scholarly journals Sequence alignment of folk song melodies reveals cross-cultural mechanisms of musical evolution

2020 ◽  
Author(s):  
Patrick E. Savage ◽  
Gakuto Chiba ◽  
Thomas E. Currie ◽  
Haruo Suzuki ◽  
Quentin Atkinson
Keyword(s):  
Sequence Alignment ◽  
Neutral Theory ◽  
Folk Song ◽  
Sequence Evolution ◽  
Functional Roles ◽  
Physical Constraints ◽  
Molecular Sequence ◽  
Alignment Algorithms ◽  
Creative Art ◽  
Musical Evolution

Culture, like genes, evolves, but the existence of cross-culturally universal mechanisms of cultural evolution is debated. As a diverse but cross-culturally universal phenomenon, music may provide a novel domain to test for the existence of such mechanisms. Folk song melodies are culturally transmitted sequences of notes that change over time, and the generation and transmission of these melodies may be subject to similar cognitive and acoustic/physical constraints, such that general laws of melodic evolution may apply across cultures. Modeling changes in musical notes as analogous to the process of molecular sequence evolution allows us to quantitatively test such hypotheses. Here we adapt sequence alignment algorithms from molecular genetics to analyze musical evolution in a sample of 10,062 melodies from musically divergent Japanese and English folk song traditions. Our analysis identifies 328 pairs of highly related melodies, within which rates of change vary in ways predicted by a neutral theory of melodic evolution in which note changes are more likely when they have smaller impacts on a song's melody. Specifically: 1) notes are most likely to change to neighboring notes, 2) rarer notes are more likely to change, and 3) notes with stronger functional roles are less likely to change. These results are consistent across samples despite using different scales with different probabilities of change between notes, suggesting they may apply universally. Our findings demonstrate that even a creative art form such as music is subject to evolutionary constraints analogous to those governing the evolution of genes, languages, and other domains of culture.

scholarly journals Species tree-aware simultaneous reconstruction of gene and domain evolution

2018 ◽  
Author(s):  
Sayyed Auwn Muhammad ◽  
Bengt Sennblad ◽  
Jens Lagergren
Keyword(s):  
Sequence Alignment ◽  
Gene Tree ◽  
Phylogenetic Reconstruction ◽  
Gene Families ◽  
Species Tree ◽  
Biological Data ◽  
Sequence Evolution ◽  
Multiple Sequence ◽  
Alignment Algorithms ◽  
Tandem Duplications

AbstractMost genes are composed of multiple domains, with a common evolutionary history, that typically perform a specific function in the resulting protein. As witnessed by many studies of key gene families, it is important to understand how domains have been duplicated, lost, transferred between genes, and rearranged. Analogously to the case of evolutionary events affecting entire genes, these domain events have large consequences for phylogenetic reconstruction and, in addition, they create considerable obstacles for gene sequence alignment algorithms, a prerequisite for phylogenetic reconstruction.We introduce the DomainDLRS model, a hierarchical, generative probabilistic model containing three levels corresponding to species, genes, and domains, respectively. From a dated species tree, a gene tree is generated according to the DL model, which is a birth-death model generalized to occur in a dated tree. Then, from the dated gene tree, a pre-specified number of dated domain trees are generated using the DL model and the molecular clock is relaxed, effectively converting edge times to edge lengths. Finally, for each domain tree and its lengths, domain sequences are generated for the leaves based on a selected model of sequence evolution.For this model, we present a MCMC-based inference framework called DomainDLRS that takes a dated species tree together with a multiple sequence alignment for each domain family as input and outputs an estimated posterior distribution over reconciled gene and domain trees. By requiring aligned domains rather than genes, our framework evades the problem of aligning full-length genes that have been exposed to domain duplications, in particular non-tandem domain duplications. We show that DomainDLRS performs better than MrBayes on synthetic data and that it outperforms MrBayes on biological data. We analyse several zincfinger genes and show that most domain duplications have been tandem duplications, some involving two or more domains, but non-tandem duplications have also been common.

