Biology subjects

Tamuri, A. U.

Publications and source records attributed to Tamuri, A. U..

4 recordsLinked to original sources

bioRxiv2021

A mutation-selection model of protein evolution under persistent positive selection

We use first principles of population genetics to model the evolution of proteins under persistent positive selection (PPS). PPS may occur when organisms are subjected to persistent environmental change, during adaptive radiations, or in host-pathogen interactions. Our mutation-selection model indicates protein evolution under PPS is an irreversible Markov process, and thus proteins under PPS show a strongly asymmetrical distribution of selection coefficients among amino acid substitutions. Our model shows the criteria{omega} > 1 (where{omega} is the ratio of non-synonymous over synonymous codon substitution rates) to detect positive selection is conservative and indeed arbitrary, because in real proteins many mutations are highly deleterious and are removed by selection even at positively-selected sites. We use a penalized-likelihood implementation of our model to successfully detect PPS in plant RuBisCO and influenza HA proteins. By directly estimating selection coefficients at protein sites, our inference procedure bypasses the need for using{omega} as a surrogate measure of selection and improves our ability to detect molecular adaptation in proteins. Significance StatementUnderstanding how natural selection acts on proteins is important as it can inform studies from adaptive radiations to host-pathogen co-evolution. Traditionally, selection on proteins is inferred indirectly by measuring the non-synonymous to synonymous rate ratio,{omega} , with{omega} > 1, = 1, and < 1 indicating positive (adaptive) selection, neutral evolution, and negative (purifying) selection respectively. However, the theoretical underpinnings of this ratio are not well understood. Here we use first-principles of population genetics to work out how persistent changes in selection affect protein evolution and we use our new model to detect adaptive sites in plant and influenza proteins. We show measuring selection directly improves detection of adaptation in proteins.

evolutionary biology↗

bioRxiv2021

SARS-CoV-2 convergent evolution cannot be reliably inferred from phylogenetic analyses

A homoplasy is a trait shared between individuals that did not arise in a common ancestor, but rather is the result of convergent evolution. SARS-CoV-2 homoplasic mutations are important to characterise, because the evidence for a mutation conferring a fitness advantage is strengthened if this mutation has evolved independently and repeatedly in separate viral lineages. Yet detecting homoplasy is difficult due to insufficient variation between sequences to construct reliable phylogenetic trees. Here, we develop a method to more robustly identify confident homoplasies. We derive a maximum likelihood (ML) tree, with taxa bearing seemingly recurrent mutations dispersed across the tree, and then, for each potentially homoplasic mutation, we derive an alternative tree where the same taxa are constrained to one clade such that the mutation is no longer homoplasic. We then compare how well the two trees fit the sequence data. Applying this method to SARS-CoV-2 yields only a few instances where the constrained trees have significantly less statistical support than unconstrained tree, suggesting phylogenetics can provide limited support for homoplasy in SARS-CoV-2 and that caution is needed when inferring evidence of convergent evolution from phylogenetic methods in the absence of evidence from other sources.

evolutionary biology↗

bioRxiv2020

Phylogenetic Novelty Scores: a New Approach for Weighting Genetic Sequences

BackgroundMany important applications in bioinformatics, including sequence alignment and protein family profiling, employ sequence weighting schemes to mitigate the effects of non-independence of homologous sequences and under- or over-representation of certain taxa in a dataset. These schemes aim to assign high weights to sequences that are novel compared to the others in the same dataset, and low weights to sequences that are over-represented. ResultsWe formalise this principle by rigorously defining the evolutionary novelty of a sequence within an alignment. This results in new sequence weights that we call phylogenetic novelty scores. These scores have various desirable properties, and we showcase their use by considering, as an example application, the inference of character frequencies at an alignment column -- important, for example, in protein family profiling. We give computationally efficient algorithms for calculating our scores and, using simulations, show that they improve the accuracy of character frequency estimation compared to existing sequence weighting schemes. ConclusionsOur phylogenetic novelty scores can be useful when an evolutionarily meaningful system for adjusting for uneven taxon sampling is desired. They have numerous possible applications, including estimation of evolutionary conservation scores and sequence logos, identification of targets in conservation biology, and improving and measuring sequence alignment accuracy.

bioinformatics↗

bioRxiv2018

Haplotype assignment of virus NGS data using co-variation of variant frequencies

Longitudinal deep sequencing of viruses can provide detailed information about intra-host evolutionary dynamics including how viruses interact with and transmit between hosts. Many analyses require haplotype reconstruction, identifying which variants are co-located on the same genomic element. Most current methods to perform this reconstruction are based on a high density of variants and cannot perform this reconstruction for slowly evolving viruses. We present a new approach, HaROLD (HAplotype Reconstruction Of Longitudinal Deep sequencing data), which performs this reconstruction based on identifying co-varying variant frequencies using a probabilistic framework. We test this method with synthetic data sets of mixed cytomegalovirus and norovirus genomes, demonstrating high accuracy when longitudinal samples are available.

genomics↗