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Representational oligonucleotide microarray analysis

Representational oligonucleotide microarray analysis is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Representational oligonucleotide microarray analysis rather than just read about it. In short: Representational oligonucleotide microarray analysis (ROMA) is a technique that was developed by Michael Wigler and Rob Lucito at the Cold Spring Harbor Laboratory (CSHL) in 2003. Wigler and Lucito currently run laboratories at CSHL using ROMA to explore genomic copy number variation in cancer and other genetic diseases.

Representational oligonucleotide microarray analysis — main illustration
Representational oligonucleotide microarray analysis — illustration

Key takeaways

  • Representational oligonucleotide microarray analysis belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Representational oligonucleotide microarray analysis to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Representational oligonucleotide microarray analysis from memory before moving on to harder problems.

Reference excerpt

Representational oligonucleotide microarray analysis (ROMA) is a technique that was developed by Michael Wigler and Rob Lucito at the Cold Spring Harbor Laboratory (CSHL) in 2003. Wigler and Lucito currently run laboratories at CSHL using ROMA to explore genomic copy number variation in cancer and other genetic diseases. In this technique, two genomes are compared for their differences in copy number on a microarray. The ROMA technology emerged from a previous method called representational difference analysis (RDA). ROMA, in comparison to other comparative genomic hybridization (CGH) techniques, has the advantage of reducing the complexity of a genome with a restriction enzyme which highly increases the efficiency of genomic fragment hybridization to a microarray. In ROMA, a genome is digested with a restriction enzyme, ligated with adapters specific to the restriction fragment sticky ends and amplified by PCR. After the PCR step, representations of the entire genome (restriction fragments) are amplified to pronounce relative increases, decreases or preserve equal copy number in the two genomes. The representations of the two different genomes are labeled with different fluorophores and co-hybridized to a microarray with probes specific to locations across the entire human genome. After analysis of the ROMA microarray image is completed, a copy number profile of the entire human genome is generated. This allows researchers to detect with high accuracy amplifications (amplicons) and deletions that occur across the entire genome. In cancer, the genome becomes very unstable, resulting in specific regions that may be deleted (if they contain a tumor suppressor) or amplified (if they contain an oncogene). Amplifications and deletions have also been observed in the normal human population and are referred to as Copy Number Polymorphisms (CNPs). Jonathan Sebat was one of the first researchers to report in the journal 'Science' in 2004 that these CNPs give rise to human genomic variation and may contribute to our phenotypic differences. Tremendous research efforts are being conducted now to understand the role of CNPs in normal human variation and neurological diseases such as autism. By understanding which regions of the genome have undergone copy number polymorphisms in disease, scientists can ultimately identify genes that are overexpressed or deleted and design drugs to compensate for these genes to cure genetic diseases.

References

Lucito, R. et al. (2003) Representational oligonucleotide microarray analysis: a high-resolution method to detect genome copy number variation. Genome Res. 13, 2291–2305

Illustrations

Representational oligonucleotide microarray analysis: ROMA
ROMA

Worked examples

Example 1 — a first encounter with Representational oligonucleotide microarray analysis

Start with the simplest possible case. Write down what Representational oligonucleotide microarray analysis claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Representational oligonucleotide microarray analysis before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Representational oligonucleotide microarray analysis ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Representational oligonucleotide microarray analysis

In research
Representational oligonucleotide microarray analysis appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Representational oligonucleotide microarray analysis in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Representational oligonucleotide microarray analysis is common in secondary-school and first-year university syllabi. It links to neighbouring topics Microarrays, so understanding it makes those chapters shorter.
In everyday life
Look for Representational oligonucleotide microarray analysis outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Representational oligonucleotide microarray analysis in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Representational oligonucleotide microarray analysis means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Representational oligonucleotide microarray analysis out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Representational oligonucleotide microarray analysis in simple terms?

Representational oligonucleotide microarray analysis (ROMA) is a technique that was developed by Michael Wigler and Rob Lucito at the Cold Spring Harbor Laboratory (CSHL) in 2003. Wigler and Lucito currently run laboratories at CSHL using ROMA to explore genomic copy number variation in cancer and…

Why does Representational oligonucleotide microarray analysis matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Representational oligonucleotide microarray analysis?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Representational oligonucleotide microarray analysis.

Tags

  • Microarrays

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