DNA microarray

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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 DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Each DNA spot contains picomoles (10−12 moles) of a specific DNA sequence, known as probes (or reporters or oligos ). These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA (also called anti-sense RNA) sample (called target) under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. The original nucleic acid arrays were macro arrays approximately 9 cm × 12 cm and the first computerized image based analysis was published in 1981. [1] It was invented by Patrick O. Brown. An example of its application is in SNPs arrays for polymorphisms in cardiovascular diseases, cancer, pathogens and GWAS analysis. It is also used for the identification of structural variations and the measurement of gene expression.

Contents

Principle

Hybridization of the target to the probe NA hybrid.svg
Hybridization of the target to the probe

The core principle behind microarrays is hybridization between two DNA strands, the property of complementary nucleic acid sequences to specifically pair with each other by forming hydrogen bonds between complementary 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, and the identity of the feature is known by its position.

The steps required in a microarray experiment Microarray exp horizontal.svg
The steps required in a microarray experiment

Uses and types

Two Affymetrix chips. A match is shown at bottom left for size comparison. Affymetrix-microarray.jpg
Two Affymetrix chips. A match is shown at bottom left for size comparison.

Many types of arrays exist and the broadest distinction is whether they are spatially arranged on a surface or on coded beads:

DNA microarrays can be used to detect DNA (as in comparative genomic hybridization), or detect RNA (most commonly as cDNA after reverse transcription) that may or may not be translated into proteins. The process of measuring gene expression via cDNA is called expression analysis or expression profiling.

Applications include:

Application or technologySynopsis
Gene expression profiling In an mRNA or gene expression profiling experiment the expression levels of thousands of genes are simultaneously monitored to study the effects of certain treatments, diseases, and developmental stages on gene expression. For example, microarray-based gene expression profiling can be used to identify genes whose expression is changed in response to pathogens or other organisms by comparing gene expression in infected to that in uninfected cells or tissues. [2]
Comparative genomic hybridization Assessing genome content in different cells or closely related organisms, as originally described by Patrick Brown, Jonathan Pollack, Ash Alizadeh and colleagues at Stanford. [3] [4]
GeneIDSmall microarrays to check IDs of organisms in food and feed (like GMO ), mycoplasms in cell culture, or pathogens for disease detection, mostly combining PCR and microarray technology.
Chromatin immunoprecipitation on Chip DNA sequences bound to a particular protein can be isolated by immunoprecipitating that protein (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 immunoprecipitate are histone modifications (H3K27me3, H3K4me2, H3K9me3, etc.), Polycomb-group protein (PRC2:Suz12, PRC1:YY1) and trithorax-group protein (Ash1) to study the epigenetic landscape or RNA polymerase II to study the transcription landscape.
DamID Analogously to ChIP, genomic regions bound by a protein of interest can be isolated and used to probe a microarray to determine binding site occupancy. Unlike ChIP, DamID does not require antibodies but makes use of adenine methylation near the protein's binding sites to selectively amplify those regions, introduced by expressing minute amounts of protein of interest fused to bacterial DNA adenine methyltransferase.
SNP detection Identifying single nucleotide polymorphism among alleles within or between populations. [5] Several applications of microarrays make use of SNP detection, including genotyping, forensic analysis, measuring predisposition to disease, identifying drug-candidates, evaluating germline mutations in individuals or somatic mutations in cancers, assessing loss of heterozygosity, or genetic linkage analysis.
Alternative splicing detectionAn exon junction array design uses probes specific to the expected or potential splice sites of predicted exons for a gene. It is of intermediate density, or coverage, to a typical gene expression array (with 1–3 probes per gene) and a genomic tiling array (with hundreds or thousands of probes per gene). It is used to assay the expression of alternative splice forms of a gene. Exon arrays have a different design, employing probes designed to detect each individual exon for known or predicted genes, and can be used for detecting different splicing isoforms.
Fusion genes microarrayA fusion gene microarray can detect fusion transcripts, e.g. from cancer specimens. The principle behind this is building on the alternative splicing microarrays. The oligo design strategy enables combined measurements of chimeric transcript junctions with exon-wise measurements of individual fusion partners.
Tiling array Genome tiling arrays consist of overlapping probes designed to densely represent a genomic region of interest, sometimes as large as an entire human chromosome. The purpose is to empirically detect expression of transcripts or alternatively spliced forms which may not have been previously known or predicted.
Double-stranded B-DNA microarraysRight-handed double-stranded B-DNA microarrays can be used to characterize novel drugs and biologicals that can be employed to bind specific regions of immobilized, intact, double-stranded DNA. This approach can be used to inhibit gene expression. [6] [7] They also allow for characterization of their structure under different environmental conditions.
Double-stranded Z-DNA microarraysLeft-handed double-stranded Z-DNA microarrays can be used to identify short sequences of the alternative Z-DNA structure located within longer stretches of right-handed B-DNA genes (e.g., transcriptional enhancement, recombination, RNA editing). [6] [7] The microarrays also allow for characterization of their structure under different environmental conditions.
Multi-stranded DNA microarrays (triplex-DNA microarrays and quadruplex-DNA microarrays)Multi-stranded DNA and RNA microarrays can be used to identify novel drugs that bind to these multi-stranded nucleic acid sequences. This approach can be used to discover new drugs and biologicals that have the ability to inhibit gene expression. [6] [7] [8] [9] These microarrays also allow for characterization of their structure under different environmental conditions.

