Experimental and computational methods to assign gene function to maize genes

Maize is an important crop species and is the highest produced cereal crop in the world as well as a model species for genetics and genomics research. For this reason, researchers have been very successful in translating understanding of basic biological processes into improved crops for over 100 years. Maize researchers have a long history of utilizing genetic techniques to dissect the function of genes that control biological processes. Characterizing and cloning mutants precisely defines gene function but is a slow process that can take years to accomplish. Alternatively, computational methods provide a faster way to assign predicted function to genes by leveraging the vast knowledge base of gene function gathered by experimental and curatorial efforts in multiple species. Computational methods can be used to predict functions for genes at a genome-wide scale. Ideally, improved computational predictions would narrow and target experiments that would be used to test gene function, thus speeding the process of experimental characterization. We have created methods to improve discrete steps in both experimental characterization and computational prediction of gene function in maize. For the experimental work, we have developed molecular methods, leveraging the decreasing high-throughput sequencing cost, and bioinformatics analysis pipelines, capitalizing the availability of multiple maize genome assemblies, that improve positional cloning of maize mutants. We have also focused on methods to improve identification of T-DNA integration locations genome-wide for maize. Genes responsible for mutant phenotypes are often studied using transgenic techniques to manipulate function at a molecular level. These techniques typically integrate a transfer DNA (T-DNA) fragment into the host genome, where genome integration context may have crucial effects on transgene expression. Current methods to identify T-DNA integration locations are either cumbersome or imprecise for repetitive rich genomes like maize. We developed a molecular protocol that utilizes long-read sequencing to enrich genomic T-DNA flanks, thus revealing T-DNA placement more precisely. Working to identify and characterize genetic variants responsible for specific phenotypes gives insight into how critical the quality of predicted gene function annotations can be to inform and guide experimental investigation. Functional annotation data are used for the interpretation of results from large-scale studies such as transcriptomics and proteomics. In addition, these data are also used to inform and prioritize candidate genes potentially responsible for a phenotype for positional cloning, genetic association, and other studies. To improve the quality of predicted gene functions available for all researchers working in maize, we generated a high-coverage, high-confidence, and reproducible functional annotation dataset for maize genes using the Gene Ontology. Methods we used to generate GO annotations for maize are generic and applicable to other plants. To enable application to other species, we formalized the method used to annotate maize as a containerized pipeline called GOMAP. GOMAP has been optimized for use in high- performance computing environments and has been tested on additional maize lines and other plant species

An annotated genetic map of loblolly pine based on microsatellite and cDNA markers

BACKGROUND: Previous loblolly pine (Pinus taeda L.) genetic linkage maps have been based on a variety of DNA polymorphisms, such as AFLPs, RAPDs, RFLPs, and ESTPs, but only a few SSRs (simple sequence repeats), also known as simple tandem repeats or microsatellites, have been mapped in P. taeda. The objective of this study was to integrate a large set of SSR markers from a variety of sources and published cDNA markers into a composite P. taeda genetic map constructed from two reference mapping pedigrees. A dense genetic map that incorporates SSR loci will benefit complete pine genome sequencing, pine population genetics studies, and pine breeding programs. Careful marker annotation using a variety of references further enhances the utility of the integrated SSR map. RESULTS: The updated P. taeda genetic map, with an estimated genome coverage of 1,515 cM((Kosambi)) across 12 linkage groups, incorporated 170 new SSR markers and 290 previously reported SSR, RFLP, and ESTP markers. The average marker interval was 3.1 cM. Of 233 mapped SSR loci, 84 were from cDNA-derived sequences (EST-SSRs) and 149 were from non-transcribed genomic sequences (genomic-SSRs). Of all 311 mapped cDNA-derived markers, 77% were associated with NCBI Pta UniGene clusters, 67% with RefSeq proteins, and 62% with functional Gene Ontology (GO) terms. Duplicate (i.e., redundant accessory) and paralogous markers were tentatively identified by evaluating marker sequences by their UniGene cluster IDs, clone IDs, and relative map positions. The average gene diversity, H(e), among polymorphic SSR loci, including those that were not mapped, was 0.43 for 94 EST-SSRs and 0.72 for 83 genomic-SSRs. The genetic map can be viewed and queried at http://www.conifergdb.org/pinemap. CONCLUSIONS: Many polymorphic and genetically mapped SSR markers are now available for use in P. taeda population genetics, studies of adaptive traits, and various germplasm management applications. Annotating mapped genes with UniGene clusters and GO terms allowed assessment of redundant and paralogous EST markers and further improved the quality and utility of the genetic map for P. taeda

