- Research article
- Open Access
Characterization of a newly developed chicken 44K Agilent microarray
© Li et al; licensee BioMed Central Ltd. 2008
Received: 21 November 2007
Accepted: 31 January 2008
Published: 31 January 2008
The development of microarray technology has greatly enhanced our ability to evaluate gene expression. In theory, the expression of all genes in a given organism can be monitored simultaneously. Sequencing of the chicken genome has provided the crucial information for the design of a comprehensive chicken transcriptome microarray. A long oligonucleotide microarray has been manually curated and designed by our group and manufactured using Agilent inkjet technology. This provides a flexible and powerful platform with high sensitivity and specificity for gene expression studies.
A chicken 60-mer oligonucleotide microarray consisting of 42,034 features including the entire Marek's disease virus, two avian influenza virus (H5N2 and H5N3), and 150 chicken microRNAs has been designed and tested. In an important validation study, total RNA isolated from four major chicken tissues: cecal tonsil (C), ileum (I), liver (L), and spleen (S) were used for comparative hybridizations. More than 95% of spots had high signal noise ratio (SNR > 10). There were 2886, 2660, 358, 3208, 3355, and 3710 genes differentially expressed between liver and spleen, spleen and cecal tonsil, cecal tonsil and ileum, liver and cecal tonsil, liver and ileum, spleen and ileum (P < 10-7), respectively. There were a number of tissue-selective genes for cecal tonsil, ileum, liver, and spleen identified (95, 71, 535, and 108, respectively; P < 10-7). Another highlight of these data revealed that the antimicrobial peptides GAL1, GAL2, GAL6 and GAL7 were highly expressed in the spleen compared to other tissues tested.
A chicken 60-mer oligonucleotide 44K microarray was designed and validated in a comprehensive survey of gene expression in diverse tissues. The results of these tissue expression analyses have demonstrated that this microarray has high specificity and sensitivity, and will be a useful tool for chicken functional genomics. Novel data on the expression of putative tissue specific genes and antimicrobial peptides is highlighted as part of this comprehensive microarray validation study. The information for accessing and ordering this 44K chicken array can be found at http://people.tamu.edu/~hjzhou/TAMUAgilent44KArray/
The chicken, being the first farm animal with a completely sequenced genome, has become an important animal model in the fields of evolution, development, immunology, oncology, cell biology, virology, and genetics [1, 2]. Candidate genes, QTL, and molecular markers have been widely utilized to reveal the genetic basis of economically important traits in chickens [3–5]. There are also many new genetic and bioinformatics resources available that are based upon chicken genome information, including genetic and physical maps , EST databases , and SNP maps [1, 8]. Global gene expression profiling will provide a complementary tool improving our ability to study regulation of complex and economically important traits in chickens.
The development of high-throughput microarray has accelerated the study of gene expression by interrogating thousands of genes simultaneously [9–11]. Microarray technologies provide an important tool to infer gene networks and to identify highly conserved genetic pathways in plants and animals. There have been many important studies contributing to gene expression profiling in agricultural animals including pigs [12, 13], rabbits , and cattle [15, 16]. Several chicken cDNA or oligonucleotide probe (oligo) arrays have also been developed and utilized in gene expression studies. These arrays include a 3,011 lymphocyte array , a 3,072 intestinal array , an 11K heart specific array , a 14,718 macrophage specific array , a 13K cDNA transcriptome array , a 5K immune related array [21, 22], a 20K long oligo chicken genome array , and a 33K Affymetrix chicken genome array .
Short and long oligo arrays have several advantages over cDNA arrays in terms of specificity, sensitivity, and reproducibility . Both microarray technologies can provide comprehensive and reliable data for global expression analyses. However, oligos are more uniform in concentrations and annealing temperature, more gene-specific, flexible, and economic. Long oligos can provide increased signal intensity compared to short ones [26, 27]. Long oligo arrays generated by Agilent Technology may be able to detect down to single transcript per cell . This 60-mer 44K chicken whole genome custom array which was developed by our group and manufactured using the Agilent Technology will provide a comprehensive and powerful functional genomics tool for the agricultural community.
Genes selected on the array
The signal-to-noise ratio (SNR) for each element was calculated using the difference of the median intensity, minus the median background, divided by the standard deviation of the background . The percentage of high quality spots (SNR > 10) were calculated as the number of high quality spots divided by the total number of spots on the array. For all 24 arrays, the average percentage of high quality spots was determined to be 96.55 ± 4.89%.
Gene expression in different tissues
Before normalization, signal intensities of each feature were filtered against negative controls in the array. The ratio of signal intensity for each gene and the average signal intensity of negative control elements were calculated. An arbitrary ratio of 1.5 was used to determine if a particular gene was expressed in a given tissue. It was found that 43.83% of all genes on the array were expressed within all four tissues. Looking at each tissue individually, it was found that 71.11%, 80.05%, 75.37%, and 80.22% of the genes on the array were expressed in cecal tonsil, ileum, liver, and spleen, respectively.
A comparative study was conducted by comparing gene expression profiles between each of the four selected tissues (cecal tonsil, ileum, liver, and spleen). There were 3710, 3355, 3208, 2886, 2660, and 358 genes significantly and differentially expressed between spleen and ileum, liver and ileum, liver and cecal tonsil, liver and spleen, spleen and cecal tonsil, and cecal tonsil and ileum at the cut-off of P < 10-7. The corresponding false discovery rate (FDR) for each comparison was calculated and shown to be 4.46 × 10-7, 4.14 × 10-7, 4.37 × 10-7, 5.02 × 10-7, 7.39 × 10-7 and 9.11 × 10-6, respectively. Out of the 150 chicken microRNAs included in this microarray, it was shown that 15, 36, 31, 24, 15, and 11 microRNAs were differentially expressed when comparing spleen and ileum, liver and ileum, liver and cecal tonsil, liver and spleen, spleen and cecal tonsil, and cecal tonsil and ileum (P < 0.05).
