science

Multiple EM for Motif Elicitation

Multiple EM for Motif Elicitation 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 Multiple EM for Motif Elicitation rather than just read about it. In short: Multiple Expectation maximizations for Motif Elicitation (MEME) is a tool for discovering motifs in a group of related DNA or protein sequences. A motif is a sequence pattern that occurs repeatedly in a group of related protein or DNA sequences and is often associated with some biological function.

Key takeaways

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

Reference excerpt

Multiple Expectation maximizations for Motif Elicitation (MEME) is a tool for discovering motifs in a group of related DNA or protein sequences. A motif is a sequence pattern that occurs repeatedly in a group of related protein or DNA sequences and is often associated with some biological function. MEME represents motifs as position-dependent letter-probability matrices which describe the probability of each possible letter at each position in the pattern. Individual MEME motifs do not contain gaps. Patterns with variable-length gaps are split by MEME into two or more separate motifs. MEME takes as input a group of DNA or protein sequences (the training set) and outputs as many motifs as requested. It uses statistical modeling techniques to automatically choose the best width, number of occurrences, and description for each motif. MEME is the first of a collection of tools for analyzing motifs called the MEME suite.

Definition The MEME algorithm could be understood from two different perspectives. From a biological point of view, MEME identifies and characterizes shared motifs in a set of unaligned sequences. From the computer science aspect, MEME finds a set of non-overlapping, approximately matching substrings given a starting set of strings.

Use MEME can be used to find similar biological functions and structures in different sequences. It is necessary to take into account that the sequences variation can be significant and that the motifs are sometimes very small. It is also useful to take into account that the binding sites for proteins are very specific. This makes it easier to reduce wet-lab experiments (saving cost and time). Indeed, to better discover the motifs relevant from a biological point it is necessary to carefully choose: the best width of motifs, the number of occurrences in each sequence, and the composition of each motif.

Algorithm components The algorithm uses several types of well known functions:

Expectation maximization (EM). EM based heuristic for choosing the EM starting point. Maximum likelihood ratio based (LRT-based) heuristic for determining the best number of model-free parameters. Multi-start for searching over possible motif widths. Greedy search for finding multiple motifs. However, one often doesn't know where the starting position is. Several possibilities exist: exactly one motif per sequence, or one or zero motif per sequence, or any number of motifs per sequence.

See also Sequence motif Sequence alignment

References

External links The MEME Suite — Motif-based sequence analysis tools GPU Accelerated version of MEME EXTREME — An online EM implementation of the MEME model for fast motif discovery in large ChIP-Seq and DNase-Seq Footprinting data

Worked examples

Example 1 — a first encounter with Multiple EM for Motif Elicitation

Start with the simplest possible case. Write down what Multiple EM for Motif Elicitation 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 Multiple EM for Motif Elicitation 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 Multiple EM for Motif Elicitation 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 Multiple EM for Motif Elicitation

In research: Multiple EM for Motif Elicitation 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 Multiple EM for Motif Elicitation 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: Multiple EM for Motif Elicitation 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 Multiple EM for Motif Elicitation 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 Multiple EM for Motif Elicitation in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Multiple EM for Motif Elicitation means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
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Frequently asked questions

What is Multiple EM for Motif Elicitation in simple terms?

Why does Multiple EM for Motif Elicitation 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 Multiple EM for Motif Elicitation?

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 Multiple EM for Motif Elicitation.