Finding our way through phenotypes.

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.

Automated update, revision, and quality control of the maize genome annotations using MAKER-P improves the B73 RefGen_v3 gene models and identifies new genes.

The large size and relative complexity of many plant genomes make creation, quality control, and dissemination of high-quality gene structure annotations challenging. In response, we have developed MAKER-P, a fast and easy-to-use genome annotation engine for plants. Here, we report the use of MAKER-P to update and revise the maize (Zea mays) B73 RefGen_v3 annotation build (5b+) in less than 3 h using the iPlant Cyberinfrastructure. MAKER-P identified and annotated 4,466 additional, well-supported protein-coding genes not present in the 5b+ annotation build, added additional untranslated regions to 1,393 5b+ gene models, identified 2,647 5b+ gene models that lack any supporting evidence (despite the use of large and diverse evidence data sets), identified 104,215 pseudogene fragments, and created an additional 2,522 noncoding gene annotations. We also describe a method for de novo training of MAKER-P for the annotation of newly sequenced grass genomes. Collectively, these results lead to the 6a maize genome annotation and demonstrate the utility of MAKER-P for rapid annotation, management, and quality control of grasses and other difficult-to-annotate plant genomes.

MAKER-P: a tool kit for the rapid creation, management, and quality control of plant genome annotations.

We have optimized and extended the widely used annotation engine MAKER in order to better support plant genome annotation efforts. New features include better parallelization for large repeat-rich plant genomes, noncoding RNA annotation capabilities, and support for pseudogene identification. We have benchmarked the resulting software tool kit, MAKER-P, using the Arabidopsis (Arabidopsis thaliana) and maize (Zea mays) genomes. Here, we demonstrate the ability of the MAKER-P tool kit to automatically update, extend, and revise the Arabidopsis annotations in light of newly available data and to annotate pseudogenes and noncoding RNAs absent from The Arabidopsis Informatics Resource 10 build. Our results demonstrate that MAKER-P can be used to manage and improve the annotations of even Arabidopsis, perhaps the best-annotated plant genome. We have also installed and benchmarked MAKER-P on the Texas Advanced Computing Center. We show that this public resource can de novo annotate the entire Arabidopsis and maize genomes in less than 3 h and produce annotations of comparable quality to those of the current The Arabidopsis Information Resource 10 and maize V2 annotation builds.

Maize chromosomal knobs are located in gene-dense areas and suppress local recombination.

Knobs are conspicuous heterochromatic regions found on the chromosomes of maize and its relatives. The number, locations, and sizes of knobs vary dramatically, with most lines containing between four and eight knobs in mid-arm positions. Prior data suggest that some knobs may reduce recombination. However, comprehensive tests have not been carried out, primarily because most knobs have not been placed on the genetic map. We used fluorescent in situ hybridization and two recombinant inbred populations to map seven knobs and to accurately place three knobs from the B73 inbred on the genomic sequence assembly. The data show that knobs lie in gene-dense regions of the maize genome. Comparisons to 23 other recombinant inbred populations segregating for knobs at the same sites confirm that large knobs can locally reduce crossing over by as much as twofold on a cM/Mb scale. These effects do not extend beyond regions ~10 cM to either side of knobs and do not appear to affect linkage disequilibrium among genes within and near knob repeat regions of the B73 RefGen_v2 assembly.

The Locus Lookup tool at MaizeGDB: identification of genomic regions in maize by integrating sequence information with physical and genetic maps.

SUMMARY: Methods to automatically integrate sequence information with physical and genetic maps are scarce. The Locus Lookup tool enables researchers to define windows of genomic sequence likely to contain loci of interest where only genetic or physical mapping associations are reported. Using the Locus Lookup tool, researchers will be able to locate specific genes more efficiently that will ultimately help them develop a better maize plant. With the availability of the well-documented source code, the tool can be easily adapted to other biological systems. AVAILABILITY: The Locus Lookup tool is available on the web at http://maizegdb.org/cgi-bin/locus_lookup.cgi. It is implemented in PHP, Oracle and Apache, with all major browsers supported. Source code is freely available for download at http://ftp.maizegdb.org/open_source/locus_lookup/.

