science

Sequence clustering

Sequence clustering 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 Sequence clustering rather than just read about it. In short: In bioinformatics, sequence clustering algorithms attempt to group biological sequences that are somehow related. The sequences can be either of genomic, "transcriptomic" (ESTs) or protein origin.

Key takeaways

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

Reference excerpt

In bioinformatics, sequence clustering algorithms attempt to group biological sequences that are somehow related. The sequences can be either of genomic, "transcriptomic" (ESTs) or protein origin. For proteins, homologous sequences are typically grouped into families. For EST data, clustering is important to group sequences originating from the same gene before the ESTs are assembled to reconstruct the original mRNA. Some clustering algorithms use single-linkage clustering, constructing a transitive closure of sequences with a similarity over a particular threshold. UCLUST and CD-HIT use a greedy algorithm that identifies a representative sequence for each cluster and assigns a new sequence to that cluster if it is sufficiently similar to the representative; if a sequence is not matched then it becomes the representative sequence for a new cluster. The similarity score is often based on sequence alignment. Sequence clustering is often used to make a non-redundant set of representative sequences. Sequence clusters are often synonymous with (but not identical to) protein families. Determining a representative tertiary structure for each sequence cluster is the aim of many structural genomics initiatives.

Sequence clustering algorithms and packages

CD-HIT UCLUST in USEARCH Starcode: a fast sequence clustering algorithm based on exact all-pairs search. OrthoFinder: a fast, scalable and accurate method for clustering proteins into gene families (orthogroups) Linclust: first algorithm whose runtime scales linearly with input set size, very fast, part of MMseqs2 software suite for fast, sensitive sequence searching and clustering of large sequence sets TribeMCL: a method for clustering proteins into related groups BAG: a graph theoretic sequence clustering algorithm JESAM: Open source parallel scalable DNA alignment engine with optional clustering software component UICluster: Parallel Clustering of EST (Gene) Sequences BLASTClust single-linkage clustering with BLAST Clusterer: extendable java application for sequence grouping and cluster analyses PATDB: a program for rapidly identifying perfect substrings nrdb: a program for merging trivially redundant (identical) sequences CluSTr: A single-linkage protein sequence clustering database from Smith-Waterman sequence similarities; covers over 7 mln sequences including UniProt and IPI ICAtools - original (ancient) DNA clustering package with many algorithms useful for artifact discovery or EST clustering Skipredudant EMBOSS tool to remove redundant sequences from a set CLUSS Algorithm to identify groups of structurally, functionally, or evolutionarily related hard-to-align protein sequences. CLUSS webserver CLUSS2 Algorithm for clustering families of hard-to-align protein sequences with multiple biological functions. CLUSS2 webserver

Non-redundant sequence databases PISCES: A Protein Sequence Culling Server RDB90 UniRef: A non-redundant UniProt sequence database Uniclust: A clustered UniProtKB sequences at the level of 90%, 50% and 30% pairwise sequence identity. Virus Orthologous Clusters: A viral protein sequence clustering database; contains all predicted genes from eleven virus families organized into ortholog groups by BLASTP similarity

See also Cluster analysis Social sequence analysis

References

Worked examples

Example 1 — a first encounter with Sequence clustering

Start with the simplest possible case. Write down what Sequence clustering 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 Sequence clustering 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 Sequence clustering 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 Sequence clustering

In research: Sequence clustering 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 Sequence clustering 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: Sequence clustering 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 Sequence clustering 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 Sequence clustering in 20 minutes

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

Frequently asked questions

What is Sequence clustering in simple terms?

In bioinformatics, sequence clustering algorithms attempt to group biological sequences that are somehow related. The sequences can be either of genomic, "transcriptomic" (ESTs) or protein origin.

Why does Sequence clustering 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 Sequence clustering?

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 Sequence clustering.