Biology subjects

Sedlazeck, F. J.

Publications and source records attributed to Sedlazeck, F. J..

At least 19 recordsLinked to original sources

bioRxiv2026

IsoMatch enables population-scale transcript integration and reveals reference-dependent isoform novelty

Transcriptome analyses increasingly generate large collections of transcript models that must be integrated, compared and interpreted across samples and reference annotations. Transcript novelty depends on structural equivalence criteria and reference annotation. Here we present IsoMatch, a scalable framework that harmonizes transcript merging and reference-based classification through explicit, configurable structural criteria. IsoMatch preserves source-GTF and original-transcript traceability throughout the workflow, allowing every merged and classified model to be traced back to its contributing inputs. Benchmarking showed that IsoMatch preserves distinct transcript models during integration through configurable matching parameters for both splice junction chains and transcript ends. With its index-based architecture, IsoMatch merged 163 ENCODE transcript catalogs and classified 7.9 million transcript models with peak memory usage of 0.49 GB and 1.1 GB, respectively. Integrating GENCODE, RefSeq and CHESS revealed that 71.2% of transcript groups remained annotation-specific even without terminal constraints. For 4.84 million merged transcript models from 119 long-read transcriptomes from the 1000 Genomes Project, the integrated reference increased the full-splice match proportion from 12.0-13.6% with individual annotations to 18.0%. IsoMatch enables scalable transcript integration and classification with explicit structural criteria and preserved traceability, demonstrating how reference choice shapes estimates of transcript novelty.

bioinformatics↗

bioRxiv2026

The Genome In A Bottle HG002 assembly-based variant benchmark set enables comprehensive benchmarking of small and structural variants.

High-accuracy, high-resolution benchmark data are fundamental to improving the accuracy and generalizability of sequencing technologies and associated algorithms. Most existing benchmarks for DNA sequencing rely on mapping to a single linear reference, omitting inaccessible and highly repetitive complex genomic regions. Here, we present the Genome in a Bottle (GIAB) Consortium's HG002 v5.0q benchmark set, a more comprehensive variant benchmark derived from a fully phased, highly curated, and accurate diploid assembly of HG002. Our approach leverages recent advances in long-read sequencing and assembly to provide confident small- and structural-variant calls across previously inaccessible regions, including segmental duplications, homopolymers, tandem repeats, and complex immune loci. The v5.0 benchmark significantly expands the callable genome relative to v4.2.1, adding x200 Mbp of sequence and increasing variant detection by 18 % for small variants and 3-fold for structural variants compared to previous benchmarks. The benchmark regions include more variants in repetitive regions, including 1.2 x segmental duplications, 1.8 x tandem repeats, and 2.0 x homopolymers, compared to v4.2.1. Separate and combined small- and structural-variant benchmarks are available for three versions of the human reference genome (GRCh37, GRCh38, and CHM13). We validate the reliability of this benchmark set by comparing multiple sequencing technologies and variant calling pipelines and curating differences. The v5.0q benchmark provides a critical resource for the development and validation of next-generation sequencing technologies and analysis methods.

genomics↗

bioRxiv2026

Isocall enables scalable transcript identification from long-read RNA-sequencing data

Long-read RNA sequencing directly resolves the full structures of RNA transcripts. Advances in throughput now enable the generation of deeply sequenced cohorts of hundreds of samples, making joint transcript discovery across large datasets possible. However, existing transcript identification methods were designed for small datasets, which limits their applicability at this scale. Here, we present Isocall, a scalable and deterministic computational method for jointly calling transcripts from multiple PacBio long-read RNA sequencing samples. Isocall converts aligned full-length non-concatemer reads into compact per-sample transcript profiles, merges these profiles across samples, and jointly identifies known and novel transcripts supported by reads in the analyzed dataset. Filtering is tunable: presets provide coarse control and individual parameters, including relative abundance and internal priming thresholds, provide fine control. Isocall demonstrated high precision in our accuracy benchmarks, including the WTC11 SIRV spike-in controls, for which Isocall reported 0-2 false-positive transcripts per sample across the three SIRV mixes at default settings. To demonstrate scalability, we applied Isocall to 206 samples, totalling 3.5 billion raw reads, from the Human Pangenome Reference Consortium. After parallelized pbmm2 alignment and Isocall profile, the call step performed joint calling across the entire dataset in 25 minutes, using 1.3 GB of peak memory and 8 threads. Finally, in Genome in a Bottle samples with matched SNP genotypes, splice-site polymorphisms provide an additional measure of call accuracy: Isocall recovered 337 polymorphic splice sites, including a de novo donor site in BTN3A1 that corresponds to a complete isoform switch on the mutant allele.