The number of tissue selective genes at certain cut-off P values
Quantitative real time PCR
The gene symbol, accession number, and primers of genes used for quantitative RT-PCR
Forward primer (5'-3')
Reverse primer (5'-3')
Microarray and qRT-PCR results of 23 selected genes for each pair of comparison
Liver vs. spleen
Spleen vs. cecal tonsil
Cecal tonsil vs. ileum
Microarray and qRT-PCR results of 23 selected genes for each pair of comparison
Liver vs. cecal tonsil
Liver vs. ileum
Spleen vs. ileum
Utilization of the array
MIAME information about this chicken transcriptome microarray has been deposited in NCBI's Gene Expression Omnibus (GEO) . The accession numbers are: Platform, GPL4993; Series, GSE7452; Samples, GSM180391–GSM180406, GSM180426, GSM180428, GSM180430, GSM180433, GSM180434, GSM180436, GSM180438, GSM180441.
Three different types of microarrays have been widely utilized in genome research including cDNA (long strands of amplified cDNA sequences), short oligonucleotide (25–30 nt), and long oligonucleotide (50–80 nt). Several studies have compared the performance of different platforms [10, 13, 31–34]. Annotation and identity of the commercial oligonucleotides are reliable and the probe performance is excellent . Commercial microarrays can provide higher precision than homemade microarrays . This custom long-oligo array was generated by the Agilent SurePrint ink-jet technology, which also provides a flexible platform for revising and updating oligonucleotide probes in the array without additional cost [25, 35]. Only small amount of RNA is needed for labelling (50 ng to 5 μg of total RNA or 10–100 ng of poly (A)+ RNA) , compared to at least 20–30 μg total RNA using cDNA array. This is especially important for those applications that generate limited amounts of RNA, such as laser-capture.
Chicken, as a major food animal, plays a key role in nutrition and food safety for human health, and is a model organism in developmental biology and for disease research including virology, oncology, and immunology . There were several chicken whole genome microarrays as noted in the introduction. The currently described 44K long oligonucleotide (60-mer) microarray has shown overall high array quality and specificity compared to cDNA and 25-mer oligo arrays . In addition, the 4 × 44K platform in the array design has the feature of four independent arrays in one slide, which is more cost effective and can also reduce variations among the arrays within a slide. The design of this array was based on expressed sequences selected by walking over the chicken genome sequences in the UCSC genome browser. This manual approach allowed us to maximize genome coverage and minimize gene redundancy.
High background levels in an array platform can obscure the signal from low-expressed genes and impede accurate quantification. The magnitude of SNR can affect the sensitivity of the microarray, and a higher SNR indicates high sensitivity and low background. In general, SNR > 3 was used as the lower-bound threshold for spot detection  in the current microarray studies and a SNR > 10 was the indication of high quality spots . More than 95% of the spots with SNR > 10 in the array compared to 86.3 to 88.9% with SNR > 3 for the chicken cDNA array  have demonstrated the high sensitivity of the current array. The average SNR of the current microarray was 921.93, which was much higher than the SNR of most cDNA array platforms (35.1 to 38.3). This will promote sufficient signal generation for the detection of even low copy genes.
Quantitative real time PCR has become the gold standard for the gene expression and generally used to validate the microarray results . At the criterion of P < 5 × 10-4 in the microarray analysis, false positives could be effectively controlled (95.5% consistency between microarray and qRT-PCR). For those 4.5% inconsistent ones, large variations were observed between four biological replicates within each tissue using the more sensitive qRT-PCR method, which caused higher P values. On the other hand, the results from qRT-PCR demonstrated that type II errors (false negatives) can be controlled, given certain cut-off P value from microarray analysis (100% true false, given P > 0.05). These results indicated that microarray analyses from the current array were statistically reliable and accurate.
Genes on the microarray
This whole genome 44 K microarray consists of probes designed from all potential genes and was designed based on the February 2004 chicken (Gallus gallus) v1.0 draft assembly. The current array design includes all of the available (150) chicken microRNAs from miRBase 8.1 [39, 40], all known Marek's disease virus and two avian influenza virus (H5N2 and H5N3) transcripts. This array platform will provide a unique opportunity to study host-pathogen interaction using the same array simultaneously. This is important as we currently face potential emergence of an avian influenza virus epidemic. A second version of this array based on May 2006 chicken (Gallus gallus) v2.1 draft assembly has been updated and is now available.
A strict statistical criterion has been applied in the current analysis. Several thousand genes were differentially expressed between every two tissue comparison even at P < 10-7. Because there were more than 40 thousand genes analyzed in this microarray experiment; therefore, it is important to control the proportion of false positives . False discovery rates (FDR) based on P values is the expected proportion of true null hypotheses rejected in relation to the total number of null hypotheses rejected . FDR is a more convenient and natural scale than the P-value scale, and it can provide the probability of a gene value to be false positive . In this study, the FDRs were less than 5% for a P value of 10-7, which demonstrated the reliable results of the current microarray experiment. Similar FDR were observed in gene expression profiling between different tissues using a long oligo swine array .
