Biology subjects

Soylev, A.

Publications and source records attributed to Soylev, A..

8 recordsLinked to original sources

bioRxiv2026

Synteny-aware microbial pangenome graphs reveal blueprints of genomic variation

Pangenomics quantifies the conserved and variable gene repertoire among genomes, but popular implementations ignore gene synteny. Graph-based approaches incorporate both gene homology and synteny, but become difficult to interpret due to pervasive rearrangements. Here we present network-pruning and graph-layout algorithms that enable interactive, synteny-aware quantification and visualization of gene conservation and variability. Applied to 29 genomes of the marine genus Undatipelagibacter (formerly SAR11 subclade Ia.3.VI), we find that genomic variability forms not a few hypervariable islands against a static backbone but a structured continuum, whose variable regions differ in scale, topology, function, and evolutionary character. Genome variation spans from ancient, specialized regions of hundreds of genes whose propensity to vary is conserved across genera, to single hypervariable genes shaped by epistatic co-selection with partners dispersed genome-wide, and shows that chromosomal context carries evolutionary information synteny-unaware pangenomics cannot capture, and some evolutionary processes act on entire functional subsystems throughout a pangenome.

microbiology↗

bioRxiv2024

gaftools: a toolkit for analyzing and manipulating pangenome alignments

Linear reference genomes are ubiquitously used in genomics research, despite known biases associated with their use. In recent years, there has a been a shift towards using graph-based reference genomes to address some of these biases, which has required new algorithms and file formats to be developed. This has created the need to develop new tools capable of utilizing these formats and performing operations similar to those carried out by traditional methods.In this paper we present "gaftools", a multi-purpose tool that introduces several utilities for processing graph alignments in GAF format. Gaftools enables users to index and sort alignments, with graph ordering serving as a necessary step for the sorting process. Additionally it allows users to view subsets of alignments and perform realignment using the wavefront alignment algorithm, among other useful features. Many of these functionalities are inspired by SAMtools, which provide similar operations for linear genomes, while gaftools adapts and extends them for pangenomes. Availabilitygaftools is available under MIT license at https://github.com/marschall-lab/gaftools.

bioinformatics↗

bioRxiv2024

Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project

Structural variants (SVs) contribute significantly to human genetic diversity and disease1-4. Previously, SVs have remained incompletely resolved by population genomics, with short-read sequencing facing limitations in capturing the whole spectrum of SVs at nucleotide resolution5-7. Here we leveraged nanopore sequencing8 to construct an intermediate coverage resource of 1,019 long-read genomes sampled within 26 human populations from the 1000 Genomes Project. By integrating linear and graph-based approaches for SV analysis via pangenome graph-augmentation, we uncover 167,291 sequence-resolved SVs in these samples, considerably advancing SV characterization compared to population-wide short-read sequencing studies3,4. Our analysis details diverse SV classes--deletions, duplications, insertions, and inversions--at population-scale. LINE-1 and SVA retrotransposition activities frequently mediate transductions9,10 of unique sequences, with both mobile element classes transducing sequences at either the 3'- or 5'-end, depending on the source element locus. Furthermore, analyses of SV breakpoint junctions suggest a continuum of homology-mediated rearrangement processes are integral to SV formation, and highlight evidence for SV recurrence involving repeat sequences. Our open-access dataset underscores the transformative impact of long-read sequencing in advancing the characterisation of polymorphic genomic architectures, and provides a resource for guiding variant prioritisation in future long-read sequencing-based disease studies.

genomics↗

bioRxiv2024

SVarp: pangenome-based structural variant discovery

The linear human reference genome that we use today does not represent the haplotypic diversity of the global human population. This raises bias in genomic read alignment and limits our ability to call large structural variations (SV), especially at highly polymorphic loci. Thus, many SV alleles remain unresolved. Recent efforts to transition to a graph-based reference genome resulted in the generation of the first draft human pangenome reference, but tools to call SVs relative to the pangenome reference are presently lacking. In this study, we present the SVarp algorithm, aiming to discover haplotype resolved SVs on top of a pangenome reference using long sequencing reads. SVarp outputs local assemblies of SV alleles, termed svtigs, instead of a VCF file of SV breakpoints, which we propose as a general exchange format allowing for flexible downstream analyses. In order to assess the accuracy of svtigs, we used simulated and real human genomes. Simulations allowed us to make exact breakpoint comparisons against the true callsets. We observed [~]96% recall with deletions, insertions and duplications larger than 1,000bp, showing that SVarp can reliably detect genomic structural variants not yet represented in the graph. On the other hand, we compared SVarp output for ONT sequencing data at 20X coverage against independent genome assemblies of the same samples and found that [~]82% of our svtig predictions are validated by the assemblies by a match with more than 85% sequence identity. SVarp was implemented using C++ and its source code is available at https://github.com/asylvz/SVarp under MIT license.

