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On this page, you will find useful references pertaining to different services and technologies.
Publications using information from UHNMAC printed microarrays can be found here.

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Service Platforms
Selected publications using the Affymetrix platform:
Myocardial Infarction Genetics Consortium, Kathiresan S, et al. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nature Genet 2009, 41:334

Publications using UHNMAC Affymetrix Service:
Jones RA, et al. Characterization of a Novel Primary Mammary Tumor Cell Line Reveals that Cyclin D1 Is Regulated by the Type I Insulin-Like Growth Factor Receptor. Mol Cancer Res, 2008, 6:819

Tone AA, et al. Gene expression profiles of luteal phase fallopian tube epithelium from BRCA mutation carriers resemble high-grade serous carcinoma. Clin Cancer Res, 2008, 14(13):4067

Lovegrove FE, et al. Expression Microarray Analysis Implicates Apoptosis and Interferon-Responsive Mechanisms in Susceptibility to Experimental Cerebral Malaria. Am J Pathol 2007, 171(6):1

Selected publications using the Agilent platform:
Yang H, et al. MicroRNA Expression Signatures in Barrett's Esophagus and Esophageal Adenocarcinoma. Clin Cancer Res 2009, 15:5744

Publications using UHNMAC Agilent Service:
Kovalenko A, et al. Caspase-8 deficiency in epidermal keratinocytes triggers an inflammatory skin disease. J Exp Med 2009, 206(10):2161-2177

Ponzielli R, Boutros PC, Katz S, Stojanova A, Hanley AP, Khosravi F, Bros C, Jurisica I, Penn LZ. Optimization of experimental design parameters for high-throughput chromatin immunoprecipitation studies. Nucleic Acids Res 2008, 36(21):e144

Xuan W, et al. Interleukin-24 Induces Expression of β4 Integrin but Suppresses Anchorage-Independent Growth of Rat Mammary Tumor Cells by a Mechanism That Is Independent of β4. Mol Cancer Res 2009, 7:433

Zippo A, et al. PIM1-dependent phosphorylation of histone H3 at serine 10 is required for MYC-dependent transcriptional activation and oncogenic transformation. Nature Cell Biol, 2007, 9(8):932

Bio-Plex and xMAP® assays
Selected publications using xMAP® assays:
Kambas K, et al. C5a and TNF-α Up-Regulate the Expression of Tissue Factor in Intra-Alveolar Neutrophils of Patients with the Acute Respiratory Distress Syndrome. J Immunol 2008, 180(11):7368 (Procarta® Human Cytokine Profiling kit; Panomics)

Marquis JF, et al. Disseminated and rapidly fatal tuberculosis in mice bearing a defective allele at IFN regulatory factor 8. J Immunol 2009, 182(5):3008 (Mouse Cytokine Panel; Millipore)

Niu, X, et al. Altered cytokine profiles in patients with Chuvash polycythemia. Am J Hematol 2008, 84(2):74 (Human Th1/Th2 Multiplex Cytokine Kit; Bio-Rad)

Siawaya, JFD, et al. An Evaluation of Commercial Fluorescent Bead-Based Luminex Cytokine Assays. Plos ONE 2008, 3(7):e2535

Sikkeland LIB, et al. Circulating lipopolysaccharides in the blood from "bioprotein" production workers. Occup Environ Med 2008, 65(3):211 (BioSource™ assay; Invitrogen)

Whitcomb BW, et al. Circulating Chemokine Levels and Miscarriage. Am J Epidemiol 2007, 166(3):323 (Fluorokine® Human Cytokine Panel A; R&D Systems)

Selected publications using the Illumina platform:
Chen J, et al. Highly sensitive and specific microRNA expression profiling using BeadArray technology. Nucleic Acids Research 2008, 36(14):e87

Laurent LC, et al. Comprehensive microRNA profiling reveals a unique human embryonic stem cell signature dominated by a single seed sequence. Embyronic Stem Cells 2008, 26(6):1506

Woelk CH, et al. Gene expression before HAART initiation predicts HIV-infected individuals at risk of poor CD4+ T-cell recovery. AIDS 2010, 24(2):217

Zhou Q, et al. In vivo reprogramming of adult pancreatic exocrine cells to ?-cells. Nature 2008, 455:627

NanoString Technology
Selected publications using the NanoString platform:
Amit I, et al. Unbiased Reconstruction of a Mammalian Transcriptional Network Mediating Pathogen Responses. Science 2009, 326(5950):257

Fortina P & Surrey S. Digital mRNA profiling. Nature Biotechnology 2008, 26(3):293

Payton JE, et al. High throughput digital quantification of mRNA abundance in primary human acute myeloid leukemia samples. J Clin Invest, 2009, Epub May 18, 2009

Malkov VA, et al. Multiplexed measurements of gene signatures in different analytes using the Nanostring nCounterTM Assay System. BMC Research Notes 2009, 2:80

Types of experiments
Array Comparative Genomic Hybridisation (aCGH)
Selected publications involving aCGH:
Genome Technology Array CGH Tech Guide, A Troubleshooting Guide: Experts share their advice on performing array comparative genomic hybridisation. September 2008 (Genome Web).

