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Oligonucleotide Arrays

Oligonucleotide Arrays are a powerful tool for genetic analysis, allowing researchers to study gene expression, DNA methylation, and genomic variations on a large scale.
These arrays consist of short, synthetic DNA or RNA sequences (oligonucleotides) immobilized on a solid surface, such as a glass slide or silicon chip.
By hybridizing samples to the array, researchers can measure the abundance of thousands of different genetic sequences simultaneously.
Oligonucleotide Arrays offer high-throughput, cost-effective, and reproducible results, making them indispensable for a wide range of applications, including disease diagnosis, drug discovery, and population genetics.
However, optimizing the protocols and selecting the right products for these experiments can be challenging.
PubCompare.ai is an AI-driven platform that helps researchers find the best protocols and products from literature, pre-prints, and patents, ensuring reproducibility and accuracy in their Oligonucleotide Array experiments.
With PubCompare.ai, researchers can take the guesswork out of their work and trust their results.

Most cited protocols related to «Oligonucleotide Arrays»

1
Genotyping and Haplotyping of Human SNPs
Of approximately 6.9 million SNPs in dbSNP release 122 approximately 4.7 million were selected for genotyping by Perlegen. 2.5 million SNPs were excluded because no assay could be designed and a further 350,000 were excluded for other reasons (see Methods). Perlegen performed genotyping using custom high-density oligonucleotide arrays as previously described15 (link). Additional genotype submissions are described in the text. QC filters were applied as previously described3 (link). Where multiple submissions met the QC criteria the submission with the lowest missing data rate was chosen for inclusion in the non-redundant filtered data set. Haplotypes were estimated from genotype data as described previously3 (link). Ancestral states at SNPs were inferred by parsimony by comparison to orthologous bases in the chimpanzee (panTro2) and rhesus macaque (rheMac2) assemblies. Recombination rates and the location of recombination hotspots were estimated as described previously3 (link). Additional details can be found in the Methods section and the Supplementary Information. The data described in this paper are in release 21 of the International HapMap Project.
A second generation human haplotype map of over 3.1 million SNPs. (2007). Nature, 449(7164), 851-861.
Publication 2007
Biological Assay Genotype Haplotypes Macaca mulatta Oligonucleotide Arrays Pan troglodytes Recombination, Genetic Single Nucleotide Polymorphism
2
Gene Expression Data Normalization Protocol
We started from data sets that were already normalized for their respective study without any additional normalization procedure to account for different platform derivation. For the signal intensity data generated by one-channel oligonucleotide microarrays, Affymetrix's GeneChip, we applied a lower threshold of 20U and a upper threshold of 16,000U. For the log2 transformed ratio data generated by cDNA microarrays, we first removed genes whose values were missing in more than 5% of the samples, and then imputed the missing values for the rest of the genes using a k-nearest neighbor algorithm [15] (link) (ImputeMissingValues.KNN, in the GenePattern software package, http://www.broad.mit.edu/genepattern/).
Before marker gene selection, we used following gene filtering. For the oligonucleotide array data, only genes exhibiting at least 3-fold differential expression and an absolute difference of at least 100 units across the samples in the experiment were included. For the cDNA array data, only genes with an absolute log2 ratio greater than one and whose difference in log2 ratio across all the samples in the data set was greater than one were included.
Before applying the SubMap, each microarray probe ID was converted into its corresponding HUGO gene symbol (http://www.gene.ucl.ac.uk/nomenclature/), and multiple probe data corresponding to a single gene symbol was averaged. The number of genes remaining for our analyses of multiple tissue types, DLBCL, breast cancer, and DLBCL (with survival data) data sets were 5565, 661, 1213, and 3795, respectively.
Hoshida Y., Brunet J.P., Tamayo P., Golub T.R, & Mesirov J.P. (2007). Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets. PLoS ONE, 2(11), e1195.
