Biology subjects

Popic, V.

Publications and source records attributed to Popic, V..

12 recordsLinked to original sources

bioRxiv2026

Scalable single-cell isoform profiling with sequencing-by-expansion

Single-cell RNA sequencing has transformed our understanding of cellular systems, yet the reliance on short-read sequencing restricts analysis to gene-level quantification and obscures the immense biological diversity generated by alternative splicing. While long-read sequencing technologies can capture full-length RNA and resolve transcript isoforms, current platforms remain constrained by throughput and high per-base costs, rendering them impractical for modern million-cell applications. To address this critical limitation, we developed and optimized sequencing-by-expansion (SBX) chemistry for high-throughput single-cell RNA isoform profiling. Integrated within the AXELIOS 1 sequencing platform, SBX employs a unique biochemical conversion process that transforms complementary DNA into expanded surrogate high signal-to-noise polymers called Xpandomers which are sequenced via translocation through a dense nanopore array yielding over 9.5 billion reads in a two-hour run. To leverage this unique data type for long-read single-cell RNA isoform sequencing, we developed the Consensus UMI Deduplication using Longest Length (CUDLL) algorithm, which computationally consolidates variable-length raw SBX reads into single, high-fidelity consensus reads, elevating sequence accuracy to 99.83% and maximizing per transcript read length. We demonstrate that this consensus approach successfully captures the vast isoform diversity of single-cell libraries and enables the accurate measure of differential isoform expression across distinct cell types in peripheral blood mononuclear cells. Furthermore, SBX coupled with CUDLL efficiently resolves T-cell and B-cell receptor clonotypes directly from whole-transcriptome libraries without the need for VDJ-specific target enrichment. Ultimately, this work establishes SBX and the AXELIOS 1 as a transformative platform for high-scale single-cell isoform sequencing.

genetics↗

bioRxiv2026

Integrating Long-Read Structural Variant Analysis with single-nucleus RNA-seq to Elucidate Gene Expression Effects in Disease

Structural variants (SVs) are a major source of genetic diversity, yet how they impact cell types in complex brain diseases remains largely unexplored, partially due to limitations of short-read sequencing. Here, we addressed this fundamental question in Parkinsons disease (PD). generating long-read whole-genome sequencing (WGS) data for 100 post-mortem brain samples from a PD cohort, constructing a high-confidence catalog of 74,552 SVs. To resolve their functional impact, we integrated single-nucleus RNA-sequencing data from two brain regions from the same samples and focused functional analyses on SVs proximal to genes previously nominated as cis-regulated, potential causal targets of PD-associated GWAS loci. Using expression quantitative trait locus and allele-specific expression analyses, we uncovered SVs significantly associated with expression in specific cell types as well as effects shared across cell types. This study demonstrates the power of uniting long-read WGS with transcriptomics to uncover SVs underlying complex disease architecture with cell type resolution.

genomics↗

bioRxiv2026

Accurate strand-specific long-read transcript isoform discovery and quantification at bulk, single-cell, and single-nucleus resolution

Recent advances in long-read transcriptome sequencing enable high-throughput profiling of full-length RNA isoforms in bulk, single-cell, and single-nucleus samples. However, long-read datasets typically contain a mixture of complete and partial transcripts, leading to pervasive ambiguity in read-to-isoform assignment and complicating accurate isoform identification and quantification, particularly in the absence of reliable reference annotations. These challenges are further amplified in single-cell and single-nucleus samples, where coverage is sparse and transcriptional heterogeneity is high. Here, we present the Long Read Alignment Assembler (LRAA), a unified and versatile computational framework for isoform identification and quantification from long-read RNA sequencing data across bulk, single-cell, and single-nucleus transcriptomic samples. LRAA combines splice-graph based structural modeling with expectation maximization based optimization to probabilistically resolve ambiguous read assignments and improve isoform abundance estimation. The framework supports quantification-only, reference-guided, and fully reference-free (de novo) modes of analysis within a single methodological paradigm. We benchmarked LRAA using both simulated and genuine long-read datasets spanning sequencing standards and whole transcriptomes. Central to this evaluation is a novel benchmarking strategy based on Multiplexed Overexpression of Regulatory Factors (MORFs), which provides biologically expressed, barcoded isoforms with unambiguous read-level ground truth. Across all benchmarks, including MORFs, synthetic spike-ins, and whole-transcriptome datasets, LRAA consistently outperformed state-of-the-art methods in isoform identification accuracy, sensitivity, and expression quantification. Finally, we demonstrate the biological utility of LRAA by resolving cell-type-specific isoform usage across peripheral blood immune cell populations and by detecting a pathogenic cryptic isoform of STMN2 with associated transcriptional changes in single-nucleus RNA-seq data from frontal cortex tissue of an individual with frontotemporal dementia (FTD). Together, these results establish LRAA as a robust and general solution for resolving transcript diversity in complex biological systems, from development to disease.

