Biology subjects

Noskova, E.

Publications and source records attributed to Noskova, E..

9 recordsLinked to original sources

bioRxiv2026

SweepLink: Joint Inference of Demography and Linked~Selection from Time-series Data

Genome-wide time-series data, i.e. allele frequency trajectories tracked across multiple sampling times, are among the richest sources of information for inferring selection. Beyond a beneficial allele's own rise in frequency, such data capture how it drags nearby loci upward via linkage, an effect known as genetic hitch-hiking. Yet most existing tools are single-locus, treating loci independently: they infer site-specific selection coefficients in isolation, then rely on ad hoc window statistics to account for hitch-hiking. Many existing tools further require a predefined population size, or scale poorly when jointly inferring selection and demography, and their power is highly sensitive to a significance threshold. To address these shortcomings, we here present SweepLink, a two-layer Hidden Markov Model that overcomes these limitations by jointly inferring demography and linked selection genome-wide: a spatial layer captures correlations between neighboring selection coefficients, coupled with a temporal Wright-Fisher diffusion layer. As we show with extensive simulations, this setup pushes drift-driven false signals toward neutrality while reinforcing loci that receive support from neighbouring loci, thereby increasing the sensitivity for weak and moderate selection, while matching the power of existing tools to detect strong selection. These simulations further show that SweepLink yields confident posteriors that remain stable at maximal significance, removing the need for arbitrary thresholds. We applied SweepLink to ancient DNA time-series data from the British population, previously analysed with a single-locus tool. SweepLink recovers four of the previously reported signals (LCT, SLC45A2, DHCR7, HERC2), and partially recovers the MHC/HLA signal. It also identifies additional candidate regions, including DPYD, FADS1/2 and OAS1, missed by the prior scan but supported by independent studies.

evolutionary biology↗

bioRxiv2025

GHIST 2024: The 1st Genomic History Inference Strategies Tournament

Evaluating population genetic inference methods is challenging due to the complexity of evolutionary histories, potential model misspecification, and unconscious biases in self-assessment. The Genomic History Inference Strategies Tournament (GHIST) is a community-driven competition designed to evaluate methods for inferring evolutionary history from population genomic data. The inaugural GHIST competition ran from July to November 2024 and featured four demographic history inference challenges of varying complexity: a bottleneck model, a split with isolation model, a secondary contact model with demographic complexity, and an archaic admixture model. Data were provided as error-free VCF files, and participants submitted numerical parameter estimates that were scored by relative root mean squared error. Approximately 60 participants competed, using diverse approaches. Results revealed the current dominance of methods based on site frequency spectra, while highlighting the advantages of flexible model-building approaches for complex demographic histories. We discuss insights regarding the competition and outline the next iteration, which is ongoing with expanded challenge diversity. By providing standardized benchmarks and highlighting areas for improvement, GHIST represents a substantial step toward more reliable inference of evolutionary history from genomic data.

evolutionary biology↗

bioRxiv2023

ORFanes in mitochondrial genomes of marine polychaete Polydora

Most characterised metazoan mitochondrial genomes are compact and encode a small set of proteins that are essential for oxidative phosphorylation. However, in rare cases, invertebrate taxa have additional open reading frames (ORFs) in their mtDNA sequences. Here, we sequenced and analysed the mitochondrial genome of a polychaete worm, Polydora cf. ciliata, part of whose life cycle takes place in low-oxygen conditions. In the mitogenome, we found three "ORFane" regions (1063, 427, and 519 bp) that have no resemblance to any standard metazoan mtDNA gene but lack stop codons in one of the reading frames. Similar regions are found in the mitochondrial genomes of three other Polydora species and Bocardiella hamata. All five species share the same gene order in their mitogenomes, which differ from that of other known spionidae mitogenomes. By analysing the ORFane sequences, we found that they are under negative selection pressure, contain conservative regions, and harbour predicted transmembrane domains.The codon adaptation indices (CAIs) of the ORFan genes were in the same range of values as the CAI of conventional protein-coding genes in corresponding mitochondrial genomes. Together, this suggests that ORFanes encode functional proteins. We speculate that the ORFanes originated from the conventional mitochondrial protein-coding genes which were duplicated when the Polydora/Bocardiella species complex separated from the rest of the Spionidae. Significance statementMetazoan mitochondrial genomes usually contain a conservative set of genes and features. However, mitogenomes of some species contain ORFanes - putative protein-coding genes without clear homology with other known sequences. In this study, we analysed three ORFanes in mitochondria of species of the genera Polydora and Bocardiella, which were absent in all other representatives of Spionidae. To the best of our knowledge, ORFanes havent been described in Annelida before. Sequence analysis of the ORFanes suggests they contain conservative regions and are likely translated into functional proteins. Our study features an uncommon case where new protein-coding genes emerged in the mitochondrial genomes of metazoa.