bioinformatics↗

bioRxiv2026

Integrated map of somatic mosaicism across human tissues in 25 individuals

Although all cells in the body descend from one genome, they accumulate distinct genetic and epigenetic changes over a lifetime, producing a mosaic of somatic variation that can shape development, aging, and disease. This mosaicism is often studied in isolation, leaving unclear how these forms relate within and between individuals. Here, we present the first integrated analysis of the Somatic Mosaicism across Human Tissues (SMaHT) Network's production resource, profiling up to 20 tissues from 25 donors using short- and long-read, duplex, single-cell, transcriptomic, and epigenomic sequencing, alongside donor-specific near-telomere-to-telomere assemblies. Somatic mutation burden cannot be captured by a single data type or metric, as tissues accumulate distinct variant classes largely independently of one another. Long-read and single-cell data resolved cell-type-specific mutational processes, traced mobile element insertions to source loci, and revealed the developmental timing and functional consequences of individual mutations. Donor-specific assemblies uncovered elevated mutation rates within centromeres and segmental duplications inaccessible to standard reference genomes, while haplotype-resolved chromatin and methylation data showed that nongenetically-deterministic epigenetic states are pervasive across tissues. Together, these findings provide an integrated, multi-scale portrait of somatic mosaicism across the human body, establishing a baseline against which its contributions to aging and disease can be measured.

genomics↗

bioRxiv2026

IsoAtlas: Visual interpretation of known and novel transcript isoforms using population-scale long-read evidence

Long-read RNA sequencing has revealed extensive transcript diversity, but newly observed isoforms remain difficult to interpret beyond their classification as known or novel. Here, we present IsoAtlas, an interactive multispecies database for visual exploration and population-scale interpretation of transcript isoforms using 1, 035 human and 414 mouse uniformly processed long-read RNA-sequencing samples. Users can search annotated genes and transcripts or submit novel transcript models in GTF format, visualize their structures, and examine sample-level support, prevalence, expression, tissue and disease context, and sequencing-platform evidence. IsoAtlas integrates structurally equivalent transcripts across GENCODE, RefSeq and CHESS, consolidating evidence that would otherwise be distributed across annotation-specific identifiers. It further links corresponding human and mouse transcript models, enabling users to assess cross-species conservation and enabling users to assess cross-species conservation and inform the suitability of mouse models for isoform-specific studies. IsoAtlas can also evaluate arbitrary user-supplied transcript structures directly against accumulated long-read evidence. IsoAtlas therefore complements established reference annotations with an extensible evidence layer that connects transcript structure to population prevalence, biological context and cross-species support. IsoAtlas is freely available at https://www.isoatlas.org/. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/744438v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@89bd72org.highwire.dtl.DTLVardef@f48bbforg.highwire.dtl.DTLVardef@102ae37org.highwire.dtl.DTLVardef@fbbc2b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Long-read sequencing maps transposable element variation and its regulatory and epigenetic effects in the human brain

Transposable elements (TEs) are mobile DNA sequences that shape genome architecture and gene regulation, yet their roles in the human brain remain largely unresolved. Short-read sequencing lacks the resolution to accurately map TE insertions, detect associated structural variants, and resolve highly repetitive regions. Here, we leverage long-read whole-genome sequencing to profile germline TE insertions in postmortem brain tissue from two ancestrally diverse cohorts: the North American Brain Expression Consortium (NABEC; European ancestry, n = 205) and the Human Brain Collection Core (HBCC; African and African-admixed ancestry, n = 146). We identified 2,842 and 1,660 high-confidence non-reference insertions in HBCC and NABEC, respectively, spanning Alu, LINE-1, and SVA elements. We then also further characterized complex short tandem repeat and variable number tandem repeat variation within reference SVA and Alu loci. Reference TEs were also found to mediate complex structural variants at loci implicated in brain development and neurodegenerative disease, with several showing ancestry-specific patterns. Integration of bulk RNA-sequencing data identified TE expression quantitative trait loci, including insertions that modulate neuronal gene expression. Single-nucleus RNA sequencing revealed cell-type-specific effects of TE regulation across cortical populations. Long-read methylation profiling further demonstrated age-associated epigenetic regulation of both reference and non-reference Alu elements. As a community resource, we release a catalog of TE insertions, allele frequencies, and ancestry-specific distributions to enable future functional and disease-focused investigations. Together, these findings highlight the widespread regulatory and epigenetic influence of TEs in the human brain and establish long-read sequencing as a powerful approach for uncovering cell-type- and population-specific TE dynamics.

