, Carolina. University of Georgia; Estados Unidos Fil: Cameron, Connor. National Center for Genome Resources; Estados Unidos Fil: Campbell, Jacqueline. University of Iowa; Estados Unidos Fil: Chavarro, Carolina. University of Georgia; Estados Unidos Fil: Chitikineni, Annapurna. Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); India Fil: Chu, Ye. University of Georgia; Estados Unidos Fil: Dash, Sudhansu. National Center for Genome Resources; Estados Unidos Fil: El Baidouri, Moaine. Centre National de la Recherche Scientifique; Francia Fil: Guo, Baozhu. University of Georgia; Estados Unidos Fil: Huang, Wei. University of Iowa; Estados Unidos Fil: Kim, Kyung Do. University of Georgia; Estados Unidos. Corporate R&D, LG Chem; Corea del Sur Fil: Korani, Walid. University of Georgia; Estados Unidos Fil: Lanciano, Sophie. Centre National de la Recherche Scientifique; Francia Fil: Lui, Christopher G.. The Center for Genome Architecture; Estados Unidos Fil: Mirouze, Marie. Centre National de la Recherche Scientifique; Francia Fil: Moretzsohn, Márcio C.. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil Fil: Pham, Melanie. The Center for Genome Architecture; Estados Unidos Fil: Shin, Jin Hee. University of Georgia; Estados Unidos Fil: Shirasawa, Kenta Shirasawa. Department of Frontier Research and Development, Kazusa DNA Research Institute; Japón Fil: Sinharoy, Senjuti. National Institute of Plant Genome Research; India Fil: Sreedasyam, Avinash. Hudson Alpha Institute of Biotechnology; Estados Unidos Fil: Weeks, Nathan T.. United States Department of Agriculture; Estados Unidos Fil: Zhang, Xinyou. Henan Academy of Agricultural Sciences; China Fil: Zheng, Zheng. Henan Academy of Agricultural Sciences; China Fil: Sun, Ziqi. Henan Academy of Agricultural Sciences; China Fil: Froenicke, Lutz. University of California at Davis; Estados Unidos Fil: Aiden, Erez L.. The Center for Genome Architecture; Estados Unidos Fil: Michelmore, Richard. University of California at Davis; Estados Unidos Fil: Varshney, Rajeev K.. Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); India Fil: Holbrook, C. Corley. United States Department of Agriculture; Estados Unidos Fil: Cannon, Ethalinda K. S.. University of Iowa; Estados Unidos Fil: Scheffler, Brian E.. United States Department of Agriculture; Estados Unidos Fil: Grimwood, Jane. Hudson Alpha Institute of Biotechnology; Estados Unidos Fil: Ozias-Akins, Peggy. University of Georgia; Estados Unidos Fil: Cannon, Steven B.. United States Department of Agriculture; Estados Unidos Fil: Jackson, Scott A.. University of Georgia; Estados Unidos Fil: Schmutz, Jeremy. Hudson Alpha Institute of Biotechnology; Estados Unidos