evolutionary biology↗

bioRxiv2022

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗

bioRxiv2022

Bayesian optimization for demographic inference

MotivationInference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a models parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware resources, critically more so for larger population counts. Although genetic algorithm based solution have proven efficient for demographic inference in the past, it struggles to deal with log-likelihoods in the setting of more than three populations. Different tools are therefore needed to handle such scenarios. ResultsWe introduce a new specialized optimization pipeline for demographic inference with time-consuming log-likelihood evaluations. It is based on Bayesian optimization, a prominent technique for optimizing expensive black box functions. Comparing to the existing widely used genetic algorithm solution, we demonstrate new pipelines superiority in time limited conditions for demographic inference with four and five populations when using log-likelihoods provided by the moments tool. Moreover, we expect this behavior to generalize just as well to other expensive-to-evaluate log-likelihood functions in the field. AvailabilityThe proposed method was implemented as part of the GADMA software framework and is freely and openly available on GitHub: https://github.com/ctlab/GADMA. Contactekaterina.e.noskova@gmail.com, viacheslav.borovitskiy@gmail.com Supplementary informationSupplementary materials are available as a separate document.

bioinformatics↗

bioRxiv2022

GADMA2: more efficient and flexible demographic inference from genetic data

BackgroundInference of complex demographic histories is a source of information about events that happened in the past of studied populations. Existing methods for demographic inference typically require input from the researcher in the form of a parameterized model. With an increased variety of methods and tools, each with its own interface, the model specification becomes tedious and error-prone. Moreover, optimization algorithms used to find model parameters sometimes turn out to be inefficient, for instance, by being not properly tuned or highly dependent on a user-provided initialization. The open-source software GADMA addresses these problems, providing automatic demographic inference. It proposes a common interface for several likelihood engines and provides global parameters optimization based on a genetic algorithm. ResultsHere, we introduce the new GADMA2 software and provide a detailed description of the added and expanded features. It has a renovated core code base, new likelihood engines, an updated optimization algorithm and a flexible setup for automatic model construction. We provide a full overview of GADMA2 enhancements, compare the performance of supported likelihood engines on simulated data and demonstrate an example of GADMA2 usage on two empirical datasets. ConclusionsWe demonstrate the better performance of a genetic algorithm in GADMA2 by comparing it to the initial version and other existing optimization approaches. Our experiments on simulated data indicate that GADMA2s likelihood engines are able to provide accurate estimations of demographic parameters even for misspecified models. We improve model parameters for two empirical datasets of inbred species.

bioinformatics↗

bioRxiv2022

Demes: a standard format for demographic models

Understanding the demographic history of populations is a key goal in population genetics, and with improving methods and data, ever more complex models are being proposed and tested. Demographic models of current interest typically consist of a set of discrete populations, their sizes and growth rates, and continuous and pulse migrations between those populations over a number of epochs, which can require dozens of parameters to fully describe. There is currently no standard format to define such models, significantly hampering progress in the field. In particular, the important task of translating the model descriptions in published work into input suitable for population genetic simulators is labor intensive and error prone. We propose the Demes data model and file format, built on widely used technologies, to alleviate these issues. Demes provides a well-defined and unambiguous model of populations and their properties that is straightforward to implement in software, and a text file format that is designed for simplicity and clarity. We provide thoroughly tested implementations of Demes parsers in multiple languages including Python and C, and showcase initial support in several simulators and inference methods. An introduction to the file format and a detailed specification are available at:https://popsim-consortium.github.io/demes-spec-docs/.

genetics↗

bioRxiv2019

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

evolutionary biology↗

bioRxiv2018

GADMA: Genetic Algorithm for Automatic Inferring Joint Demographic History of Multiple Populations from Allele Frequency Spectrum

The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum or AFS, the distribution of allele frequencies in populations. The joint allele frequency spectrum is commonly used to reconstruct the demographic history of multiple populations and several methods based on diffusion approximation (e.g.,{partial} a{partial}i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint allele frequency spectrum data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Analysis, https://github.com/ctlab/GADMA). We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer demographic models at different local optima close to the global one, making it is possible to detect more biology corrected model during further research.

evolutionary biology↗