Explorer Facilitating functional annotation of chicken microarray data (original) (raw)
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Comparative and Functional Genomics, 2004
The genetic networks that govern the differentiation and growth of major tissues of economic importance in the chicken are largely unknown. Under a functional genomics project, our consortium has generated 30 609 expressed sequence tags (ESTs) and developed several chicken DNA microarrays, which represent the Chicken Metabolic/Somatic (10 K) and Neuroendocrine/Reproductive (8 K) Systems (http://udgenome.ags.udel.edu/cogburn/). One of the major challenges facing functional genomics is the development of mathematical models to reconstruct functional gene networks and regulatory pathways from vast volumes of microarray data. In initial studies with liver-specific microarrays (3.1 K), we have examined gene expression profiles in liver during the peri-hatch transition and during a strong metabolic perturbation -fasting and re-feeding -in divergently selected broiler chickens (fast vs. slow-growth lines). The expression of many genes controlling metabolic pathways is dramatically altered by these perturbations. Our analysis has revealed a large number of clusters of functionally related genes (mainly metabolic enzymes and transcription factors) that control major metabolic pathways. Currently, we are conducting transcriptional profiling studies of multiple tissues during development of two sets of divergently selected broiler chickens (fast vs. slow growing and fat vs. lean lines). Transcriptional profiling across multiple tissues should permit construction of a detailed genetic blueprint that illustrates the developmental events and hierarchy of genes that govern growth and development of chickens. This review will briefly describe the recent acquisition of chicken genomic resources (ESTs and microarrays) and our consortium's efforts to help launch the new era of functional genomics in the chicken.
IMAD: flexible annotation of microarray sequences
BMC proceedings, 2009
Accurate and current functional annotation of microarray probes is essential for the analysis and interpretation of the biological processes involved. As gene structures and functional annotation are updated in genome databases, the annotation attached to microarray probes must be updated so that scientists have access to the latest information with which to analyse their data. We have designed a pipeline and database for the annotation of microarray probes using publically available databases. The pipeline is based on NCBI BLAST, Perl and MySQL. The pipeline was used to annotate a subset of 791 differentially expressed ArkGenomics chicken probes from an experiment involving chickens infected with the protozoan parasite Eimeria. Using our pipeline, 770 of the probes were assigned at least one entry in either the Ensembl, UniGene or the DFCI gene indices databases. The pipeline described here provides a simple and robust way of maintaining up-to-date and accurate annotation for micro...
Systems-wide Chicken DNA Microarrays, Gene Expression Profiling, and Discovery of Functional Genes
2003
The goal of our current consortium project is to launch a new era-functional genomics of poultryby providing genomic resources [expressed sequence tags (EST) and DNA microarrays] and by examining global gene expression in target tissues of chickens. DNA microarray analysis has been a fruitful strategy for the identification of functional genes in several model organisms (i.e., human, rodents, fruit fly, etc.). We have constructed and normalized five tissue-specific or multiple-tissue chicken cDNA libraries [liver, fat, breast, and leg muscle/ epiphyseal growth plate, pituitary/hypothalamus/pineal, and reproductive tract (oviduct/ovary/testes)] for high-throughput DNA sequencing of EST. DNA sequence clustering was used to build contigs of overlapping sequence and to identify unique, non-redundant EST clones (unigenes), which permitted printing of systems-wide chicken DNA microarrays. One of the most promising genetic resources for gene exploration and functional gene mapping is provided by two sets of experimental lines of broiler-type chickens developed at INRA, France, by divergent selection for extremes in growth traits (fastgrowing versus slow-growing; fatness versus leanness at (Key words: chicken gene index, expressed sequence tag (EST), DNA microarray, functional genomics, gene expression profiling) 2003 Poultry Science 82:939-951 Abbreviation Key: BBSRC = Biotechnology and Biological Sciences Research Council; C/EBPα = CCAAT/enhancer binding protein-α; CAP3 = cluster assembly program 3; CFAR = Centre for Food and Animal Research; DBI = Delaware Biotechnology Institute; EST = expressed sequence tags; FGL = fast-growing line; FL = fat line; LL = lean line; M = morgan; PTU = propyl-thiouracil; SGL = slow-growing line; SPARC = secreted protein acidic and rich in cysteine; TIGR = The Institute for Genomic Research; THIG = thyroid hormone-inducible gene; THRG = thyroid hormone-repressible gene; T 3 = triiodothyronine; UD = University of Delaware.
Functional Genomics of the Chicken--A Model Organism
Poultry Science, 2007
Since the sequencing of the genome and the development of high-throughput tools for the exploration of functional elements of the genome, the chicken has reached model organism status. Functional genomics focuses on understanding the function and regulation of genes and gene products on a global or genome-wide scale. Systems biology attempts to integrate functional information derived from multiple high-content data sets into a holistic view of all biological processes within a cell or organism. Generation of a large collection (∼600K) of chicken expressed sequence tags, representing most tissues and developmental stages, has enabled the construction of high-density microarrays for transcriptional profiling. Comprehensive analysis of this large expressed sequence tag collection and a set of ∼20K full-length cDNA sequences indicate that the transcriptome of the chicken represents approximately 20,000 genes. Furthermore, comparative analyses of these sequences have facilitated functional annotation of the genome and the creation of several bioinformatic resources for the chicken.
