Researchers often identify similarities between newly discovered biological sequences, such as DNA or proteins, and those already cataloged. This process is fundamental to understanding gene function, evolutionary relationships, and disease mechanisms. However, the immense size of sequence databases means some similarity can arise purely by chance. Computational tools are indispensable for navigating this data, allowing scientists to pinpoint significant resemblances.
Understanding BLAST Searches
The Basic Local Alignment Search Tool (BLAST) is a widely used computational method to find regions of similarity between biological sequences. It compares a query sequence against a large database of known sequences. BLAST identifies local alignments, which are segments of sequences that match well, rather than requiring entire sequences to align perfectly. This heuristic approach makes BLAST faster than algorithms that guarantee optimal alignments.
Researchers input a sequence into BLAST, which then rapidly searches through extensive databases. The output provides a list of database sequences, often called “hits,” that show significant similarity to the query. Each hit is accompanied by statistical scores and metrics indicating the alignment’s quality and significance.
The Meaning of E-value
The Expect value, or E-value, is a crucial statistical measure in BLAST that assesses the significance of a sequence alignment. It represents the expected number of alignments with a score equal to or better than the observed score that would occur by chance in a database of a given size. It quantifies the background noise one might expect to see. For instance, an E-value of 0.05 indicates one might expect to find 0.05 such matches purely by chance.
A lower E-value suggests higher statistical significance, implying the observed similarity is less likely a random event and more likely a true biological relationship. Conversely, a higher E-value indicates the alignment could easily have occurred by random chance, diminishing its biological significance. An E-value of 1.0, for example, means one expects to find one match with a similar score by chance. It is important to remember that an E-value is an estimate of expected random hits, not a direct probability (p-value) that the match occurred by chance.
How E-value is Determined
The E-value calculation is influenced by several factors. The size of the database being searched plays a significant role; a larger database increases the probability of finding random matches, leading to a higher E-value for a given alignment score. This means a sequence hit will have a better (lower) E-value if found in a smaller database.
The length of the query sequence also impacts the E-value. Shorter query sequences have a higher likelihood of matching randomly, resulting in relatively higher E-values even for identical matches. The raw alignment score, which reflects the quality of the match, is another determinant. Higher raw scores generally lead to lower E-values, indicating a stronger alignment. The bit score, a normalized version of the raw alignment score, is also used in E-value calculations and offers a standardized measure of alignment quality.
Applying E-value to Search Results
Researchers use E-values to filter and interpret BLAST search results, focusing on meaningful similarities. A common practice involves setting a threshold, such as an E-value of 0.01 or 0.001, to consider a match statistically significant. For an E-value of 0.01, there is only a 1 in 100 chance of finding a match this good or better by random chance. The default E-value threshold on the NCBI BLAST web page is often 10, but more stringent thresholds are frequently applied for research purposes.
While a low E-value is generally desired for identifying genuine biological relationships, it is not the sole determinant of significance. Other metrics, such as percent identity and query coverage, should also be considered. The appropriate E-value cutoff can vary depending on the specific research question and the nature of the sequences being compared. For instance, searching for distant evolutionary relatives might involve a higher E-value cutoff than searching for nearly identical sequences.