Biology subjects

Ghareghani, M.

Publications and source records attributed to Ghareghani, M..

6 recordsLinked to original sources

bioRxiv2026

TandemTwister: Scalable genotyping and advanced visualization of tandem repeats

Tandem repeats are genomic regions consisting of consecutively repeated units with variable copy numbers and possible mutations. They are used in DNA fingerprinting and have been implicated in complex traits and genetic disorders, including neurodegenerative and developmental diseases. The vast and expanding number of tandem repeat loci in the human genome underscores the need for fast and scalable tools for accurate genotyping and visualization. An accurate tool for characterizing these variants is essential for understanding their functional impacts and associations with phenotypes. We developed TandemTwister, a novel algorithm implemented in C++, as a highly scalable and parallelized tool for tandem repeat copy number genotyping. Additionally, we created an interactive visualization tool to facilitate quick manual inspection, displaying exact motif occurrences, counts, and population information across haplotypes. TandemTwister demonstrates high accuracy and runtime efficiency for tandem repeat genotyping across all long-read sequencing technologies and assembled genomes. We evaluated the performance of TandemTwister in Ashkenazim trio on different sequencing technologies on a set of 1.2 million annotated tandem repeat regions. TandemTwister was the fastest and most accurate genotyping tool available for tandem repeats in comparison to the state of the art tools. For PacBio Hifi data as an example, TandemTwister was run in 17 minutes on 32 CPU cores resulting in 99.4% recall, 98.0% Mendelian consistency, and 94% sequence accuracy. We also showed a successful super-population clustering and examined inheritance patterns of tandem repeats and haplotype blocks in three trio sets. TandemTwister demonstrated its ability to detect pathogenic repeat expansions. We applied it in a cohort of 31 individuals with neurodegenerative and developmental disorders, successfully distinguishing healthy from pathogenic copy numbers.

genomics↗

bioRxiv2025

Accurate detection of pathogenic structural variants guided by multi-platform comparison

Structural variants (SVs) are a common cause of human diseases and greatly contribute to inter-individual variability. Their detection represents a significant challenge due to their diversity, size, and enrichment in repetitive regions. With the use of high quality long-read technologies, the majority of these challenges can now be mitigated. However, many downstream applications ranging from clinical diagnostics and genome-wide association studies to large-scale aggregation of population variants continue to rely on short-read-sequencing data due to its high through-put and cost-effectiveness. Thus, the challenges of short-read sequencing SV detection remain a constant and relevant obstacle. We created dicast, a machine-learning method to improve the identification of true-positive SVs from short-read sequencing using genomic context and alignment features. Dicast is driven by a novel and comprehensive benchmark call-set created through the combination of several sequencing technologies and rigorous manual curation. This benchmark set served as the basis for a systematic evaluation of five sequencing platforms and fifteen SV detection methods across different SV classes, sizes, and genomic contexts, enabling us to quantify the strengths and weaknesses of each and inform the training of our model. Leveraging these insights, dicast outperforms state-of-the-art short-read-based callers and consensus approaches, identifying considerably more true-positive variants while maintaining a high precision. We also demonstrate the methods applicability in diagnostic scenarios using putative pathogenic candidates in a limb malformation cohort, as well as known pathogenic variants in atrial fibrillation and neuromuscular disease cohorts. Dicast identifies all known pathogenic variants and 20% more manually confirmed candidate deletions than a consensus approach, and therefore can be used to reliably reduce time-consuming manual inspection in diagnostics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/655285v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1c1702aorg.highwire.dtl.DTLVardef@aaecddorg.highwire.dtl.DTLVardef@1a4de08org.highwire.dtl.DTLVardef@ed0811_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Phasing Diploid Genome Assembly Graphs with Single-Cell Strand Sequencing

Haplotype information is crucial for biomedical and population genetics research. However, current strategies to produce de-novo haplotype-resolved assemblies often require either difficult-to-acquire parental data or an intermediate haplotype-collapsed assembly. Here, we present Graphasing, a workflow which synthesizes the global phase signal of Strand-seq with assembly graph topology to produce chromosome-scale de-novo haplotypes for diploid genomes. Graphasing readily integrates with any assembly workflow that both outputs an assembly graph and has a haplotype assembly mode. Graphasing performs comparably to trio-phasing in contiguity, phasing accuracy, and assembly quality, outperforms Hi-C in phasing accuracy, and generates human assemblies with over 18 chromosome-spanning haplotypes.

bioinformatics↗

bioRxiv2020

De novo assembly of 64 haplotype-resolved human genomes of diverse ancestry and integrated analysis of structural variation

Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average contig N50: 26 Mbp) integrate all forms of genetic variation across even complex loci such as the major histocompatibility complex. We focus on 107,590 structural variants (SVs), of which 68% are inaccessible by short-read sequencing. We identify new SV hotspots (spanning megabases of gene-rich sequence), characterize 130 of the most active mobile element source elements, and find that 63% of all SVs arise by homology-mediated mechanisms--a twofold increase from previous studies. Our resource now enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1,525 expression quantitative trait loci (SV-eQTLs) as well as SV candidates for adaptive selection within the human population.

genetics↗

bioRxiv2019

A fully phased accurate assembly of an individual human genome

The prevailing genome assembly paradigm is to produce consensus sequences that "collapse" parental haplotypes into a consensus sequence. Here, we leverage the chromosome-wide phasing and scaffolding capabilities of single-cell strand sequencing (Strand-seq)1,2 and combine them with high-fidelity (HiFi) long sequencing reads3, in a novel reference-free workflow for diploid de novo genome assembly. Employing this strategy, we produce completely phased de novo genome assemblies separately for each haplotype of a single individual of Puerto Rican origin (HG00733) in the absence of parental data. The assemblies are accurate (QV > 40), highly contiguous (contig N50 > 25 Mbp) with low switch error rates (0.4%) providing fully phased single-nucleotide variants (SNVs), indels, and structural variants (SVs). A comparison of Oxford Nanopore and PacBio phased assemblies identifies 150 regions that are preferential sites of contig breaks irrespective of sequencing technology or phasing algorithms.

bioinformatics↗

bioRxiv2019

Single cell tri-channel-processing reveals structural variation landscapes and complex rearrangement processes

Structural variation (SV), where rearrangements delete, duplicate, invert or translocate DNA segments, is a major source of somatic cell variation. It can arise in rapid bursts, mediate genetic heterogenity, and dysregulate cancer-related pathways. The challenge to systematically discover SVs in single cells remains unsolved, with copy-neutral and complex variants typically escaping detection. We developed single cell tri-channel-processing (scTRIP), a computational framework that jointly integrates read depth, template strand and haplotype phase to comprehensively discover SVs in single cells. We surveyed SV landscapes of 565 single cell genomes, including transformed epithelial cells and patient-derived leukemic samples, and discovered abundant SV classes including inversions, translocations and large-scale genomic rearrangements mediating oncogenic dysregulation. We dissected the molecular karyotype of the leukemic samples and examined their clonal structure. Different from prior methods, scTRIP also enabled direct detection and discrimination of SV mutational processes in individual cells, including breakage-fusion-bridge cycles. scTRIP will facilitate studies of clonal evolution, genetic mosaicism and somatic SV formation, and could improve disease classification for precision medicine.

genomics↗