E-value

Definition

The e-value, or expectation value, is a statistical measure used in bioinformatics to indicate the number of hits one can expect to see by chance when searching a database. It helps assess the significance of sequence alignments and is crucial for evaluating results in sequence database searches, as it accounts for the size of the database and the scoring system used in alignments.

5 Must Know Facts For Your Next Test

1. The e-value is inversely related to the score of the alignment; a lower e-value indicates a more significant match between sequences.
2. E-values can vary depending on the size of the database being searched; larger databases generally lead to higher e-values for the same alignment.
3. In the context of BLAST searches, an e-value threshold can be set to filter out less significant matches, focusing on those most likely to be biologically relevant.
4. An e-value of 1 suggests that one would expect to see one match by random chance in the database, while an e-value of 0.01 indicates a very strong match with low probability of occurring by chance.
5. When performing motif discovery or functional annotation, e-values help researchers identify biologically meaningful sequences or patterns that are statistically significant.

Review Questions

• How does the e-value impact the interpretation of sequence alignments in bioinformatics?
  • The e-value significantly impacts how researchers interpret sequence alignments by providing a measure of statistical significance. A lower e-value indicates a greater likelihood that the observed match is not due to random chance, which helps researchers focus on biologically relevant sequences. By considering e-values alongside alignment scores, scientists can make more informed decisions about which sequences warrant further investigation.
• In what ways does the size of a database influence the e-value returned in sequence search results?
  • The size of a database directly influences the e-value because larger databases increase the likelihood of random matches. This means that as the database grows, an alignment that may have a specific score could result in a higher e-value due to increased chances of finding similar sequences by chance. Thus, it's essential to consider database size when interpreting e-values to determine their biological significance accurately.
• Evaluate how setting different e-value thresholds can affect results in functional annotation and motif discovery.
  • Setting different e-value thresholds can significantly affect outcomes in functional annotation and motif discovery by altering which sequences are considered significant. A very low threshold may yield only highly significant matches but could miss potentially interesting sequences that are biologically relevant but fall just outside this stringent cutoff. Conversely, a high threshold might include many matches that are not meaningful, potentially leading to false positives. Balancing these thresholds is crucial for obtaining accurate and useful insights from bioinformatics analyses.

Related terms

Sequence Alignment:

A method for arranging sequences of DNA, RNA, or proteins to identify regions of similarity that may indicate functional or evolutionary relationships.

BLAST:

Basic Local Alignment Search Tool, a widely used algorithm for comparing an input sequence against a database to find regions of similarity.

P-value: A statistical measure that helps determine the significance of results obtained in hypothesis testing, often compared with the e-value in bioinformatics.