Biology subjects

Skums, P.

Publications and source records attributed to Skums, P..

12 recordsLinked to original sources

bioRxiv2026

Reconstructing intra-tumor fitness landscapes from scSeq CNA genotypes via simulation-based Bayesian inference and Deep Learning

Inferring the selective effects of copy-number alterations (CNAs) from clonal tumor data is essential for understanding tumor evolution. In practice, intra-tumor evolutionary parameters are typically estimated by fitting population genetic models to observed data using maximum likelihood or Bayesian methods. However, realistic mechanistic models often lead to intractable likelihoods, limiting the applicability of conventional inference approaches. Here, we introduce a likelihood-free, simulation-based framework for inferring intra-tumor selection coefficients directly from clonal CNA profiles. Our approach employs neural posterior estimation to amortize inference across simulated tumors and uses normalizing flows to flexibly parameterize high-dimensional posterior distributions while enabling robust uncertainty quantification. Our primary model, CloneMLP-NPE, learns representations of whole-tumor CNA genotypes using a multilayer perceptron (MLP)-based encoder. We compare this model against two baselines: (i) a Set Transformer encoder applied to the same whole-tumor CNA profiles, and (ii) a consensus-based approach that relies only on the CNA profile of the most abundant clone. On held-out simulations, CloneMLP-NPE achieves the strongest overall performance, yielding well-calibrated posterior distributions and more accurate posterior mean estimates than both baselines.

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bioRxiv2026

Identification of different sequence properties between HIV-1 DNA and RNA across subtypes using the k-mer-based approach

Advanced analytical tools that enable mining of the masked features hidden in intricate datasets and strengthening the biological interpretation of multigenomic outputs hold paramount importance. At present, HIV-1 subtyping remains a challenging task in a great part due to analytical tool discordance. To tackle this issue, in this study, we present an updated version of a k-mer-based approach, PORT-EK-v2, a streamlined bioinformatic pipeline, allowing for a comparison of multiple genomic datasets and identification of over-represented genomic regions, k-mers, related to specific origins of datasets. Using PORT-EK-v2, we exemplified that intrinsic sequence patterns between HIV-1 DNA and RNA are distinct across group M HIV-1 subtypes. Furthermore, we showcased that "isolate k-mer count", a predictive variable computed in this work, could serve as a default choice in classifying the HIV-1 DNA versus RNA sequences across subtypes. Lastly, results based on network-based analyses and Markov chain Monte Carlo modeling unveiled a clear discontinuation of a random walk throughout the network properties corresponding to each tested group of HIV-1 subtypes, confirming the specificity of enriched k-mer retrieved by PORT-EK-v2 and the genomic diversity across group M HIV-1 subtypes. Source code for PORT-EK-v2 is at https://github.com/Quantitative-Virology-Research-Group/PORT-EK-version-2 and is freely available.

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bioRxiv2023

Community structure and temporal dynamics of SARS-CoV-2 epistatic network allows for early detection of emerging variants with altered phenotypes

The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.

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bioRxiv2022

SOPHIE: viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework

Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, while common source outbreaks violate this assumption. We propose a maximum likelihood framework SOPHIE (SOcial and PHilogenetic Investigation of Epidemics) based on integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modelled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity and accurately infers transmissions without case-specific epidemiological data. SOPHIE code is freely available at https://github.com/compbel/SOPHIE/

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bioRxiv2021

From Alpha to Zeta: Identifying variants and subtypes of SARS-CoV-2 via clustering

The availability of millions of SARS-CoV-2 sequences in public databases such as GISAID and EMBL-EBI (UK) allows a detailed study of the evolution, genomic diversity and dynamics of a virus like never before. Here we identify novel variants and sub-types of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intra-host viral populations. We asses our results using clustering entropy -- the first time it has been used in this context. Our clustering approach reaches lower entropies compared to other methods, and we are able to boost this even further through gap filling and Monte Carlo based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the UK and GISAID datasets, but is also able to detect the much less represented (< 1% of the sequences) Beta (South Africa), Epsilon (California), Gamma and Zeta (Brazil) variants in the GISAID dataset. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large datasets.

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bioRxiv2020

Primary case inference in viral outbreaks through analysis of intra-host variant population

Investigation of outbreaks to identify the primary case is crucial for the interruption and prevention of transmission of infectious diseases. These individuals may have a higher risk of participating in near future transmission events when compared to the other patients in the outbreak, so directing more transmission prevention resources towards these individuals is a priority. Genetic characterization of intra-host viral populations, although highly efficient in the identification of transmission clusters, is not as efficient in routing transmissions during outbreaks, owing to complexity of viral evolution. Here, we present a new computational framework, PYCIVO: primary case inference in viral outbreaks. This framework expands upon our earlier work in development of QUENTIN, which builds a probabilistic disease transmission tree based on simulation of evolution of intra-host hepatitis C virus (HCV) variants between cases involved in direct transmission during an outbreak. PYCIVO improves upon QUENTIN by also implementing a custom heterogeneity index which empowers PYCIVO to make the important No primary case prediction. One or more samples, possibly including the primary case, may have not been sampled, and this designation is meant to account for these scenarios. These approaches were validated using a set of 105 sequence samples from 11 distinct HCV transmission clusters identified during outbreak investigations, in which the primary case was epidemiologically verified. Both models can detect the correct primary case in 9 out of 11 transmission clusters (81.8%). However, while QUENTIN issues erroneous predictions on the remaining 2 transmission clusters, PYCIVO issues a null output for these clusters, giving it an effective prediction accuracy of 100%. To further evaluate accuracy of the inference, we created 10 modified transmission clusters in which the primary case had been removed. In this scenario, PYCIVO was able to correctly identify that there was no primary case in 8/10 (80%) of these modified clusters. This model was validated with HCV; however, this approach may be applicable to other microbial pathogens. A version of this software is publicly available at the following url: https://www.github.com/walkergussler/PYCIVO

