Biology subjects

Nabieva, E.

Publications and source records attributed to Nabieva, E..

3 recordsLinked to original sources

bioRxiv2019

Senescence and entrenchment in evolution of amino acid sites

Amino acid propensities at a site change in the course of protein evolution. This may happen for two reasons. Changes may be triggered by substitutions at epistatically interacting sites elsewhere in the genome; alternatively, they may arise due to environmental changes that are external to the genome. Here, we design a framework for distinguishing between these alternatives. Using analytical modelling and simulations, we show that they cause opposite dynamics of the fitness of the allele currently occupying the site: its fitness tends to increase with the time since its origin due to epistasis (\"entrenchment\"), but to decrease due to random environmental fluctuations (\"senescence\"). We analyse the phylogenetic distribution of substitutions in nuclear genomes, and show that among the amino acids originating at negatively selected sites of vertebrates, nearly all experience strong entrenchment. By contrast, among the amino acids originating at positively selected sites, 18% experience senescence. A similar pattern is observed in phylogenies of insects. We propose that senescence of the current allele is a cause of adaptive evolution.

evolutionary biology↗

bioRxiv2019

SELVa: Simulator of Evolution with Landscape Variation

Organisms evolve to increase their fitness, a process that may be described as climbing the fitness landscape. However, the fitness landscape of an individual site, i.e., the vector of fitness values corresponding to different variants at this site, can change with time due to changes in the environment or substitutions at other epistatically interacting sites. We present SELVa, the Simulator of Evolution with Landscape Variation, aimed at modeling the substitution process under a changing single position fitness landscape in a set of evolving lineages forming a phylogeny of arbitrary shape. Written in Java and distributed as an executable jar file, SELVa provides a flexible framework that allows the user to choose from a number of implemented rules governing landscape change.\n\nAvailabilityhttps://github.com/bazykinlab/SELVa

evolutionary biology↗

bioRxiv2019

Accurate Fetal Variant Calling in the Presence of Maternal Cell Contamination

High-throughput sequencing of fetal DNA is a promising and increasingly common method for the discovery of all (or all coding) genetic variants in the fetus, either as part of prenatal screening or diagnosis, or for genetic diagnosis of spontaneous abortions. In many cases, the fetal DNA (from chorionic villi, amniotic fluid, or abortive tissue) can be contaminated with maternal cells, resulting in the mixture of fetal and maternal DNA. This maternal cell contamination (MCC) undermines the assumption, made by traditional variant callers, that each allele in a heterozygous site is covered, on average, by 50% of the reads, and therefore can lead to erroneous genotype calls. We present a panel of methods for reducing the genotyping error in the presence of MCC. All methods start with the output of GATK HaplotypeCaller on the sequencing data for the (contaminated) fetal sample and both of its parents, and additionally rely on information about the MCC fraction (which itself is readily estimated from the high-throughput sequencing data). The first of these methods uses a Bayesian probabilistic model to correct the fetal genotype calls produced by MCC-unaware HaplotypeCaller. The other two methods “learn” the genotype-correction model from examples. We use simulated contaminated fetal data to train and test the models. Using the test sets, we show that all three methods lead to substantially improved accuracy when compared with the original MCC-unaware HaplotypeCaller calls. We then apply the best-performing method to three chorionic villus samples from spontaneously terminated pregnancies.Code and training data availability https://github.com/bazykinlab/ML-maternal-cell-contaminationCompeting Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