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↗

bioRxiv2025

Multi-platform evaluation and optimization of single-cell RNA isoform sequencing

Long-read transcriptomics enables isoform identification and quantification at single-cell resolution, but analysis is complicated by artifacts introduced during library preparation. We characterize the distinct artifact profiles of three popular single-cell platforms and demonstrate their impact on isoform identification. We introduce a new method for the identification of a wide range of artifacts in cDNA libraries and novel biochemical and bioinformatic strategies to significantly reduce their impact on downstream applications. Finally, we provide a comprehensive framework for RNA isoform sequencing analysis and interpretation as the field continues to develop.

genetics↗

bioRxiv2025

ralphi: a deep reinforcement learning framework for haplotype assembly

Haplotype assembly is the problem of reconstructing the combination of alleles on the maternally and paternally inherited chromosome copies. Individual haplotypes are essential to our understanding of how combinations of different variants impact phenotype. In this work, we focus on read-based haplotype assembly of individual diploid genomes, which reconstructs the two haplotypes directly from read alignments at variant loci. We introduce ralphi, a novel deep reinforcement learning framework for haplotype assembly, which integrates the representational power of deep learning with reinforcement learning to accurately partition read fragments into their respective haplotype sets. To set the reward objective for reinforcement learning, our approach uses the classic reduction of the problem to the maximum fragment cut formulation on fragment graphs, where nodes correspond to reads and edge weights capture the conflict or agreement of the reads at shared variant sites. We trained ralphi on a diverse dataset of fragment graph topologies derived from genomes in the 1000 Genomes Project. We show that ralphi consistently achieves lower error rates at comparable or longer haplotype block lengths over the state of the art for short and long ONT reads at varying coverage in standard human genome benchmarks. ralphi is available at https://github.com/PopicLab/ralphi.

bioinformatics↗

bioRxiv2025

A Murine Database of Structural Variants Enables the Genetic Architecture of a Spontaneous Murine Lymphoma to be Characterized

A more complete map of the pattern of genetic variation among inbred mouse strains is essential for characterizing the genetic architecture of the many available mouse genetic models of important biomedical traits. Although structural variants (SVs) are a major component of genetic variation, they have not been adequately characterized among inbred strains due to methodological limitations. To address this, we generated high-quality long-read sequencing data for 40 inbred strains; and designed a pipeline to optimally identify and validate different types of SVs. This generated a database for 40 inbred strains with 573,191SVs, which included 10,815 duplications and 2,115 inversions, which also has 70 million SNPs and 7.5 million insertions/deletions. Analysis of this SV database identified an SV that is one component of a bi-genic model for lymphoma susceptibility in SJL mice, which provides mechanistic insight into the genetic basis for susceptibility to murine (and potentially human) lymphomas.

