用生物信息学方法分析4万首爱尔兰舞曲，揭示音乐演化与蛋白质演化的不同约束机制。
Contrasting statistical patterns in melodic and molecular evolution reveal distinctive constraints in a culturally evolving system
- 开发节奏感知的序列比对方法，实现大规模旋律自动对齐。
- 发现旋律演化模式与分子演化显著不同，体现记忆、动作和社交偏好影响。
- 为文化演化研究提供可量化的分析框架，适合跨学科研究者参考。
进化序列可用于推断演化规律。口传民谣是演化序列的一种，其一维结构且来自有限音符集合的特性，使其适合应用生物信息学方法研究文化演化。主要障碍在于旋律包含节奏信息，破坏了标准序列比对算法的假设。本文提出一种节奏感知的比对方法，并应用于40,000个爱尔兰舞曲变体，首次实现大规模自动化旋律对齐。通过四项经典生物信息学分析——突变率、替换矩阵、位置保守性与协变性——揭示了与分子演化不同的模式，反映出蛋白演化受生化与物理约束，而旋律演化则受记忆、动作及社会偏好的驱动。结果表明，生物信息学不仅提供算法工具，更是一种强大的概念框架，可用于系统研究文化演化。尽管音乐的文化传播已讨论数百年，本文首次实现其大规模量化分析。
原文摘要 · Abstract (English)
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.
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