Biology subjects

Zelikovsky, A.

Publications and source records attributed to Zelikovsky, A..

13 recordsLinked to original sources

bioRxiv2026

A comprehensive benchmark of discrepancies across microbial genome reference databases

Metagenomic analysis of microbial communities relies significantly on the quality and completeness of reference genomes, which allow researchers to compare sequencing reads against reference genome collections to reveal essential community characteristics. However, the reliability of these analyses is often compromised by substantial discrepancies across existing reference resources, including differences in genome content, assembly fragmentation, taxonomic representation, and metadata completeness. While these inconsistencies are known to introduce bias, the extent of divergence between major databases remains largely unknown. Here, we present a comprehensive benchmark of discrepancies across multiple widely used microbial genome reference resources. We developed the Cross-DB Genomic Comparator (CDGC), which utilizes reference genome alignments to systematically capture discrepancies in genome assemblies across reference databases. Applying this framework, we found that 99% of viral genomes were identical across databases, indicating strong consistency in viral reference resources. In contrast, fungal genomes showed substantially greater variability: although 82% of assemblies exhibited at least 90% similarity, only 7% were identical across databases. More concerning, we identified a subset of 461 assemblies with less than 50% similarity, suggesting the presence of technical artifacts, incomplete assemblies, or damaged genome files that require closer examination. Collectively, these results demonstrate that systematic cross-database benchmarking provides a critical mechanism for refining the accuracy of individual reference databases and advancing efforts towards more unified and reliable universal reference genomes.

bioinformatics↗

bioRxiv2026

Leveraging a hybrid cross-disciplinary training model to accelerate global bioinformatics capacity

Disparities in formal bioinformatics training exacerbate the global skills gap, impeding the democratized application of advanced genomic technologies. To bridge this divide, we introduce a scalable, hybrid training framework designed to rapidly accelerate regional bioinformatics capacity. We exemplify this approach through the Eastern European Bioinformatics and Genomics (EEBG) workshop series -- a cross-disciplinary initiative that pairs international faculty with local institutions to deliver modular, hands-on curricula. Functioning as a structured knowledge-transfer pipeline, the series has catalyzed a sustainable educational ecosystem, evidenced by the establishment of multiple independent summer schools across the region. The assessment of the 2025 EEBG workshop in Krakow, Poland, validates the models viability; participant metrics confirm high efficacy in skill acquisition (mean satisfaction: 4.4/5.0) and community building. Crucially, the hybrid delivery mode dismantled geographic barriers, serving as a vital mechanism for maintaining scientific continuity for researchers facing displacement and crisis. Synthesizing these outcomes, we define the core features of a replicable blueprint for scientific readiness in resource-constrained environments. We conclude by presenting a strategic roadmap -- organized around infrastructure standardization, governance sustainability, and geographical expansion -- for adapting this regional proof-of-concept into a global export-ready model, offering a critical path toward ensuring universal access to genomic innovation.

scientific communication and education↗

bioRxiv2026

FASTR: Reimagining FASTQ via Compact Image-inspired Representation

MotivationHigh-throughput sequencing (HTS) enables population-scale genomics but generates massive datasets, creating bottlenecks in storage, transfer, and analysis. FASTQ, the standard format for over two decades, stores one byte per base and one byte per quality score, leading to inefficient I/O, high storage costs, and redundancy. Existing compression tools can mitigate some issues, but often introduce costly decompression or complex dependency issues. ResultsWe introduce FASTR, a lossless, computation-native successor to FASTQ that encodes each nucleotide together with its base quality score into a single 8-bit value. FASTR reduces file size by at least 2x while remaining fully reversible and directly usable for downstream analyses. Applying general-purpose compression tools on FASTR consistently yields higher compression ratios, 2.47, 3.64, and 4.8x faster compression, and 2.34, 1.96, 1.75x faster decompression than on FASTQ across Illumina, HiFi, and ONT reads. FASTR is machine-learning-ready, allowing reads to be consumed directly as numerical vectors or image-like representations. We provide a highly parallel software ecosystem for FASTQ-FASTR conversion and show that FASTR integrates with existing tools, such as minimap2, with minimal interface changes and no performance overhead. By eliminating decompression costs and reducing data movement, FASTR lays the foundation for scalable genomics analyses and real-time sequencing workflows. Availability and Implementationhttps://github.com/ALSER-Lab/FASTR

bioinformatics↗

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.