genomics↗

bioRxiv2026

A complete human pancreatic cancer genome

Cancer genome sequencing is essential for understanding tumor evolution and advancing precision medicine.1 However, reference gaps and germline variants obscure detection of small and large somatic variants and methylation in repetitive regions.1-3 It is common for tumor cells to gain or lose chromosome arms due to somatic structural changes that occur inside highly repetitive satellite DNA sequences in the centromeres.4 To identify the full spectrum of somatic variants, including complex rearrangements, we construct and curate near-complete, haplotype-resolved assemblies of the most recent common ancestor of an early-passage broadly-consented hypodiploid pancreatic cancer cell line and matched normal tissues. The tumor assembly completely recapitulates all 35 tumor chromosomes observed with karyotyping, with multiple translocation-induced hybrid chromosomes. The hybrid chromosomes contain putative functional dicentric and fused centromeres, nested foldback inversions causing 14 breakpoints with a haplotype switch in a single event, and centromeric satellite tandem duplications up to 136 kbp. Direct comparison of tumor and normal assembly haplotypes uncovers >7,000 variants altering >1 Mbp of sequence in repetitive regions that have been hidden by reference gaps and germline variants. 44 % of somatic small variants change representation because they alter germline variants on GRCh38, impacting mutational signatures and kataegis/omikli clusters. Most somatic LINE insertions originate from two hypomethylated non-reference germline LINE insertions, highlighting their impact on insertion mutation burden. These assemblies demonstrate that centromeric, acrocentric, and telomeric regions conventionally excluded from analysis harbor extensive somatic and epigenetic changes. Resolving complete tumor genomes enables a deeper understanding of cancer structural plasticity and the endpoints of breakage-fusion-bridge cycles. These assembled, curated paired normal-tumor benchmarks will serve as a critical foundation for developing future algorithms to characterize the most intractable regions of cancer genomes.

genomics↗

bioRxiv2026

Rapid phylogenomic analysis for viral surveillance and metagenomic profiling with Omni2Tree

Phylogenomic surveillance is limited not by sequencing throughput, but by the difficulty of converting heterogeneous raw data into reliable evolutionary inference, particularly for low-titer and contaminated viral field samples. Here we present Omni2Tree, an assembly-free framework that reconstructs viral phylogenies directly from raw sequencing reads and generates easily shareable interactive reports and genome-wide entropy profiles to identify diversification. In H5N1 benchmark analyses, Omni2Tree maintained accurate placement and topological stability even under low coverage, unlike assembly or reference based methods. Omni2Tree generated an annotated phylogeny for 64-sample H5N1 field surveillance dataset from the eastern USA in under 3 hours. Omni2Tree recovered known phylogenetic structure and key variability insights across 1,328 hepatitis C virus and 707 human cytomegalovirus datasets, and resolved co-infecting respiratory viruses in clinical metagenomic samples. By enabling direct analysis from raw reads, Omni2Tree supports faster, more portable, and more decentralized phylogenomic surveillance across outbreak, clinical, and resource-limited settings.