A Comprehensive Analysis of Sequence Alignment Algorithms for LongRead Sequencing

2016 ◽  
Vol 11 (3) ◽  
pp. 375-381
Author(s):  
Yu Zhang ◽  
Jian Tai He ◽  
Yangde Zhang ◽  
Ke Zuo
Keyword(s):  
Sequence Alignment ◽  
Comprehensive Analysis ◽  
Alignment Algorithms

Similarity and Homology

1995 ◽  
Author(s):  
David J. States ◽  
Mark S. Boguski
Keyword(s):  
Sequence Alignment ◽  
Common Ancestor ◽  
Sequence Data ◽  
Sequence Similarity ◽  
Biological Macromolecules ◽  
Clear Understanding ◽  
Molecular Sequence Data ◽  
Molecular Sequence ◽  
Automated Tools ◽  
Similarity Relationships

Properly approached, molecular sequence data is a rich source of knowledge capable of teaching us much about the structure, function, and evolution of biological macromolecules. To effectively realize this potential, however, some understanding of the process of and theoretical basis for sequence comparison is needed as well as a variety of practical tools to access and manipulate the data. The volume of molecular sequence data has long since surpassed human information processing capacity for even simple tasks such as searching for related sequences, and with the ever increasing rate at which new sequences are being produced, the need for computer-assisted analysis becomes more and more acute. Automated tools can extend human capabilities by orders of magnitude in both speed and accuracy. The educated application of these automated tools is an essential part of modern molecular biology research. This chapter considers the theory and practice of analyzing sequence similarity as it applies to database searching and sequence alignment. Five major areas will be examined. First, we describe the use of dot matrix plots to elucidate the structures and features relating a sequence pair. Secondly, we discuss optimal pairwise alignment of sequences using dynamic programming algorithms. Thirdly, we examine fast, approximate techniques for detecting local similarities. Fourthly, the uses of and techniques for multiple sequence alignment are described. Finally, the statistical significance of sequence similarity is considered. In the analysis of molecular sequences, the terms similarity andhomology are often used without a clear understanding of their distinct implications. Similarity is a descriptive term which only implies that two sequences, by some criterion, resemble each other and carries no suggestion as to their origins or ancestry. Homology refers specifically to similarity due to descent from a common ancestor (Patterson, 1988;Reeck etal., 1987). On the basis of similarity relationships among a group of sequences, it may be possible to infer homology, but outside of an explicit laboratory model system, descent from a common ancestor remains hypothetical. There are philosophical issues in the inference of homology as well as practical ones. In classical morphology, conjunction (the occurrence of two traits in a single individual) is considered evidence that they are not homologous (Patterson, 1982).

scholarly journals Generalizing rate heterogeneity across sites in statistical phylogenetics

2020 ◽  
Vol 20 (4) ◽  
pp. 410-436
Author(s):  
Sarah E Heaps ◽  
Tom MW Nye ◽  
Richard J Boys ◽  
Tom A Williams ◽  
Svetlana Cherlin ◽  
...  
Keyword(s):  
Linear Models ◽  
Sequence Data ◽  
Sequence Evolution ◽  
Rate Matrix ◽  
Molecular Sequence Data ◽  
Instantaneous Rate ◽  
Sequence Composition ◽  
Molecular Sequence ◽  
Selective Constraints ◽  
Posterior Inference

Phylogenetics uses alignments of molecular sequence data to learn about evolutionary trees relating species. Along branches, sequence evolution is modelled using a continuous-time Markov process characterized by an instantaneous rate matrix. Early models assumed the same rate matrix governed substitutions at all sites of the alignment, ignoring variation in evolutionary pressures. Substantial improvements in phylogenetic inference and model fit were achieved by augmenting these models with multiplicative random effects that describe the result of variation in selective constraints and allow sites to evolve at different rates which linearly scale a baseline rate matrix. Motivated by this pioneering work, we consider an extension using a quadratic, rather than linear, transformation. The resulting models allow for variation in the selective coefficients of different types of point mutation at a site in addition to variation in selective constraints. We derive properties of the extended models. For certain non-stationary processes, the extension gives a model that allows variation in sequence composition, both across sites and taxa. We adopt a Bayesian approach, describe an MCMC algorithm for posterior inference and provide software. Our quadratic models are applied to alignments spanning the tree of life and compared with site-homogeneous and linear models.

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