Biology subjects

Tamura, K.

Publications and source records attributed to Tamura, K..

5 recordsLinked to original sources

bioRxiv2018

An averaging strategy to reduce variability in target-decoy estimates of false discovery rate

Decoy database search with target-decoy competition (TDC) provides an intuitive, easy-to-implement method for estimating the false discovery rate (FDR) associated with spectrum identifications from shotgun proteomics data. However, the procedure can yield different results for a fixed dataset analyzed with different decoy databases, and this decoy-induced variability is particularly problematic for smaller FDR thresholds, datasets or databases. In such cases, the nominal FDR might be 1% but the true proportion of false discoveries might be 10%. The averaged TDC protocol combats this problem by exploiting multiple independently shuffled decoy databases to provide an FDR estimate with reduced variability. We provide a tutorial introduction to aTDC, describe an improved variant of the protocol that offers increased statistical power, and discuss how to deploy aTDC in practice using the Crux software toolkit.

bioinformatics↗

bioRxiv2018

Modelling the emergence of an egalitarian society in the n-player game framework

Unlike other primates, human foragers have an egalitarian society. Therefore, the evolution of egalitarian behaviour has been the subject of long-standing debate in a wide variety of disciplines. A recent hypothesis states that a social control against potentially dominant individuals played an important role in the emergence of an egalitarian society, although this has not been modelled directly. In the present study, we modelled this hypothesis based on the n-player game framework, in which the owner, who may attempt to monopolise resources, could be punished by a coalition of other group members. Our results suggest that a potentially despotic payoff structure can promote the evolution of egalitarian behaviour. Besides, large group size, small cost of competition, and variation in the strengths of individuals can promote the evolution of egalitarian behaviour. Our results suggest the importance of both social control against dominant individuals and benefits of a coalition for the evolution of egalitarian behaviour.

animal behavior and cognition↗

bioRxiv2018

Adaptive landscape of protein variation in human exomes

The human genome contains hundreds of thousands of missense mutations. However, only a handful of these variants are known to be adaptive, which implies that adaptation through protein sequence change is an extremely rare phenomenon in human evolution. Alternatively, existing methods may lack the power to pinpoint adaptive variation. We have developed and applied an Evolutionary Probability Approach (EPA) to discover candidate adaptive polymorphisms (CAPs) through the discordance between allelic evolutionary probabilities and their observed frequencies in human populations. EPA reveals thousands of missense CAPs, which suggest that a large number of previously optimal alleles had experienced a reversal of fortune in the human lineage. We explored non-adaptive mechanisms to explain CAPs, including the effects of demography, mutation rate variability, and negative and positive selective pressures in modern humans. Our analyses suggest that a large proportion of CAP alleles have increased in frequency due to beneficial selection. This conclusion is supported by the facts that a vast majority of adaptive missense variants discovered previously in humans are CAPs, and that hundreds of CAP alleles are protective in genotype-phenotype association data. Our integrated phylogenomic and population genetic EPA approach predicts the existence of thousands of signatures of non-neutral evolution in the human proteome. We expect this collection to be enriched in beneficial variation. EPA approach can be applied to discover candidate adaptive variation in any protein, population, or species for which allele frequency data and reliable multispecies alignments are available.

evolutionary biology↗

bioRxiv2017

RelTime relaxes the strict molecular clock throughout the phylogeny

The RelTime method estimates divergence times when evolutionary rates vary among lineages. Theoretical analyses show that RelTime relaxes the strict molecular clock throughout a molecular phylogeny, and it performs well in the analysis of empirical and computer simulated datasets in which evolutionary rates are variable. Lozano-Fernandez et al. (2017) found that the application of RelTime to one metazoan dataset (Erwin et al. 2011) produced equal rates for several ancient lineages, which led them to speculate that RelTime imposes a strict molecular clock for deep animal divergences. RelTime does not impose a strict molecular clock. The pattern observed by Lozano-Fernandez et al. (2017) was a result of the use of an option to assign the same rate to lineages in RelTime when the rates are not statistically significantly different. The median rate difference was 5% for many deep metazoan lineages for Erwin et al. (2011) dataset, so the rate equality was not rejected. In fact, RelTime analysis with and without the option to test rate differences produced very similar time estimates. We found that the Bayesian time estimates vary widely depending on the root priors assigned, and that the use of less restrictive priors produce Bayesian divergence times that are concordant with those from RelTime for Erwin et al. (2011) dataset. Therefore, it is prudent to discuss Bayesian estimates obtained under a range of priors in any discourse about molecular dating, including method comparisons.

evolutionary biology↗

bioRxiv2017

Theoretical foundation of the RelTime method for estimating divergence times from variable evolutionary rates

RelTime estimates divergence times by relaxing the assumption of a strict molecular clock in a phylogeny. It showed excellent performance in estimating divergence times for both simulated and empirical molecular sequence datasets in which evolutionary rates varied extensively throughout the tree. RelTime is computationally efficient and scales well with increasing size of datasets. Until now, however, RelTime has not had a formal mathematical foundation. Here, we show that the basis of the RelTime approach is a relative rate framework (RRF) that combines comparisons of evolutionary rates in sister lineages with the principle of minimum rate change between an evolutionary lineage and its descendants. We present analytical solutions for estimating relative lineage rates and divergence times under RRF. We also discuss the relationship of RRF with other approaches, including the Bayesian framework. We conclude that RelTime will be also useful for phylogenies with branch lengths derived not only from molecular data, but also morphological and biochemical traits.

evolutionary biology↗