Biology subjects

Sarvar, A.

Publications and source records attributed to Sarvar, A..

4 recordsLinked to original sources

bioRxiv2026

G4SCOPE identifies sequence features and epigenetic contexts of chromatin G-quadruplexes

Cancer-associated epigenetic remodeling involves a complex interplay between DNA secondary structures and covalent cytosine modifications, yet predicting endogenously folded, chromatin-constrained G-quadruplex (G4) dynamics remains challenging. Here, we develop G4SCOPE, a multi-task deep learning framework that models chromatin-associated G4 occupancy and reconstructs quantitative high-resolution CUT&Tag profiles directly from primary DNA sequence alone. We initially trained G4SCOPE on genotype-stratified G4 enriched cut&tag landscapes generated across an isogenic series of human breast epithelial cells (WT, TP53-/-, TP53-/-;BRCA1-/-, and TP53-/-;BRCA2-/-). G4SCOPE integrates convolutional motif learning with transformer self-attention to decode long-range sequence contexts. The model robustly distinguishes chromatin-associated G4s from GC-matched loci with intrinsic in vitro folding potential, generalizes across distinct breast lineages, and transfers accurately to an independent K562 cellular context. Strikingly, genotype-resolved mapping reveals a profound divergence in genome maintenance states: BRCA1 deficiency indicated a global inflation of chromatin-embedded G4 density, whereas BRCA2 loss was associated with systemic, chromosome-wide structural depletion. Furthermore, unsupervised clustering of model-derived latent sequence representations identified distinct biological G4 subclasses that occupy unique regulatory class genomic environments associated with distinct local 5-methylcytosine and 5-hydroxymethylcytosine landscapes. Together, our framework decodes the primary sequence grammar governing cell-based G4 occupancy and demonstrates that sequence-defined G4 topologies harbor intrinsic and distinct local epigenetic features.

bioinformatics↗

bioRxiv2026

Chromosomal mutational signatures of DNA damaging agents at single cell resolution

The chromosomal-scale mutational spectrum of small molecules that interact with DNA has been hard to study at scale, as mutational events are distributed in location and occur in parallel in different cells. Here, we present a framework that pairs phylogenetic ancestry reconstruction with mutational signature decomposition to characterise recent, cell-private copy number alteration (CNA) mutational patterns at single-cell resolution. We used this framework to characterise the cell-wise mutational spectrum of contemporaneous CNAs generated by double-strand-break-inducing chemotherapeutic drugs. We demonstrate that platinum salts, G-quadruplex stabilizers and topoisomerase II inhibitors, although mechanistically distinct, converge on a mutational signature dominated by telomere-bounded copy-number gains and losses. This signature is observed in different genetic backgrounds and in vivo in drug-treated patient-derived xenografts. We also observe a high rate of endogenous telomere-bounded mutational foreground in BRCA1 deficient cells. We show that the single cell genome derived signature exposures are drug dose-dependent, and use this to identify the decay of mutational load after drug withdrawal. We observe that both cisplatin and a G4 binder molecule (CX5461) exhibit foreground mutational signature persistence for at least 3 weeks after drug withdrawal, suggesting that residual effects of exposure may last longer than anticipated. Finally, extending the framework to serially drug-treated patient-derived xenograft (PDX) models, we show that telomere-bounded CNA signature exposure is associated with tumoural response to drug, consistent with loss of mutational activity on the genome after acquired resistance emerges. Together, our results show that our framework applied on scWGS identifies contemporaneous chromosomal mutation patterns induced by small molecules in human tissues.

cancer biology↗

bioRxiv2026

The UV potentiated mutational signature of clinical stage G-quadruplex binder CX5461.

Drug-related UVA-induced photoreactions have been reported for several therapeutic compounds, including fluoroquinolones. CX5461 is a clinically relevant quinolone-derived anti-cancer small molecule with documented UVA-sensitising activity. Here we compared, by bulk and clonal whole genome sequencing (WGS) under light-protected conditions, the mutational signatures in human retinal pigment epithelial cells (RPE1) exposed to UVA, CX5461, or co-exposed to UVA and CX5461. Treatment with CX5461 or UVA alone resulted in a low SNV burden and background-like mutational profiles. In contrast, bulk sequencing of human cells co-exposed to UVA and CX5461 had a markedly higher SNV burden characterized by T>A and T>C substitutions. Furthermore, single-cell clonal expansion and sequencing of CX5461 alone, UVA alone or CX5461+UVA treatments confirmed that the pattern was only observed when cells were exposed to both UVA and CX5461. The CX5461+UVA-associated SNV signature we report arises only when CX5461-treated cells are exposed to UVA, and is not observed when CX5461-treated cells are shielded from light. We do not observe strong single base mutagenic activity of CX5461 alone, under light protected conditions. Our data emphasise the need for appropriate controls and light-exposure precautions when studying base mutagenesis activity of known photosensitiser molecules.

cancer biology↗

bioRxiv2025

Genome misassembly detection using Stash: A data structure based on stochastic tile hashing

MotivationAnalyzing large data from high-throughput sequencing technologies presents significant challenges in terms of memory and computational requirements. It is crucial to develop efficient data structures and computational methods that handle sequencing information. These challenges impact bioinformatics studies, including de novo genome assembly which serves as the foundation of genomics. Issues like read errors or limitations of heuristic decisions in assembly algorithms lead to genome misassemblies and inaccurate genomic representations, compromising the quality of downstream analyses. Hence, de novo assemblies can benefit from misassembly detection and correction, to produce a more optimal assembly. ResultsWe present Stash, a novel hash-based data structure designed for storing and querying large sequencing data. For an input sequence, Stash uses sliding windows of spaced seed patterns to extract and hash k-mers. The hash values combined with the sequence ID determine the value stored in Stash. A filled Stash can be used to query whether two genomic regions are covered by the same set of reads. This can be used in genome misassembly detection. We demonstrate the effectiveness of Stash in detecting misassemblies in human genome assemblies generated by Flye and Shasta, using Pacbio HiFi reads from the human cell line NA24385. We observed that scaffolding Stash-cut assemblies reduces 7.6% and 3.4% of misassemblies in the Flye and Shasta assemblies, respectively. This is accomplished in 310 minutes utilizing 8 GB of memory. Stash is comparable to alternative long read misassembly correction methods and can result in superior assemblies compared to the baseline. AvailabilityStash is implemented in C++ as a static library and is available here: https://github.com/bcgsc/Stash. Contactasarvar@bccrc.ca or inanc.birol@ubc.ca

bioinformatics↗