bioinformatics↗

bioRxiv2026

Population-scale interpretation of RNA isoform diversity enabled by Isopedia

Alternative splicing generates extensive transcriptomic complexity, yet "novelty" is often inflated because of incomplete reference annotations, with 20-70% of transcripts in RNA-Seq studies labeled as novel. Isopedia provides an expandable data structure for reference-agnostic isoform annotation, which we demonstrate here through a population-scale catalog of 1,007 long-read datasets spanning 37 diverse biological contexts. By transitioning from reference-dependent to evidence-weighted annotation, Isopedia provides the frequency-based context necessary to distinguish stochastic noise from biologically active isoforms. In HG002 benchmarks, Isopedia reduced apparent isoform novelty by up to 26-fold, achieving a >95% annotation rate even for low-abundance isoforms typically missed by standard catalogs. The framework further supports systematic exploration of challenging loci such as pseudogenes and gene fusions. Isopedia transforms isoform discovery into a systematic interpretation of the human transcriptome, providing a critical foundation for clinical and functional RNA research. Isopedia is open source and freely available: https://github.com/zhengxinchang/isopedia.

bioinformatics↗

bioRxiv2025

Benchmark for simple and complex genome inversions

BackgroundInversions represent a consequential yet under-characterized form of structural variation, with roles in genomic disorders, evolution, and genome instability. However, their detection remains technically challenging, particularly within repeat-dense regions and for complex multi-breakpoint events. A lack of dedicated, high-quality benchmarks has hindered algorithmic improvement, performance comparison, and robust biological interpretation. ResultsHere, we present a comprehensive, multi-genome benchmark for simple and complex inversions derived from Strand-seq and phased long-read based assemblies across five reference samples, with breakpoint refinement using haplotype-resolved long-read assemblies. This tiered resource spans a broad spectrum of inversion classes, sizes, and zygosity states, and captures challenging genomic contexts including segmental duplications, inverted repeats, and composite rearrangements. We used this benchmark to systematically assess leading structural variant callers and alignment strategies across short-read, PacBio HiFi, and Oxford Nanopore data. Performance varied substantially by inversion class and genomic context: simple inversions were recovered with high sensitivity at sufficient coverage, whereas complex and heterozygous events remained difficult. Sniffles2 and Severus achieved the strongest recall for complex inversions, despite increased false-positive rates. We additionally benchmarked two commonly used long-read alignment pipelines (Minimap2 and VACmap), demonstrating that the mapper choice has a substantial impact on inversion detection in repetitive regions. ConclusionTogether, this work provides the first unified, high-resolution inversion benchmark and reveals clear strengths and limitations of current methods across platforms. Our resource establishes a foundation for principled tool development, evaluation, and tuning, enabling the community to more accurately resolve inversion variation and its biological and clinical consequences.

bioinformatics↗

bioRxiv2025

Comprehensive benchmarking of somatic structural variant detection at ultra-low allele fractions

Postzygotic mosaicism gives rise to somatic structural variants (SVs) at ultra-low variant allele fractions (VAFs), which pose challenges for detection due to the high-coverage sequencing required and noise introduced by sequencing artifacts. Although somatic SV detection has been extensively studied in cancer, these studies are not directly applicable to the study of tissue mosaicism, as they rely on matched normals, target higher VAF ranges, and are enriched for different types of SVs. We present comprehensive benchmark data and best practices for non-cancer somatic SV detection. We created a synthetic mosaic sample by combining six HapMap individuals at varying proportions, generating allele fractions as low as 0.25%. This sample was sequenced to [~]2,300x total coverage using Illumina, PacBio, and Nanopore technologies across multiple sequencing centers. A high-confidence benchmark SV set containing over 21,000 pseudo-somatic insertions and deletions [≥]50bp was derived from haplotype-resolved assemblies. We evaluated 12 SV discovery pipelines and identified caller-specific strengths and sequencing platform-specific shortcomings. We find that short read-based approaches show reduced recall for insertions and repeat-associated SVs, whereas long-read sequencing achieves high accuracy throughout the genome, increasing linearly with coverage. The best algorithms sensitivity exceeded 80% for VAFs [≥]4% and 15% for VAFs of 0.5-1% with 60x coverage. The publicly available benchmarking data and comparative analysis of current methods provide a foundation for robust discovery of SV mosaicism in non-cancer tissues..

bioinformatics↗

bioRxiv2024

Long-read sequencing of hundreds of diverse brains provides insight into the impact of structural variation on gene expression and DNA methylation