Gene expression profiles of different normal tissues provide information about the biological function of the tissue and are expected to be conserved during evolution. Liver, spleen, and ileum have been widely utilized in gene expression profiling studies in human [29, 44–46] and swine [12, 47]. There were some common gene ontology terms enriched with tissue comparison between spleen and ileum in both human  and chickens such as protein biosynthesis, energy pathways, and immune response. But there were some distinct enrich terms between human and chickens including cytochrome C oxidase activity in human, and cell death, development, M phase, macromolecule metabolism, and physiological process in chicken. For the comparison of liver and spleen, energy pathways, main pathways of carbohydrate metabolism, and fatty acid oxidation were enriched in human , while generation of precursor metabolites and energy, cellular carbohydrate metabolism, cellular lipid metabolism, tricarboxylic acid cycle organic acid metabolism were enriched in chickens (Figures 4A, B).
Cecal tonsil and ileum are two proximal tissues in the digestive tract. Both of these are critical components of gut-associated immune system. They might share many common functions, which means, there might be fewer genes differentially expressed between them. The lowest number of differentially expressed genes (358) was found in the comparison of cecal tonsil and ileum. These findings support the expectation that tissues with similar gene expression might have similar biological functions.
Liver is a major organ with important biological functions like lipid metabolism. More genes specifically expressed in liver than spleen and ileum and/or other tissues in human and swine [12, 44, 46]. Similar results were observed in chickens. In human, oxidoreductase activity, lipid metabolism, complement activation, steroid metabolism, alcohol metabolism, cytochrome P450 activity, urea cycle, coagulation, amino acid metabolism, bile acid biosynthesis, and carbohydrate metabolism were liver-selective [29, 44, 45, 48]. In swine, coagulation pathway, alcohol metabolism, lipid processing, bile metabolism and xenobiotic metabolism were liver-specific . In the current study, energy, metabolism, especial fatty acid metabolism-related genes, fatty acid or lipid binding protein, fatty acid synthase, cholesterol hydroxylase, lectin, adenyl nucleotide binding, ATPase, hydrolase, coagulation factor, cytochrome P450, lyase, C3, C4B, C8, phosphorylase, and oxidoreductase, were found liver-selective (see additional file 3).
Spleen is one of the major immune organs. Many immune-related genes were more highly expressed in spleen than the other three tissues in chickens. Similar results have been observed using northern blot hybridization , moreover, it was reported that immune response genes were selectively expressed in human spleen  and porcine small intestine . Ileum is one of the more important tissues involved as part of bacterial pathogenesis studies in agricultural animals. Genes related to interaction between organisms and viral life cycle were specifically expressed in porcine ileum cDNA libraries . In chickens, class II histocompatibility antigen, B-L beta chain and C7 were found ileum-selective (see additional file 2). No ileum-selective genes were available in human from the previous studies. The conserved gene expression profiles in tissue comparisons among species have provide a solid basis for comparative genomics study. The tissue-selective genes could be potentially used as markers for the origin of pathogen, like gut-related pathogens.
Perhaps one of the most important and interesting findings in the study was in relation to antimicrobial peptides (AMPs). AMPs are essential for the innate immune response in plants, flies, mammals, and chickens. There are two major families of AMPs: defensins and cathelicidins. Fourteen β-defensins, known as gallinacins (GAL) and cathelicidin have been described in chickens [51–53]. In the present study, GAL1, GAL2 and GAL6-7 showed strong comparative induction in spleen and weakest expression in the ileum. Macrophage receptor with collagenous structure (MARCO) mediates alveolar macrophages to bind, ingest and clear the inhaled particle and bacteria . MARCO only expressed on the marginal zone macrophage of the spleen and macrophages of meullary cord in lymph nodes in normal mice . The current study corroborates this as we also found MARCO was highly expressed in spleen compared to other tissues.
To our knowledge, this is the first study to characterize tissue expression in chickens using a whole genome array. A total of four tissues were selected for this study. Two of these (liver and spleen) are complete organs, which play significant roles in many sophisticated biological functions of the animals. The liver is responsible for lipid, amino acid, and carbohydrate metabolism, while the spleen is an essential part of immune function in animals. The other two tissues (ileum and cecal tonsil) may have less complicated functions than liver and spleen. The GO analysis of global gene expression profiling among these four tissues supported the notion that more clusters of genes would be significantly enriched in the comparisons of organ (liver and spleen) against tissues such as ileum and cecal tonsil (Figures 4A, 4B, 5). The majority of functional enrichments associated with gene regulation in the liver comparisons were consistent with the roles of liver . In the spleen, there were many immune-related (cell death, apoptosis, response to stimulus etc) clusters enriched. In summary, the results above demonstrated that this newly developed chicken 44K whole genome array is a powerful genomic tool to investigate different biological processes in chickens.
We have characterized a newly developed chicken 44K whole genome oligonucleotide microarray using four major tissues. This microarray in theory consists of probes designed from the whole chicken transcriptome as well as 150 microRNAs, the entire genome sequences of Marek's disease virus and two avian influenza virus genomes. Comparison of gene expression among 2 organs and 2 tissues has been submitted to GEO providing valuable comparative gene expression data to the scientific community. Novel findings related to defensins and cathelicidin expression in the spleen is highlighted. Additionally, the custom tracks for sequences and probes used in this array have been built for Chicken Genome Browser Gateway in UCSC providing an efficient tool to link genomic information from this powerful genome browser to our expression data. This array will be a complimentary platform for the scientific community to study genetics, immunology, developmental biology, genomics, nutrition, and food safety in chickens.