Specialised arrays tailored to particular crops are becoming increasingly popular in molecular breeding applications. In the future they could be used to screen seedlings at early stages to lower the number of unneeded seedlings tried out in breeding operations. [10]

Fabrication

Microarrays can be manufactured in different ways, depending on the number of probes under examination, costs, customization requirements, and the type of scientific question being asked. Arrays from commercial vendors may have as few as 10 probes or as many as 5 million or more micrometre-scale probes.

Spotted vs. in situ synthesised arrays

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, [11] [12] or electrochemistry on microelectrode arrays.

In spotted microarrays, the probes are oligonucleotides, cDNA or small fragments of PCR products that correspond to mRNAs. The probes are 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. This technique is used by research scientists around the world to produce "in-house" printed microarrays in their own labs. These arrays may be easily customized for each experiment, because researchers can choose the probes and printing locations on the arrays, synthesize the probes in their own lab (or collaborating facility), and spot the arrays. They can then generate their own labeled samples for hybridization, hybridize the samples to the array, and finally scan the arrays with their own equipment. This provides a relatively low-cost microarray that may be customized for each study, and avoids the costs of purchasing often more expensive commercial arrays that may represent vast numbers of genes that are not of interest to the investigator. Publications exist which indicate in-house spotted microarrays may not provide the same level of sensitivity compared to commercial oligonucleotide arrays, [13] possibly owing to the small batch sizes and reduced printing efficiencies when compared to industrial manufactures of oligo arrays.

In oligonucleotide microarrays, the probes are short sequences designed to match parts of the sequence of known or predicted open reading frames. Although oligonucleotide probes are often used in "spotted" microarrays, the term "oligonucleotide array" most often refers to a specific technique of manufacturing. Oligonucleotide arrays are produced by printing short oligonucleotide sequences designed to represent a single gene or family of gene splice-variants by synthesizing this sequence directly onto the array surface instead of depositing intact sequences. Sequences may be longer (60-mer probes such as the Agilent design) or shorter (25-mer probes produced by Affymetrix) depending on the desired purpose; longer probes are more specific to individual target genes, shorter probes may be spotted in higher density across the array and are cheaper to manufacture. One technique used to produce oligonucleotide arrays include photolithographic synthesis (Affymetrix) on a silica substrate where light and light-sensitive masking agents are used to "build" a sequence one nucleotide at a time across the entire array. [14] Each applicable probe is selectively "unmasked" prior to bathing the array in a solution of a single nucleotide, then a masking reaction takes place and the next set of probes are unmasked in preparation for a different nucleotide exposure. After many repetitions, the sequences of every probe become fully constructed. More recently, Maskless Array Synthesis from NimbleGen Systems has combined flexibility with large numbers of probes. [15]

Two-channel vs. one-channel detection

Diagram of typical dual-colour microarray experiment Microarray-schema.jpg
Diagram of typical dual-colour microarray experiment

Two-color microarrays or two-channel microarrays are typically hybridized with cDNA prepared from two samples to be compared (e.g. diseased tissue versus healthy tissue) and that are labeled with two different fluorophores. [16] Fluorescent dyes commonly used for cDNA labeling include Cy3, which has a fluorescence emission wavelength of 570 nm (corresponding to the green part of the light spectrum), and Cy5 with a fluorescence emission wavelength of 670 nm (corresponding to the red part of the light spectrum). The two Cy-labeled cDNA samples are mixed and hybridized to a single microarray that is then scanned in a microarray scanner to visualize fluorescence of the two fluorophores after excitation with a laser beam of a defined wavelength. Relative intensities of each fluorophore may then be used in ratio-based analysis to identify up-regulated and down-regulated genes. [17]

Oligonucleotide microarrays often carry control probes designed to hybridize with RNA spike-ins. The degree of hybridization between the spike-ins and the control probes is used to normalize the hybridization measurements for the target probes. Although absolute levels of gene expression may be determined in the two-color array in rare instances, the relative differences in expression among different spots within a sample and between samples is the preferred method of data analysis for the two-color system. Examples of providers for such microarrays includes Agilent with their Dual-Mode platform, Eppendorf with their DualChip platform for colorimetric Silverquant labeling, and TeleChem International with Arrayit.