Structural variants (SVs) drive gene expression in the human brain and are causative of many neurological conditions. However, most existing genetic studies have been based on short-read sequencing methods, which capture fewer than half of the SVs present in any one individual. Long-read sequencing (LRS) enhances our ability to detect disease-associated and functionally relevant structural variants; however, its application in large-scale genomic studies has been limited by challenges in sample preparation and high costs. Here, we leverage a new scalable wet-lab protocol and computational pipeline for whole-genome Oxford Nanopore Technologies sequencing and apply it to neurologically normal control samples from the North American Brain Expression Consortium (NABEC) (European ancestry) and Human Brain Collection Core (HBCC) (African or African admixed ancestry) cohorts. Through this work, we present a publicly available long-read resource from 351 human brain samples (median N50: 27 Kbp and at an average depth of ~40x genome coverage). We discover approximately 234,905 SVs and produce locally phased assemblies that cover 95% of all protein-coding genes in GRCh38. To resolve cis-regulatory effects, we develop ASM-LR, a method for allele-specific methylation analysis from long-read data, revealing both strong and subtle regulatory effects, including numerous novel methylation QTLs masked in unphased models. Our results highlight the power of haplotype-resolved methylation to uncover regulatory mechanisms and establish a foundational resource for exploring how genetic variation shapes gene expression and epigenetic architecture across diverse ancestries.

genomics↗

bioRxiv2024

Clair3-RNA: A deep learning-based small variant caller for long-read RNA sequencing data

Variant calling using long-read RNA sequencing (lrRNA-seq) can be applied to diverse tasks, such as capturing full-length isoforms and gene expression profiling. It poses challenges, however, due to higher error rates than DNA data, the complexities of transcript diversity, RNA editing events, etc. In this paper, we propose Clair3-RNA, the first deep learning-based variant caller tailored for lrRNA-seq data. Clair3-RNA leverages the strengths of the Clair series pipelines and incorporates several techniques optimized for lrRNA-seq data, such as uneven coverage normalization, refinement of training materials, editing site discovery, and the incorporation of phasing haplotype to enhance variant-calling performance. Clair3-RNA is available for various platforms, including PacBio and ONT complementary DNA sequencing (cDNA), and ONT direct RNA sequencing (dRNA). Our results demonstrated that Clair3-RNA achieved a [~]91% SNP F1-score on the ONT platform using the latest ONT SQK-RNA004 kit (dRNA004) and a [~]92% SNP F1-score in PacBio Iso-Seq and MAS-Seq for variants supported by at least four reads. The performance reached a [~]95% and [~]96% F1-score for ONT and PacBio, respectively, with at least ten supporting reads and disregarding the zygosity. With read phased, the performance reached [~]97% for ONT and [~]98% for PacBio. Extensive evaluation of various GIAB samples demonstrated that Clair3-RNA consistently outperformed existing callers and is capable of distinguishing RNA high-quality editing sites from variants accurately. Clair3-RNA is open-source and available at (https://github.com/HKU-BAL/Clair3-RNA).

bioinformatics↗

bioRxiv2024

K-mer analysis of long-read alignment pileups for structural variant genotyping

Accurately genotyping structural variant (SV) alleles is crucial to genomics research. We present a novel method (kanpig) for genotyping SVs that leverages variant graphs and k-mer vectors to rapidly generate accurate SV genotypes. We benchmark kanpig against the latest SV benchmarks and show single-sample genotyping concordance of 82.1%, which is higher than existing genotypers averaging 66.3%. We explore kanpigs applicability to multi-sample projects by benchmarking project-level VCFs containing 47 genetically diverse samples and find kanpig accurately genotypes complex loci (e.g. SVs neighboring other SVs), achieving much higher genotyping concordance than other tools. Kanpig requires only 43 seconds to process a single samples 20x long-reads and can be run on PacBio or ONT long-reads.

bioinformatics↗

bioRxiv2024

Defining a tandem repeat catalog and variation clusters for genome-wide analyses and population databases