Primer Server

A project to store information related to Primer Server project in Vollbrecht Lab at Iowa State Universit

Experimental and computational methods to assign gene function to maize genes

Maize is an important crop species and is the highest produced cereal crop in the world as well as a model species for genetics and genomics research. For this reason, researchers have been very successful in translating understanding of basic biological processes into improved crops for over 100 years. Maize researchers have a long history of utilizing genetic techniques to dissect the function of genes that control biological processes. Characterizing and cloning mutants precisely defines gene function but is a slow process that can take years to accomplish. Alternatively, computational methods provide a faster way to assign predicted function to genes by leveraging the vast knowledge base of gene function gathered by experimental and curatorial efforts in multiple species. Computational methods can be used to predict functions for genes at a genome-wide scale. Ideally, improved computational predictions would narrow and target experiments that would be used to test gene function, thus speeding the process of experimental characterization. We have created methods to improve discrete steps in both experimental characterization and computational prediction of gene function in maize. For the experimental work, we have developed molecular methods, leveraging the decreasing high-throughput sequencing cost, and bioinformatics analysis pipelines, capitalizing the availability of multiple maize genome assemblies, that improve positional cloning of maize mutants. We have also focused on methods to improve identification of T-DNA integration locations genome-wide for maize. Genes responsible for mutant phenotypes are often studied using transgenic techniques to manipulate function at a molecular level. These techniques typically integrate a transfer DNA (T-DNA) fragment into the host genome, where genome integration context may have crucial effects on transgene expression. Current methods to identify T-DNA integration locations are either cumbersome or imprecise for repetitive rich genomes like maize. We developed a molecular protocol that utilizes long-read sequencing to enrich genomic T-DNA flanks, thus revealing T-DNA placement more precisely. Working to identify and characterize genetic variants responsible for specific phenotypes gives insight into how critical the quality of predicted gene function annotations can be to inform and guide experimental investigation. Functional annotation data are used for the interpretation of results from large-scale studies such as transcriptomics and proteomics. In addition, these data are also used to inform and prioritize candidate genes potentially responsible for a phenotype for positional cloning, genetic association, and other studies. To improve the quality of predicted gene functions available for all researchers working in maize, we generated a high-coverage, high-confidence, and reproducible functional annotation dataset for maize genes using the Gene Ontology. Methods we used to generate GO annotations for maize are generic and applicable to other plants. To enable application to other species, we formalized the method used to annotate maize as a containerized pipeline called GOMAP. GOMAP has been optimized for use in high- performance computing environments and has been tested on additional maize lines and other plant species.</p

Maize RefGen_v3 GO Annotations

These are the GO Annotations downloaded from Gramene and Phytozome for Maize REfGen_v3.<br><br><u><b>Please cite Gramene and Phytozome projects if you use these datasets</b></u><div><br></div

GOMAP Process Flowchart

The GOMAP Process Flowchart provides a visualization of the files generated from each step of the Gene Ontology Meta Annotator for Plants (GOMAP) Pipeline

Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants

BACKGROUND: Genome-wide gene function annotations are useful for hypothesis generation and for prioritizing candidate genes potentially responsible for phenotypes of interest. We functionally annotated the genes of 18 crop plant genomes across 14 species using the GOMAP pipeline. RESULTS: By comparison to existing GO annotation datasets, GOMAP-generated datasets cover more genes, contain more GO terms, and are similar in quality (based on precision and recall metrics using existing gold standards as the basis for comparison). From there, we sought to determine whether the datasets across multiple species could be used together to carry out comparative functional genomics analyses in plants. To test the idea and as a proof of concept, we created dendrograms of functional relatedness based on terms assigned for all 18 genomes. These dendrograms were compared to well-established species-level evolutionary phylogenies to determine whether trees derived were in agreement with known evolutionary relationships, which they largely are. Where discrepancies were observed, we determined branch support based on jackknifing then removed individual annotation sets by genome to identify the annotation sets causing unexpected relationships. CONCLUSIONS: GOMAP-derived functional annotations used together across multiple species generally retain sufficient biological signal to recover known phylogenetic relationships based on genome-wide functional similarities, indicating that comparative functional genomics across species based on GO data holds promise for generating novel hypotheses about comparative gene function and traits