Cecal tonsil (C), ileum (I), liver (L), and spleen (S) were collected from six two-week commercial broilers. Total of 24 samples were immersed into 10 volumes of RNAlater (Ambion, Austin, USA) and stored at -20°C until RNA isolation.
Tissues were homogenized using a Tissue Miser (Fisher Scientific, Houston, TX). Total RNA was isolated from each homogenized tissue using Trizol extraction method as described by the manufacturer (Invitrogen, Carlsbad, CA). All of DNA was removed from the samples using TURBO DNA free™ Kit (Ambion, Austin, TX) according to the manufacturer's protocol. The RNA quantity and purity were determined by NanoDrop ND-1000 spectrophotometer at 260/280 nm (Nano Drop Technologies, Wilmington, Delaware). The integrity of total RNA was assessed with an Agilent Bioanalyzer 2100 and RNA 6000 Nano LabChip Kit (Agilent Technologies, Palo Alto, CA). The RNA Integrity Numbers (RINs) for the samples were obtained. Only RNA samples with RIN values of 6, or higher, were used for the analysis.
A 500 ng of aliquot of total RNA was reverse transcribed into cDNA using the Low RNA Input Fluorescent Linear Amplification Kit (Agilent Technologies). The synthesized cDNA was transcribed into cRNA and labelled with either cyanine 3 or cyanine 5-labelled nucleotide (Perkin Elmer, Wellesley, MA). Labelled cRNA was purified with RNeasy Mini columns (Qiagen, Valecia, CA). The quality of each cRNA sample was verified by total yield and specificity calculated based on NanoDrop ND-1000 spectrophotometer measurement (NanoDrop Technologies).
Labelled cRNAs with specificity greater than 8 were used for hybridization using the in situ hybridization kit plus (Agilent Technologies). Arrays were incubated at 65°C for 17 h in Agilent's microarray hybridization chambers. After hybridization, arrays were washed according to the Agilent protocol.
Arrays were scanned at 5-μm resolution using GenePix Personal 4100A (Molecular Devices Corporation, Sunnyvale, CA) and images were saved as TIFF format. Auto Photomultiplier tube (PMT) settings were selected and adjusted to get the ratio of the overall intensities between two channels (Cy3 and Cy5) to 0.95 to 1.05. The signal intensities of all spots on each image were quantified by Genepix pro 6.0 software (Molecular Devices Corporation, Downingtown, PA), and data were saved as .txt files for further analysis.
Normalization and statistical analysis
The signal intensity of each probe was divided by that of negative control to filter the genes which were not expressed. The signal intensity of each gene was globally normalized using LOWESS within the R statistics package . A mixed model that included the fixed effects of dye (Cy3 and Cy5), tissue, and random effect of slide and array, was used to analyze the normalized data by SAS (SAS Institute, Cary, NC). P value and fold changes between each comparison for each gene were calculated. One tissue was included in three comparisons, the significantly expressed genes among these three comparisons were joined together to derive the selectively expressed tissue genes. False discovery rate (FDR) (q values) were calculated by R program according to Benjamini and Hochberg's method .
An unreleased version of the High Throughput Gene Ontology Functional Annotation Toolkit (HTGOFAT) was utilized [58, 59] to assign updated Gene Ontology  numbers, Enzyme Commission  numbers, mappings to Kyoto Encylopedia of Genes and Genomes (KEGG) Pathways  and updated definitions. Additionally, differentially regulated genes were mapped to Protein Information Resource (PIR) keywords  and COG  functional annotations through the use of the Database for Annotation, Visualization and Integrated Discovery (DAVID) . Statistics related to over representation of functional categories were performed using DAVID, which is based upon a Fisher Exact statistic methodology similar to that described by Al-Shahrour et al . A P < 0.001 was considered as significant.
Quantitative real-time PCR
Both up-regulated and down regulated genes from each comparison were selected for quantitative real time PCR (qRT-PCR). Four tissue samples from each chicken, and total of sixteen samples from 4 chickens were used. All reagents for qRT-PCR were loaded by Eppendorf ep Motion 5070 workstation (Eppendorf, Westbury, NY). The primers were designed with Primer 3 . Gene symbols and primers are listed in Table 2. A 1 ug aliquot of total RNA was used to synthesize first-strand cDNA using random hexamers and Thermoscript™ RT-PCR system (Invitrogen) in a reaction volume of 20 μL. The PCR reactions were performed in a 10 ul volume containing a 1×SYBR Green Master Mix, 50 ng cDNA, 300 nM of forward primers, 300 nM of reverse primers on an ABI Prism 7900HT sequence detection system (Applied Biosystems, Foster City, CA). The amplification condition was 50°C for 2 min; 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 59°C for 1 min; a final soak at 4°C was also incorporated. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the internal control. All of the samples were measured in duplicate. Two measurements of each tissue sample were averaged for further analysis. The comparative Ct method was used to calculate the relative gene expression level across the tissues. Relative expression level of each gene in one tissue (ΔCt) was calculated by Ct target gene-Ct GAPDH; relative expression of each gene in two different tissues (ΔΔCt) was calculated by ΔCt A -ΔCt B.