Tandem repeat (TR) catalogs are important components of repeat genotyping studies as they define the genomic coordinates and expected motifs of all TR loci being analyzed. In recent years, genome-wide studies have used catalogs ranging in size from fewer than 200,000 to over 7 million loci. Where these catalogs overlapped, they often disagreed on locus boundaries, hindering the comparison and reuse of results across studies. Now, with multiple groups developing public databases of TR variation in large population cohorts, there is a risk that, without sufficient consensus in the choice of locus definitions, the use of divergent repeat catalogs will lead to confusion, fragmentation, and incompatibility across resources. In this paper, we compare existing TR catalogs and discuss desirable features of a comprehensive genome-wide catalog. We then present a new, richly annotated catalog designed for large-scale analyses and population databases. This new catalog, which we call the TRExplorer catalog v1.0, contains 4.86 million TR loci and, unlike most catalogs, is designed to be useful for both short-read and long-read analyses. It consists of 4,803,366 STRs and 59,675 VNTRs, of which 780,607 STRs and 21,888 VNTRs are both polymorphic and entirely absent from widely-used catalogs previously developed for short-read analyses. Additionally, our catalog stratifies TRs into two groups: 1) isolated TRs suitable for repeat copy number analysis using short-read or long-read data and 2) so-called variation clusters that contain TRs within wider polymorphic regions that are best studied through sequence-level analysis. To define variation clusters, we present a novel algorithm that leverages long-read HiFi sequencing data to group repeats with surrounding polymorphisms. We show that the human genome contains at least 25,000 complex variation clusters, most of which span over 120 bp and contain five or more TRs. Resolving the sequence of entire variation clusters instead of individually genotyping constituent TRs leads to a more accurate analysis of these regions and enables us to profile variation that would have been missed otherwise. We also share the trexplorer.broadinstitute.org portal which allows anyone to search, visualize, and download the catalog along with variation clusters and annotations.

genomics↗

bioRxiv2024

STIX: Long-reads based Accurate Structural Variation Annotation at Population Scale

The prioritization of Structural Variants (SV), which is needed to rank and identify potential pathogenic alleles, is still in its infancy. This is exemplified over gnomAD only being able to annotate 33.5% of GIAB SVs. To overcome this, we present the first long-read based annotation resource for both GRCh38 and CHM13-T2T reference using STIX. In contrast to previous methods, STIX indexes SV-informative long-reads themselves, can thus be easily extended and accurately annotate all SV types including insertions. STIX successfully annotated 95.9% of GIAB Tier1 SVs. STIX further improved cancer based SV prioritization by highlighting 3,563 SV from COSMIC being common in the population. We further showcase that mosaic SV can be independently gained and may be widely spread throughout the population. This highlights the need for accurate SV population frequency annotation to further facilitate the adoption of SV via long-read sequencing in medical research and clinical applications.

bioinformatics↗

bioRxiv2024

Comprehensive and accurate genome analysis at scale using DRAGEN accelerated algorithms

Research and medical genomics require comprehensive and scalable solutions to drive the discovery of novel disease targets, evolutionary drivers, and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size (e.g., SNV/SV) or location (e.g., repeats). Here we present DRAGEN that utilizes novel methods based on multigenomes, hardware acceleration, and machine learning based variant detection to provide novel insights into individual genomes with [~]30min computation time (from raw reads to variant detection). DRAGEN outperforms all other state-of-the-art methods in speed and accuracy across all variant types (SNV, indel, STR, SV, CNV) and further incorporates specialized methods to obtain key insights in medically relevant genes (e.g., HLA, SMN, GBA). We showcase DRAGEN across 3,202 genomes and demonstrate its scalability, accuracy, and innovations to further advance the integration of comprehensive genomics for research and medical applications.

genomics↗

bioRxiv2023

Benchmarking of small and large variants across tandem repeats

Tandem repeats (TRs) are highly polymorphic in the human genome, have thousands of associated molecular traits, and are linked to over 60 disease phenotypes. However, their complexity often excludes them from at-scale studies due to challenges with variant calling, representation, and lack of a genome-wide standard. To promote TR methods development, we create a comprehensive catalog of TR regions and explore its properties across 86 samples. We then curate variants from the GIAB HG002 individual to create a tandem repeat benchmark. We also present a variant comparison method that handles small and large alleles and varying allelic representation. The 8.1% of the genome covered by the TR catalog holds [~]24.9% of variants per individual, including 124,728 small and 17,988 large variants for the GIAB HG002 TR benchmark. We work with the GIAB community to demonstrate the utility of this benchmark across short and long read technologies.

bioinformatics↗