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Publication 2007
cDNA Microarrays Gene Chips Genes Genetic Selection Histocompatibility Testing Malignant Neoplasm of Breast Microarray Analysis Oligonucleotide Arrays Strains
3
Breast Cancer Genetic Association Study Protocol
Cases for stage 1 were identified through clinical genetics centres in the UK and a national study of bilateral breast cancer. Cases in stage 2 were drawn from a population-based study of breast cancer (SEARCH)32 (link). Controls for stages 2 and 3 were drawn from EPIC-Norfolk, a population-based study of diet and cancer33 (link).
Cases and controls for stage 3 were identified through case-control studies in Europe, North America, South-East Asia and Australia participating in the Breast Cancer Association Consortium (Supplementary Table 2)34 (link).
Genotyping for stages 1 and 2 was conducted using high-density oligonucleotide microarrays. For the main analyses, we excluded samples called on ≤80% of SNPs in either stage. We also excluded SNPs that achieved a call rate of ≤90% in stage 1 and ≤95% in stage 2, and SNPs whose frequency deviated from Hardy–Weinberg equilibrium in controls at P<0.00001. Genotyping for stage 3, and for the fine-scale mapping of the FGFR2 locus, was conducted using either a 5′ nuclease assay (Taqman, Applied Biosystems) or MALDI-TOF mass spectrometry using the Sequenom iPLEX system. For each centre, we excluded any sample called on ≤80% of SNPs, and any SNP with a call rate of ≤95% or a deviation from Hardy–Weinberg equilibrium in controls at P<0.00001. Tests of association were 1 d.f. Cochran-Armitage tests, stratified for stage, centre and ethnic group (European or Asian). Odds ratios for each SNP were estimated using stratified logistic regression, using the stage 3 data only.
Easton D.F., Pooley K.A., Dunning A.M., Pharoah P.D., Thompson D., Ballinger D.G., Struewing J.P., Morrison J., Field H., Luben R., Wareham N., Ahmed S., Healey C.S., Bowman R., Meyer K.B., Haiman C.A., Kolonel L.K., Henderson B.E., Marchand L.L., Brennan P., Sangrajrang S., Gaborieau V., Odefrey F., Shen C.Y., Wu P.E., Wang H.C., Eccles D., Evans D.G., Peto J., Fletcher O., Johnson N., Seal S., Stratton M.R., Rahman N., Chenevix-Trench G., Bojesen S.E., Nordestgaard B.G., Axelsson C.K., Garcia-Closas M., Brinton L., Chanock S., Lissowska J., Peplonska B., Nevanlinna H., Fagerholm R., Eerola H., Kang D., Yoo K.Y., Noh D.Y., Ahn S.H., Hunter D.J., Hankinson S.E., Cox D.G., Hall P., Wedren S., Liu J., Low Y.L., Bogdanova N., Schürmann P., Dörk T., Tollenaar R.A., Jacobi C.E., Devilee P., Klijn J.G., Sigurdson A.J., Doody M.M., Alexander B.H., Zhang J., Cox A., Brock I.W., MacPherson G., Reed M.W., Couch F.J., Goode E.L., Olson J.E., Meijers-Heijboer H., van den Ouweland A., Uitterlinden A., Rivadeneira F., Milne R.L., Ribas G., Gonzalez-Neira A., Benitez J., Hopper J.L., McCredie M., Southey M., Giles G.G., Schroen C., Justenhoven C., Brauch H., Hamann U., Ko Y.D., Spurdle A.B., Beesley J., Chen X., Mannermaa A., Kosma V.M., Kataja V., Hartikainen J., Day N.E., Cox D.R., Ponder B.A., Luccarini C., Conroy D., Shah M., Munday H., Jordan C., Perkins B., West J., Redman K., Driver K., Aghmesheh M., Amor D., Andrews L., Antill Y., Armes J., Armitage S., Arnold L., Balleine R., Begley G., Beilby J., Bennett I., Bennett B., Berry G., Blackburn A., Brennan M., Brown M., Buckley M., Burke J., Butow P., Byron K., Callen D., Campbell I., Chenevix-Trench G., Clarke C., Colley A., Cotton D., Cui J., Culling B., Cummings M., Dawson S.J., Dixon J., Dobrovic A., Dudding T., Edkins T., Eisenbruch M., Farshid G., Fawcett S., Field M., Firgaira F., Fleming J., Forbes J., Friedlander M., Gaff C., Gardner M., Gattas M., George P., Giles G., Gill G., Goldblatt J., Greening S., Grist S., Haan E., Harris M., Hart S., Hayward N., Hopper J., Humphrey E., Jenkins M., Jones A., Kefford