This project was supported by National Research Initiative Grant no. 2007-35604-17903 from the USDA Cooperative State Research, Education, and Extension Service Animal Genome program. Thanks Angie Hinrichs from UCSC CBSE for providing custom track for microarray, and Juan M. Anzola from Texas A&M University for probe custom track. Thanks to Michael H. Kogut and Christina L. Swaggerty from Southern Plain Agricultural Research Center, USDA-ARS for providing the samples.
- International Chicken Genome Sequencing Consortium: Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature. 2004, 432 (7018): 695-716. 10.1038/nature03154.View ArticleGoogle Scholar
- Siegel PB, Dodgson JB, Andersson L: Progress from chicken genetics to the chicken genome. Poult Sci. 2006, 85 (12): 2050-2060.PubMedView ArticleGoogle Scholar
- Cheng HH, Crittenden LB: Microsatellite markers for genetic mapping in the chicken. Poult Sci. 1994, 73 (4): 539-546.PubMedView ArticleGoogle Scholar
- Jacobsson L, Park HB, Wahlberg P, Fredriksson R, Perez-Enciso M, Siegel PB, Andersson L: Many QTLs with minor additive effects are associated with a large difference in growth between two selection lines in chickens. Genet Res. 2005, 86 (2): 115-125. 10.1017/S0016672305007767.PubMedView ArticleGoogle Scholar
- Park HB, Jacobsson L, Wahlberg P, Siegel PB, Andersson L: QTL analysis of body composition and metabolic traits in an intercross between chicken lines divergently selected for growth. Physiol Genomics. 2006, 25 (2): 216-223. 10.1152/physiolgenomics.00113.2005.PubMedView ArticleGoogle Scholar
- Wallis JW, Aerts J, Groenen MA, Crooijmans RP, Layman D, Graves TA, Scheer DE, Kremitzki C, Fedele MJ, Mudd NK, Cardenas M, Higginbotham J, Carter J, McGrane R, Gaige T, Mead K, Walker J, Albracht D, Davito J, Yang SP, Leong S, Chinwalla A, Sekhon M, Wylie K, Dodgson J, Romanov MN, Cheng H, de Jong PJ, Osoegawa K, Nefedov M, Zhang H, McPherson JD, Krzywinski M, Schein J, Hillier L, Mardis ER, Wilson RK, Warren WC: A physical map of the chicken genome. Nature. 2004, 432 (7018): 761-764. 10.1038/nature03030.PubMedView ArticleGoogle Scholar
- BBSRC Chicken EST database. [http://www.chick.manchester.ac.uk/]
- Wong GK, Liu B, Wang J, Zhang Y, Yang X, Zhang Z, Meng Q, Zhou J, Li D, Zhang J, Ni P, Li S, Ran L, Li H, Zhang J, Li R, Li S, Zheng H, Lin W, Li G, Wang X, Zhao W, Li J, Ye C, Dai M, Ruan J, Zhou Y, Li Y, He X, Zhang Y, Wang J, Huang X, Tong W, Chen J, Ye J, Chen C, Wei N, Li G, Dong L, Lan F, Sun Y, Zhang Z, Yang Z, Yu Y, Huang Y, He D, Xi Y, Wei D, Qi Q, Li W, Shi J, Wang M, Xie F, Wang J, Zhang X, Wang P, Zhao Y, Li N, Yang N, Dong W, Hu S, Zeng C, Zheng W, Hao B, Hillier LW, Yang SP, Warren WC, Wilson RK, Brandstrom M, Ellegren H, Crooijmans RP, van der Poel JJ, Bovenhuis H, Groenen MA, Ovcharenko I, Gordon L, Stubbs L, Lucas S, Glavina T, Aerts A, Kaiser P, Rothwell L, Young JR, Rogers S, Walker BA, van Hateren A, Kaufman J, Bumstead N, Lamont SJ, Zhou H, Hocking PM, Morrice D, de Koning DJ, Law A, Bartley N, Burt DW, Hunt H, Cheng HH, Gunnarsson U, Wahlberg P, Andersson L, Kindlund E, Tammi MT, Andersson B, Webber C, Ponting CP, Overton IM, Boardman PE, Tang H, Hubbard SJ, Wilson SA, Yu J, Wang J, Yang H: A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms. Nature. 2004, 432 (7018): 717-722. 10.1038/nature03156.PubMedView ArticleGoogle Scholar
- Barrett JC, Kawasaki ES: Microarrays: the use of oligonucleotides and cDNA for the analysis of gene expression. Drug Discov Today. 2003, 8 (3): 134-141. 10.1016/S1359-6446(02)02578-3.PubMedView ArticleGoogle Scholar
- Hardiman G: Microarray platforms--comparisons and contrasts. Pharmacogenomics. 2004, 5 (5): 487-502. 10.1517/14622418.104.22.1687.PubMedView ArticleGoogle Scholar
- Held GA, Duggar K, Stolovitzky G: Comparison of Amersham and Agilent microarray technologies through quantitative noise analysis. Omics. 2006, 10 (4): 532-544. 10.1089/omi.2006.10.532.PubMedView ArticleGoogle Scholar
- Zhao SH, Recknor J, Lunney JK, Nettleton D, Kuhar D, Orley S, Tuggle CK: Validation of a first-generation long-oligonucleotide microarray for transcriptional profiling in the pig. Genomics. 2005, 86 (5): 618-625. 10.1016/j.ygeno.2005.08.001.PubMedView ArticleGoogle Scholar
- Tsai S, Mir B, Martin A, Estrada J, Bischoff S, Hsieh W, Cassady J, Freking B, Nonneman D, Rohrer G, Piedrahita J: Detection of transcriptional difference of porcine imprinted genes using different microarray platforms. BMC Genomics. 2006, 7 (1): 328-10.1186/1471-2164-7-328.PubMedPubMed CentralView ArticleGoogle Scholar