In single-channel microarrays or one-color microarrays, the arrays provide intensity data for each probe or probe set indicating a relative level of hybridization with the labeled target. However, they do not truly indicate abundance levels of a gene but rather relative abundance when compared to other samples or conditions when processed in the same experiment. Each RNA molecule encounters protocol and batch-specific bias during amplification, labeling, and hybridization phases of the experiment making comparisons between genes for the same microarray uninformative. The comparison of two conditions for the same gene requires two separate single-dye hybridizations. Several popular single-channel systems are the Affymetrix "Gene Chip", Illumina "Bead Chip", Agilent single-channel arrays, the Applied Microarrays "CodeLink" arrays, and the Eppendorf "DualChip & Silverquant". One strength of the single-dye system lies in the fact that an aberrant sample cannot affect the raw data derived from other samples, because each array chip is exposed to only one sample (as opposed to a two-color system in which a single low-quality sample may drastically impinge on overall data precision even if the other sample was of high quality). Another benefit is that data are more easily compared to arrays from different experiments as long as batch effects have been accounted for.

One channel microarray may be the only choice in some situations. Suppose samples need to be compared: then the number of experiments required using the two channel arrays quickly becomes unfeasible, unless a sample is used as a reference.

number of samplesone-channel microarraytwo channel microarray

two channel microarray (with reference)

1111
2211
3332
4463

A typical protocol

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 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. Summary of RNA Microarray.svg
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 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.

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.

  1. The two samples to be compared (pairwise comparison) are grown/acquired. In this example treated sample (case) and untreated sample (control).
  2. 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 immunoprecipitated (ChIP-on-chip) for epigenetic or regulation studies. In this example total RNA is isolated (both nuclear and cytoplasmic) by guanidinium thiocyanate-phenol-chloroform extraction (e.g. Trizol) which isolates most RNA (whereas column methods have a cut off of 200 nucleotides) and if done correctly has a better purity.
  3. 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., >1μg, although the required amount varies by microarray platform), the experiment can proceed.
  4. The labeled product is generated via reverse transcription and followed by an optional 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). miRNA microarrays ligate an oligonucleotide to the purified small RNA (isolated with a fractionator), which is then reverse transcribed and amplified.
    • The label is added either during the reverse transcription step, or following amplification if it is performed. The sense labeling is dependent on the microarray; e.g. if the label is added with the RT mix, the cDNA is antisense and the microarray probe is sense, except in the case of negative controls.
    • The label is typically fluorescent; only one machine uses radiolabels.
    • The labeling can be direct (not used) or indirect (requires a coupling stage). For two-channel arrays, the coupling stage occurs before hybridization, using aminoallyl uridine triphosphate (aminoallyl-UTP, or aaUTP) and NHS amino-reactive dyes (such as cyanine dyes); for single-channel arrays, the coupling stage occurs after hybridization, using biotin and labeled streptavidin. The modified nucleotides (usually in a ratio of 1 aaUTP: 4 TTP (thymidine triphosphate)) are added enzymatically in a low ratio to normal nucleotides, typically resulting in 1 every 60 bases. The aaDNA is then purified with a column (using a phosphate buffer solution, as Tris contains amine groups). The aminoallyl group is an amine group on a long linker attached to the nucleobase, which reacts with a reactive dye.
      • A form of replicate known as a dye flip can be performed to control for dye artifacts in two-channel experiments; for a dye flip, a second slide is used, with the labels swapped (the sample that was labeled with Cy3 in the first slide is labeled with Cy5, and vice versa). In this example, aminoallyl-UTP is present in the reverse-transcribed mixture.
  5. The labeled samples are then mixed with a proprietary hybridization solution which can consist of SDS, SSC, dextran sulfate, a blocking agent (such as Cot-1 DNA, salmon sperm DNA, calf thymus DNA, PolyA, or PolyT), Denhardt's solution, or formamine.
  6. The mixture is denatured and added to the pinholes of the microarray. The holes are sealed and the microarray hybridized, either in a hyb oven, where the microarray is mixed by rotation, or in a mixer, where the microarray is mixed by alternating pressure at the pinholes.
  7. After an overnight hybridization, all nonspecific binding is washed off (SDS and SSC).
  8. The microarray is dried and scanned by a machine that uses a laser to excite the dye and measures the emission levels with a detector.
  9. The image is gridded with a template and the intensities of each feature (composed of several pixels) is quantified.
  10. The raw data is normalized; the simplest normalization method is to subtract background intensity and scale so that the total intensities of the features of the two channels are equal, or to use the intensity of a reference gene to calculate the t-value for all of the intensities. More sophisticated methods include z-ratio, loess and lowess regression and RMA (robust multichip analysis) for Affymetrix chips (single-channel, silicon chip, in situ synthesized short oligonucleotides).

