Contrasting statistical patterns in melodic and molecular evolution reveal distinctive constraints in a culturally evolving system

Evolved sequences can be used to infer the rules of evolution.

Orally transmitted folk melodies are evolved sequences whose similarity to protein sequences (one-dimensional, drawn from a limited alphabet) invites application of bioinformatics methods to study cultural evolution.

A major obstacle is that melodies encode rhythm, which breaks some assumptions of standard sequence-alignment algorithms.

We develop a rhythm-aware alignment method and apply it to \num{40000} Irish dance tune variants, enabling the first large-scale automated melodic alignment.

Four canonical bioinformatics analyses -- mutability, substitution matrices, positional conservation, and covariance -- reveal patterns distinct from those of molecular evolution, revealing the forces that shape each domain: biochemical and biophysical constraints for proteins; memory, motor, and social biases for melodies.

Together the results show that bioinformatics provides a powerful framework -- conceptual as much as algorithmic -- for studying cultural evolution.

Although the cultural transmission of music has been discussed for centuries, here we show how to analyze it at large scale.