biology

MAFFT

MAFFT is a biology 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 MAFFT rather than just read about it. In short: In bioinformatics, MAFFT (multiple alignment using fast Fourier transform) is a program used to create multiple sequence alignments of amino acid or nucleotide sequences. Published in 2002, the first version used an algorithm based on progressive alignment, in which the sequences were clustered with the help of the fast Fourier transform.

Key takeaways

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

Reference excerpt

In bioinformatics, MAFFT (multiple alignment using fast Fourier transform) is a program used to create multiple sequence alignments of amino acid or nucleotide sequences. Published in 2002, the first version used an algorithm based on progressive alignment, in which the sequences were clustered with the help of the fast Fourier transform. Subsequent versions of MAFFT have added other algorithms and modes of operation, including options for faster alignment of large numbers of sequences, higher accuracy alignments, alignment of non-coding RNA sequences, and the addition of new sequences to existing alignments.

History There have been many variations of the MAFFT software, some of which are listed below: MAFFT – The first version, created by Kazutaka Katoh in 2002, used an algorithm based on progressive alignment, in which the sequences were clustered with the help of the fast Fourier transform. MAFFT v5 – The second generation software, released in 2005, was a rewrite of the original software. This generation introduced a simplified scoring system that performs well for reducing CPU time and increasing accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. MAFFT v6 – The third generation, released in 2006, again improved on prior versions. It implemented group-to-group alignment, guide trees which had an approximate but faster O(N log N) tree-building algorithm, and made the version usable with larger datasets of ~50,000 sequences. MAFFT v7 – The fourth generation, released in 2012, improved the speed and accuracy substantially. MAFFT v7.511 – A more recent version, released in December 2022, improved on version 7 with various bug fixes. One of the most notable being an overhaul to the --merge option, which now includes, enabling iterative refinement, creating a single MSA from multiple sub-MSAs, as well as the combination of --merge and --seed. There were also several minor enhancements to the speed and accuracy of MAFFT v7.

Algorithm The MAFFT algorithm works following these 5 steps Pairwise Alignment, Distance Calculation, Guide Tree Construction, Progressive Alignment, Iterative Refinement.

Pairwise Alignment – This step is used to identify the regions that are similar between the sequences inputted. The algorithm starts by using the inputted sequences executing pairwise alignments across all the sequences. This step's time complexity is O(L^2) where L is the sequence. Distance Matrix – Using the calculated pairwise alignments, a distance matrix calculation is done to evaluate the dissimilarity between the alignments based on their alignment scores. The distance calculation step helps organize the sequences based on their similarity. The Distance Matrix's time complexity is O(N^2L^2) where N is the number of sequences and L is the length of the sequence. This time complexity is because the distance calculation between pairs of sequences requires comparing every position of each sequence. Guide Tree – Using the distance matrix a guide tree is constructed where there is a hierarchical representation of the clusters (each node is a cluster) and the branches included are the distance between the clusters. O(N^2L) is the time complexity for the guide tree construction, where N is the number of sequences. Progressive Alignment – Using the guide tree progressive alignment is performed from the leaves to the root. The algorithm uses the inputted sequences and aligns the child nodes to calculate a consensus alignment for the parent node. This step is done until the entire tree is traversed to result a final multiple sequence alignment. The progressive alignment method's time complexity is O(N^2L) + O(NL^2). This is because the first term corresponds to the guide tree calculation stated earlier along with the second term that corresponds to group to group alignment. Iterative Alignment – The iterative refinement step repeats the entire process with adjustments to the positions of gaps and insertions to improve alignment accuracy. The time complexity of the iterative alignment depends on the number of iterations that occur. But generally the time complexity of this method is O(N2L) + O(NL2) where N is the number of sequences, and L is the length of the sequence.

Input/output

Web form

Input This program can take in multiple sequences as input, which can be entered in two ways:

Sequence input window

The user can directly enter three or more sequences in the input window in any of the following formats: GCG, FASTA, EMBL (nucleotide only), GenBank, PIR, NBRF, PHYLIP, or UniProtKB/Swiss-Prot (protein only). Partly formatted sequences are not accepted, and adding a return to the end of the sequence may help certain applications understand the input. It is also advised to avoid using data from word processors as hidden/control characters may be present.

Sequence file upload The user can upload a file containing three or more valid sequences in any format mentioned above. Word processor files may yield unpredictable results due to the presence of hidden/control characters, so it is best to save files with the Unix format option to avoid hidden Windows characters. Once the file is uploaded, it can be used as input for multiple sequence alignment.

Output The user will have the option to request the Multiple Sequence Alignment (MSA) to be generated in one of the two available formats:

Default value is: Pearson/FASTA [fasta]

Settings There are many settings that affect how the MAFFT algorithm works. Adjusting the settings to needs is the best way to get accurate and meaningful results. The most important settings to understand are: the Scoring Matrix, Gap Open Penalty, and Gap Extension Penalty.

… excerpt ends here. Continue reading the full article.

Illustrations

MAFFT: Steps of how to use MAFFT with other programs to view a MSA — Steps of how to use MAFFT with other programs to view a MSA

Worked examples

Example 1 — a first encounter with MAFFT

Start with the simplest possible case. Write down what MAFFT claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, 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 MAFFT 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 MAFFT 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 MAFFT

In research: MAFFT appears in biology 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 MAFFT 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: MAFFT is common in secondary-school and first-year university syllabi. It links to neighbouring topics Phylogenetics software, so understanding it makes those chapters shorter.
In everyday life: Look for MAFFT 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 MAFFT in 20 minutes

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

Frequently asked questions

What is MAFFT in simple terms?

Why does MAFFT matter?

Because it connects several biology 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 MAFFT?

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 MAFFT.