- Popp MP, Liu L, Timmers A, Esson DW, Shiroma L, Meyers C, Berceli S, Tao M, Wistow G, Schultz GS, Sherwood MB: Development of a microarray chip for gene expression in rabbit ocular research. Mol Vis. 2007, 13: 164-173.PubMedPubMed CentralGoogle Scholar
- Band MR, Olmstead C, Everts RE, Liu ZL, Lewin HA: A 3800 gene microarray for cattle functional genomics: comparison of gene expression in spleen, placenta, and brain. Anim Biotechnol. 2002, 13 (1): 163-172. 10.1081/ABIO-120005779.PubMedView ArticleGoogle Scholar
- Suchyta SP, Sipkovsky S, Kruska R, Jeffers A, McNulty A, Coussens MJ, Tempelman RJ, Halgren RG, Saama PM, Bauman DE, Boisclair YR, Burton JL, Collier RJ, DePeters EJ, Ferris TA, Lucy MC, McGuire MA, Medrano JF, Overton TR, Smith TP, Smith GW, Sonstegard TS, Spain JN, Spiers DE, Yao J, Coussens PM: Development and testing of a high-density cDNA microarray resource for cattle. Physiol Genomics. 2003, 15 (2): 158-164.PubMedView ArticleGoogle Scholar
- Neiman PE, Ruddell A, Jasoni C, Loring G, Thomas SJ, Brandvold KA, Lee R, Burnside J, Delrow J: Analysis of gene expression during myc oncogene-induced lymphomagenesis in the bursa of Fabricius. Proc Natl Acad Sci U S A. 2001, 98 (11): 6378-6383. 10.1073/pnas.111144898.PubMedPubMed CentralView ArticleGoogle Scholar
- van Hemert S, Ebbelaar BH, Smits MA, Rebel JM: Generation of EST and microarray resources for functional genomic studies on chicken intestinal health. Anim Biotechnol. 2003, 14 (2): 133-143. 10.1081/ABIO-120026483.PubMedView ArticleGoogle Scholar
- Afrakhte M, Schultheiss TM: Construction and analysis of a subtracted library and microarray of cDNAs expressed specifically in chicken heart progenitor cells. Dev Dyn. 2004, 230 (2): 290-298. 10.1002/dvdy.20059.PubMedView ArticleGoogle Scholar
- Bliss TW, Dohms JE, Emara MG, Keeler CL: Gene expression profiling of avian macrophage activation. Vet Immunol Immunopathol. 2005, 105 (3-4): 289-299. 10.1016/j.vetimm.2005.02.013.PubMedView ArticleGoogle Scholar
- Burnside J, Neiman P, Tang J, Basom R, Talbot R, Aronszajn M, Burt D, Delrow J: Development of a cDNA array for chicken gene expression analysis. BMC Genomics. 2005, 6 (1): 13-10.1186/1471-2164-6-13.PubMedPubMed CentralView ArticleGoogle Scholar
- Smith J, Speed D, Hocking P, Talbot R, Degen W, Schijns V, Glass E, Burt D: Development of a chicken 5 K microarray targeted towards immune function. BMC Genomics. 2006, 7 (1): 49-10.1186/1471-2164-7-49.PubMedPubMed CentralView ArticleGoogle Scholar
- Operon Gallus gallus (chicken) Roslin/ARK CoRe Array V1.0. 2007, [https://www.operon.com/arrays/oligosets_chicken.php?]
- Affymetrix Chicken Genome Array. 2007, [http://www.affymetrix.com/products/arrays/specific/chicken.affx]
- Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW, Lefkowitz SM, Ziman M, Schelter JM, Meyer MR, Kobayashi S, Davis C, Dai H, He YD, Stephaniants SB, Cavet G, Walker WL, West A, Coffey E, Shoemaker DD, Stoughton R, Blanchard AP, Friend SH, Linsley PS: Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotech. 2001, 19 (4): 342-347. 10.1038/86730.View ArticleGoogle Scholar
- Relogio A, Schwager C, Richter A, Ansorge W, Valcarcel J: Optimization of oligonucleotide-based DNA microarrays. Nucleic Acids Res. 2002, 30 (11): e51-10.1093/nar/30.11.e51.PubMedPubMed CentralView ArticleGoogle Scholar
- Shippy R, Sendera T, Lockner R, Palaniappan C, Kaysser-Kranich T, Watts G, Alsobrook J: Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations. BMC Genomics. 2004, 5 (1): 61-10.1186/1471-2164-5-61.PubMedPubMed CentralView ArticleGoogle Scholar
- Leiske D, Karimpour-Fard A, Hume P, Fairbanks B, Gill R: A comparison of alternative 60-mer probe designs in an in-situ synthesized oligonucleotide microarray. BMC Genomics. 2006, 7 (1): 72-10.1186/1471-2164-7-72.PubMedPubMed CentralView ArticleGoogle Scholar
- Hsiao LL, Dangond F, Yoshida T, Hong R, Jensen RV, Misra J, Dillon W, Lee KF, Clark KE, Haverty P, Weng Z, Mutter GL, Frosch MP, Macdonald ME, Milford EL, Crum CP, Bueno R, Pratt RE, Mahadevappa M, Warrington JA, Stephanopoulos G, Stephanopoulos G, Gullans SR: A compendium of gene expression in normal human tissues. Physiol Genomics. 2001, 7 (2): 97-104.PubMedView ArticleGoogle Scholar
- Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R: NCBI GEO: mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res. 2007, 35 (Database issue): D760-5. 10.1093/nar/gkl887.PubMedPubMed CentralView ArticleGoogle Scholar
- Petersen D, Chandramouli GVR, Geoghegan J, Hilburn J, Paarlberg J, Kim C, Munroe D, Gangi L, Han J, Puri R, Staudt L, Weinstein J, Barrett JC, Green J, Kawasaki E: Three microarray platforms: an analysis of their concordance in profiling gene expression. BMC Genomics. 2005, 6 (1): 63-10.1186/1471-2164-6-63.PubMedPubMed CentralView ArticleGoogle Scholar
- Woo Y, Affourtit J, Daigle S, Viale A, Johnson K, Naggert J, Churchill G: A comparison of cDNA, oligonucleotide, and Affymetrix GeneChip gene expression microarray platforms. J Biomol Tech. 2004, 15 (4): 276-284.PubMedPubMed CentralGoogle Scholar
- de Reynies A, Geromin D, Cayuela JM, Petel F, Dessen P, Sigaux F, Rickman DS: Comparison of the latest commercial short and long oligonucleotide microarray technologies. BMC Genomics. 2006, 7: 51-10.1186/1471-2164-7-51.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang Y, Barbacioru C, Hyland F, Xiao W, Hunkapiller K, Blake J, Chan F, Gonzalez C, Zhang L, Samaha R: Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays. BMC Genomics. 2006, 7 (1): 59-10.1186/1471-2164-7-59.PubMedPubMed CentralView ArticleGoogle Scholar
- Wolber PK, Collins PJ, Lucas AB, De Witte A, Shannon KW: The Agilent in situ-synthesized microarray platform. Methods Enzymol. 2006, 410: 28-57. 10.1016/S0076-6879(06)10002-6.PubMedView ArticleGoogle Scholar
- Dequeant ML, Pourquie O: Chicken genome: new tools and concepts. Dev Dyn. 2005, 232 (4): 883-886. 10.1002/dvdy.20266.PubMedView ArticleGoogle Scholar
- Agilent: Performance comparison of Agilent's 60-mer and 25-mer in situ synthesized oligonucleotide microarrays. 2005Google Scholar
- Dallas P, Gottardo N, Firth M, Beesley A, Hoffmann K, Terry P, Freitas J, Boag J, Cummings A, Kees U: Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR ?how well do they correlate?. BMC Genomics. 2005, 6 (1): 59-10.1186/1471-2164-6-59.PubMedPubMed CentralView ArticleGoogle Scholar
- Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006, 34 (Database issue): D140-4. 10.1093/nar/gkj112.PubMedPubMed CentralView ArticleGoogle Scholar
- Griffiths-Jones S: The microRNA Registry. Nucleic Acids Res. 2004, 32 (Database issue): D109-11. 10.1093/nar/gkh023.PubMedPubMed CentralView ArticleGoogle Scholar
- Tsai CA, Hsueh HM, Chen JJ: Estimation of false discovery rates in multiple testing: application to gene microarray data. Biometrics. 2003, 59 (4): 1071-1081. 10.1111/j.0006-341X.2003.00123.x.PubMedView ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological). 1995, 57 (1): 289-300.Google Scholar
- Pawitan Y, Michiels S, Koscielny S, Gusnanto A, Ploner A: False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics. 2005, 21 (13): 3017-3024. 10.1093/bioinformatics/bti448.PubMedView ArticleGoogle Scholar
- Son CG, Bilke S, Davis S, Greer BT, Wei JS, Whiteford CC, Chen QR, Cenacchi N, Khan J: Database of mRNA gene expression profiles of multiple human organs. Genome Res. 2005, 15 (3): 443-450. 10.1101/gr.3124505.PubMedPubMed CentralView ArticleGoogle Scholar
- Shyamsundar R, Kim YH, Higgins JP, Montgomery K, Jorden M, Sethuraman A, van de Rijn M, Botstein D, Brown PO, Pollack JR: A DNA microarray survey of gene expression in normal human tissues. Genome Biol. 2005, 6 (3): R22-10.1186/gb-2005-6-3-r22.PubMedPubMed CentralView ArticleGoogle Scholar
- Saito-Hisaminato A, Katagiri T, Kakiuchi S, Nakamura T, Tsunoda T, Nakamura Y: Genome-wide profiling of gene expression in 29 normal human tissues with a cDNA microarray. DNA Res. 2002, 9 (2): 35-45. 10.1093/dnares/9.2.35.PubMedView ArticleGoogle Scholar
- Hornshoj H, Conley LN, Hedegaard J, Sorensen P, Panitz F, Bendixen C: Microarray Expression Profiles of 20.000 Genes across 23 Healthy Porcine Tissues. PLoS ONE. 2007, 2 (11): e1203-10.1371/journal.pone.0001203.PubMedPubMed CentralView ArticleGoogle Scholar
- Levine DM, Haynor DR, Castle JC, Stepaniants SB, Pellegrini M, Mao M, Johnson JM: Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways. Genome Biol. 2006, 7 (10): R93-10.1186/gb-2006-7-10-r93.PubMedPubMed CentralView ArticleGoogle Scholar
- Takahashi T, Iwase T, Tachibana T, Komiyama K, Kobayashi K, Chen CL, Mestecky J, Moro I: Cloning and expression of the chicken immunoglobulin joining (J)-chain cDNA. Immunogenetics. 2000, 51 (2): 85-91. 10.1007/s002510050016.PubMedView ArticleGoogle Scholar