Microarrays and bioinformatics

Gene expression values from microarray experiments can be represented as heat maps to visualize the result of data analysis. Heatmap.png
Gene expression values from microarray experiments can be represented as heat maps to visualize the result of data analysis.

The advent of inexpensive microarray experiments created several specific bioinformatics challenges: [19] the multiple levels of replication in experimental design (Experimental design); the number of platforms and independent groups and data format (Standardization); the statistical treatment of the data (Data analysis); mapping each probe to the mRNA transcript that it measures (Annotation); the sheer volume of data and the ability to share it (Data warehousing).

Experimental design

Due to the biological complexity of gene expression, the considerations of experimental design that are discussed in the expression profiling article are of critical importance if statistically and biologically valid conclusions are to be drawn from the data.

There are three main elements to consider when designing a microarray experiment. First, replication of the biological samples is essential for drawing conclusions from the experiment. Second, technical replicates (e.g. two RNA samples obtained from each experimental unit) may help to quantitate precision. The biological replicates include independent RNA extractions. Technical replicates may be two aliquots of the same extraction. Third, spots of each cDNA clone or oligonucleotide are present as replicates (at least duplicates) on the microarray slide, to provide a measure of technical precision in each hybridization. It is critical that information about the sample preparation and handling is discussed, in order to help identify the independent units in the experiment and to avoid inflated estimates of statistical significance. [20]

Standardization

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 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 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 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. [21] The MGED Society has developed standards for the representation of gene expression experiment results and relevant annotations.

Data analysis

National Center for Toxicological Research scientist reviews microarray data. Toxicology Research at FDA (NCTR 1470) (6009042166).jpg
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. Statistical challenges include taking into account effects of background noise and appropriate normalization of the data. Normalization methods may be suited to specific platforms and, in the case of commercial platforms, the analysis may be proprietary. [22] Algorithms that affect statistical analysis include:

Microarray data may require further processing aimed at reducing the dimensionality of the data to aid comprehension and more focused analysis. [33] Other methods permit analysis of data consisting of a low number of biological or technical replicates; for example, the Local Pooled Error (LPE) test pools standard deviations of genes with similar expression levels in an effort to compensate for insufficient replication. [34]

Annotation

The relation between a probe and the mRNA that it is expected to detect is not trivial. [35] Some mRNAs may cross-hybridize probes in the array that are supposed to detect another mRNA. In addition, mRNAs may experience amplification bias that is sequence or molecule-specific. Thirdly, probes that are designed to detect the mRNA of a particular gene may be relying on genomic EST information that is incorrectly associated with that gene.

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 BioMart, have been created for the specific purpose of integrating diverse biological datasets, and also support analysis.

Alternative technologies

Advances in massively parallel sequencing has led to the development of RNA-Seq technology, that enables a whole transcriptome shotgun approach to characterize and quantify gene expression. [36] [37] Unlike microarrays, which need a reference genome and transcriptome to be available before the microarray itself can be designed, RNA-Seq can also be used for new model organisms whose genome has not been sequenced yet. [37]

Glossary

See also

Related Research Articles

Molecular biology is a branch of biology that seeks to understand the molecular basis of biological activity in and between cells, including biomolecular synthesis, modification, mechanisms, and interactions.

<span class="mw-page-title-main">Northern blot</span> Molecular biology technique

The northern blot, or RNA blot, is a technique used in molecular biology research to study gene expression by detection of RNA in a sample.

<span class="mw-page-title-main">Reverse transcription polymerase chain reaction</span> Laboratory technique to multiply an RNA sample for study

Reverse transcription polymerase chain reaction (RT-PCR) is a laboratory technique combining reverse transcription of RNA into DNA and amplification of specific DNA targets using polymerase chain reaction (PCR). It is primarily used to measure the amount of a specific RNA. This is achieved by monitoring the amplification reaction using fluorescence, a technique called real-time PCR or quantitative PCR (qPCR). Confusion can arise because some authors use the acronym RT-PCR to denote real-time PCR. In this article, RT-PCR will denote Reverse Transcription PCR. Combined RT-PCR and qPCR are routinely used for analysis of gene expression and quantification of viral RNA in research and clinical settings.

