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Research in the past decade has demonstrated that a single reference genome is not representative of a species’ diversity. MaizeGDB introduces a pan-genomic approach to hosting genomic data, leveraging the large number of diverse maize genomes and their associated datasets to quickly and efficiently connect genomes, gene models, expression, epigenome, sequence variation, structural variation, transposable elements, and diversity data across genomes so that researchers can easily track the structural and functional differences of a locus and its orthologs across maize. We believe our framework is unique and provides a template for any genomic database poised to host large-scale pan-genomic data. Supplementary Information The online version contains supplementary material available at 10.1186/s12870-021-03173-5.

The Maize Genetics and Genomics Database (MaizeGDB) team prepared a survey to identify breeders’ needs for visualizing pedigrees, diversity data and haplotypes in order to prioritize tool development and curation efforts at MaizeGDB. The survey was distributed to the maize research community on behalf of the Maize Genetics Executive Committee in Summer 2015. The survey garnered 48 responses from maize researchers, of which more than half were self-identified as breeders. The survey showed that the maize researchers considered their top priorities for visualization as: (i) displaying single nucleotide polymorphisms in a given region for a given list of lines, (ii) showing haplotypes for a given list of lines and (iii) presenting pedigree relationships visually. The survey also asked which populations would be most useful to display. The following two populations were on top of the list: (i) 3000 publicly available maize inbred lines used in Romay et al. (Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol, 2013;14:R55) and (ii) maize lines with expired Plant Variety Protection Act (ex-PVP) certificates. Driven by this strong stakeholder input, MaizeGDB staff are currently working in four areas to improve its interface and web-based tools: (i) presenting immediate progenies of currently available stocks at the MaizeGDB Stock pages, (ii) displaying the most recent ex-PVP lines described in the Germplasm Resources Information Network (GRIN) on the MaizeGDB Stock pages, (iii) developing network views of pedigree relationships and (iv) visualizing genotypes from SNP-based diversity datasets. These survey results can help other biological databases to direct their efforts according to user preferences as they serve similar types of data sets for their communities. Database URL: https://www.maizegdb.org

Fully automated text mining (TM) systems promote efficient literature searching,\ud retrieval, and review but are not sufficient to produce ready-to-consume curated documents.\ud These systems are not meant to replace biocurators, but instead to assist them in\ud one or more literature curation steps. To do so, the user interface is an important aspect\ud that needs to be considered for tool adoption. The BioCreative Interactive task (IAT) is a\ud track designed for exploring user-system interactions, promoting development of useful\ud TM tools, and providing a communication channel between the biocuration and the TM\ud communities. In BioCreative V, the IAT track followed a format similar to previous interactive\ud tracks, where the utility and usability of TM tools, as well as the generation of use\ud cases, have been the focal points. The proposed curation tasks are user-centric and\ud formally evaluated by biocurators. In BioCreative V IAT, seven TM systems and 43 biocurators\ud participated. Two levels of user participation were offered to broaden curator involvement\ud and obtain more feedback on usability aspects. The full level participation\ud involved training on the system, curation of a set of documents with and without TM\ud assistance, tracking of time-on-task, and completion of a user survey. The partial level\ud participation was designed to focus on usability aspects of the interface and not the performance\ud per se. In this case, biocurators navigated the system by performing predesigned\ud tasks and then were asked whether they were able to achieve the task and the\ud level of difficulty in completing the task. In this manuscript, we describe the development of the interactive task, from planning to execution and discuss major findings for the systems tested.

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.

To thrive, the field that links biologists and their data urgently needs structure, recognition and support.

There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most current versions of genetic and physical maps, tools with the ability to recognize novel phenotypes or classify known phenotypes, and an intimate knowledge of the biochemical processes generating physiological and phenotypic effects. They must also know how all of these factors change and differ among species, diverse alleles, germplasms, and environmental conditions. While there are robust databases, such as MaizeGDB, for some of these types of raw data, other crucial components are missing. Moreover, the management of visually observed mutant phenotypes is still in its infant stage, let alone the complex query methods that can draw upon high-level and aggregated information to answer the questions of geneticists. In this paper, we address the scientific challenge and propose to develop a robust framework for managing the knowledge of visually observed phenotypes, mining the correlation of visual characteristics with genetic maps, and discovering the knowledge relating to cross-species conservation of visual and genetic patterns. The ultimate goal of this research is to allow a geneticist to submit phenotypic and genomic information on a mutant to a knowledge base and ask, "What genes or environmental factors cause this visually observed phenotype?".