High-throughput linkage analysis of Mutator insertion sites in maize.

Insertional mutagenesis is a cornerstone of functional genomics. High-copy transposable element systems such as Mutator (Mu) in maize (Zea mays) afford the advantage of high forward mutation rates but pose a challenge for identifying the particular element responsible for a given mutation. Several large mutant collections have been generated in Mu-active genetic stocks, but current methods limit the ability to rapidly identify the causal Mu insertions. Here we present a method to rapidly assay Mu insertions that are genetically linked to a mutation of interest. The method combines elements of MuTAIL (thermal asymmetrically interlaced) and amplification of insertion mutagenized sites (AIMS) protocols and is applicable to the analysis of single mutants or to high-throughput analyses of mutant collections. Briefly, genomic DNA is digested with a restriction enzyme and adapters are ligated. Polymerase chain reaction is performed with TAIL cycling parameters, using a fluorescently labeled Mu primer, which results in the preferential amplification and labeling of Mu-containing genomic fragments. Products from a segregating line are analyzed on a capillary sequencer. To recover a fragment of interest, PCR products are cloned and sequenced. Sequences with lengths matching the size of a band that co-segregates with the mutant phenotype represent candidate linked insertion sites, which are then confirmed by PCR. We demonstrate the utility of the method by identifying Mu insertion sites linked to seed-lethal mutations with a preliminary success rate of nearly 50%.

PlantGDB: a resource for comparative plant genomics.

PlantGDB (http://www.plantgdb.org/) is a genomics database encompassing sequence data for green plants (Viridiplantae). PlantGDB provides annotated transcript assemblies for >100 plant species, with transcripts mapped to their cognate genomic context where available, integrated with a variety of sequence analysis tools and web services. For 14 plant species with emerging or complete genome sequence, PlantGDB's genome browsers (xGDB) serve as a graphical interface for viewing, evaluating and annotating transcript and protein alignments to chromosome or bacterial artificial chromosome (BAC)-based genome assemblies. Annotation is facilitated by the integrated yrGATE module for community curation of gene models. Novel web services at PlantGDB include Tracembler, an iterative alignment tool that generates contigs from GenBank trace file data and BioExtract Server, a web-based server for executing custom sequence analysis workflows. PlantGDB also hosts a plant genomics research outreach portal (PGROP) that facilitates access to a large number of resources for research and training.

A standardized kinesin nomenclature.

In recent years the kinesin superfamily has become so large that several different naming schemes have emerged, leading to confusion and miscommunication. Here, we set forth a standardized kinesin nomenclature based on 14 family designations. The scheme unifies all previous phylogenies and nomenclature proposals, while allowing individual sequence names to remain the same, and for expansion to occur as new sequences are discovered.

MaizeGDB's new data types, resources and activities.

MaizeGDB is the Maize Genetics and Genomics Database. Available at MaizeGDB are diverse data that support maize research including maps, gene product information, loci and their various alleles, phenotypes (both naturally occurring and as a result of directed mutagenesis), stocks, sequences, molecular markers, references and contact information for maize researchers worldwide. Also available through MaizeGDB are various community support service bulletin boards including the Editorial Board's list of high-impact papers, information about the Annual Maize Genetics Conference and the Jobs board where employment opportunities are posted. Reported here are data updates, improvements to interfaces and changes to standard operating procedures that have been made during the past 2 years. MaizeGDB is freely available and can be accessed online at http://www.maizegdb.org.

Predicting chromosomal locations of genetically mapped loci in maize using the Morgan2McClintock Translator.

The Morgan2McClintock Translator permits prediction of meiotic pachytene chromosome map positions from recombination-based linkage data using recombination nodule frequency distributions. Its outputs permit estimation of DNA content between mapped loci and help to create an integrated overview of the maize nuclear genome structure.

MaizeGDB.

MaizeGDB is the community database for biological information about the crop plant Zea mays. Genetic, genomic, sequence, gene product, functional characterization, literature reference, and person/organization contact information are among the data types stored at MaizeGDB. At the project's website (http://www.maizegdb.org) are standardized custom interfaces enabling researchers to browse data and to seek out specific information matching explicit search criteria. In addition, pre-compiled reports are made available for particular types of data, and bulletin boards are provided to facilitate communication and coordination among members of the community of maize geneticists.