ANEXdb: An Integrated Animal ANnotation and Microarray EXpression Database.
To determine annotations of the sequence elements on microarrays used for transcriptional profiling experiments in livestock species, currently researchers must either use the sparse direct annotations available for these species or create their own annotations. ANEXdb (http:// www.anexdb.org) is an open-source web application that supports integrated access of two databases that house microarray expression (ExpressDB) and EST annotation (AnnotDB) data. The expression database currently supports storage and querying of Affymetrix-based expression data as well as retrieval of experiments in a form ready for NCBI-GEO submission; these services are available online. AnnotDB currently houses a novel assembly of approximately 1.6 million unique porcine-expressed sequence reads called the Iowa Porcine Assembly (IPA), which consists of 140,087 consensus sequences, the Iowa Tentative Consensus (ITC) sequences, and 103,888 singletons. The IPA has been annotated via transfer of information from homologs identified through sequence alignment to NCBI RefSeq. These annotated sequences have been mapped to the Affymetrix porcine array elements, providing annotation for 22,569 of the 23,937 (94%) porcinespecific probe sets, of which 19,253 (80%) are linked to an NCBI RefSeq entry. The ITC has also been mined for sequence variation, providing evidence for up to 202,383 SNPs, 62,048 deletions, and 958 insertions in porcineexpressed sequence. These results create a single location to obtain porcine annotation of and sequence variation in differently expressed genes in expression experiments, thus permitting possible identification of causal variants in such genes of interest. The ANEXdb application is open source and available from SourceForge.net.
Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome
BMC Genomics, 2007
The chicken genome was sequenced because of its phylogenetic position as a nonmammalian 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.
Genetic architecture of gene expression in the chicken
BMC Genomics, 2013
Background: The annotation of many genomes is limited, with a large proportion of identified genes lacking functional assignments. The construction of gene co-expression networks is a powerful approach that presents a way of integrating information from diverse gene expression datasets into a unified analysis which allows inferences to be drawn about the role of previously uncharacterised genes. Using this approach, we generated a condition-free gene co-expression network for the chicken using data from 1,043 publically available Affymetrix GeneChip Chicken Genome Arrays. This data was generated from a diverse range of experiments, including different tissues and experimental conditions. Our aim was to identify gene co-expression modules and generate a tool to facilitate exploration of the functional chicken genome.
Strategies for enhanced annotation of a microarray probe set
International Journal of Bioinformatics Research and Applications, 2010
We aim to determine the biological relevance of genes identified through microarray-mediated transcriptional profiling of Xenopus sensory organs and brain tissue. Genetic data analysis depends on how, and to what extent, the probe sets for genes represented on the microarray are characterized. Difficulties can arise when probe set annotation is used to interpret expression data because of limitations to the amount of available information and the lack of a universal gene nomenclature. To address these potential impediments, we used three curation strategies to augment the annotation of probe sets on a commercially available microarray by using sequence based and semantic linking methods in combination with computational approaches. Our curation efforts enabled linkage of our probe sets and expression data to public access databases. We found that each curation process contributed to the enhancement of the vendor supplied annotation and also assisted with the tentative identification of previously unidentified probe set IDs. Our methods provide an alternative to the use of proprietary data analysis software to gain biological significance from microarray data. These approaches may be especially useful for laboratories investigating less studied organisms and species whose genomes have not been sequenced.
Microarray data mining using Bioconductor packages
BMC proceedings, 2009
Background: This paper describes the results of a Gene Ontology (GO) term enrichment analysis of chicken microarray data using the Bioconductor packages. By checking the enriched GO terms in three contrasts, MM8-PM8, MM8-MA8, and MM8-MM24, of the provided microarray data during this workshop, this analysis aimed to investigate the host reactions in chickens occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. The results of GO enrichment analysis using GO terms annotated to chicken genes and GO terms annotated to chicken-human orthologous genes were also compared. Furthermore, a locally adaptive statistical procedure (LAP) was performed to test differentially expressed chromosomal regions, rather than individual genes, in the chicken genome after Eimeria challenge.
Development of a cDNA array for chicken gene expression analysis
BMC Genomics, 2005
BACKGROUND: The application of microarray technology to functional genomic analysis in the chicken has been limited by the lack of arrays containing large numbers of genes. RESULTS: We have produced cDNA arrays using chicken EST collections generated by BBSRC, University of Delaware and the Fred Hutchinson Cancer Research Center. From a total of 363,838 chicken ESTs representing 24 different adult