R., Kirk J., Kollias J., Kovalenko S., Lakhani S., Leary J., Lim J., Lindeman G., Lipton L., Lobb L., Maclurcan M., Mann G., Marsh D., McCredie M., McKay M., McLachlan S.A., Meiser B., Milne R., Mitchell G., Newman B., O'Loughlin I., Osborne R., Peters L., Phillips K., Price M., Reeve J., Reeve T., Richards R., Rinehart G., Robinson B., Rudzki B., Salisbury E., Sambrook J., Saunders C., Scott C., Scott E., Scott R., Seshadri R., Shelling A., Southey M., Spurdle A., Suthers G., Taylor D., Tennant C., Thorne H., Townshend S., Tucker K., Tyler J., Venter D., Visvader J., Walpole I., Ward R., Waring P., Warner B., Warren G., Watson E., Williams R., Wilson J., Winship I., Young M.A., Bowtell D., Green A., deFazio A., Chenevix-Trench G., Gertig D, & Webb P. (2007). Genome-wide association study identifies novel breast cancer susceptibility loci. Nature, 447(7148), 1087-1093.
Publication 2007
Asian Americans Biological Assay Diet Ethnicity Europeans FGFR2 protein, human Iplex Malignant Neoplasm of Breast Mass Spectrometry Oligonucleotide Arrays Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
4
Microarray-based Detection of SPI-7 Genetic Elements
A custom oligonucleotide probe-based array was designed as previously described [92] (link) in order to detect genes related to the absence and presence of SPI-7. After labelling, probes were purified and applied to microarray slides [93] (link). Genomic DNA was sonicated to yield 200–500 bp fragments, purified and labelled with Cy3-dCTP using the BioPrime DNA Labelling System (Invitrogen–BioSciences Ltd., Dun Laoghaire, Ireland). Duplicate slides were hybridized with the dCTP labelled DNAs in 48% formamide at 55oC for 16–20 hrs in a humid chamber. The slides were washed at RT, washed again at 50oC, scanned (GenepixR 4000B laser scanner, Axon Instruments, Redwood City, Calif.) and processed (GenePix Pro 3.0). The full dataset was analyzed using R (www.r-project.org), and Bioconductor (www.bioconductor.org) as described [94] (link). In brief, the bimodal distribution that was observed was treated as two overlapping Normal distributions. Means and 95% confidence intervals were determined for each distribution. Probes were scored “absent” if the log2 intensity was within or below the 95% CI for the “low” peak; “present” if the log2 intensity was within or above the 95% CI for the “high” peak and intermediate values were scored as “uncertain”. As a control, PCR tests similar to those described previously [95] (link) were used to screen for presence or absence of larger regions of SPI-7.
Achtman M., Wain J., Weill F.X., Nair S., Zhou Z., Sangal V., Krauland M.G., Hale J.L., Harbottle H., Uesbeck A., Dougan G., Harrison L.H, & Brisse S. (2012). Multilocus Sequence Typing as a Replacement for Serotyping in Salmonella enterica. PLoS Pathogens, 8(6), e1002776.
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Publication 2012
2'-deoxycytidine 5'-triphosphate Axon DNA formamide Genes Genome Microarray Analysis Oligonucleotide Arrays Redwood
5
Profiling Neuroblastoma Gene Expression
Single-color gene expression profiles from 649 neuroblastoma tumors and 2 neuroblastoma cell lines were generated using 44K oligonucleotide microarrays as described previously.43 (link) Total RNA of HOXC9- and GFP-expressing SK-N-AS and IMR-32 cells was isolated at 0, 6, 12, 24, 48 and 96 h using Trizol (Invitrogen, Karlsruhe, Germany). To determine global differences in the expression profiles of HOXC9- and GFP-induced SK-N-AS and IMR-32 cells, mean expression levels of each gene between HOXC9-induced and control cells were compared. Gene Ontology Tree Machine (GOTM)44 was used to identify functional categories associated with the condition of the respective cell line (Supplementary Materials and Methods). All raw and normalized microarray data are available through the Gene Expression Omnibus database (Accession: GSE45480).