bioinformatics↗

bioRxiv2021

CONGA: Copy number variation genotyping in ancient genomes and low-coverage sequencing data

AO_SCPLOWBSTRACTC_SCPLOWTo date, ancient genome analyses have been largely confined to the study of single nucleotide polymorphisms (SNPs). Copy number variants (CNVs) are a major contributor of disease and of evolutionary adaptation, but identifying CNVs in ancient shotgun-sequenced genomes is hampered by typical low coverage (<1 x) and short fragments (<80 bps), precluding standard CNV detection software to be effectively applied to ancient genomes. Here we present CONGA, tailored for genotyping CNVs at low coverage. Simulations and down-sampling experiments suggest that CONGA can genotype deletions >1 kbps with F-scores >0.75 at [≥]1x, and distinguish between heterozygous and homozygous states. We applied CONGA to genotype 10,002 outgroup-ascertained deletions across a heterogenous set of 71 ancient human genomes spanning the last 50,000 years, produced using variable experimental protocols. A fraction of these (21/71) display divergent deletion profiles unrelated to their population origin, but attributable to technical factors such as coverage and read length. The majority of the sample (50/71), despite originating from nine different laboratories and having coverages 0.44x-26x (median 4x) and read lengths 52-121 bp (median 69), exhibit coherent deletion frequencies. Across these 50 genomes, inter-individual genetic diversity measured using SNPs and CONGA-genotyped deletions are strongly correlated. CONGA-genotyped deletions also display purifying selection signatures, as expected. CONGA thus paves the way for systematic CNV analyses in ancient genomes, despite the technical challenges posed by low and variable genome coverage.

genomics↗

bioRxiv2021

Adaptive ecological processes and metabolic independence drive microbial colonization and resilience in the human gut

Changes in microbial community composition as a function of human health and disease states have sparked remarkable interest in the human gut microbiome. However, establishing reproducible insights into the determinants of microbial succession in disease has been a formidable challenge. Here we use fecal microbiota transplantation (FMT) as an in natura experimental model to investigate the association between metabolic independence and resilience in stressed gut environments. Our genome-resolved metagenomics survey suggests that FMT serves as an environmental filter that favors populations with higher metabolic independence, the genomes of which encode complete metabolic modules to synthesize critical metabolites, including amino acids, nucleotides, and vitamins. Interestingly, we observe higher completion of the same biosynthetic pathways in microbes enriched in IBD patients. These observations suggest a general mechanism that underlies changes in diversity in perturbed gut environments, and reveal taxon-independent markers of dysbiosis that may explain why widespread yet typically low abundance members of healthy gut microbiomes can dominate under inflammatory conditions without any causal association with disease.

microbiology↗

bioRxiv2019

A robust benchmark for germline structural variant detection

New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. Translating these methods to routine research and clinical practice requires robust benchmark sets. We developed the first benchmark set for identification of both false negative and false positive germline SVs, which complements recent efforts emphasizing increasingly comprehensive characterization of SVs. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods, both alignment- and de novo assembly-based, from short-, linked-, and long-read sequencing, as well as optical and electronic mapping. The final benchmark set contains 12745 isolated, sequence-resolved insertion and deletion calls [≥]50 base pairs (bp) discovered by at least 2 technologies or 5 callsets, genotyped as heterozygous or homozygous variants by long reads. The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.66 Gbp and 9641 SVs supported by at least one diploid assembly. Support for SVs was assessed using svviz with short-, linked-, and long-read sequence data. In general, there was strong support from multiple technologies for the benchmark SVs, with 90 % of the Tier 1 SVs having support in reads from more than one technology. The Mendelian genotype error rate was 0.3 %, and genotype concordance with manual curation was >98.7 %. We demonstrate the utility of the benchmark set by showing it reliably identifies both false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.

genomics↗

bioRxiv2018

Discovery of tandem and interspersed segmental duplications using high throughput sequencing

MotivationSeveral algorithms have been developed that use high throughput sequencing technology to characterize structural variations. Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions, and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions; and duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants. ResultsWe developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing data sets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real data sets. In the simulation experiments, using a 30x coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state of the art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (less than 5% for the top 50 predictions). AvailabilityTARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/ Contactfhormozd@ucdavis.edu and calkan@cs.bilkent.edu.tr

bioinformatics↗