Biology subjects

Marschall, T.

Publications and source records attributed to Marschall, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Haplotype-resolved assembly of a tetraploid potato genome using long reads and low-depth offspring data

Potato is one of the worlds major staple crops and like many important crop plants it has a polyploid genome. Polyploid haplotype assembly poses a major computational challenge, hindering the use of genomic data in breeding strategies. Here, we introduce a novel strategy for the assembly of polyploid genomes and present an assembly of the autotetraploid potato cultivar Altus. Our method uses low-depth sequencing data from an offspring population, which is available in many plant breeding settings, to achieve chromosomal clustering and haplotype phasing directly on the assembly graph. This involves a novel strategy for the analysis of k-mers unique to specific graph nodes. Our approach generates assemblies of individual chromosomes with phased haplotig N50 values of up to 13 Mb and haplotig lengths of up to 31 Mb. This major advance provides high-quality assemblies with haplotype-specific sequence resolution of whole chromosome arms and can be applied in common breeding scenarios where collections of offspring are available.

genomics↗

bioRxiv2022

Automated assembly of high-quality diploid human reference genomes

The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has greatly benefited society1, 2. However, it still has many gaps and errors, and does not represent a biological human genome since it is a blend of multiple individuals3, 4. Recently, a high-quality telomere-to-telomere reference genome, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a duplicate genome, and is thus nearly homozygous5. To address these limitations, the Human Pangenome Reference Consortium (HPRC) recently formed with the goal of creating a collection of high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity6. Here, in our first scientific report, we determined which combination of current genome sequencing and automated assembly approaches yields the most complete, accurate, and cost-effective diploid genome assemblies with minimal manual curation. Approaches that used highly accurate long reads and parent-child data to sort haplotypes during assembly outperformed those that did not. Developing a combination of all the top performing methods, we generated our first high- quality diploid reference assembly, containing only [~]4 gaps (range 0-12) per chromosome, most within + 1% of CHM13s length. Nearly 1/4th of protein coding genes have synonymous amino acid changes between haplotypes, and centromeric regions showed the highest density of variation. Our findings serve as a foundation for assembling near-complete diploid human genomes at the scale required for constructing a human pangenome reference that captures all genetic variation from single nucleotides to large structural rearrangements.

genomics↗

bioRxiv2021

Haplotype-resolved inversion landscape reveals hotspots of mutational recurrence associated with genomic disorders

Unlike copy number variants (CNVs), inversions remain an underexplored genetic variation class. By integrating multiple genomic technologies, we discover 729 inversions in 41 human genomes. Approximately 85% of inversions <2 kbp form by twin-priming during L1-retrotransposition; 80% of the larger inversions are balanced and affect twice as many base pairs as CNVs. Balanced inversions show an excess of common variants, and 72% are flanked by segmental duplications (SDs) or mobile elements. Since this suggests recurrence due to non-allelic homologous recombination, we developed complementary approaches to identify recurrent inversion formation. We describe 40 recurrent inversions encompassing 0.6% of the genome, showing inversion rates up to 2.7x10-4 per locus and generation. Recurrent inversions exhibit a sex- chromosomal bias, and significantly co-localize to the critical regions of genomic disorders. We propose that inversion recurrence results in an elevated number of heterozygous carriers and structural SD diversity, which increases mutability in the population and predisposes to disease- causing CNVs.

genomics↗

bioRxiv2021

Haplotype-aware single-cell multiomics uncovers functional effects of somatic structural variation

Somatic structural variants (SVs) are widespread in cancer genomes, however, their impact on tumorigenesis and intra-tumour heterogeneity is incompletely understood, since methods to functionally characterize the broad spectrum of SVs arising in cancerous single-cells are lacking. We present a computational method, scNOVA, that couples SV discovery with nucleosome occupancy analysis by haplotype-resolved single-cell sequencing, to systematically uncover SV effects on cis-regulatory elements and gene activity. Application to leukemias and cell lines uncovered SV outcomes at several loci, including dysregulated cancer-related pathways and mono-allelic oncogene expression near SV breakpoints. At the intra-patient level, we identified different yet overlapping subclonal SVs that converge on aberrant Wnt signaling. We also deconvoluted the effects of catastrophic chromosomal rearrangements resulting in oncogenic transcription factor dysregulation. scNOVA directly links SVs to their functional consequences, opening the door for single-cell multiomics of SVs in heterogeneous cell populations.