MaizeGDB, the community database for maize genetics and genomics.

The Maize Genetics and Genomics Database (MaizeGDB) is a central repository for maize sequence, stock, phenotype, genotypic and karyotypic variation, and chromosomal mapping data. In addition, MaizeGDB provides contact information for over 2400 maize cooperative researchers, facilitating interactions between members of the rapidly expanding maize community. MaizeGDB represents the synthesis of all data available previously from ZmDB and from MaizeDB-databases that have been superseded by MaizeGDB. MaizeGDB provides web-based tools for ordering maize stocks from several organizations including the Maize Genetics Cooperation Stock Center and the North Central Regional Plant Introduction Station (NCRPIS). Sequence searches yield records displayed with embedded links to facilitate ordering cloned sequences from various groups including the Maize Gene Discovery Project and the Clemson University Genomics Institute. An intuitive web interface is implemented to facilitate navigation between related data, and analytical tools are embedded within data displays. Web-based curation tools for both designated experts and general researchers are currently under development. MaizeGDB can be accessed at http://www.maizegdb.org/.

MaizeGDB: The Maize Genetics and Genomics Database.

MaizeGDB is the community database for biological information about the crop plant Zea mays. Genomic, genetic, sequence, gene product, functional characterization, literature reference, and person/organization contact information are among the datatypes stored at MaizeGDB. At the project's website ( http://www.maizegdb.org ) are custom interfaces enabling researchers to browse data and to seek out specific information matching explicit search criteria. In addition, pre-compiled reports are made available for particular types of data and bulletin boards are provided to facilitate communication and coordination among members of the community of maize geneticists.

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.

G-quadruplex (G4) motifs in the maize (Zea mays L.) genome are enriched at specific locations in thousands of genes coupled to energy status, hypoxia, low sugar, and nutrient deprivation.

The G-quadruplex (G4) elements comprise a class of nucleic acid structures formed by stacking of guanine base quartets in a quadruple helix. This G4 DNA can form within or across single-stranded DNA molecules and is mutually exclusive with duplex B-form DNA. The reversibility and structural diversity of G4s make them highly versatile genetic structures, as demonstrated by their roles in various functions including telomere metabolism, genome maintenance, immunoglobulin gene diversification, transcription, and translation. Sequence motifs capable of forming G4 DNA are typically located in telomere repeat DNA and other non-telomeric genomic loci. To investigate their potential roles in a large-genome model plant species, we computationally identified 149,988 non-telomeric G4 motifs in maize (Zea mays L., B73 AGPv2), 29% of which were in non-repetitive genomic regions. G4 motif hotspots exhibited non-random enrichment in genes at two locations on the antisense strand, one in the 5' UTR and the other at the 5' end of the first intron. Several genic G4 motifs were shown to adopt sequence-specific and potassium-dependent G4 DNA structures in vitro. The G4 motifs were prevalent in key regulatory genes associated with hypoxia (group VII ERFs), oxidative stress (DJ-1/GATase1), and energy status (AMPK/SnRK) pathways. They also showed statistical enrichment for genes in metabolic pathways that function in glycolysis, sugar degradation, inositol metabolism, and base excision repair. Collectively, the maize G4 motifs may represent conditional regulatory elements that can aid in energy status gene responses. Such a network of elements could provide a mechanistic basis for linking energy status signals to gene regulation in maize, a model genetic system and major world crop species for feed, food, and fuel.

Multi-source and ontology-based retrieval engine for maize mutant phenotypes.