Kocak H., Ackermann S., Hero B., Kahlert Y., Oberthuer A., Juraeva D., Roels F., Theissen J., Westermann F., Deubzer H., Ehemann V., Brors B., Odenthal M., Berthold F, & Fischer M. (2013). Hox-C9 activates the intrinsic pathway of apoptosis and is associated with spontaneous regression in neuroblastoma. Cell Death & Disease, 4(4), e586-.
Publication 2013
Cell Lines Cells Gene Expression Microarray Analysis Neoplasms Neuroblastoma Oligonucleotide Arrays Trees trizol

Most recents protocols related to «Oligonucleotide Arrays»

1
Agilent CGH Microarray Protocol
CGH 4x44K micro-array was performed using the agilent platform as previously described (11 (link),12 (link)). Agilent® oligonucleotide array was performed according to the manufacturer’s instructions (Agilent Human Genome CGH Microarray kit 44K®).
Rjiba K., Slimani W., Gaddas M., Hassine I.H., Jelloul A., Khelifa H.B., El Amri F., Zaouali M., Mcelreavey K., Saad A, & Mougou-Zerelli S. (2023). Anomalies in Human Sex Determination: Usefulness of a Combined Cytogenetic Approach to Characterize an Additional Case with Xp Functional Disomy Associated with 46,XY Gonadal Dysgenesis. Journal of Clinical Research in Pediatric Endocrinology, 15(1), 25-34.
Publication 2023
Genome, Human Microarray Analysis Oligonucleotide Arrays
2
Integrative Analysis of Hematopoietic Datasets
BloodSpot, Stemformatics, and GEO are open-access downloaded bio-database that provide visualization and are analyzing tools for large-scale genomics datasets. In particular, BloodSpot (https://www.bloodspot.eu) provides gene expression profiles of healthy and malignant hematopoiesis in humans or mice, encompassing a total of more than 5,000 samples analyzed using a oligonucleotide microarray chip and by RNA-seq assay (Bagger et al., 2016 (link)). Stemformatics (https://www.stemformatics.org/) is an established gene expression data portal containing over 420 public gene expression datasets derived from microarray, RNA sequencing, and single-cell profiling technologies. Its major focus is on pluripotency, tissue stem cells, and staged differentiation (Choi et al., 2019 (link)). The Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo) is an open functional genomics database of a high-throughput resource (Barrett, 2004 (link)).
Analysis of data from database IDs 6326 and 6610 from Stemformatics (accessed on 4 November 2022) and GSE24759 and GSE13159 from GEO was performed in silico (accessed on 13 December 2022). The hierarchical tree was analyzed in the BloodSpot online database (accessed on 13 December 2022).
Maggi F., Morelli M.B., Aguzzi C., Zeppa L., Nabissi M., Polidori C., Santoni G, & Amantini C. (2023). Calcium influx, oxidative stress, and apoptosis induced by TRPV1 in chronic myeloid leukemia cells: Synergistic effects with imatinib. Frontiers in Molecular Biosciences, 10, 1129202.