bioinformatics↗

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.

bioinformatics↗

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

bioinformatics↗

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.

bioinformatics↗

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

bioinformatics↗

bioRxiv2018

Solving scaffolding problem with repeats

One of the most important steps in genome assembly is scaffolding. Increasing the length of sequencing reads allows assembling short genomes but assembly of long repeat-rich genomes remains one of the most interesting and challenging problems in bioinformatics. There is a high demand in developing computational approaches for repeat aware scaffolding. In this paper, we propose a novel repeat-aware scaffolder BATISCAF based on the optimization formulation for filtering out repeated and short contigs. Our experiments with five benchmarking datasets show that the proposed tool BATISCAF outperforms state-of-the-art tools. BATISCAF is freely available on GitHub: https://github.com/mandricigor/batiscaf.

bioinformatics↗

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

bioinformatics↗

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.

bioinformatics↗

bioRxiv2017

Repeat aware evaluation of scaffolding tools

SummaryGenomic sequences are assembled into a variable, but large number of contigs that should be scaffolded (ordered and oriented) for facilitating comparative or functional analysis. Finding scaffolding is computationally challenging due to misassemblies, inconsistent coverage across the genome, and long repeats. An accurate assessment of scaffolding tools should take into account multiple locations of the same contig on the reference scaffolding rather than matching a repeat to a single best location. This makes mapping of inferred scaffoldings onto the reference a computationally challenging problem. This paper formulates the repeat-aware scaffolding evaluation problem which is to find a mapping of the inferred scaffolding onto the reference maximizing number of correct links and proposes a scalable algorithm capable of handling large whole-genome datasets. Our novel scaffolding validation pipeline has been applied to assess the most of state-of-the-art scaffolding tools on the representative subset of GAGE datasets.\n\nAvailabilityThe source code of this evaluation framework is available at https://github.com/mandricigor/repeat-aware. The documentation is hosted at https://mandricigor.github.io/repeat-aware.

bioinformatics↗

bioRxiv2016

Profiling adaptive immune repertoires across multiple human tissues by RNA Sequencing

Assay-based approaches provide a detailed view of the adaptive immune system by profiling T and B cell receptor repertoires. However, these methods come at a high cost and lack the scale of standard RNA sequencing (RNA-seq). Here we report the development of ImReP, a novel computational method for rapid and accurate profiling of the adaptive immune repertoire from regular RNA-Seq data. We applied it to 8,555 samples across 544 individuals from 53 tissues from the Genotype-Tissue Expression (GTEx v6) project. ImReP is able to efficiently extract TCR- and BCR- derived reads from the RNA-Seq data and accurately assemble the complementarity determining regions 3 (CDR3s), the most variable regions of B- and T-cell receptors determining their antigen specificity. Using ImReP, we have created the systematic atlas of immunological sequences for B- and T-cell repertoires across a broad range of tissue types, most of which have not been studied for B and T cell receptor repertoires. We have also examined the compositional similarities of clonal populations between the GTEx tissues to track the flow of T- and B- clonotypes across immune-related tissues, including secondary lymphoid organs and organs encompassing mucosal, exocrine, and endocrine sites. The atlas of T- and B-cell receptor receptors, freely available at https://sergheimangul.wordpress.com/atlas-immune-repertoires/, is the largest collection of CDR3 sequences and tissue types. We anticipate this recourse will enhance future studies in areas such as immunology and advance development of therapies for human diseases. ImReP is freely available at https://sergheimangul.wordpress.com/imrep/.

immunology↗