This work will allow bio-informaticians to analyze the ever-increasing gene sequence data, discover valuable knowledge in maize biology and related plant; development, and understand subtle variations among different phenotypes. Furthermore, successful measuring of visual phenotypes will advance plant research by finding the genes and/or environmental factors that cause a given visual phenotype. In what follows, the field of plant genetics is introduced (particularly quantitative trait loci and disease scoring) to the signal processing community, discuss the challenges involved, and present an image analysis system for precisely quantifying and mapping immeasurable phenotypes in maize

Formal description of plant phenotypes and standardized annotation of gene expression and protein localization data require uniform terminology that accurately describes plant anatomy and morphology. This facilitates cross species comparative studies and quantitative comparison of phenotypes and expression patterns. A major drawback is variable terminology that is used to describe plant anatomy and morphology in publications and genomic databases for different species. The same terms are sometimes applied to different plant structures in different taxonomic groups. Conversely, similar structures are named by their species-specific terms. To address this problem, we created the Plant Structure Ontology (PSO), the first generic ontological representation of anatomy and morphology of a flowering plant. The PSO is intended for a broad plant research community, including bench scientists, curators in genomic databases, and bioinformaticians. The initial releases of the PSO integrated existing ontologies for Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and rice (Oryza sativa); more recent versions of the ontology encompass terms relevant to Fabaceae, Solanaceae, additional cereal crops, and poplar (Populus spp.). Databases such as The Arabidopsis Information Resource, Nottingham Arabidopsis Stock Centre, Gramene, MaizeGDB, and SOL Genomics Network are using the PSO to describe expression patterns of genes and phenotypes of mutants and natural variants and are regularly contributing new annotations to the Plant Ontology database. The PSO is also used in specialized public databases, such as BRENDA, GENEVESTIGATOR, NASCArrays, and others. Over 10,000 gene annotations and phenotype descriptions from participating databases can be queried and retrieved using the Plant Ontology browser. The PSO, as well as contributed gene associations, can be obtained at www.plantontology.org.

Plant growth stages are identified as distinct morphological landmarks in a continuous developmental process. The terms describing these developmental stages record the morphological appearance of the plant at a specific point in its life cycle. The widely differing morphology of plant species consequently gave rise to heterogeneous vocabularies describing growth and development. Each species or family specific community developed distinct terminologies for describing whole-plant growth stages. This semantic heterogeneity made it impossible to use growth stage description contained within plant biology databases to make meaningful computational comparisons. The Plant Ontology Consortium (http://www.plantontology.org) was founded to develop standard ontologies describing plant anatomical as well as growth and developmental stages that can be used for annotation of gene expression patterns and phenotypes of all flowering plants. In this article, we describe the development of a generic whole-plant growth stage ontology that describes the spatiotemporal stages of plant growth as a set of landmark events that progress from germination to senescence. This ontology represents a synthesis and integration of terms and concepts from a variety of species-specific vocabularies previously used for describing phenotypes and genomic information. It provides a common platform for annotating gene function and gene expression in relation to the developmental trajectory of a plant described at the organismal level. As proof of concept the Plant Ontology Consortium used the plant ontology growth stage ontology to annotate genes and phenotypes in plants with initial emphasis on those represented in The Arabidopsis Information Resource, Gramene database, and MaizeGDB.

The Plant Ontology Consortium (POC) (www.plantontology.org) is a collaborative effort among several plant databases and experts in plant systematics, botany and genomics. A primary goal of the POC is to develop simple yet robust and extensible controlled vocabularies that accurately reflect the biology of plant structures and developmental stages. These provide a network of vocabularies linked by relationships (ontology) to facilitate queries that cut across datasets within a database or between multiple databases. The current version of the ontology integrates diverse vocabularies used to describeArabidopsis, maize and rice (Oryzasp.) anatomy, morphology and growth stages. Using the ontology browser, over 3500 gene annotations from three species-specific databases, The Arabidopsis Information Resource (TAIR) forArabidopsis, Gramene for rice and MaizeGDB for maize, can now be queried and retrieved.

Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation. Database URL: http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/.

