Biology subjects

Lipovac, J.

Publications and source records attributed to Lipovac, J..

6 recordsLinked to original sources

bioRxiv2026

Sourmash accurately profiles strains from long-read metagenomes preserving gene content

Background. Metagenomic strain-level profiling remains challenging because closely related bacterial strains often share more than 99% average nucleotide identity (ANI). However, they can differ substantially in gene content and functional potential. Sourmash uses k-mer sketches with FracMinHash to compare sequencing reads against reference databases, which makes it an interesting approach for strain-level profiling. Because it compares sketches rather than full alignments, it enables fast searches against large reference databases. However, its ability to recover biologically relevant strains and preserve community gene content has not been specifically assessed. Here, we evaluated the performance of sourmash for strain-level identification abundance, functional gene content recovery, and abundance estimations using five mock communities of increasing complexity with known composition. Results. We first assessed whether nucleotide similarity reflects functional similarity between strains. Gene content similarity was generally consistent with ANI but revealed important divergences between closely related strains. For example, Escherichia coli strains sharing more than 98% ANI showed gene content similarities ranging from 20% to 57%, highlighting that nucleotide similarity alone is insufficient to assess functional proximity and consequently the accuracy of strain identification. Above sufficient genome coverage, sourmash identified strains either exactly or with closely related strains sharing highly conserved gene content. These closely related strains provided a biologically relevant representation of the expected functional potential of the mock communities. Gene content recovery was highly accurate, with F1 scores ranging from 0.942 to 0.998 across the five mock communities. Genome abundances estimated against sourmash-identified genomes closely matched the expected community composition: Pearson correlation coefficients ranged from 0.823 to 1 at strain level and from 0.978 to 1 at species level. Conclusions. Our results show that sourmash provides a biologically relevant approach for strain-level characterisation in microbial ecosystems. Even when the exact strain could not be identified, closely related strains retained most of the expected gene content and abundance estimations were accurate, supporting the use of sourmash for downstream functional profiling or metabolic network reconstruction.

bioinformatics↗

bioRxiv2026

Using Mapping-Profiles to Refine Strain-Level Metagenomic Classification

Metagenomic classification at the strain level remains challenging due to high sequence similarity among closely related genomes, which leads to ambiguous read mappings and frequent false-positive strain detections. Reducing such errors improves the reliability of strain-level analyses, which is critical for applications such as pathogen detection. We introduce StrainRefine, a post-mapping refinement method that analyzes read-reference mapping profiles to resolve ambiguous assignments among highly similar genomes. The method represents candidate reference genomes using binary profiles that capture read-support patterns and measures similarity between references based on profile overlap. The method clusters references based on similar mapping profiles, filters weakly supported genomes, and reassigns reads to representative references, reducing redundant reporting of near-identical strains. StrainRefine substantially reduces false-positive strain detections while preserving recall and improving agreement between predicted and true abundance profiles. On large-scale metagenomic datasets, it achieves a substantially improved precision-recall balance compared to existing mapping-based approaches, with the standalone method obtaining the highest read-level classification accuracy on the most complex evaluated dataset. Unlike many strain-level tools designed for individual species, StrainRefine operates without prior assumptions about sample composition or curated species-specific reference collections, while still achieving comparable performance in single-species settings on species-specific reference databases. These results highlight mapping-profile similarity as an effective signal for improving strain-level metagenomic classification.

bioinformatics↗

bioRxiv2025

A complete diploid human genome benchmark for personalized genomics

Human genome resequencing typically involves mapping reads to a reference genome to call variants; however, this approach suffers from both technical and reference biases, leaving many duplicated and structurally polymorphic regions of the genome unmapped. Consequently, existing variant benchmarks, generated by the same methods, fail to assess these complex regions. To address this limitation, we present a telomere-to-telomere genome benchmark that achieves near-perfect accuracy (i.e. no detectable errors) across 99.4% of the complete, diploid HG002 genome. This benchmark adds 701.4 Mb of autosomal sequence and both sex chromosomes (216.8 Mb), totaling 15.3% of the genome that was absent from prior benchmarks. We also provide a diploid annotation of genes, transposable elements, segmental duplications, and satellite repeats, including 39,144 protein-coding genes across both haplotypes. To facilitate application of the benchmark, we developed tools for measuring the accuracy of sequencing reads, phased variant call sets, and genome assemblies against a diploid reference. Genome-wide analyses show that state-of-the-art de novo assembly methods resolve 2-7% more sequence and outperform variant calling accuracy by an order of magnitude, yielding just one error per 100 kb across 99.9% of the benchmark regions. Adoption of genome-based benchmarking is expected to accelerate the development of cost-effective methods for complete genome sequencing, expanding the reach of genomic medicine to the entire genome and enabling a new era of personalized genomics.

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↗

bioRxiv2025

MADRe: Strain-Level Metagenomic Classification Through Assembly-Driven Database Reduction

AbstractStrain-level metagenomic classification is essential for understanding microbial diversity and functional potential, but remains challenging, par- ticularly in the absence of prior knowledge about the composition of the sample. In this paper we present MADRe, a modular and scalable pipeline for long-read strain-level metagenomic classification, enhanced with Metagenome Assembly-Driven Database Reduction. MADRe com- bines long-read metagenome assembly, contig-to-reference mapping reas- signment based on an expectation-maximization algorithm for database reduction, and probabilistic read mapping reassignment to achieve sensi- tive and precise classification. We extensively evaluated MADRe on sim- ulated datasets, mock communities, and a real anaerobic digester sludge metagenome, demonstrating that it consistently outperforms existing tools by achieving higher precision with reduced false positives. MADRes de- sign allows users to apply either the database reduction or read classi- fication step individually. Using only the read classification step shows results on par with other tested tools. MADRe is open source and pub- licly available at https://github.com/lbcb-sci/MADRe.

bioinformatics↗

bioRxiv2024

The Hitchhiker's Guide to Sequencing Data Types and Volumes for Population-Scale Pangenome Construction

Long-read (LR) technologies from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) have transformed genomics research by providing diverse data types like HiFi, Duplex, and ultra-long ONT (ULONT). Despite recent strides in achieving haplotype-phased gapless genome assemblies using long-read technologies, concerns persist regarding the representation of genetic diversity, prompting the development of pangenome references. However, pangenome studies face challenges related to data types, volumes, and cost considerations for each assembled genome, while striving to maintain sensitivity. The absence of comprehensive guidance on optimal data selection exacerbates these challenges. To fill this gap, our study evaluates available data types, their significance, and the required volumes for robust de novo assembly in population-level pangenome projects. The results show that achieving chromosome-level haplotype-resolved assembly requires 20x high-quality long reads (HQLR) such as PacBio HiFi or ONT duplex, combined with 15-20x of ULONT per haplotype and 30x of long-range data such as Omni-C. High-quality long reads from both platforms yield assemblies with comparable contiguity, with HiFi excelling in NG50 and phasing accuracies, while usage of duplex generates more T2T contigs. As Long-Read Technologies advance, our study reevaluates recommended data types and volumes, providing practical guidelines for selecting sequencing platforms and coverage. These insights aim to be vital to the pangenome research community, contributing to their efforts and pushing genomic studies with broader impacts.

bioinformatics↗