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[[File:From_spit_to_DNA-sample.webm|thumb|''How to use a microarray for genotyping.'' The video shows the process of extracting genotypes from a human spit sample using microarrays. Genotyping is a major use of DNA microarrays, but with some modifications they can also be used for other purposes such as measurement of gene expression and epigenetic markers.]]
A '''DNA microarray''' (also commonly known as
== Principle ==
[[File:NA hybrid.svg|thumb|Hybridization of the target to the probe]]▼
{{Main|Nucleic acid hybridization}}
{{Further|#A typical protocol}}
<!-- KEEP SECTION SIMPLE -->
▲[[File:NA hybrid.svg|thumb|Hybridization of the target to the probe]]
The core principle behind microarrays is hybridization between two DNA strands, the property of [[Complementarity (molecular biology)|complementary]] nucleic acid sequences to specifically pair with each other by forming [[hydrogen bond]]s between complementary [[Nucleotide|nucleotide base pairs]]. A high number of complementary base pairs in a nucleotide sequence means tighter [[non-covalent]] bonding between the two strands. After washing off non-specific bonding sequences, only strongly paired strands will remain hybridized. Fluorescently labeled target sequences that bind to a probe sequence generate a signal that depends on the hybridization conditions (such as temperature), and washing after hybridization. Total strength of the signal, from a spot (feature), depends upon the amount of target sample binding to the probes present on that spot. Microarrays use relative quantitation in which the intensity of a feature is compared to the intensity of the same feature under a different condition, <!-- 2 channel experiments are mentioned below! so do not repeat --> and the identity of the feature is known by its position.
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| [[Comparative genomic hybridization]]
| Assessing genome content in different cells or closely related organisms, as originally described by [[Patrick O. Brown|Patrick Brown]], Jonathan Pollack, [[Ash Alizadeh]] and colleagues at [[Stanford University|Stanford]].<ref name="Pollack et al.">{{cite journal|author=Pollack JR|author2=Perou CM|author3=Alizadeh AA|author4=Eisen MB|author5=Pergamenschikov A|author6=Williams CF|author7=Jeffrey SS|author8=Botstein D|author9=Brown PO|date= 1999|title=Genome-wide analysis of DNA copy-number changes using cDNA microarrays|journal=Nat Genet|volume=23|pages=41–46|pmid=10471496|doi=10.1038/12640|issue=1|s2cid=997032|url=https://cdr.lib.unc.edu/downloads/sj139421j }}</ref><ref name="Moran et al.">{{cite journal|author=Moran G|author2=Stokes C|author3=Thewes S|author4=Hube B|author5=Coleman DC|author6=Sullivan D|date= 2004|title=Comparative genomics using Candida albicans DNA microarrays reveals absence and divergence of virulence-associated genes in Candida dubliniensis|journal=Microbiology|volume=150|pages=3363–3382|pmid=15470115|doi=10.1099/mic.0.27221-0|issue=Pt 10|doi-access=free|hdl=2262/6097|hdl-access=free}}</ref>
|-
| GeneID
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| [[ChIP-on-chip|Chromatin immunoprecipitation on Chip]]
| DNA sequences bound to a particular protein can be isolated by [[immunoprecipitation|immunoprecipitating]] that protein ([[Chromatin immunoprecipitation|ChIP]]), these fragments can be then hybridized to a microarray (such as a [[tiling array]]) allowing the determination of protein binding site occupancy throughout the genome. Example protein to [[Chromatin immunoprecipitation|immunoprecipitate]] are histone modifications ([[H3K27me3]], H3K4me2, H3K9me3, etc.), [[Polycomb-group protein]] (PRC2:Suz12, PRC1:YY1) and [[trithorax-group protein]] (Ash1) to study the [[epigenetics|epigenetic landscape]] or [[RNA
|-
| [[DNA adenine methyltransferase identification|DamID]]
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| [[Fusion gene]]s microarray
| A
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| [[Tiling array]]
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Specialised arrays tailored to particular [[crop]]s are becoming increasingly popular in [[molecular breeding]] applications. In the future they could be used to screen [[seedling]]s at early stages to lower the number of unneeded seedlings tried out in breeding operations.<ref name="Rasheed-et-al-2017">{{cite journal | last1=Rasheed | first1=Awais | last2=Hao | first2=Yuanfeng | last3=Xia | first3=Xianchun | last4=Khan | first4=Awais | last5=Xu | first5=Yunbi | last6=Varshney | first6=Rajeev K. | last7=He | first7=Zhonghu | title=Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives | journal=[[Molecular Plant]] | publisher=[[Chinese Academy of Sciences|Chin Acad Sci]]+[[Chinese Society for Plant Biology|Chin Soc Plant Bio]]+[[Shanghai Institutes for Biological Sciences|Shanghai Inst Bio Sci]] ([[Elsevier]]) | volume=10 | issue=8 | year=2017 | issn=1674-2052 | doi=10.1016/j.molp.2017.06.008 | pages=1047–1064 | s2cid=33780984 | pmid=28669791| doi-access=free | bibcode=2017MPlan..10.1047R | url=http://oar.icrisat.org/10133/1/S1674-2052%2817%2930174-0.pdf }}</ref>