Model Organism Databases, including the various plant genome databases, collect and enable access to massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc, as well as textual descriptions of many of these entities. While a variety of basic browsing and search capabilities are available to allow researchers to query and peruse the names and attributes of phenotypic data, next-generation search mechanisms that allow querying and ranking of text descriptions are much less common. In addition, the plant community needs an innovative way to leverage the existing links in these databases to search groups of text descriptions simultaneously. Furthermore, though much time and effort have been afforded to the development of plant-related ontologies, the knowledge embedded in these ontologies remains largely unused in available plant search mechanisms. Addressing these issues, we have developed a unique search engine for mutant phenotypes from MaizeGDB. This advanced search mechanism integrates various text description sources in MaizeGDB to aid a user in retrieving desired mutant phenotype information. Currently, descriptions of mutant phenotypes, loci and gene products are utilized collectively for each search, though expansion of the search mechanism to include other sources is straightforward. The retrieval engine, to our knowledge, is the first engine to exploit the content and structure of available domain ontologies, currently the Plant and Gene Ontologies, to expand and enrich retrieval results in major plant genomic databases. Database URL: http:www.PhenomicsWorld.org/QBTA.php.

MaizeGDB: curation and outreach go hand-in-hand.

First released in 1991 with the name MaizeDB, the Maize Genetics and Genomics Database, now MaizeGDB, celebrates its 20th anniversary this year. MaizeGDB has transitioned from a focus on comprehensive curation of the literature, genetic maps and stocks to a paradigm that accommodates the recent release of a reference maize genome sequence, multiple diverse maize genomes and sequence-based gene expression data sets. The MaizeGDB Team is relatively small, and relies heavily on the research community to provide data, nomenclature standards and most importantly, to recommend future directions, priorities and strategies. Key aspects of MaizeGDB's intimate interaction with the community are the co-location of curators with maize research groups in multiple locations across the USA as well as coordination with MaizeGDB's close partner, the Maize Genetics Cooperation--Stock Center. In this report, we describe how the MaizeGDB Team currently interacts with the maize research community and our plan for future interactions that will support updates to the functional and structural annotation of the B73 reference genome.

The MaizeGDB Genome Browser tutorial: one example of database outreach to biologists via video.

Video tutorials are an effective way for researchers to quickly learn how to use online tools offered by biological databases. At MaizeGDB, we have developed a number of video tutorials that demonstrate how to use various tools and explicitly outline the caveats researchers should know to interpret the information available to them. One such popular video currently available is 'Using the MaizeGDB Genome Browser', which describes how the maize genome was sequenced and assembled as well as how the sequence can be visualized and interacted with via the MaizeGDB Genome Browser. Database

POPcorn: An Online Resource Providing Access to Distributed and Diverse Maize Project Data.

The purpose of the online resource presented here, POPcorn (Project Portal for corn), is to enhance accessibility of maize genetic and genomic resources for plant biologists. Currently, many online locations are difficult to find, some are best searched independently, and individual project websites often degrade over time-sometimes disappearing entirely. The POPcorn site makes available (1) a centralized, web-accessible resource to search and browse descriptions of ongoing maize genomics projects, (2) a single, stand-alone tool that uses web Services and minimal data warehousing to search for sequence matches in online resources of diverse offsite projects, and (3) a set of tools that enables researchers to migrate their data to the long-term model organism database for maize genetic and genomic information: MaizeGDB. Examples demonstrating POPcorn's utility are provided herein.

BioExtract server--an integrated workflow-enabling system to access and analyze heterogeneous, distributed biomolecular data.

Many in silico investigations in bioinformatics require access to multiple, distributed data sources and analytic tools. The requisite data sources may include large public data repositories, community databases, and project databases for use in domain-specific research. Different data sources frequently utilize distinct query languages and return results in unique formats, and therefore researchers must either rely upon a small number of primary data sources or become familiar with multiple query languages and formats. Similarly, the associated analytic tools often require specific input formats and produce unique outputs which make it difficult to utilize the output from one tool as input to another. The BioExtract Server (http://bioextract.org) is a Web-based data integration application designed to consolidate, analyze, and serve data from heterogeneous biomolecular databases in the form of a mash-up. The basic operations of the BioExtract Server allow researchers, via their Web browsers, to specify data sources, flexibly query data sources, apply analytic tools, download result sets, and store query results for later reuse. As a researcher works with the system, their "steps" are saved in the background. At any time, these steps can be preserved long-term as a workflow simply by providing a workflow name and description.