Gene function curation via Gene Ontology (GO) annotation is a common task among Model Organism Database groups. Owing to its manual nature, this task is considered one of the bottlenecks in literature curation. There have been many previous attempts at automatic identification of GO terms and supporting information from full text. However, few systems have delivered an accuracy that is comparable with humans. One recognized challenge in developing such systems is the lack of marked sentence-level evidence text that provides the basis for making GO annotations. We aim to create a corpus that includes the GO evidence text along with the three core elements of GO annotations: (i) a gene or gene product, (ii) a GO term and (iii) a GO evidence code. To ensure our results are consistent with real-life GO data, we recruited eight professional GO curators and asked them to follow their routine GO annotation protocols. Our annotators marked up more than 5000 text passages in 200 articles for 1356 distinct GO terms. For evidence sentence selection, the inter-annotator agreement (IAA) results are 9.3% (strict) and 42.7% (relaxed) in F1-measures. For GO term selection, the IAAs are 47% (strict) and 62.9% (hierarchical). Our corpus analysis further shows that abstracts contain ∼10% of relevant evidence sentences and 30% distinct GO terms, while the Results/Experiment section has nearly 60% relevant sentences and >70% GO terms. Further, of those evidence sentences found in abstracts, less than one-third contain enough experimental detail to fulfill the three core criteria of a GO annotation. This result demonstrates the need of using full-text articles for text mining GO annotations. Through its use at the BioCreative IV GO (BC4GO) task, we expect our corpus to become a valuable resource for the BioNLP research community. Database URL: http://www.biocreative.org/resources/corpora/bc-iv-go-task-corpus/.

A framework for understanding the synthesis and catalysis of metabolites and other biochemicals by proteins is crucial for unraveling the physiology of cells. To create such a framework for Zea mays L. subsp. mays (maize), we developed MaizeCyc, a metabolic network of enzyme catalysts, proteins, carbohydrates, lipids, amino acids, secondary plant products, and other metabolites by annotating the genes identifi ed in the maize reference genome sequenced from the B73 variety. MaizeCyc version 2.0.2 is a collection of 391 maize pathways involving 8889 enzyme mapped to 2110 reactions and 1468 metabolites. We used MaizeCyc to describe the development and function of maize organs including leaf, root, anther, embryo, and endosperm by exploring the recently published microarray-based maize gene expression atlas. We found that 1062 differentially expressed metabolic genes mapped to 524 unique enzymatic reactions associated with 310 pathways. The MaizeCyc pathway database was created by running a library of evidences collected from the maize genome annotation, genebased phylogeny trees, and comparison to known genes and pathways from rice (Oryza sativa L.) and Arabidopsis thaliana (L.) Heynh. against the PathoLogic module of Pathway Tools. The network and the database that were also developed as a community resource are freely accessible online at http:// maizecyc.maizegdb.org to facilitate analysis and promote studies on metabolic genes in maize. M

It is anticipated that the coming years will see the generation of large datasets including diagnostic markers in several plant species with emphasis on crop plants. To use these datasets effectively in any breeding program, it is essential to have the information available via public databases. For some major crop species, databases exist including GrainGenes (for small grain cereals), Gramene (for rice and related grasses), MaizeGDB (for maize), SoyBase (for soybean), and the Sol Genomics Network (SGN; for tomato, potato, etc.) to name a few. These databases are both extensive and useful, and require constant improvement to meet the ever-changing needs of the scientists they seek to serve. This article is devoted to describing tools and resources that are currently available for use and outlines some lesser-known, unique repositories and tools that aim to meet the data management and analysis needs of breeders and other plant biologists.

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.

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

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 URL: http://www.maizegdb.org/

Advances in sequencing technology have made it possible to sequence the 2500 MB B73 maize genome, both cheaply and in a relatively short time. Nearly simultaneously, other sequencing-based data are on the leading edge of a data tsunami: sequenced differences (currently >300,000 SNP for >1000 inbred lines and related species) and gene expression data. The MaizeGDB team integrates access to these datasets after considerable cooperation among both data users and suppliers, and typically while the data are being generated. In this way, data suppliers have resources in place to format data ready for integration with MaizeGDB. And, we learn what details, including links to other resources, are needed to support our user community and what interface development is required. An interesting result is that we inadvertently serve as outreach for the data suppliers, explaining the caveats and expected availabilities of data not yet at MaizeGDB. Contact with users is at conferences, by online tutorials, personal visits, or by surveys from the Maize Executive Genetics Committee addressed to all persons listed in MaizeGDB as a Cooperator.