Barrett MT, et al. Comparative genomics hybridization using oligonucleotide microarrays and total genomic DNA. PNAS 2004, 101(51):17765
This study demonstrates that oligonucleotide arrays designed for CGH are able to detect chromosomal alterations using full-complexity genomic samples.

Bruno DL, et al. Detection of cryptic pathogenic copy number variations and constitutional loss of heterozygosity using high resolution SNP microarray analysis in 117 patients referred for cytogenetic analysis and impact on clinical practice. J Med Genet 2009, 46:123-131

Chi B, et al. SeeGH - A software tool for visualization of whole genome array comparative genomic hybridization data. BMC Bioinformatics, 2004, 5:13
This paper provides information about SeeGH, a software tool used to view and analyse aCGH data.

Davies JD, et al. Array CGH technologies and their applications to cancer genomes. Chromomsome Research, 2005, 13(3):237
A review of various aCGH platforms and their use in the study of cancer genomics.

Neuvial P, et al. Spatial normalization of array-CGH data. BMC Bioinformatics, 2006, 7:264
This paper describes a normalisation technique that properly corrects spatial effects.

Pounds S, et al. Reference alignment of SNP microarray signals for copy number analysis of tumors. Bioinformatics 2009, 25(3):315-321

Ylstra B, et al. BAC to the future! Or oligonucleotides: a perspective for micro array comparative genomic hybridization (array CGH). Nucleic Acids Research, 2006, 34(2):445
This paper reviews different platforms used for aCGH.

ChIP-on-Chip/CpG Island Arrays
Selected publications involving ChIP-on-Chip:
Heisler LE, et al. CpG Island microarray probe sequences derived from a physical library are representative of CpG Islands annotated on the human genome. Nucleic Acids Research, 2005, 33(9);2952
This article describes the design and construction of CGI arrays, an effective tool for the global analysis of protein-chromatin interactions and DNA methylation status.

Komashko VM, et al. Using ChIP-chip technology to reveal common principles of transcriptional repression in normal and cancer cells. Genome Res 2008, 18(4):521

Kondo Y, et al. Chromatin immunoprecipitation microarrays for identification of genes silenced by histone H3 lysine 9 methylation. PNAS, 2004, 101(19);7398

Nouzova M, et al. Epigenomic Changes during Leukemia Cell Differentiation: Analysis of Histone Acetylation and Cytosine Methylation Using CpG Island Microarrays. Journal of Pharmacology and Experimental Therapeutics, 2004, 311(3);968

Oberley MJ, et al. High-Throughput Screening of Chromatin Immunoprecipitates Using CpG-Island Microarrays. Methods in Enzymology, 2003, 376;315

Differential Methylation Hybridisation (DMH)
Selected publications involving DMH:
Balch C, et al. Antimitogenic and chemosensitizing effects of the methylation inhibitor zebularine in ovarian cancer. Mol Cancer Ther, 2005, 4:1505

Goto Y, et al. Epigenetic Profiles Distinguish Malignant Pleural Mesothelioma from Lung Adenocarcinoma. Cancer Res 2009, 69:9073

Huang T H-M, et al. Methylation profiling of CpG islands in human breast cancer cells. Hum Mol Genet, 1999, 8:459-470
This paper describes the development of the array-based differential methylation hybridisation (DMH) technique, which allows the genome-wide screening of hypermethylated CpG islands in tumour cells.

Schumacher A, et al. Microarray-based DNA methylation profiling: technology and applications. Nucleic Acids Research, 2006, 34(2):528-42

Yan PS, et al. Use of CpG island microarrays to identify colorectal tumors with a high degree of concurrent methylation. Methods, 2002, 27:162

miRNA profiling
Selected publications involving miRNA profiling:
Ambros V, et al. A uniform system for microRNA annotation. RNA, 2003, 9:277
This paper outlines the guidelines for the identification and annotation of new miRNAs.

Castoldi M, et al. A sensitive array for microRNA expression profiling (miChip) based on locked nucleic acids (LNA). RNA, 2006, 12:913
A novel microarray platform for profiling mature miRNAs using locked nucleic acid (LNA)-modified capture probes. Sample RNA size selection and/or amplification is not required due to the superior detection sensitivity of LNA-modified probes.

Chaudhuri K, Chatterjee R. MicroRNA Detection and Target Prediction: Integration of Computational and Experimental Approaches. DNA Cell Biol, 2007, 26(5):321
A review of the various computational methods for identifying miRNAs and their targets as well as the technologies used to validate the predictions.

