Biology subjects

Vrcek, L.

Publications and source records attributed to Vrcek, L..

3 recordsLinked to original sources

bioRxiv2025

DipGNNome: Diploid de novo genome assembly with geometric deep learning and beam-search

De novo genome assembly remains a central challenge in computational biology, particularly for diploid genomes where maternal and paternal haplotypes must be accurately resolved. Existing assemblers achieve impressive results through carefully designed heuristics, yet modern deep learning methods remain largely unexplored in the diploid setting. We present DipGNNome, the first deep learning-based framework for diploid de novo genome assembly. Our approach formulates assembly as an edge classification and traversal problem on haplotype-aware assembly graphs, training graph neural networks (GNNs) to guide contig construction. To enable this, we establish a novel pipeline for generating diploid graphs with ground-truth edge labels, providing the first systematic way to produce training data for machine learning models in this domain. This framework creates a foundation for applying and extending graph-based deep learning to diploid assembly. DipGNNome creates assemblies comparable to state-of-the-art and demonstrates the feasibility of deep learning for diploid assembly and introduces a paradigm that bridges algorithmic genomics with graph representation learning. Our code, dataset and trained model is openly available at https://github.com/lbcb-sci/DipGNNome.

genomics↗

bioRxiv2025

A Complete Telomere-to-Telomere Diploid Reference Genome for Indian Population

Human reference genomes have been instrumental in advancing genomic and biomedical research, but South and Southeast Asian populations are underrepresented, despite accounting for a large proportion of world population. As a part of effort on generating reference genomes for these populations, we present the first gapless, telomere-to-telomere (T2T) diploid genome assembly created by using a trio sample set of Indian ancestry (I002C), with NG50 of 154.89 Mb and 146.27 Mb for the maternal and paternal haplotypes, including the fully assembled rDNA array for the maternal chromosome 21 and Y chromosome. With the Merqury QVs of 82.05, 83.08 and 82.64 for the maternal, paternal and haploid assemblies respectively, I002C represents the highest-quality human genome assembled in both diploid and haploid forms to date. Compared to CHM13, the I002C genome displays substantial sequence diversity, resulting in 14,943 structural variants, including 3,236 novel variants absent from public databases. Analysis of trio-phased haplotypes further revealed elevated inter-haplotype divergence within centromeric and subtelomeric regions, along with identification of differentially methylated regions (DMRs) as candidates for novel imprinting loci. As a result of substantial SVs between them, I002C is a more suitable reference than CHM13 for the genomic analysis of South Asian samples with less reference bias and better performance in mapping and variant calling, particularly for long read sequencing data. As the first high-quality T2T diploid reference genome for Indian, the largest worlds population, I002C contributes to the growing set of population-specific reference genomes and helps to overcome a significant gap in human genome diversity.

genomics↗

bioRxiv2024

Geometric deep learning framework for de novo genome assembly

The critical stage of every de novo genome assembler is identifying paths in assembly graphs that correspond to the reconstructed genomic sequences. The existing algorithmic methods struggle with this, primarily due to repetitive regions causing complex graph tangles, leading to fragmented assemblies. Here, we introduce GNNome, a framework for path identification based on geometric deep learning that enables training models on assembly graphs without relying on existing assembly strategies. By leveraging symmetries inherent to the problem, GNNome reconstructs assemblies with similar or superior contiguity compared to the state-of-the-art tools across several species, sequenced with PacBio HiFi or Oxford Nanopore. With every new genome assembled telomere-to-telomere, the amount of reliable training data at our disposal increases. Combining the straightforward generation of abundant simulated data for diverse genomic structures with the AI approach makes the proposed framework a plausible cornerstone for future work on reconstructing complex genomes with different ploidy and aneuploidy degrees. To facilitate such developments, we make the framework and the best-performing model publicly available, provided as a tool that can directly be used to assemble new haploid genomes.

bioinformatics↗