genomics↗

bioRxiv2021

BubbleGun: Enumerating Bubbles and Superbubbles in Genome Graphs

MotivationWith the fast development of third generation sequencing machines, de novo genome assembly is becoming a routine even for larger genomes. Graph-based representations of genomes arise both as part of the assembly process, but also in the context of pangenomes representing a population. In both cases, polymorphic loci lead to bubble structures in such graphs. Detecting bubbles is hence an important task when working with genomic variants in the context of genome graphs. ResultsHere, we present a fast general-purpose tool, called BubbleGun, for detecting bubbles and superbubbles in genome graphs. Furthermore, BubbleGun detects and outputs runs of linearly connected bubbles and superbubbles, which we call bubble chains. We showcase its utility on de Bruijn graphs and compare our results to vgs snarl detection. We show that BubbleGun is considerably faster than vg especially in bigger graphs, where it reports all bubbles in less than 30 minutes on a human sample de Bruijn graph of around 2 million nodes. AvailabilityBubbleGun is available and documented at https://github.com/fawaz-dabbaghieh/bubble_gun under MIT license. Contactfawaz@hhu.de or tobias.marschall@hhu.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Pangenome-based genome inference

AO_SCPLOWBSTRACTC_SCPLOWTypical analysis workflows map reads to a reference genome in order to detect genetic variants. Generating such alignments introduces references biases, in particular against insertion alleles absent in the reference and comes with substantial computational burden. In contrast, recent k-mer-based genotyping methods are fast, but struggle in repetitive or duplicated regions of the genome. We propose a novel algorithm, called PanGenie, that leverages a pangenome reference built from haplotype-resolved genome assemblies in conjunction with k-mer count information from raw, short-read sequencing data to genotype a wide spectrum of genetic variation. The given haplotypes enable our method to take advantage of linkage information to aid genotyping in regions poorly covered by unique k-mers and provides access to regions otherwise inaccessible by short reads. Compared to classic mapping-based approaches, our approach is more than 4x faster at 30x coverage and at the same time, reached significantly better genotype concordances for almost all variant types and coverages tested. Improvements are especially pronounced for large insertions (> 50bp), where we are able to genotype > 99.9% of all tested variants with over 90% accuracy at 30x short-read coverage, where the best competing tools either typed less than 60% of variants or reached accuracies below 70%. PanGenie now enables the inclusion of this commonly neglected variant type in downstream analyses.

bioinformatics↗

bioRxiv2020

A multi-platform reference for somatic structural variation detection

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality gold standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines for comprehensive somatic SV detection. Here, we approached this challenge by genome-wide somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different technologies: Illumina HiSeq, Oxford Nanopore, Pacific Biosciences and 10x Genomics. Based on the evidence from multiple technologies combined with extensive experimental validation, including Bionano optical mapping data and targeted detection of candidate breakpoint junctions, we compiled a comprehensive set of true somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance of each technology as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects and data analysis tool evaluation. The reference truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

genomics↗

bioRxiv2020

MBG: Minimizer-based Sparse de Bruijn Graph Construction

MotivationDe Bruijn graphs can be constructed from short reads efficiently and have been used for many purposes. Traditionally long read sequencing technologies have had too high error rates for de Bruijn graph-based methods. Recently, HiFi reads have provided a combination of long read length and low error rate, which enables de Bruijn graphs to be used with HiFi reads. ResultsWe have implemented MBG, a tool for building sparse de Bruijn graphs from HiFi reads. MBG outperforms existing tools for building dense de Bruijn graphs, and can build a graph of 50x coverage whole human genome HiFi reads in four hours on a single core. MBG also assembles the bacterial E. coli genome into a single contig in 8 seconds. AvailabilityPackage manager: https://anaconda.org/bioconda/mbg and source code: https://github.com/maickrau/MBG

bioinformatics↗

bioRxiv2020

Expectations and blind spots for structural variation detection from short-read alignment and long-read assembly

