Biology subjects

Ashyralyyev, A.

Publications and source records attributed to Ashyralyyev, A..

3 recordsLinked to original sources

bioRxiv2026

Somatic structural variation discovery on cancer pangenome graphs

Somatic structural variants (SVs) drive tumorigenesis across most cancers, yet low variant allele frequencies in subclonal populations often necessitate multi-sample sequencing per patient. Existing somatic SV callers rely on whole-genome read alignments against a single linear reference, incurring high computational costs and reference bias. We present JANUS, which discovers somatic SVs directly within a personal pangenome, a colored de Bruijn-like graph of locally consistent parsing (LCP) cores built from matched normal and tumor PacBio HiFi reads. JANUS identifies candidate SV sites traversed exclusively by tumor reads without prior alignment, mapping only short read snippets surrounding each site. Across five tumor-normal pairs in the CASTLE panel, JANUS leads the 18-caller consensus with a mean F1 score of 0.887 (next-best caller: 0.858), widening its lead under low-signal regimes (0.489 versus 0.362 at 5x; 0.683 versus 0.520 at 20% purity). End to end, JANUS analyzes a tumor-normal pair in ~11 minutes on average with 16 threads, which is ~24x faster than read alignment alone on the same machine. In a 20-tumor patient simulation, joint graph calling outperforms independent pairwise runs in speed and accuracy, particularly at low depth. By processing multi-sample cohorts jointly and maintaining sensitivity at low coverage and purity, JANUS provides an efficient, reference-unbiased framework for cancer genomics. JANUS is available at https://github.com/BilkentCompGen/janus.

genomics↗

bioRxiv2026

GenCore: Genomic distance estimation using Locally Consistent Parsing

In the era of exponential data generation, a fast, consistent, and efficient string processing technique is necessary to represent extensive genomic data. One of the earliest string processing techniques, predating MinHash and minimizer-based sketching, is Locally Consistent Parsing (LCP). This technique partitions an input string and identifies short, exactly occurring substrings called cores, which collectively cover the input string while maintaining Partition and Labeling Consistency. The iterative application of LCP yields progressively longer cores in a compressed format, thereby substantially enhancing the efficiency of genomic sequence representation and subsequent downstream analysis. We have previously developed Lcptools as the first iterative implementation of LCP for the DNA alphabet and demonstrated its effectiveness in identifying cores with minimal collisions. Here, we introduce GO_SCPLOWENC_SCPLOWCO_SCPLOWOREC_SCPLOW, a computational method that leverages LCP cores for the first time to sketch and estimate genomic distances for closely related large genomes, and successfully reconstruct simulated progression trees. GO_SCPLOWENC_SCPLOWCO_SCPLOWOREC_SCPLOW also successfully recapitulates primate phylogeny using both telomere-totelomere (T2T) assemblies and the PacBio HiFi reads for assembly-free comparisons. AvailabilityGO_SCPLOWENC_SCPLOWCO_SCPLOWOREC_SCPLOW is available at https://github.com/BilkentCompGen/gencore

bioinformatics↗

bioRxiv2023

Dynamic proximity interaction profiling suggests that YPEL2 is involved in cellular stress surveillance

YPEL2 is a member of the evolutionarily conserved YPEL family involved in cellular proliferation, mobility, differentiation as well as senescence and death. However, the mechanism by which YPEL2, or YPEL proteins, mediates its effects is yet unknown. Proteins perform their functions in a network of proteins whose identities, amounts, and compositions change spatiotemporally in a lineage-specific manner in response to internal and external stimuli. We here explored interaction partners of YPEL2 by using dynamic TurboID-coupled mass spectrometry analyses to infer a function for the protein. Our results using inducible transgene expressions in COS7 cells indicate that proximity interaction partners of YPEL2 are largely involved in RNA and mRNA metabolic processes, ribonucleoprotein complex biogenesis, regulation of gene silencing by miRNA, and cellular responses to stress. We showed that YPEL2 interacts with RNA binding protein ELAVL1 and selective autophagy receptor SQSTM1. We also found that YPEL2 participates in events associated with the formation/disassembly of stress granules in response to sodium arsenite an oxidative stress inducer. Establishing a point of departure in the delineation of structural/functional features of YPEL2, our results suggest that YPEL2 may be involved in stress surveillance mechanisms.

cell biology↗