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

Fluorescence <i>in situ</i> hybridization Genetic testing technique

Fluorescence in situ hybridization (FISH) is a molecular cytogenetic technique that uses fluorescent probes that bind to only particular parts of a nucleic acid sequence with a high degree of sequence complementarity. It was developed by biomedical researchers in the early 1980s to detect and localize the presence or absence of specific DNA sequences on chromosomes. Fluorescence microscopy can be used to find out where the fluorescent probe is bound to the chromosomes. FISH is often used for finding specific features in DNA for use in genetic counseling, medicine, and species identification. FISH can also be used to detect and localize specific RNA targets in cells, circulating tumor cells, and tissue samples. In this context, it can help define the spatial-temporal patterns of gene expression within cells and tissues.

<span class="mw-page-title-main">Gene expression profiling</span> Detection of mRNA molecules

In the field of molecular biology, gene expression profiling is the measurement of the activity of thousands of genes at once, to create a global picture of cellular function. These profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment. Many experiments of this sort measure an entire genome simultaneously, that is, every gene present in a particular cell.

<span class="mw-page-title-main">RNA spike-in</span>

An RNA spike-in is an RNA transcript of known sequence and quantity used to calibrate measurements in RNA hybridization assays, such as DNA microarray experiments, RT-qPCR, and RNA-Seq.

<span class="mw-page-title-main">Microarray analysis techniques</span>

Microarray analysis techniques are used in interpreting the data generated from experiments on DNA, RNA, and protein microarrays, which allow researchers to investigate the expression state of a large number of genes – in many cases, an organism's entire genome – in a single experiment. Such experiments can generate very large amounts of data, allowing researchers to assess the overall state of a cell or organism. Data in such large quantities is difficult – if not impossible – to analyze without the help of computer programs.

<span class="mw-page-title-main">ChIP-on-chip</span> Molecular biology method

ChIP-on-chip is a technology that combines chromatin immunoprecipitation ('ChIP') with DNA microarray ("chip"). Like regular ChIP, ChIP-on-chip is used to investigate interactions between proteins and DNA in vivo. Specifically, it allows the identification of the cistrome, the sum of binding sites, for DNA-binding proteins on a genome-wide basis. Whole-genome analysis can be performed to determine the locations of binding sites for almost any protein of interest. As the name of the technique suggests, such proteins are generally those operating in the context of chromatin. The most prominent representatives of this class are transcription factors, replication-related proteins, like origin recognition complex protein (ORC), histones, their variants, and histone modifications.

Within computational biology, an MA plot is an application of a Bland–Altman plot for visual representation of genomic data. The plot visualizes the differences between measurements taken in two samples, by transforming the data onto M and A scales, then plotting these values. Though originally applied in the context of two channel DNA microarray gene expression data, MA plots are also used to visualise high-throughput sequencing analysis.

ChIP-sequencing, also known as ChIP-seq, is a method used to analyze protein interactions with DNA. ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. It can be used to map global binding sites precisely for any protein of interest. Previously, ChIP-on-chip was the most common technique utilized to study these protein–DNA relations.

<span class="mw-page-title-main">Tiling array</span>

Tiling arrays are a subtype of microarray chips. Like traditional microarrays, they function by hybridizing labeled DNA or RNA target molecules to probes fixed onto a solid surface.

The Illumina Methylation Assay using the Infinium I platform uses 'BeadChip' technology to generate a comprehensive genome-wide profiling of human DNA methylation. Similar to bisulfite sequencing and pyrosequencing, this method quantifies methylation levels at various loci within the genome. This assay is used for methylation probes on the Illumina Infinium HumanMethylation27 BeadChip. Probes on the 27k array target regions of the human genome to measure methylation levels at 27,578 CpG dinucleotides in 14,495 genes. In 2008, Illumina released the Infinium HumanMethylation450 BeadChip array, which targets over 450,000 methylation sites. In 2016, the Infinium MethylationEPIC BeadChip ("EPIC") was released, which interrogates over 850,000 methylation sites across the human genome.

<span class="mw-page-title-main">MAGIChip</span>

MAGIChips, also known as "microarrays of gel-immobilized compounds on a chip" or "three-dimensional DNA microarrays", are devices for molecular hybridization produced by immobilizing oligonucleotides, DNA, enzymes, antibodies, and other compounds on a photopolymerized micromatrix of polyacrylamide gel pads of 100x100x20 μm or smaller size. This technology is used for analysis of nucleic acid hybridization, specific binding of DNA, and low-molecular weight compounds with proteins, and protein-protein interactions.

