Calculate Shannon entropy to measure sequence complexity and information content in biological sequences
Shannon entropy is a measure of information content and complexity in sequences. Higher entropy indicates more randomness and complexity, while lower entropy suggests more order and predictability. It's calculated as H = -Σ(p_i * log2(p_i)) where p_i is the probability of each character.
Q: What does low entropy mean?
A: Low entropy indicates repetitive or biased sequences with limited diversity.
Q: How is normalized entropy calculated?
A: It's the ratio of actual entropy to maximum possible entropy (H/H_max).