=== Fabrication ===
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[[File:Microarray printing.ogv|thumb|A DNA microarray being printed by a [[robot]] at the [[University of Delaware]] ]]
Microarrays can be fabricated using a variety of technologies, including printing with fine-pointed pins onto glass slides, [[photolithography]] using pre-made masks, photolithography using dynamic micromirror devices, ink-jet printing,<ref>J Biochem Biophys Methods. 2000 Mar 16;42(3):105–10. DNA-printing: utilization of a standard inkjet printer for the transfer of nucleic acids to solid supports. Goldmann T, Gonzalez JS.</ref><ref>{{cite journal|journal=Genome Biology | title=POSaM: a fast, flexible, open-source, inkjet oligonucleotide synthesizer and microarrayer| author=Lausted C| volume = 5 |
In ''spotted microarrays'', the probes are [[oligonucleotide synthesis|oligonucleotide]]s, [[cDNA]] or small fragments of [[Polymerase chain reaction|PCR]] products that correspond to [[mRNA]]s. The probes are [[oligonucleotide synthesis|synthesized]] prior to deposition on the array surface and are then "spotted" onto glass. A common approach utilizes an array of fine pins or needles controlled by a robotic arm that is dipped into wells containing DNA probes and then depositing each probe at designated locations on the array surface. The resulting "grid" of probes represents the nucleic acid profiles of the prepared probes and is ready to receive complementary cDNA or cRNA "targets" derived from experimental or clinical samples.
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===A typical protocol===
[[File:Summary of RNA Microarray.svg|thumb|''Examples of levels of application of microarrays.'' Within the organisms, genes are transcribed and spliced to produce mature mRNA transcripts (red). The mRNA is extracted from the organism and reverse transcriptase is used to copy the mRNA into stable ds-cDNA (blue). In microarrays, the ds-cDNA is fragmented and fluorescently labelled (orange). The labelled fragments bind to an ordered array of complementary oligonucleotides, and [[Fluorometer|measurement of fluorescent intensity]] across the array indicates the abundance of a predetermined set of sequences. These sequences are typically specifically chosen to report on genes of interest within the organism's genome.<ref>{{Cite journal|last1=Shafee|first1=Thomas|last2=Lowe|first2=Rohan|date=2017|title=Eukaryotic and prokaryotic gene structure|journal=WikiJournal of Medicine|language=en|volume=4|issue=1|doi=10.15347/wjm/2017.002|issn=2002-4436|doi-access=free|s2cid=35766676 }}</ref>]]
This is an example of a '''DNA microarray experiment''' which includes details for a particular case to better explain DNA microarray experiments, while listing modifications for RNA or other alternative experiments.
# The two samples to be compared (pairwise comparison) are grown/acquired. In this example treated sample ([[Case-control|case]]) and untreated sample ([[Case-control|control]]).
# The [[nucleic acid]] of interest is purified: this can be [[RNA]] for [[expression profiling]], [[DNA]] for [[comparative hybridization]], or DNA/RNA bound to a particular [[protein]] which is [[Chromatin immunoprecipitation|immunoprecipitated]] ([[ChIP-on-chip]]) for [[Epigenetics|epigenetic]] or regulation studies. In this example total RNA is isolated (both nuclear and [[cytoplasm]]ic) by [[
# The purified RNA is analysed for quality (by [[capillary electrophoresis]]) and quantity (for example, by using a [[NanoDrop]] or NanoPhotometer [[spectrometer]]). If the material is of acceptable quality and sufficient quantity is present (e.g., >1[[μg]], although the required amount varies by microarray platform), the experiment can proceed.
# The labeled product is generated via [[reverse transcription]] and followed by an optional [[Polymerase chain reaction|PCR]] amplification. The RNA is reverse transcribed with either polyT primers (which amplify only [[mRNA]]) or random primers (which amplify all RNA, most of which is [[rRNA]]). [[MicroRNA|miRNA]] microarrays ligate an oligonucleotide to the purified small RNA (isolated with a fractionator), which is then reverse transcribed and amplified.
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[[Image:Heatmap.png|right|thumb|Gene expression values from microarray experiments can be represented as [[heat map]]s to visualize the result of data analysis.]]