Methylated DNA immunoprecipitation is a large-scale purification technique in molecular biology that is used to enrich for methylated DNA sequences. It consists of isolating methylated DNA fragments via an antibody raised against 5-methylcytosine (5mC). This technique was first described by Weber M. et al. in 2005 and has helped pave the way for viable methylome-level assessment efforts, as the purified fraction of methylated DNA can be input to high-throughput DNA detection methods such as high-resolution DNA microarrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). Nonetheless, understanding of the methylome remains rudimentary; its study is complicated by the fact that, like other epigenetic properties, patterns vary from cell-type to cell-type.

The reverse northern blot is a method by which gene expression patterns may be analyzed by comparing isolated RNA molecules from a tester sample to samples in a control cDNA library. It is a variant of the northern blot in which the nucleic acid immobilized on a membrane is a collection of isolated DNA fragments rather than RNA, and the probe is RNA extracted from a tissue and radioactively labelled. A reverse northern blot can be used to profile expression levels of particular sets of RNA sequences in a tissue or to determine presence of a particular RNA sequence in a sample. Although DNA Microarrays and newer next-generation techniques have generally supplanted reverse northern blotting, it is still utilized today and provides a relatively cheap and easy means of defining expression of large sets of genes.

Massive parallel signature sequencing (MPSS) is a procedure that is used to identify and quantify mRNA transcripts, resulting in data similar to serial analysis of gene expression (SAGE), although it employs a series of biochemical and sequencing steps that are substantially different.

Suspension array technology is a high throughput, large-scale, and multiplexed screening platform used in molecular biology. SAT has been widely applied to genomic and proteomic research, such as single nucleotide polymorphism (SNP) genotyping, genetic disease screening, gene expression profiling, screening drug discovery and clinical diagnosis. SAT uses microsphere beads to prepare arrays. SAT allows for the simultaneous testing of multiple gene variants through the use of these microsphere beads as each type of microsphere bead has a unique identification based on variations in optical properties, most common is fluorescent colour. As each colour and intensity of colour has a unique wavelength, beads can easily be differentiated based on their wavelength intensity. Microspheres are readily suspendable in solution and exhibit favorable kinetics during an assay. Similar to flat microarrays, an appropriate receptor molecule, such as DNA oligonucleotide probes, antibodies, or other proteins, attach themselves to the differently labeled microspheres. This produces thousands of microsphere array elements. Probe-target hybridization is usually detected by optically labeled targets, which determines the relative abundance of each target in the sample.

Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.

Spatial transcriptomics is a method for assigning cell types to their locations in the histological sections. Recent work demonstrated that the subcellular localization of mRNA molecules, for example, in the nucleus can also be studied.

