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Facilitating functional annotation of chicken microarray data.

ABSTRACT: BACKGROUND: 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. RESULTS: 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 (AGOM) tool to help researchers to quickly retrieve corresponding functional information for their dataset. CONCLUSION: 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 AGOM 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.

SUBMITTER: Buza TJ

PROVIDER: S-EPMC3226191 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Facilitating functional annotation of chicken microarray data.

Buza Teresia J TJ Kumar Ranjit R Gresham Cathy R CR Burgess Shane C SC McCarthy Fiona M FM

BMC bioinformatics 20091008

<h4>Background</h4>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 li ...[more]

PMID: 19811685

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Project description:UNLABELLED: The widespread availability of microarray technology has driven functional genomics to the forefront as scientists seek to draw meaningful biological conclusions from their microarray results. Gene annotation enrichment analysis is a functional analysis technique that has gained widespread attention and for which many tools have been developed. Unfortunately, most of these tools have limited support for agricultural species. Here, we evaluate and compare four publicly available computational tools (Onto-Express, EasyGO, GOstat, and DAVID) that support analysis of gene expression datasets in agricultural species. We use AgBase as the functional annotation reference for agricultural species. The selected tools were evaluated based on i) available features, usage and accessibility, ii) implemented statistical computational methods, and iii) annotation and enrichment performance analysis. Annotation was assessed using a randomly selected test gene annotation set and an experimental differentially expressed gene-set--both from chicken. The experimental set was also used to evaluate identification of enriched functional groups.Comparison of the tools shows that they produce different sets of annotations for the two datasets and different functional groups for the experimental dataset. While DAVID, GOstat and Onto-Express annotate comparable numbers of genes, DAVID provides by far the most annotations per gene. However, many of DAVID's annotations appear to be redundant or are at very high levels in the GO hierarchy. The GOSlim distribution of annotations shows that GOstat, Onto-Express and EasyGO provide similar GO distributions to those found in AgBase while annotations from DAVID show a different GOSlim distribution, again probably due to duplication and many non-specific terms. No consistent trends were found in results of GO term over/under representation analysis applied to the experimental data using different tools. While GOstat, David and Onto-Express could retrieve some significantly enriched terms, EasyGO did not show any significantly enriched terms. There was little agreement about the enriched terms identified by the tools. CONCLUSION: Different tools for functionally annotating gene sets and identifying significantly enriched GO categories differ widely in their results when applied to a test annotation gene set and an experimental dataset from chicken. These results emphasize the need for care when interpreting the results of such analysis and the lack of standardization of approaches.

Project description:BackgroundThe 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.ResultsWe analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the in vivo 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.ConclusionWe 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.

Project description:BackgroundMicroarray technology has matured over the past fifteen years into a cost-effective solution with established data analysis protocols for global gene expression profiling. The Agilent-016047 maize 44 K microarray was custom-designed from EST sequences, but only reporter sequences with EST accession numbers are publicly available. The following information is lacking: (a) reporter - gene model match, (b) number of reporters per gene model, (c) potential for cross hybridization, (d) sense/antisense orientation of reporters, (e) position of reporter on B73 genome sequence (for eQTL studies), and (f) functional annotations of genes represented by reporters. To address this, we developed a strategy to annotate the Agilent-016047 maize microarray, and built a publicly accessible annotation database.DescriptionGenomic annotation of the 42,034 reporters on the Agilent-016047 maize microarray was based on BLASTN results of the 60-mer reporter sequences and their corresponding ESTs against the maize B73 RefGen v2 "Working Gene Set" (WGS) predicted transcripts and the genome sequence. The agreement between the EST, WGS transcript and gDNA BLASTN results were used to assign the reporters into six genomic annotation groups. These annotation groups were: (i) "annotation by sense gene model" (23,668 reporters), (ii) "annotation by antisense gene model" (4,330); (iii) "annotation by gDNA" without a WGS transcript hit (1,549); (iv) "annotation by EST", in which case the EST from which the reporter was designed, but not the reporter itself, has a WGS transcript hit (3,390); (v) "ambiguous annotation" (2,608); and (vi) "inconclusive annotation" (6,489). Functional annotations of reporters were obtained by BLASTX and Blast2GO analysis of corresponding WGS transcripts against GenBank.The annotations are available in the Maize Microarray Annotation Database http://MaizeArrayAnnot.bi.up.ac.za/, as well as through a GBrowse annotation file that can be uploaded to the MaizeGDB genome browser as a custom track.The database was used to re-annotate lists of differentially expressed genes reported in case studies of published work using the Agilent-016047 maize microarray. Up to 85% of reporters in each list could be annotated with confidence by a single gene model, however up to 10% of reporters had ambiguous annotations. Overall, more than 57% of reporters gave a measurable signal in tissues as diverse as anthers and leaves.ConclusionsThe Maize Microarray Annotation Database will assist users of the Agilent-016047 maize microarray in (i) refining gene lists for global expression analysis, and (ii) confirming the annotation of candidate genes before functional studies.

Dataset Information

Facilitating functional annotation of chicken microarray data.

Publications

Facilitating functional annotation of chicken microarray data.

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