ArticleslgStudy

science

Horizontal correlation

Horizontal correlation 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 Horizontal correlation rather than just read about it. In short: Horizontal correlation is a methodology for gene sequence analysis. Rather than referring to one specific technique, horizontal correlation instead encompasses a variety of approaches to sequence analysis that are unified by two specific themes: Sequence analysis is performed by making comparisons horizontally, along the length of a single genetic sequence; this is in contrast to vertical methods that make compariso…

Key takeaways

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

Reference excerpt

Horizontal correlation is a methodology for gene sequence analysis. Rather than referring to one specific technique, horizontal correlation instead encompasses a variety of approaches to sequence analysis that are unified by two specific themes:

Sequence analysis is performed by making comparisons horizontally, along the length of a single genetic sequence; this is in contrast to vertical methods that make comparisons across several different genetic sequences. The comparisons made generally measure information theoretic quantities such as value of the mutual information function between two regions of the sequence. The core ideas of the horizontal correlation approach were first presented in a year 2000 paper by Grosse, Herzel, Buldyrev, and Stanley (Grosse, et al. 2000). In this first formulation, Grosse and colleagues sought to characterize a large genetic sequence by dividing the sequence into coding and non-coding regions. Whereas traditional approaches to the coding-vs.-non-coding problem generally relied on sophisticated pattern recognition systems that were first trained on small inputs and then run over the entire sequence (Ohler, et al. 1999), the horizontal correlation approach of Grosse and colleagues worked instead by breaking the sequence into many relatively short sequence fragments, each only 500 base pairs in length. They then sought to characterize each of these fragments as either coding or non-coding. This was accomplished by comparing each size 3 window along the length of a fragment with the first size 3 window in that fragment, then measuring the value of the mutual information function between the two windows. Coding sequences were found to display a stylized pattern of 3-periodicity that non-coding sequences did not. Such a pattern was easy to recognize, and enabled significantly more rapid, more species-independent identification of coding regions (Grosse, et al. 2000). Since 2000, horizontal correlation methodologies emphasizing the measurement of information theoretic quantities along the length of a gene sequence have been put to widespread use, and have even found application in shotgun sequencing fragment assembly (Otu & Sayood, 2004).

References Grosse, Ivo; Herzel, Hanspeter; Buldyrev, Sergey V.; Stanley, H. Eugene (2000-05-01). "Species independence of mutual information in coding and noncoding DNA". Physical Review E. 61 (5). American Physical Society (APS): 5624–5629. Bibcode:2000PhRvE..61.5624G. doi:10.1103/physreve.61.5624. ISSN 1063-651X. PMID 11031617. Ohler, U.; Harbeck, S.; Niemann, H.; Noth, E.; Reese, M. G. (1999-05-01). "Interpolated markov chains for eukaryotic promoter recognition". Bioinformatics. 15 (5). Oxford University Press (OUP): 362–369. doi:10.1093/bioinformatics/15.5.362. ISSN 1367-4803. PMID 10366656. Otu, H. H.; Sayood, K. (2003-01-01). "A divide-and-conquer approach to fragment assembly". Bioinformatics. 19 (1). Oxford University Press (OUP): 22–29. doi:10.1093/bioinformatics/19.1.22. ISSN 1367-4803. PMID 12499289.

Worked examples

Example 1 — a first encounter with Horizontal correlation

Start with the simplest possible case. Write down what Horizontal correlation 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 Horizontal correlation 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 Horizontal correlation 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 Horizontal correlation

In research
Horizontal correlation 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 Horizontal correlation 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
Horizontal correlation is common in secondary-school and first-year university syllabi. It links to neighbouring topics Bioinformatics, so understanding it makes those chapters shorter.
In everyday life
Look for Horizontal correlation 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.
Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Horizontal correlation” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Horizontal correlation in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Horizontal correlation 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 Horizontal correlation out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Horizontal correlation in simple terms?

Horizontal correlation is a methodology for gene sequence analysis. Rather than referring to one specific technique, horizontal correlation instead encompasses a variety of approaches to sequence analysis that are unified by two specific themes: Sequence analysis is performed by making comparisons…

Why does Horizontal correlation 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 Horizontal correlation?

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 Horizontal correlation.

Tags

  • Bioinformatics

Keep exploring