Maize is a major cereal crop and an important model system for basic biological research. Knowledge gained from maize research can also be used to genetically improve its grass relatives such as sorghum, wheat, and rice. The primary objective of the Maize Genome Sequencing Consortium (MGSC) was to generate a reference genome sequence that was integrated with both the physical and genetic maps. Using a previously published integrated genetic and physical map, combined with in-coming maize genomic sequence, new sequence-based genetic markers, and an optical map, we dynamically picked a minimum tiling path (MTP) of 16,910 bacterial artificial chromosome (BAC) and fosmid clones that were used by the MGSC to sequence the maize genome. The final MTP resulted in a significantly improved physical map that reduced the number of contigs from 721 to 435, incorporated a total of 8,315 mapped markers, and ordered and oriented the majority of FPC contigs. The new integrated physical and genetic map covered 2,120 Mb (93%) of the 2,300-Mb genome, of which 405 contigs were anchored to the genetic map, totaling 2,103.4 Mb (99.2% of the 2,120 Mb physical map). More importantly, 336 contigs, comprising 94.0% of the physical map (∼1,993 Mb), were ordered and oriented. Finally we used all available physical, sequence, genetic, and optical data to generate a golden path (AGP) of chromosome-based pseudomolecules, herein referred to as the B73 Reference Genome Sequence version 1 (B73 RefGen_v1)., Author Summary Maize has been a cultural icon and staple food crop of Americans since the discovery of the new world in 1492. Contemporary society is now faced with growing demands for food and fuel in the face of global climate change and the potential for increased disease pressure. To provide a comprehensive foundation to systematically understand maize biology with the goal of breeding higher yielding, disease-resistant, and drought-tolerant cultivars, our consortium sequenced the B73 genome of maize. In this study, we used a comprehensive physical and genetic framework map to develop a minimum tiling path (MTP) of over 16,000 BAC clones across the genome. The MTP was generated dynamically and integrated numerous data types, such as in-coming genome sequence, over 8,000 sequence-based genetic markers, and the maize optical map. This allowed us to genetically anchor, order, and orient the majority of the maize physical map and genome sequence to the genetic map. Post-genome sequencing, we constructed a golden path (AGP) of sequence-based pseudomolecules representing the ten chromosomes of the maize B73 genome (B73 RefGen_v1). This unprecedented integration of genetic, physical, and genomic sequence into one framework will greatly facilitate all aspects of plant biological research.

The first formal phenotype summaries for maize can be found in early editions of the Maize Genetics Cooperation Newsletter (MNL). Prior to on-line databases, the MNL served to systematize available mutant stocks, genetic maps, and addresses for maize geneticists. In fact, the first phenotypic data entered into MaizeGDB, in 1991, was from the MNL stock center report. To support renewed, community submission of similar data, the MNL has initiated a new on-line journal, called the ‘maize gene review’ (MGR, www.maizegenereview.org). It represents an extension of the Mutants of Maize (1997), eds MG Neuffer, EH Coe, SR Wessler, where text summaries of individual mutants were provided by experts in the field, often along with photographs. These were subsequently incorporated into text descriptions and image files at MaizeGDB, with authors credited. In the on-line journal MGR, each gene has a page, and an author(s), responsible for supplying a minimal set of information, and updating as new information becomes available. Reviewers familiar with the types of mutants described are requested to provide criticism and updates, which are forwarded to the contributing author, as suggestions on updating their page(s). Reciprocal links are provided to the MaizeGDB for genome and map information and the short summary of each mutant is added to the MaizeGDB locus record. This journal adds to the current community curation tools at MaizeGDB for community curation and has had an excellent response. The MNL is supported by an endowment at the University of Missouri provided by the Maize Cooperators. MaizeGDB is a USDA Agricultural Research Service funded database where the presenting author is a curator.

In 2001 maize became the number one production crop in the world with the Food and Agriculture Organization of the United Nations reporting over 614 million tonnes produced. Its success is due to the high productivity per acre in tandem with a wide variety of commercial uses. Not only is maize an excellent source of food, feed, and fuel, but also its by-products are used in the production of various commercial products. Maize's unparalleled success in agriculture stems from basic research, the outcomes of which drive breeding and product development. In order for basic, translational, and applied researchers to benefit from others' investigations, newly generated data must be made freely and easily accessible. MaizeGDB is the maize research community's central repository for genetics and genomics information. The overall goals of MaizeGDB are to facilitate access to the outcomes of maize research by integrating new maize data into the database and to support the maize research community by coordinating group activities.

