What Is Sequence Similarity and How Does BLAST Measure It?

Sequence similarity is the degree to which two biological sequences — DNA, RNA, or protein — match in local regions when aligned side by side. BLAST (Basic Local Alignment Search Tool) measures it by comparing your query sequence against a sequence database, finding regions of local similarity, and calculating the statistical significance of those matches. You use BLAST when you have a sequence and want to know what it resembles, whether that is to infer function, trace evolutionary relationships, or identify which gene family a sequence belongs to.

Similarity, identity, and homology are not the same thing

These three terms get used interchangeably in casual conversation, but they answer different questions:

Similarity and identity are measurements. Homology is an inference you draw from those measurements. A high similarity score is evidence for homology, but it is not proof on its own — significance testing is what turns a raw score into a defensible claim.

How BLAST finds and scores local alignments

BLAST does not try to align your entire query against an entire database entry. It searches for local regions where the sequences match well, which is why it can detect a shared domain even when the surrounding sequence is unrelated.

The process works roughly like this:

1. Seeding. BLAST breaks the query into short words and looks for database words that score above a threshold. These seed hits are candidate alignment starting points.
2. Extension. Each seed is extended in both directions, adding matches and penalizing mismatches and gaps according to a scoring scheme (for example, a substitution matrix plus gap penalties for proteins).
3. High-scoring segment pairs (HSPs). The extension stops when the score stops improving. What remains is an HSP — a local alignment with a raw score.
4. Statistical evaluation. The raw score is converted into an E-value, which is the number of matches with that score or better you would expect to find by chance in a database of that size.

The E-value is the number that matters most for interpretation. It depends on both the alignment score and the size of the database searched, so the same alignment can produce different E-values against different databases.

Reading the E-value

The E-value answers: how often would a match this good appear by random chance?

• Very small E-values (for example, below 1e-5, and often far smaller) indicate the match is unlikely to be a random artifact. This is the range where functional and evolutionary inferences become reasonable.
• E-values near or above 1 mean you would expect matches this good by chance, so the hit carries little information.
• E-value thresholds are conventions, not laws. A common cutoff is 1e-5, but the right threshold depends on your database size, sequence length, and how much tolerance you have for false positives.

A low E-value is necessary but not sufficient for a biological conclusion. A short query, a low-complexity region, or a biased composition can all produce misleadingly good scores.

What a significant match lets you infer

When a match is statistically significant, BLAST's own documentation notes that the tool can be used to infer functional and evolutionary relationships between sequences and to help identify members of gene families. In practice:

• Function transfer. If your unknown protein aligns strongly to a characterized protein, the shared similarity suggests related function — but only as a hypothesis to test, especially if the match covers only part of the sequence.
• Evolutionary relationship. Strong similarity across a conserved region is consistent with shared ancestry. The strength and extent of the alignment shape how confident that inference is.
• Gene family membership. A significant hit to a known family member is often how a new sequence gets assigned to a family.

The key caution: significant similarity supports these inferences, it does not establish them. Domain-level matches, partial alignments, and matches to uncharacterized entries all require judgment beyond the E-value.

Practical checks before you trust a hit

• Look at alignment coverage, not just the score. A high-scoring match to a small fragment of your query tells you less than a moderate match across the full length.
• Check the database. Matches to well-annotated reference sequences carry more interpretive weight than matches to uncharacterized entries.
• Watch for low-complexity or repetitive regions, which can generate spurious high scores.
• Compare against more than one hit. A single strong hit is informative; a cluster of hits to related sequences is stronger evidence.

BLAST's specialized tools — SmartBLAST for finding highly similar proteins, Primer-BLAST for primer design, Global Align for full-length comparison, and CD-search for conserved domains — each apply similarity measurement to a narrower question, so the interpretation guidance above applies with the appropriate adjustments for each.