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bioRxiv2020

Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections

BackgroundDetection of incident hepatitis C virus (HCV) infections is crucial for identification of outbreaks and development of public health interventions. However, there is no single diagnostic assay for distinguishing recent and persistent HCV infections. HCV exists in each infected host as a heterogeneous population of genomic variants, whose evolutionary dynamics remain incompletely understood. Genetic analysis of such viral populations can be applied to the detection of incident HCV infections and used to understand intra-host viral evolution. MethodsWe studied intra-host HCV populations sampled using next-generation sequencing from 98 recently and 256 persistently infected individuals. Genetic structure of the populations was evaluated using 245,878 viral sequences from these individuals and a set of selected parameters measuring their diversity, topological structure, complexity, strength of selection, epistasis, evolutionary dynamics, and physico-chemical properties. FindingsDistributions of the viral population parameters differ significantly between recent and persistent infections. A general increase in viral genetic diversity from recent to persistent infections is frequently accompanied by decline in genomic complexity and increase in structuredness of the HCV population, likely reflecting a high level of intra-host adaptation at later stages of infection. Using these findings, we developed a Machine Learning classifier for the infection staging, which yielded a detection accuracy of 95.22%, thus providing a higher accuracy than other genomic-based models. InterpretationThe detection of a strong association between several HCV genetic factors and stages of infection suggests that intra-host HCV population develops in a complex but regular and predictable manner in the course of infection. The proposed models may serve as a foundation of cyber-molecular assays for staging infection, that could potentially complement and/or substitute standard laboratory assays. FundingAZ and PS were supported by NIH grant 1R01EB025022. PIB was supported by GSU MBD fellowship.

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bioRxiv2019

Analysis of Heterogeneous Genomic Samples Using Image Normalization and Machine Learning

BackgroundAnalysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for the analysis of sequencing data associated with such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datastes of different sizes and structures.\n\nMethodsWe propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important molecular epidemiology problems: inference of viral infection stage and detection of viral transmission clusters and outbreaks using next-generation sequencing data.\n\nResultsThe infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy.\n\nAvailabilityThe developed software is freely available at https://bitbucket.org/adv_bio_coll/chronic_vs_clinic

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bioRxiv2019

Using Earth Mover’s Distance for Viral Outbreak Investigations

RNA viruses mutate at extremely high rates forming an intra-host viral population of closely related variants (or quasi-species) [4]. High variability of Human Immunodeficiency Virus (HIV) and Hepatitis C virus (HCV) making them particularly dangerous by allowing them to evade the hosts immune system. HIV and HCV outbreaks pose a significant problem for public health for solving which it is critical to infer transmission clusters, i.e., to decide whether two viral samples belong to the same outbreak. Initial approach [10] was based on estimating relatedness between two samples as the distance between consensuses of the corresponding viral populations. The distance between closest pair of representatives from two populations, MinDist, has been shown to be significantly more accurate [2]. Unfortunately, MinDist computation requires a cumbersome RNA-seq data assembly and identification of all viral sequences from a given project. We present a novel approach that allows to bypass read assembly and estimate the distance between viral samples based on k-mer (i.e. a substring of length k) distribution in RNA-seq reads. The experimental validation using sequencing data from HCV outbreaks shows that the proposed algorithms can successfully identify genetic relatedness between viral populations, infer transmission clusters and outbreak sources, as well decide whether the primary spreader is present in the sequenced outbreak sample.

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bioRxiv2018

Inference of clonal selection in cancer populations using single-cell sequencing data

Intra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single cell sequencing, which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here we present SCIFIL, a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from single cell sequencing data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy and utility of our approach on simulated and experimental data. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. Its source code is available at https://github.com/compbel/SCIFIL

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bioRxiv2018

Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants

Many biological analysis tasks require extraction of families of genetically similar sequences from large datasets produced by Next-generation Sequencing (NGS). Such tasks include detection of viral transmissions by analysis of all genetically close pairs of sequences from viral datasets sampled from infected individuals or studying of evolution of viruses or immune repertoires by analysis of network of intra-host viral variants or antibody clonotypes formed by genetically close sequences. The most obvious na{iota}eve algorithms to extract such sequence families are impractical in light of the massive size of modern NGS datasets. In this paper, we present fast and scalable k-mer-based framework to perform such sequence similarity queries efficiently, which specifically targets data produced by deep sequencing of heterogeneous populations such as viruses. The tool is freely available for download at https://github.com/vyacheslav-tsivina/signature-sj

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bioRxiv2018

CliqueSNV: Scalable Reconstruction of Intra-Host Viral Populations from NGS Reads

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient's treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing (NGS), but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

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