Maize (Zea mays L.) is one of the most important cereal crops and a model for the study of genetics, evolution, and domestication. To better understand maize genome organization and to build a framework for genome sequencing, we constructed a sequence-ready fingerprinted contig-based physical map that covers 93.5% of the genome, of which 86.1% is aligned to the genetic map. The fingerprinted contig map contains 25,908 genic markers that enabled us to align nearly 73% of the anchored maize genome to the rice genome. The distribution pattern of expressed sequence tags correlates to that of recombination. In collinear regions, 1 kb in rice corresponds to an average of 3.2 kb in maize, yet maize has a 6-fold genome size expansion. This can be explained by the fact that most rice regions correspond to two regions in maize as a result of its recent polyploid origin. Inversions account for the majority of chromosome structural variations during subsequent maize diploidization. We also find clear evidence of ancient genome duplication predating the divergence of the progenitors of maize and rice. Reconstructing the paleoethnobotany of the maize genome indicates that the progenitors of modern maize contained ten chromosomes., Author Summary As a cash crop and a model biological system, maize is of great public interest. To facilitate maize molecular breeding and its basic biology research, we built a high-resolution physical map with two different fingerprinting methods on the same set of bacterial artificial chromosome clones. The physical map was integrated to a high-density genetic map and further serves as a framework for the maize genome-sequencing project. Comparative genomics showed that the euchromatic regions between rice and maize are very conserved. Physically we delimited these conserved regions and thus detected many genome rearrangements. We defined extensively the duplication blocks within the maize genome. These blocks allowed us to reconstruct the chromosomes of the maize progenitor. We detected that maize genome has experienced two rounds of genome duplications, an ancient one before maize–rice divergence and a recent one after tetraploidization.

The Plant Ontology (PO; http://www.plantontology.org/) is a publicly available, collaborative effort to develop and maintain a controlled, structured vocabulary (‘ontology’) of terms to describe plant anatomy, morphology and the stages of plant development. The goals of the PO are to link (annotate) gene expression and phenotype data to plant structures and stages of plant development, using the data model adopted by the Gene Ontology. From its original design covering only rice, maize and Arabidopsis, the scope of the PO has been expanded to include all green plants. The PO was the first multispecies anatomy ontology developed for the annotation of genes and phenotypes. Also, to our knowledge, it was one of the first biological ontologies that provides translations (via synonyms) in non-English languages such as Japanese and Spanish. As of Release #18 (July 2012), there are about 2.2 million annotations linking PO terms to >110,000 unique data objects representing genes or gene models, proteins, RNAs, germplasm and quantitative trait loci (QTLs) from 22 plant species. In this paper, we focus on the plant anatomical entity branch of the PO, describing the organizing principles, resources available to users and examples of how the PO is integrated into other plant genomics databases and web portals. We also provide two examples of comparative analyses, demonstrating how the ontology structure and PO-annotated data can be used to discover the patterns of expression of the LEAFY (LFY) and terpene synthase (TPS) gene homologs.

Background Molecular markers serve three important functions in physical map assembly. First, they provide anchor points to genetic maps facilitating functional genomic studies. Second, they reduce the overlap required for BAC contig assembly from 80 to 50 percent. Finally, they validate assemblies based solely on BAC fingerprints. We employed a six-dimensional BAC pooling strategy in combination with a high-throughput PCR-based screening method to anchor the maize genetic and physical maps. Results A total of 110,592 maize BAC clones (~ 6x haploid genome equivalents) were pooled into six different matrices, each containing 48 pools of BAC DNA. The quality of the BAC DNA pools and their utility for identifying BACs containing target genomic sequences was tested using 254 PCR-based STS markers. Five types of PCR-based STS markers were screened to assess potential uses for the BAC pools. An average of 4.68 BAC clones were identified per marker analyzed. These results were integrated with BAC fingerprint data generated by the Arizona Genomics Institute (AGI) and the Arizona Genomics Computational Laboratory (AGCoL) to assemble the BAC contigs using the FingerPrinted Contigs (FPC) software and contribute to the construction and anchoring of the physical map. A total of 234 markers (92.5%) anchored BAC contigs to their genetic map positions. The results can be viewed on the integrated map of maize [1, 2]. Conclusion This BAC pooling strategy is a rapid, cost effective method for genome assembly and anchoring. The requirement for six replicate positive amplifications makes this a robust method for use in large genomes with high amounts of repetitive DNA such as maize. This strategy can be used to physically map duplicate loci, provide order information for loci in a small genetic interval or with no genetic recombination, and loci with conflicting hybridization-based information.