References

  1. Taub, Floyd (1983). "Laboratory methods: Sequential comparative hybridizations analyzed by computerized image processing can identify and quantitate regulated RNAs". DNA. 2 (4): 309–327. doi:10.1089/dna.1983.2.309. PMID   6198132.
  2. Adomas A; Heller G; Olson A; Osborne J; Karlsson M; Nahalkova J; Van Zyl L; Sederoff R; Stenlid J; Finlay R; Asiegbu FO (2008). "Comparative analysis of transcript abundance in Pinus sylvestris after challenge with a saprotrophic, pathogenic or mutualistic fungus". Tree Physiol. 28 (6): 885–897. doi:10.1093/treephys/28.6.885. PMID   18381269.
  3. Pollack JR; Perou CM; Alizadeh AA; Eisen MB; Pergamenschikov A; Williams CF; Jeffrey SS; Botstein D; Brown PO (1999). "Genome-wide analysis of DNA copy-number changes using cDNA microarrays". Nat Genet. 23 (1): 41–46. doi:10.1038/12640. PMID   10471496. S2CID   997032.
  4. Moran G; Stokes C; Thewes S; Hube B; Coleman DC; Sullivan D (2004). "Comparative genomics using Candida albicans DNA microarrays reveals absence and divergence of virulence-associated genes in Candida dubliniensis". Microbiology. 150 (Pt 10): 3363–3382. doi: 10.1099/mic.0.27221-0 . hdl: 2262/6097 . PMID   15470115.
  5. Hacia JG; Fan JB; Ryder O; Jin L; Edgemon K; Ghandour G; Mayer RA; Sun B; Hsie L; Robbins CM; Brody LC; Wang D; Lander ES; Lipshutz R; Fodor SP; Collins FS (1999). "Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays". Nat Genet. 22 (2): 164–167. doi:10.1038/9674. PMID   10369258. S2CID   41718227.
  6. 1 2 3 Gagna, Claude E.; Lambert, W. Clark (1 May 2009). "Novel multistranded, alternative, plasmid and helical transitional DNA and RNA microarrays: implications for therapeutics". Pharmacogenomics. 10 (5): 895–914. doi:10.2217/pgs.09.27. ISSN   1744-8042. PMID   19450135.
  7. 1 2 3 Gagna, Claude E.; Clark Lambert, W. (1 March 2007). "Cell biology, chemogenomics and chemoproteomics – application to drug discovery". Expert Opinion on Drug Discovery. 2 (3): 381–401. doi:10.1517/17460441.2.3.381. ISSN   1746-0441. PMID   23484648. S2CID   41959328.
  8. Mukherjee, Anirban; Vasquez, Karen M. (1 August 2011). "Triplex technology in studies of DNA damage, DNA repair, and mutagenesis". Biochimie. 93 (8): 1197–1208. doi:10.1016/j.biochi.2011.04.001. ISSN   1638-6183. PMC   3545518 . PMID   21501652.
  9. Rhodes, Daniela; Lipps, Hans J. (15 October 2015). "G-quadruplexes and their regulatory roles in biology". Nucleic Acids Research. 43 (18): 8627–8637. doi:10.1093/nar/gkv862. ISSN   1362-4962. PMC   4605312 . PMID   26350216.
  10. Rasheed, Awais; Hao, Yuanfeng; Xia, Xianchun; Khan, Awais; Xu, Yunbi; Varshney, Rajeev K.; He, Zhonghu (2017). "Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives" (PDF). Molecular Plant . 10 (8). Chin Acad Sci+Chin Soc Plant Bio+Shanghai Inst Bio Sci (Elsevier): 1047–1064. doi: 10.1016/j.molp.2017.06.008 . ISSN   1674-2052. PMID   28669791. S2CID   33780984.
  11. 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.
  12. Lausted C; et al. (2004). "POSaM: a fast, flexible, open-source, inkjet oligonucleotide synthesizer and microarrayer". Genome Biology. 5 (8): R58. doi: 10.1186/gb-2004-5-8-r58 . PMC   507883 . PMID   15287980.
  13. Bammler T, Beyer RP; Consortium, Members of the Toxicogenomics Research; Kerr, X; Jing, LX; Lapidus, S; Lasarev, DA; Paules, RS; Li, JL; Phillips, SO (2005). "Standardizing global gene expression analysis between laboratories and across platforms". Nat Methods. 2 (5): 351–356. doi:10.1038/nmeth754. PMID   15846362. S2CID   195368323.
  14. Pease AC; Solas D; Sullivan EJ; Cronin MT; Holmes CP; Fodor SP (1994). "Light-generated oligonucleotide arrays for rapid DNA sequence analysis". PNAS. 91 (11): 5022–5026. Bibcode:1994PNAS...91.5022P. doi: 10.1073/pnas.91.11.5022 . PMC   43922 . PMID   8197176.
  15. Nuwaysir EF; Huang W; Albert TJ; Singh J; Nuwaysir K; Pitas A; Richmond T; Gorski T; Berg JP; Ballin J; McCormick M; Norton J; Pollock T; Sumwalt T; Butcher L; Porter D; Molla M; Hall C; Blattner F; Sussman MR; Wallace RL; Cerrina F; Green RD (2002). "Gene Expression Analysis Using Oligonucleotide Arrays Produced by Maskless Photolithography". Genome Res. 12 (11): 1749–1755. doi:10.1101/gr.362402. PMC   187555 . PMID   12421762.
  16. Shalon D; Smith SJ; Brown PO (1996). "A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization". Genome Res. 6 (7): 639–645. doi: 10.1101/gr.6.7.639 . PMID   8796352.