The advent of inexpensive microarray experiments created several specific bioinformatics challenges:<ref>{{
=== Experimental design ===
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Microarray data is difficult to exchange due to the lack of standardization in platform fabrication, assay protocols, and analysis methods. This presents an [[interoperability]] problem in [[bioinformatics]]. Various [[grass-roots]] [[open-source model|open-source]] projects are trying to ease the exchange and analysis of data produced with non-proprietary chips:
For example, the "Minimum Information About a Microarray Experiment" ([[MIAME]]) checklist helps define the level of detail that should exist and is being adopted by many [[Scientific journal|journals]] as a requirement for the submission of papers incorporating microarray results. But MIAME does not describe the format for the information, so while many formats can support the MIAME requirements, {{as of|lc=y|2007}} no format permits verification of complete semantic compliance. The "MicroArray Quality Control (MAQC) Project" is being conducted by the US [[Food and Drug Administration]] (FDA) to develop standards and quality control metrics which will eventually allow the use of MicroArray data in drug discovery, clinical practice and regulatory decision-making.<ref>[https://web.archive.org/web/20051208055601/http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ NCTR Center for Toxicoinformatics – MAQC Project<!-- Bot generated title -->]</ref> The [[MGED Society]] has developed standards for the representation of gene expression experiment results and relevant annotations.
=== Data analysis ===
[[File:Toxicology Research at FDA (NCTR 1470) (6009042166).jpg|thumb|[[National Center for Toxicological Research]] scientist reviews microarray data]]▼
{{main|Microarray analysis techniques}}
{{See also|Gene chip analysis}}
▲[[File:Toxicology Research at FDA (NCTR 1470) (6009042166).jpg|thumb|[[National Center for Toxicological Research]] scientist reviews microarray data.]]
Microarray data sets are commonly very large, and analytical precision is influenced by a number of variables. [[Statistics|Statistical]] challenges include taking into account effects of background noise and appropriate [[Normalization (statistics)|normalization]] of the data. Normalization methods may be suited to specific platforms and, in the case of commercial platforms, the analysis may be proprietary.<ref>{{cite web|title=Prosigna {{!}} Prosigna algorithm|url=http://prosigna.com/x-us/overview/prosigna-algorithm/|website=prosigna.com|access-date=22 June 2017|archive-date=9 November 2017|archive-url=https://web.archive.org/web/20171109082205/http://prosigna.com/x-us/overview/prosigna-algorithm/|url-status=dead}}</ref> Algorithms that affect statistical analysis include:
* Image analysis: gridding, spot recognition of the scanned image (segmentation algorithm), removal or marking of poor-quality and low-intensity features (called ''flagging'').
* Data processing: background subtraction (based on global or local background), determination of spot intensities and intensity ratios, visualisation of data (e.g. see [[MA plot]]), and log-transformation of ratios, global or [[Local regression|local]] normalization of intensity ratios, and segmentation into different copy number regions using [[step detection]] algorithms.<ref>{{cite journal|last=Little|first=
* Class discovery analysis: This analytic approach, sometimes called unsupervised classification or knowledge discovery, tries to identify whether microarrays (objects, patients, mice, etc.) or genes cluster together in groups. Identifying naturally existing groups of objects (microarrays or genes) which cluster together can enable the discovery of new groups that otherwise were not previously known to exist. During knowledge discovery analysis, various unsupervised classification techniques can be employed with DNA microarray data to identify novel clusters (classes) of arrays.<ref name="Peterson">{{cite book|author=Peterson, Leif E.
|date= 2013|title=Classification Analysis of DNA Microarrays|publisher=John Wiley and Sons|isbn=978-0-470-17081-6|url=http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470170816.html}}</ref> This type of approach is not hypothesis-driven, but rather is based on iterative pattern recognition or statistical learning methods to find an "optimal" number of clusters in the data. Examples of unsupervised analyses methods include self-organizing maps, neural gas, k-means cluster analyses,<ref>De Souto M et al. (2008) Clustering cancer gene expression data: a comparative study, BMC Bioinformatics, 9(497).</ref> hierarchical cluster analysis, Genomic Signal Processing based clustering and model-based cluster analysis. For some of these methods the user also has to define a distance measure between pairs of objects. Although the Pearson correlation coefficient is usually employed, several other measures have been proposed and evaluated in the literature.<ref>{{cite journal|last1=Jaskowiak|first1=Pablo A|last2=Campello|first2=Ricardo JGB|last3=Costa|first3=Ivan G|title=On the selection of appropriate distances for gene expression data clustering|journal=BMC Bioinformatics|volume=15|issue=Suppl 2|pages=S2|doi=10.1186/1471-2105-15-S2-S2|pmid=24564555|pmc=4072854|year=2014 |doi-access=free }}</ref> The input data used in class discovery analyses are commonly based on lists of genes having high informativeness (low noise) based on low values of the coefficient of variation or high values of Shannon entropy, etc. The determination of the most likely or optimal number of clusters obtained from an unsupervised analysis is called cluster validity. Some commonly used metrics for cluster validity are the silhouette index, Davies-Bouldin index,<ref>Bolshakova N, Azuaje F (2003) Cluster validation techniques for genome expression data, Signal Processing, Vol. 83, pp. 825–833.</ref> Dunn's index, or Hubert's <math>\Gamma</math> statistic.
