E-value

Definition

The e-value, or expect value, is a statistical measure used in bioinformatics to indicate the number of times one might expect to see a match between sequences purely by chance. It helps assess the significance of alignments in various applications such as sequence databases, pairwise alignment, local alignment, and scoring matrices. A lower e-value indicates a more significant match, which is crucial for identifying biologically relevant similarities between sequences.

5 Must Know Facts For Your Next Test

1. The e-value is calculated based on the size of the database being searched and the length of the query sequence, making it dependent on these factors.
2. An e-value of 1 means you expect to see one match by chance in the database; an e-value of 0.01 suggests that there's a 1% chance of observing that match due to random alignment.
3. Different algorithms may yield different e-values for the same alignment due to variations in scoring systems and database sizes, affecting how results are interpreted.
4. Using e-values helps researchers filter out insignificant results from large databases, allowing them to focus on potentially meaningful biological information.
5. E-values are crucial for comparing multiple sequence alignments, as they provide a statistical basis for determining which alignments are worth investigating further.

Review Questions

• How does the e-value relate to the significance of sequence alignments in databases?
  • The e-value serves as a key indicator of significance when analyzing sequence alignments in databases. It quantifies the likelihood that a given alignment could occur by chance based on database size and query length. Therefore, researchers can use the e-value to prioritize findings; lower e-values suggest that the observed matches are more likely to be biologically relevant rather than random occurrences.
• What role does the e-value play in evaluating pairwise sequence alignments and their implications for homology detection?
  • In pairwise sequence alignments, the e-value helps evaluate how likely it is that two sequences share homology based on their alignment score. A low e-value indicates a strong likelihood that the sequences share a common ancestor, while higher values suggest a greater chance of random matches. This distinction is important for drawing conclusions about evolutionary relationships and functional similarities between proteins or genes.
• Discuss how variations in database size and alignment scoring affect e-values and interpretation of alignment results.
  • Variations in database size and alignment scoring can significantly influence e-values and how results are interpreted. A larger database may yield higher e-values simply due to increased chances of random matches, potentially masking biologically significant alignments. Additionally, different scoring systems can affect bit scores and thus alter the calculated e-values for an alignment. Understanding these factors is essential for accurately interpreting results and ensuring meaningful conclusions are drawn from sequence comparisons.

Related terms

Bit Score: A measure of the quality of an alignment between two sequences, taking into account the scoring system used; higher bit scores indicate better alignments.

Homology: The similarity between sequences that can be attributed to shared ancestry; homology is often inferred from low e-values in sequence comparisons.

False Positive Rate: The probability of incorrectly rejecting the null hypothesis, leading to a false identification of a significant match; the e-value helps control this rate by providing an estimate of expected matches by chance.