  17. Tang T; François N; Glatigny A; Agier N; Mucchielli MH; Aggerbeck L; Delacroix H (2007). "Expression ratio evaluation in two-colour microarray experiments is significantly improved by correcting image misalignment". Bioinformatics. 23 (20): 2686–2691. doi: 10.1093/bioinformatics/btm399 . PMID   17698492.
  18. Shafee, Thomas; Lowe, Rohan (2017). "Eukaryotic and prokaryotic gene structure". WikiJournal of Medicine. 4 (1). doi: 10.15347/wjm/2017.002 . ISSN   2002-4436.
  19. Tinker, Anna V.; Boussioutas, Alex; Bowtell, David D.L. (2006). "The challenges of gene expression microarrays for the study of human cancer". Cancer Cell. 9 (5): 333–339. doi: 10.1016/j.ccr.2006.05.001 . ISSN   1535-6108.
  20. Churchill, GA (2002). "Fundamentals of experimental design for cDNA microarrays" (PDF). Nature Genetics. supplement. 32: 490–5. doi:10.1038/ng1031. PMID   12454643. S2CID   15412245. Archived from the original (PDF) on 8 May 2005. Retrieved 12 December 2013.
  21. NCTR Center for Toxicoinformatics – MAQC Project
  22. "Prosigna | Prosigna algorithm". prosigna.com. Retrieved 22 June 2017.
  23. Little, M.A.; Jones, N.S. (2011). "Generalized Methods and Solvers for Piecewise Constant Signals: Part I" (PDF). Proceedings of the Royal Society A . 467 (2135): 3088–3114. doi:10.1098/rspa.2010.0671. PMC   3191861 . PMID   22003312.
  24. 1 2 3 Peterson, Leif E. (2013). Classification Analysis of DNA Microarrays. John Wiley and Sons. ISBN   978-0-470-17081-6.
  25. De Souto M et al. (2008) Clustering cancer gene expression data: a comparative study, BMC Bioinformatics, 9(497).
  26. Jaskowiak, Pablo A; Campello, Ricardo JGB; Costa, Ivan G (2014). "On the selection of appropriate distances for gene expression data clustering". BMC Bioinformatics. 15 (Suppl 2): S2. doi: 10.1186/1471-2105-15-S2-S2 . PMC   4072854 . PMID   24564555.
  27. Bolshakova N, Azuaje F (2003) Cluster validation techniques for genome expression data, Signal Processing, Vol. 83, pp. 825–833.
  28. Ben Gal, I.; Shani, A.; Gohr, A.; Grau, J.; Arviv, S.; Shmilovici, A.; Posch, S.; Grosse, I. (2005). "Identification of transcription factor binding sites with variable-order Bayesian networks". Bioinformatics. 21 (11): 2657–2666. doi:10.1093/bioinformatics/bti410. ISSN   1367-4803. PMID   15797905.
  29. Yuk Fai Leung and Duccio Cavalieri, Fundamentals of cDNA microarray data analysis. Trends in Genetics Vol.19 No.11 November 2003.
  30. Priness I.; Maimon O.; Ben-Gal I. (2007). "Evaluation of gene-expression clustering via mutual information distance measure". BMC Bioinformatics. 8 (1): 111. doi: 10.1186/1471-2105-8-111 . PMC   1858704 . PMID   17397530.
  31. Wei C; Li J; Bumgarner RE (2004). "Sample size for detecting differentially expressed genes in microarray experiments". BMC Genomics. 5: 87. doi: 10.1186/1471-2164-5-87 . PMC   533874 . PMID   15533245.
  32. Emmert-Streib, F. & Dehmer, M. (2008). Analysis of Microarray Data A Network-Based Approach. Wiley-VCH. ISBN   978-3-527-31822-3.
  33. Wouters L; Gõhlmann HW; Bijnens L; Kass SU; Molenberghs G; Lewi PJ (2003). "Graphical exploration of gene expression data: a comparative study of three multivariate methods". Biometrics. 59 (4): 1131–1139. CiteSeerX   10.1.1.730.3670 . doi:10.1111/j.0006-341X.2003.00130.x. PMID   14969494. S2CID   16248921.
  34. Jain N; Thatte J; Braciale T; Ley K; O'Connell M; Lee JK (2003). "Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays". Bioinformatics. 19 (15): 1945–1951. doi: 10.1093/bioinformatics/btg264 . PMID   14555628.
  35. Barbosa-Morais, N. L.; Dunning, M. J.; Samarajiwa, S. A.; Darot, J. F. J.; Ritchie, M. E.; Lynch, A. G.; Tavare, S. (18 November 2009). "A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data". Nucleic Acids Research. 38 (3): e17. doi:10.1093/nar/gkp942. PMC   2817484 . PMID   19923232.
  36. Mortazavi, Ali; Brian A Williams; Kenneth McCue; Lorian Schaeffer; Barbara Wold (July 2008). "Mapping and quantifying mammalian transcriptomes by RNA-Seq". Nat Methods. 5 (7): 621–628. doi:10.1038/nmeth.1226. ISSN   1548-7091. PMID   18516045. S2CID   205418589.
  37. 1 2 Wang, Zhong; Mark Gerstein; Michael Snyder (January 2009). "RNA-Seq: a revolutionary tool for transcriptomics". Nat Rev Genet. 10 (1): 57–63. doi:10.1038/nrg2484. ISSN   1471-0056. PMC   2949280 . PMID   19015660.