* Class prediction analysis: This approach, called supervised classification, establishes the basis for developing a predictive model into which future unknown test objects can be input in order to predict the most likely class membership of the test objects. Supervised analysis<ref name="Peterson"/> for class prediction involves use of techniques such as linear regression, k-nearest neighbor, learning vector quantization, decision tree analysis, random forests, naive Bayes, logistic regression, kernel regression, artificial neural networks, support vector machines, [[mixture of experts]], and supervised neural gas. In addition, various metaheuristic methods are employed, such as [[genetic algorithm]]s, covariance matrix self-adaptation, [[particle swarm optimization]], and [[ant colony optimization]]. Input data for class prediction are usually based on filtered lists of genes which are predictive of class, determined using classical hypothesis tests (next section), Gini diversity index, or information gain (entropy).
* Hypothesis-driven statistical analysis: Identification of statistically significant changes in gene expression are commonly identified using the [[t-test]], [[ANOVA]], [[Bayesian method]]<ref name="Ben-GalShani2005">{{cite journal|last1=Ben Gal|first1=I.|last2=Shani|first2=A.|last3=Gohr|first3=A.|last4=Grau|first4=J.|last5=Arviv|first5=S.|last6=Shmilovici|first6=A.|last7=Posch|first7=S.|last8=Grosse|first8=I.|title=Identification of transcription factor binding sites with variable-order Bayesian networks|journal=Bioinformatics|volume=21|issue=11|year=2005|pages=2657–2666|issn=1367-4803|doi=10.1093/bioinformatics/bti410|pmid=15797905|doi-access=}}</ref> [[Mann–Whitney test]] methods tailored to microarray data sets, which take into account [[multiple comparisons]]<ref>Yuk Fai Leung and Duccio Cavalieri, Fundamentals of cDNA microarray data analysis. Trends in Genetics Vol.19 No.11 November 2003.</ref> or [[cluster analysis]].<ref name="Priness2007">{{cite journal|author=Priness I.|author2=Maimon O.|author3=Ben-Gal I.|date=2007|title=Evaluation of gene-expression clustering via mutual information distance measure|journal=BMC Bioinformatics|volume=8|issue=1|page=111|doi=10.1186/1471-2105-8-111|pmid=17397530|pmc=1858704 |doi-access=free }}</ref> These methods assess statistical power based on the variation present in the data and the number of experimental replicates, and can help minimize [[
<!-- {{Citation needed|date=July 2008}}as in many other cases where authorities disagree, a sound conservative approach is to directly compare different normalization methods to determine the effects of these different methods on the results obtained. This can be done, for example, by investigating the performance of various methods on data from "spike-in" experiments. {{Citation needed|date=July 2008}} -->
* Dimensional reduction: Analysts often reduce the number of dimensions (genes) prior to data analysis.<ref name="Peterson"/> This may involve linear approaches such as principal components analysis (PCA), or non-linear manifold learning (distance metric learning) using kernel PCA, diffusion maps, Laplacian eigenmaps, local linear embedding, locally preserving projections, and Sammon's mapping.
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=== Data warehousing ===
Microarray data was found to be more useful when compared to other similar datasets. The sheer volume of data, specialized formats (such as [[MIAME]]), and curation efforts associated with the datasets require specialized databases to store the data. A number of open-source data warehousing solutions, such as [[InterMine]] and [
== Alternative technologies ==
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|label=DNA microarrays}}
{{Commons category|DNA microarrays}}
* [
* [https://pubmed.ncbi.nlm.nih.gov/14551912/ PLoS Biology Primer: Microarray Analysis]
▲* [http://www.1lec.com/microarray/ Microarray Animation] 1Lec.com
* [https://web.archive.org/web/20150924040600/http://www.genome.gov/page.cfm?pageID=10000533 Rundown of microarray technology]
* [http://www.arraymining.net ArrayMining.net]{{snd}}a free web-server for online microarray analysis
* [https://web.archive.org/web/20090223111750/http://www.unsolvedmysteries.oregonstate.edu/microarray_07 Microarray – How does it work?]
* [
* [
{{Molecular Biology}}
{{Glass science}}
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