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Publication 2023
Biological Assay Cells DNA Chips Gene Expression Hematopoiesis Homo sapiens Microarray Analysis Mus Oligonucleotide Arrays RNA-Seq Stem Cells Tissues Trees
3
Transcriptomic Analysis of Bronchial Brushings
Total RNA was extracted from bronchial brush biopsies with mRNA isolation kits (DNAGdańsk, Gdańsk, Poland), fractioned in gravity gradient, isolated in chromatographic columns, and stored at −80 °C. The quality of each sample was assessed using Qiagen® QIAxel, and RNA integrity was verified by agarose gel electrophoresis. The resulting RNA was reverse transcribed into cDNA library using Syngen® UniversalScript Reverse Transcriptase. The product was purified by Syngen ® PCR ME Mini Kit and fluorescently labeled and purified using Kreatech ® ULS Platinum Bright Red/Orange Kit. Hybridization to microarrays occurred on the Human Genomic 49K Mi ReadyArray (a Human Exonic Evidence-Based Oligonucleotide array (HEEBO); Microarray Inc., Huntsville, AL, USA) at 37 °C for 24 h.
Kozlik-Siwiec P., Buregwa-Czuma S., Zawlik I., Dziedzina S., Myszka A., Zuk-Kuwik J., Siwiec-Kozlik A., Zarychta J., Okon K., Zareba L., Soja J., Jakiela B., Kepski M., Bazan J.G, & Bazan-Socha S. (2023). Co-Expression Analysis of Airway Epithelial Transcriptome in Asthma Patients with Eosinophilic vs. Non-Eosinophilic Airway Infiltration. International Journal of Molecular Sciences, 24(4), 3789.
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Publication 2023
Acid Hybridizations, Nucleic Biopsy Bronchi cDNA Library Chromatography Electrophoresis, Agar Gel Exons Genome, Human Gravity Homo sapiens isolation Microarray Analysis Oligonucleotide Arrays Platinum RNA, Messenger RNA-Directed DNA Polymerase
4
Bioinformatic Analysis of Pyroptosis Genes
The data obtained from the oligonucleotide microarrays were analyzed using the PL-Grid Infrastructure (http://www.plgrid.pl/; accessed on 21 September 2022). The GeneSpring 13.0 platform (Agilent Technologies UK Limited, South Queensferry, UK) database was used.
Bioinformatic analysis of the selected pyroptosis-related genes at the protein level was carried out using the STRING online database (https://string-db.org/; accessed on 20 October 2022). An interaction score of >0.4 was selected as the cutoff threshold. The results of protein–protein interactions were presented graphically.
Strzalka-Mrozik B., Madej M., Kurowska N., Kruszniewska-Rajs C., Kimsa-Dudek M., Adamska J, & Gola J.M. (2023). Changes in the Expression Profile of Pyroptosis-Related Genes in Senescent Retinal Pigment Epithelial Cells after Lutein Treatment. Current Issues in Molecular Biology, 45(2), 1500-1518.
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Publication 2023
Genes Oligonucleotide Arrays Proteins Pyroptosis
5
Profiling Pyroptosis-Related Gene Expression
Evaluation of the changes in the expression profile of pyroptosis-related genes in the analyzed cells was carried out using the oligonucleotide microarray method. For this purpose, the HG-U133A 2.0 plate set (Affymetrix, Santa Clara, CA, USA) was used in accordance with the manufacturer’s recommendations.
In order to prepare the matrix for the analysis in the first step, cDNA synthesis was carried out with the SuperScript® Choice System kit (Invitrogen Life Technologies, Waltham, MA, USA). In turn, the BioArray HighYield RNA Transcript Labeling Kit (Enzo Life Sciences, Inc, Farmingdale, NY, USA) was used to obtain biotinylated cRNA. Next, the labeled cRNA were subjected to a fragmentation step using the Sample Cleanup Module kit (Qiagen GmbH, Hilden, Germany). This process was carried out for 35 min at 94 °C. Hybridization of the cRNA to HG-U133A microarrays labeled with a phycoerythrin-streptavidin complex was the final step of the analysis. The scanning was performed using a GeneArray Scanner G2500A (Agilent Technologies, Inc., Santa Clara, CA, USA).
Strzalka-Mrozik B., Madej M., Kurowska N., Kruszniewska-Rajs C., Kimsa-Dudek M., Adamska J, & Gola J.M. (2023). Changes in the Expression Profile of Pyroptosis-Related Genes in Senescent Retinal Pigment Epithelial Cells after Lutein Treatment. Current Issues in Molecular Biology, 45(2), 1500-1518.