genetics↗

bioRxiv2024

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

cancer biology↗

bioRxiv2024

Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets

Finding relatives within a study cohort is a necessary step in many genomic studies. However, when the cohort is distributed across multiple entities subject to data-sharing restrictions, performing this step often becomes infeasible. Developing a privacy-preserving solution for this task is challenging due to the significant burden of estimating kinship between all pairs of individuals across datasets. We introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across data silos. SF-Relate vastly reduces the number of individual pairs to compare while maintaining accurate detection through a novel locality-sensitive hashing approach. We assign individuals who are likely to be related together into buckets and then test relationships only between individuals in matching buckets across parties. To this end, we construct an effective hash function that captures identity-by-descent (IBD) segments in genetic sequences, which, along with a new bucketing strategy, enable accurate and practical private relative detection. To guarantee privacy, we introduce an efficient algorithm based on multiparty homomorphic encryption (MHE) to allow data holders to cooperatively compute the relatedness coefficients between individuals, and to further classify their degrees of relatedness, all without sharing any private data. We demonstrate the accuracy and practical runtimes of SF-Relate on the UK Biobank and All of Us datasets. On a dataset of 200K individuals split between two parties, SF-Relate detects 94.9% of third-degree relatives, and 99.9% of second-degree or closer relatives, within 15 hours of runtime. Our work enables secure identification of relatives across large-scale genomic datasets.

bioinformatics↗

bioRxiv2022

A deep learning framework for structural variant discovery and genotyping

Structural variants (SV) are a major driver of genetic diversity and disease in the human genome and their discovery is imperative to advances in precision medicine and our understanding of human genetics. Existing SV callers rely on hand-engineered features and heuristics to model SVs, which cannot easily scale to the vast diversity of SV types nor fully harness all the information available in sequencing datasets. Since deep neural networks can learn complex abstractions directly from the data, they offer a promising approach for general SV discovery. Here we propose an extensible deep learning framework, Cue, to call and genotype SVs. At a high level, Cue converts sequence alignments to multi-channel images that capture multiple SV-informative signals and uses a stacked hourglass convolutional neural network to predict the type, genotype, and genomic locus of the SVs captured in each image. We show that Cue outperforms the state of the art in the detection of five classes of SVs (including two types of complex SVs and subclonal SVs) on synthetic and real short-read data and that it can be easily extended to other sequencing platforms, such as long and linked read sequencing technologies, while achieving competitive performance. By design, Cue can also be automatically extended to support new SV classes: this versatility is crucial as novel SV types are discovered in ongoing population-scale sequencing initiatives.

bioinformatics↗

bioRxiv2021

High-throughput RNA isoform sequencing using programmable cDNA concatenation

Alternative splicing is a core biological process that enables profound and essential diversification of gene function. Short-read RNA sequencing approaches fail to resolve RNA isoforms and therefore primarily enable gene expression measurements - an isoform unaware representation of the transcriptome. Conversely, full-length RNA sequencing using long-read technologies are able to capture complete transcript isoforms, but their utility is deeply constrained due to throughput limitations. Here, we introduce MAS-ISO-seq, a technique for programmably concatenating cDNAs into single molecules optimal for long-read sequencing, boosting the throughput >15 fold to nearly 40 million cDNA reads per run on the Sequel IIe sequencer. We validated unambiguous isoform assignment with MAS-ISO-seq using a synthetic RNA isoform library and applied this approach to single-cell RNA sequencing of tumor-infiltrating T cells. Results demonstrated a >30 fold boosted discovery of differentially spliced genes and robust cell clustering, as well as canonical PTPRC splicing patterns across T cell subpopulations and the concerted expression of the associated hnRNPLL splicing factor. Methods such as MAS-ISO-seq will drive discovery of novel isoforms and the transition from gene expression to transcript isoform expression analyses.

genetics↗

bioRxiv2017

GATTACA: Lightweight Metagenomic Binning With Compact Indexing Of Kmer Counts And MinHash-based Panel Selection

We introduce GATTACA, a framework for rapid and accurate binning of metagenomic contigs from a single or multiple metagenomic samples into clusters associated with individual species. The clusters are computed using co-abundance profiles within a set of reference metagnomes; unlike previous methods, GATTACA estimates these profiles from k-mer counts stored in a highly compact index. On multiple synthetic and real benchmark datasets, GATTACA produces clusters that correspond to distinct bacterial species with an accuracy that matches earlier methods, while being up to 20x faster when the reference panel index can be computed offline and 6x faster for online co-abundance estimation. Leveraging the MinHash technique to quickly compare metagenomic samples, GATTACA also provides an efficient way to identify publicly-available metagenomic data that can be incorporated into the set of reference metagenomes to further improve binning accuracy. Thus, enabling easy indexing and reuse of publicly-available metagenomic datasets, GATTACA makes accurate metagenomic analyses accessible to a much wider range of researchers.

bioinformatics↗