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Cacao Genome Database

    The release of the cacao genome sequence will provide researchers with access to the latest genomic tools, enabling more efficient research and accelerating the breeding process, thereby expediting the release of superior cacao cultivars. The sequenced genotype, Matina 1-6, is representative of the genetic background most commonly found in the cacao producing countries, enabling results to be applied immediately and broadly to current commercial cultivars.  Matina 1-6 is highly homozygous which greatly reduces the complexity of the sequence assembly process. While the sequence provided is a preliminary release, it already covers 92% of the genome, with approximately 35,000 genes. We will continue to refine the assembly and annotation, working toward a complete finished sequence.

    ARS Microbial Genomic Sequence Database Server

      This database server is supported in fulfilment of the research mission of the Mycotoxin Prevention and Applied Microbiology Research Unit at the National Center for Agricultural Utilization Research in Peoria, Illinois. The linked website provides access to gene sequence databases for various groups of microorganisms, such as Streptomyces species or Aspergillus species and their relatives, that are the product of ARS research programs. The sequence databases are organized in the BIGSdb (Bacterial Isolate Genomic Sequence Database) software package developed by Keith Jolley and Martin Maiden at Oxford University.


        Ricebase ([https://ricebase.org](https://ricebase.org)) is an integrative genomic database for rice (Oryza sativa) with an emphasis on combining datasets in a way that maintains the key links between past and current genetic studies. Ricebase includes DNA sequence data, gene annotations, nucleotide variation data and molecular marker fragment size data.

        Musa Germplasm Information System

          The Musa Germplasm Information System (MGIS) contains key information on Musa germplasm diversity, including passport data, botanical classification, morpho-taxonomic descriptors, molecular studies, plant photographs and GIS information on 6829 accessions managed in 30 collections around the world, making it the most extensive source of information on banana genetic resources.

          Animal Trait Correlation Database (CorrDB)

            A genetic correlation is the proportion of shared variance between two traits that is due to genetic causes; a phenotypic correlation is the degree to which two traits co-vary among individuals in a population. In the genomics era, while gene expression, genetic association, and network analysis provide unprecedented means to decode the genetic basis of complex phenotypes, it is important to recognize the possible effects genetic progress in one trait can have on other traits. This database is designed to collect all published livestock genetic/phenotypic trait correlation data, aimed at facilitating genetic network analysis or systems biology studies.

            The Animal Quantitative Trait Loci Database (Animal QTLdb)

              The Animal Quantitative Trait Loci (QTL) Database (Animal QTLdb) strives to collect all publicly available trait mapping data, i.e. QTL (phenotype/expression, eQTL), candidate gene and association data (GWAS), and copy number variations (CNV) mapped to livestock animal genomes, in order to facilitate locating and comparing discoveries within and between species. New data and database tools are continually developed to align various trait mapping data to map-based genome features such as annotated genes.

              Genome analysis of the ubiquitous boxwood pathogen Pseudonectria foliicola: A small fungal genome with an increased cohort of genes associated with loss of virulence

                Boxwood plants are affected by many different diseases caused by fungi. Some boxwood diseases are deadly and quickly kill the infected plants, but with others, the plant can survive and even thrive when infected. The fungus that causes volutella blight is the most common of these weak boxwood pathogens. Even the healthiest boxwood plants are infected by the volutella fungus, and often there are no signs that the plants are hurt by the infection. In order to understand why the volutella blight fungus is such a weak pathogen and to understand the genetic mechanisms it uses to interact with boxwood, the complete genome of the volutella fungus was sequenced and characterized. These datasets are generated from the genome sequence of *Pseudonectria foliicola*, strain ATCC13545, the fungus responsible for volutella disease of boxwood. Datasets include the nuclear genome and mitochondrial genome assemblies (sequenced using Illumina technology), the predicted gene model dataset generated using MAKER, the multiple sequence alignment of single-copy orthologs used for phylogenetic analysis, CMAP files generated from SimpleSynteny analysis of mitogenomes, and high quality photographic images.


                  The Next Generation Cassava Breeding (NEXTGEN Cassava) project aims to significantly increase the rate of genetic improvement in cassava breeding and unlock the full potential of cassava, a staple crop central to food security and livelihoods across Africa. The project will implement and empirically test a new breeding method known as Genomic Selection that relies on statistical modeling to predict cassava performance before field-testing, and dramatically accelerates the breeding cycle.


                    Panzea is an NSF-funded project called "Biology of Rare Alleles in Maize and its Wild Relatives". We are investigating the connection between phenotype (what we see) and genotype (the genes underlying the phenotype) - of complex traits in maize and its wild relative, teosinte, and specifically in how rare genetic variations contribute to overall plant function. These studies will enrich our knowledge of evolution, sustainable agriculture, and genetic diversity and conservation. Over the 10 years of the project, we have trained many new scientists at all levels and generated key resources for the public, teachers, and scientific researchers.

                    Switchgrass ESTs and SNPs

                      As part of our project, “Developing Association Mapping in Polyploid Perennial Biofuel Grasses” (DOE-USDA Plant Feedstock Genomics for Bioenergy Program grant DE-A102-07ER64454)*, two SNP discovery initiatives were carried out. The earlier one (2009) was an approach based on EST sequences. The latest initiative (2011-12) adopted a more powerful approach, based on GBS (Genotyping by Sequencing). We believe that the SNP markers identified in these studies will greatly enhance breeding efforts that target the improvement of key biofuel traits and the development of new switchgrass cultivars.