- Gorodkin J, Cirera S, Hedegaard J, Gilchrist MJ, Panitz F, Jorgensen C, Scheibye-Knudsen K, Arvin T, Lumholdt S, Sawera M, Green T, Nielsen BJ, Havgaard JH, Rosenkilde C, Wang J, Li H, Li R, Liu B, Hu S, Dong W, Li W, Yu J, Wang J, Staefeldt HH, Wernersson R, Madsen LB, Thomsen B, Hornshoj H, Bujie Z, Wang X, Wang X, Bolund L, Brunak S, Yang H, Bendixen C, Fredholm M: Porcine transcriptome analysis based on 97 non-normalized cDNA libraries and assembly of 1,021,891 expressed sequence tags. Genome Biol. 2007, 8 (4): R45-10.1186/gb-2007-8-4-r45.PubMedPubMed CentralView ArticleGoogle Scholar
- Lynn DJ, Higgs R, Gaines S, Tierney J, James T, Lloyd AT, Fares MA, Mulcahy G, O'Farrelly C: Bioinformatic discovery and initial characterisation of nine novel antimicrobial peptide genes in the chicken. Immunogenetics. 2004, 56 (3): 170-177. 10.1007/s00251-004-0675-0.PubMedView ArticleGoogle Scholar
- Xiao Y, Hughes AL, Ando J, Matsuda Y, Cheng JF, Skinner-Noble D, Zhang G: A genome-wide screen identifies a single beta-defensin gene cluster in the chicken: implications for the origin and evolution of mammalian defensins. BMC Genomics. 2004, 5 (1): 56-10.1186/1471-2164-5-56.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhao C, Nguyen T, Liu L, Sacco RE, Brogden KA, Lehrer RI: Gallinacin-3, an inducible epithelial beta-defensin in the chicken. Infect Immun. 2001, 69 (4): 2684-2691. 10.1128/IAI.69.4.2684-2691.2001.PubMedPubMed CentralView ArticleGoogle Scholar
- Arredouani MS, Palecanda A, Koziel H, Huang YC, Imrich A, Sulahian TH, Ning YY, Yang Z, Pikkarainen T, Sankala M, Vargas SO, Takeya M, Tryggvason K, Kobzik L: MARCO is the major binding receptor for unopsonized particles and bacteria on human alveolar macrophages. J Immunol. 2005, 175 (9): 6058-6064.PubMedView ArticleGoogle Scholar
- Elomaa O, Kangas M, Sahlberg C, Tuukkanen J, Sormunen R, Liakka A, Thesleff I, Kraal G, Tryggvason K: Cloning of a novel bacteria-binding receptor structurally related to scavenger receptors and expressed in a subset of macrophages. Cell. 1995, 80 (4): 603-609. 10.1016/0092-8674(95)90514-6.PubMedView ArticleGoogle Scholar
- Yang X, Schadt EE, Wang S, Wang H, Arnold AP, Ingram-Drake L, Drake TA, Lusis AJ: Tissue-specific expression and regulation of sexually dimorphic genes in mice. Genome Res. 2006, 16 (8): 995-1004. 10.1101/gr.5217506.PubMedPubMed CentralView ArticleGoogle Scholar
- Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002, 30 (4): e15-10.1093/nar/30.4.e15.PubMedPubMed CentralView ArticleGoogle Scholar
- Dowd SE, Zaragoza J: High throughput gene ontology functional annotation toolkit (HT-GO-FAT) utilized for animal and plant [abstract]. Plant and Animal Genome Conference. 2005, San Diego, CAGoogle Scholar
- High Throughput Gene Ontology Functional Annotation Toolkit. [http://liru.ars.usda.gov/mainbioinformatics.html]
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.PubMedPubMed CentralView ArticleGoogle Scholar
- Shah I, Hunter L: Visualization based on the Enzyme Commission nomenclature. Pac Symp Biocomput. 1998, 142-152.Google Scholar
- Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999, 27 (1): 29-34. 10.1093/nar/27.1.29.PubMedPubMed CentralView ArticleGoogle Scholar
- Wu CH, Huang H, Arminski L, Castro-Alvear J, Chen Y, Hu ZZ, Ledley RS, Lewis KC, Mewes HW, Orcutt BC, Suzek BE, Tsugita A, Vinayaka CR, Yeh LS, Zhang J, Barker WC: The Protein Information Resource: an integrated public resource of functional annotation of proteins. Nucleic Acids Res. 2002, 30 (1): 35-37. 10.1093/nar/30.1.35.PubMedPubMed CentralView ArticleGoogle Scholar
- Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, Rao BS, Smirnov S, Sverdlov AV, Vasudevan S, Wolf YI, Yin JJ, Natale DA: The COG database: an updated version includes eukaryotes. BMC Bioinformatics. 2003, 4: 41-10.1186/1471-2105-4-41.PubMedPubMed CentralView ArticleGoogle Scholar
- Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4 (5): P3-10.1186/gb-2003-4-5-p3.PubMedView ArticleGoogle Scholar
- Al-Shahrour F, Diaz-Uriarte R, Dopazo J: FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics. 2004, 20 (4): 578-580. 10.1093/bioinformatics/btg455.PubMedView ArticleGoogle Scholar
- Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol. 2000, 132: 365-386.PubMedGoogle Scholar
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