Griffiths-Jones S, et al. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Research, 2006, 34:D140
A description of the miRBase database, a database that provides integrated interfaces to comprehensive miRNA sequence data, annotation and predicted gene targets. miRBase is available at

Klevebring D, et al. Genome-wide profiling of Populus small RNAs. BMC Genomics, 2009, 10:620

Meng F, et al. Involvement of Human Micro-RNA in Growth and Response to Chemotherapy in Human Cholangiocarcinoma Cell Lines. Gastroenterology, 2006, 130(7):2113

Shingara J, et al. An optimized isolation and labeling platform for accurate microRNA expression profiling. RNA, 2005, 11:1461

Soon PSH, et al. miR-195 and miR-483-5p Identified as Predictors of Poor Prognosis in Adrenocortical Cancer. Clin Cancer Res, 2009, 15(24):7684

Wiemer EA. The role of microRNAs in cancer: No small matter. Eur J Cancer, 2007, 43(10):1529

RNA Amplification
Selected publications about RNA amplification methods:
Clement-Ziza M, et al. Evaluation of methods for amplification of picogram amounts of total RNA for whole genome expression profiling. BMC Genomics, 2009, 10:246

Caretti E, et al. Comparison of RNA amplification methods and chip platforms for microarray analysis of samples processed by laser capture microdissection. J Cell Biochem, 2008, 103(2):556

Dafforn A, et al. Linear mRNA amplification from as little as 5 ng total RNA for global gene expression analysis. Biotechniques, 2004, 37(5):854
This NuGEN paper describes the Ribo-SPIA linear amplification method.

Iscove NN, et al. Representation is faithfully preserved in global cDNA amplified exponentially from sub-picogram quantities of mRNA. Nature Biotechnology, 2002, 20:940

Nygaard V, Hovig E. Options available for profiling small samples: a review of sample amplification technology when combined with microarray profiling. Nucleic Acids Research, 2006, 34(3);996

Rachman H, et al. Reliable amplification method for bacterial RNA. Journal of Biotechnology 2006, 126:61

Schindler H, et al. cRNA target preparation for microarrays: Comparison of gene expression profiles generated with different amplification procedures. Analytical Biochemistry, 2005, 344(1):92

Subkhankulova T, Livesey FJ. Comparative evaluation of linear and exponential amplification techniques for expression profiling at the single-cell level. Genome Biology 2006, 7(3):R18

Array Technology & Data Analysis
Array Technology
Guttenberg Z, et al. Planar chip device for PCR and hybridization with surface acoustic wave pump. Lab Chip, 2005, 5:308
This paper describes the SAW technology used by the SlideBooster (Advalytix, part of Beckman Coulter Genomics) for non-invasive mixing during array hybridisation.

Okamoto T, et al. Microarray fabrication with covalent attachment of DNA using Bubble Jet technology. Nature Biotechnology, 2000, 18:438-441

Peeva VK, et al. Evaluation of automated and conventional microarray hybridization: a question of data quality and best practice?. Biotechnol Appl Biochem, 2008, 50:181

Data Analysis & Experimental Design
Brown MPS, et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS Jan. 2000, 97: 262-267

Draghici S et al. Onto-Tools, The toolkit of the modern biologist: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Nucleic Acids Research, 2003, 31(13):3775-3781

Dudoit S, et al. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. 2000, Technical Report 578, Department of Statistics, UC Berkeley, CA

Holter NS et al. Fundamental patterns underlying gene expression profiles: Simplicity from complexity. PNAS 2000, 97: 8409-8414

Hu P, Beyene J, Greenwood CMT. Tests for differential gene expression using weights in oligonucleotide microarray experiments. BMC Genomics 2006, 7(33)

Lee MT, et al. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. PNAS 2000, 97(18):9834-9839

Kerr M.K. & Churchill G.A. Experimental design for gene expression microarrays. Biostatistics 2001, 2(2):183-201

Kerr MK, Martin M, Churchill GA. Analysis of Variance for Gene Expression Microarray Data, J Comput Biol. 2000, 7(6):819-837

Schuchhardt J. Normalization strategies for cDNA microarrays. Nucleic Acids Research 2000, 28(10):e47

Tseng GC, et al. Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Research 2001, 29(12):2549-2557

Xia XQ, et al. WebArrayDB: cross-platform microarray data analysis and public data repository. Bioinformatics 2009, 25(18):2425

Yang Y.H. et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variations. Nucleic Acids Research 2002, 30(4):e15

Yang YH, Speed T. Design issues for cDNA microarray experiments. Nature Reviews Genetics 2002, 3(8): 579-588

Experimental Standards
MIAME (Minimal Information About a Microarray Experiment)
Brazma A, et al. Minimum information about a microarray experiment (MIAME), towards standards for microarray data. Nature Genetics 2000, 29;365-371

MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments)
Bustin SA, et al. The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clinical Chemistry 2009, 55(4):611-622