Full text: Click here
Publication 2023
Anabolism Cells Complementary RNA Crossbreeding DNA, Complementary Microarray Analysis Oligonucleotide Arrays Phycoerythrin Pyroptosis Streptavidin

Top products related to «Oligonucleotide Arrays»

1
Rneasy mini kit by Qiagen
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The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
2
Feature extraction software by Agilent Technologies
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The Feature Extraction software is a tool used to analyze and process data from microarray experiments. It provides a standardized and automated method for extracting meaningful information from raw microarray image data.
3
Trizol reagent by Thermo Fisher Scientific
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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
4
Trizol by Thermo Fisher Scientific
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TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.
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Agilent 2100 bioanalyzer by Agilent Technologies
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The Agilent 2100 Bioanalyzer is a lab instrument that provides automated analysis of DNA, RNA, and protein samples. It uses microfluidic technology to separate and detect these biomolecules with high sensitivity and resolution.
6
2100 bioanalyzer by Agilent Technologies
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The 2100 Bioanalyzer is a lab equipment product from Agilent Technologies. It is a microfluidic platform designed for the analysis of DNA, RNA, and proteins. The 2100 Bioanalyzer utilizes a lab-on-a-chip technology to perform automated electrophoretic separations and detection.
7
Rneasy kit by Qiagen
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The RNeasy kit is a laboratory equipment product that is designed for the extraction and purification of ribonucleic acid (RNA) from various biological samples. It utilizes a silica-membrane-based technology to efficiently capture and isolate RNA molecules.
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Agilent microarray scanner by Agilent Technologies
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The Agilent Microarray Scanner is a high-performance instrument designed for the scanning and analysis of microarray data. It provides automated image acquisition and data extraction capabilities for a wide range of microarray applications.
9
Low input quick amp labeling kit by Agilent Technologies
Sourced in United States, Germany, Japan, Netherlands, Canada
The Low Input Quick Amp Labeling Kit is a sample preparation kit used for microarray analysis. It is designed to amplify and label small amounts of RNA samples for use with microarray platforms.
10
Oligonucleotide array based cgh for genomic dna analysis by Agilent Technologies
Sourced in United States
The Oligonucleotide Array-Based CGH for Genomic DNA Analysis is a laboratory equipment product from Agilent Technologies. It is designed for the analysis of genomic DNA samples using comparative genomic hybridization (CGH) technology on an oligonucleotide-based microarray platform.

More about "Oligonucleotide Arrays"

Oligonucleotide Arrays, also known as DNA microarrays or gene expression arrays, are a powerful tool for genetic analysis, enabling researchers to study gene expression, DNA methylation, and genomic variations on a large scale.
These arrays consist of short, synthetic DNA or RNA sequences (oligonucleotides) immobilized on a solid surface, such as a glass slide or silicon chip.
By hybridizing samples to the array, researchers can measure the abundance of thousands of different genetic sequences simultaneously, offering high-throughput, cost-effective, and reproducible results.
This makes Oligonucleotide Arrays indispensable for a wide range of applications, including disease diagnosis, drug discovery, and population genetics.
However, optimizing the protocols and selecting the right products for these experiments can be challenging.
PubCompare.ai is an AI-driven platform that helps researchers find the best protocols and products from literature, pre-prints, and patents, ensuring reproducibility and accuracy in their Oligonucleotide Array experiments.
Researchers can use PubCompare.ai to find the optimal protocols and products for their experiments, such as the RNeasy Mini Kit for RNA extraction, Feature Extraction software for data analysis, and the Agilent 2100 Bioanalyzer for quality control.
By taking the guesswork out of their work, researchers can trust their results and focus on their research goals.
With PubCompare.ai, researchers can also explore related techniques like Oligonucleotide Array-Based CGH for Genomic DNA Analysis, which uses Oligonucleotide Arrays to study genomic variations.
By leveraging the latest advancements in this field, researchers can stay at the forefront of their discipline and drive scientific progress forward.