Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome

<p>Abstract</p> <p>Background</p> <p>The chicken genome was sequenced because of its phylogenetic position as a non-mammalian vertebrate, its use as a biomedical model especially to study embryology and development, its role as a source of human disease organisms and its importance as the major source of animal derived food protein. However, genomic sequence data is, in itself, of limited value; generally it is not equivalent to understanding biological function. The benefit of having a genome sequence is that it provides a basis for functional genomics. However, the sequence data currently available is poorly structurally and functionally annotated and many genes do not have standard nomenclature assigned.</p> <p>Results</p> <p>We analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the <it>in vivo </it>expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology), we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO) functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines.</p> <p>Conclusion</p> <p>We demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and inform gene prediction algorithms.</p

Transcriptomic dissection of the rice – Burkholderia glumae interaction

BACKGROUND: Bacterial panicle blight caused by the bacterium Burkholderia glumae is an emerging disease of rice in the United States. Not much is known about this disease, the disease cycle or any source of disease resistance. To understand the interaction between rice and Burkholderia glumae, we used transcriptomics via next-generation sequencing (RNA-Seq) and bioinformatics to identify differentially expressed transcripts between resistant and susceptible interactions and formulate a model for rice resistance to the disease. RESULTS: Using inoculated young seedlings as sample tissues, we identified unique transcripts involved with resistance to bacterial panicle blight, including a PIF-like ORF1 and verified differential expression of some selected genes using qRT-PCR. These transcripts, which include resistance genes of the NBS-LRR type, kinases, transcription factors, transporters and expressed proteins with functions that are not known, have not been reported in other pathosystems including rice blast or bacterial blight. Further, functional annotation analysis reveals enrichment of defense response and programmed cell death (biological processes); ATP and protein binding (molecular functions); and mitochondrion-related (cell component) transcripts in the resistant interaction. CONCLUSION: Taken together, we formulated a model for rice resistance to bacterial panicle blight that involves an activation of previously unknown resistance genes and their activation partners upon challenge with B. glumae. Other interesting findings are that 1) though these resistance transcripts were up-regulated upon inoculation in the resistant interaction, some of them were already expressed in the water-inoculated control from the resistant genotype, but not in the water- and bacterium-inoculated samples from the susceptible genotype; 2) rice may have co-opted an ORF that was previously a part of a transposable element to aid in the resistance mechanism; and 3) resistance may have existed immediately prior to rice domestication. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-755) contains supplementary material, which is available to authorized users

Gene Ontology annotation quality analysis in model eukaryotes

Functional analysis using the Gene Ontology (GO) is crucial for array analysis, but it is often difficult for researchers to assess the amount and quality of GO annotations associated with different sets of gene products. In many cases the source of the GO annotations and the date the GO annotations were last updated is not apparent, further complicating a researchers’ ability to assess the quality of the GO data provided. Moreover, GO biocurators need to ensure that the GO quality is maintained and optimal for the functional processes that are most relevant for their research community. We report the GO Annotation Quality (GAQ) score, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation. As a case study, we apply the GAQ scoring method to a set of diverse eukaryotes and demonstrate how the GAQ score can be used to track changes in GO annotations over time and to assess the quality of GO annotations available for specific biological processes. The GAQ score also allows researchers to quantitatively assess the functional data available for their experimental systems (arrays or databases)

Facilitating functional annotation of chicken microarray data

<p>Abstract</p> <p>Background</p> <p>Modeling results from chicken microarray studies is challenging for researchers due to little functional annotation associated with these arrays. The Affymetrix GenChip chicken genome array, one of the biggest arrays that serve as a key research tool for the study of chicken functional genomics, is among the few arrays that link gene products to Gene Ontology (GO). However the GO annotation data presented by Affymetrix is incomplete, for example, they do not show references linked to manually annotated functions. In addition, there is no tool that facilitates microarray researchers to directly retrieve functional annotations for their datasets from the annotated arrays. This costs researchers amount of time in searching multiple GO databases for functional information.</p> <p>Results</p> <p>We have improved the breadth of functional annotations of the gene products associated with probesets on the Affymetrix chicken genome array by 45% and the quality of annotation by 14%. We have also identified the most significant diseases and disorders, different types of genes, and known drug targets represented on Affymetrix chicken genome array. To facilitate functional annotation of other arrays and microarray experimental datasets we developed an Array GO Mapper (<it>AGOM</it>) tool to help researchers to quickly retrieve corresponding functional information for their dataset.</p> <p>Conclusion</p> <p>Results from this study will directly facilitate annotation of other chicken arrays and microarray experimental datasets. Researchers will be able to quickly model their microarray dataset into more reliable biological functional information by using <it>AGOM </it>tool. The disease, disorders, gene types and drug targets revealed in the study will allow researchers to learn more about how genes function in complex biological systems and may lead to new drug discovery and development of therapies. The GO annotation data generated will be available for public use via AgBase website and will be updated on regular basis.</p

AgBase: supporting functional modeling in agricultural organisms

AgBase (http://www.agbase.msstate.edu/) provides resources to facilitate modeling of functional genomics data and structural and functional annotation of agriculturally important animal, plant, microbe and parasite genomes. The website is redesigned to improve accessibility and ease of use, including improved search capabilities. Expanded capabilities include new dedicated pages for horse, cat, dog, cotton, rice and soybean. We currently provide 590 240 Gene Ontology (GO) annotations to 105 454 gene products in 64 different species, including GO annotations linked to transcripts represented on agricultural microarrays. For many of these arrays, this provides the only functional annotation available. GO annotations are available for download and we provide comprehensive, species-specific GO annotation files for 18 different organisms. The tools available at AgBase have been expanded and several existing tools improved based upon user feedback. One of seven new tools available at AgBase, GOModeler, supports hypothesis testing from functional genomics data. We host several associated databases and provide genome browsers for three agricultural pathogens. Moreover, we provide comprehensive training resources (including worked examples and tutorials) via links to Educational Resources at the AgBase website

Improving structural and functional annotation of the chicken genome

Chicken is an important non-mammalian vertebrate model organism for biomedical research, especially for vaccine production and the study of embryology and development. Chicken is also an important agricultural species and major food source for high-quality protein worldwide. In addition, chicken is an important model organism for comparative and evolution genomics. Exploitation of this genome as a biomedical model is hindered by its incomplete structural and functional annotation. This incomplete annotation makes it difficult for researchers to model their functional genomics datasets. Improving structural and functional annotation of the chicken genome will allow researchers to derive biological meaning from their functional genomics datasets. The objectives of this study were to identify proteins expressed in multiple chicken tissues, to functionally annotate experimentally confirmed proteins expressed in different chicken tissues, to quantify and assess the Gene Ontology (GO) annotation quality, and to facilitate functional annotation of microarray data. The results of this research have proven to be fundamental resource for improving the structural and functional annotation of chicken genome. Specifically, we have improved the structural annotation of the chicken genome by adding support to predicted proteins. In addition, we have improved the functional annotation of the chicken genome by assigning useful biological information to proteomics datasets and the whole genome chicken array. The Gene Ontology Annotation Quality (GAQ) and Array GO Mapper (AGOM) tools developed in this study will sustainably continue to facilitate functional modeling of chicken arrays and high-throughput experimental datasets from microarray and proteomics studies. The ultimate positive impact of these results is to facilitate the field of biomedical research with useful information for comparative biology, better understanding of chicken biological systems, diseases, drug discovery and eventually development of therapies