Virtually all genome sequencing efforts in national biobanks, complex and Mendelian disease programs, and emerging clinical diagnostic approaches utilize short-reads (srWGS), which present constraints for genome-wide discovery of structural variants (SVs). Alternative long-read single molecule technologies (lrWGS) offer significant advantages for genome assembly and SV detection, while these technologies are currently cost prohibitive for large-scale disease studies and clinical diagnostics (∼5-12X higher cost than comparable coverage srWGS). Moreover, only dozens of such genomes are currently publicly accessible by comparison to millions of srWGS genomes that have been commissioned for international initiatives. Given this ubiquitous reliance on srWGS in human genetics and genomics, we sought to characterize and quantify the properties of SVs accessible to both srWGS and lrWGS to establish benchmarks and expectations in ongoing medical and population genetic studies, and to project the added value of SVs uniquely accessible to each technology. In analyses of three trios with matched srWGS and lrWGS from the Human Genome Structural Variation Consortium (HGSVC), srWGS captured ∼11,000 SVs per genome using reference-based algorithms, while haplotype-resolved assembly from lrWGS identified ∼25,000 SVs per genome. Detection power and precision for SV discovery varied dramatically by genomic context and variant class: 9.7% of the current GRCh38 reference is defined by segmental duplications (SD) and simple repeats (SR), yet 91.4% of deletions that were specifically discovered by lrWGS localized to these regions. Across the remaining 90.3% of the human reference, we observed extremely high concordance (93.8%) for deletions discovered by srWGS and lrWGS after error correction using the raw lrWGS reads. Conversely, lrWGS was superior for detection of insertions across all genomic contexts. Given that the non-SD/SR sequences span 90.3% of the GRCh38 reference, and encompass 95.9% of coding exons in currently annotated disease associated genes, improved sensitivity from lrWGS to discover novel and interpretable pathogenic deletions not already accessible to srWGS is likely to be incremental. However, these analyses highlight the added value of assembly-based lrWGS to create new catalogues of functional insertions and transposable elements, as well as disease associated repeat expansions in genomic regions previously recalcitrant to routine assessment.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2020

Haplotype Threading: Accurate Polyploid Phasing from Long Reads

Resolving genomes at haplotype level is crucial for understanding the evolutionary history of polyploid species and for designing advanced breeding strategies. As a highly complex computational problem, polyploid phasing still presents considerable challenges, especially in regions of collapsing haplotypes. We present WO_SCPLOWHATSC_SCPLOWHO_SCPLOWAPC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWPOLYPHASEC_SCPLOW, a novel two-stage approach that addresses these challenges by (i) clustering reads using a position-dependent scoring function and (ii) threading the haplotypes through the clusters by dynamic programming. We demonstrate on a simulated data set that this results in accurate haplotypes with switch error rates that are around three times lower than those obtainable by the current state-of-the-art and even around seven times lower in regions of collapsing haplotypes. Using a real data set comprising long and short read tetraploid potato sequencing data we show that WO_SCPLOWHATSC_SCPLOWHO_SCPLOWAPC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWPOLYPHASEC_SCPLOW is able to phase the majority of the potato genes after error correction, which enables the assembly of local genomic regions of interest at haplotype level. Our algorithm is implemented as part of the widely used open source tool WhatsHap and ready to be included in production settings.

bioinformatics↗

bioRxiv2020

AERON: Transcript quantification and gene-fusion detection using long reads

Single-molecule sequencing technologies have the potential to improve measurement and analysis of long RNA molecules expressed in cells. However, analysis of error-prone long RNA reads is a current challenge. We present AERON for the estimation of transcript expression and prediction of gene-fusion events. AERON uses an efficient read-to-graph alignment algorithm to obtain accurate estimates for noisy reads. We demonstrate AERON to yield accurate expression estimates on simulated and real datasets. It is the first method to reliably call gene-fusion events from long RNA reads. Sequencing the K562 transcriptome, we used AERON and found known as well as novel gene-fusion events.

bioinformatics↗

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↗

bioRxiv2019

A Diploid Assembly-based Benchmark for Variants in the Major Histocompatibility Complex

We develop the first human benchmark derived from a diploid assembly for the openly-consented Genome in a Bottle/Personal Genome Project Ashkenazi son (HG002). As a proof-of-principle, we focus on a medically important, highly variable, 5 million base-pair region - the Major Histocompatibility Complex (MHC). Most human genomes are characterized by aligning individual reads to the reference genome, but accurate long reads and linked reads now enable us to construct base-level accurate, phased de novo assemblies from the reads. We assemble a single haplotig (haplotype-specific contig) for each haplotype, and align reads back to each assembled haplotig to identify two regions of lower confidence. We align the haplotigs to the reference, call phased small and structural variants, and define the first small variant benchmark for the MHC, covering 21496 small variants in 4.58 million base-pairs (92 % of the MHC). The assembly-based benchmark is 99.95 % concordant with a draft mapping-based benchmark from the same long and linked reads within both benchmark regions, but covers 50 % more variants outside the mapping-based benchmark regions. The haplotigs and variant calls are completely concordant with phased clinical HLA types for HG002. This benchmark reliably identifies false positives and false negatives from mapping-based callsets, and enables performance assessment in regions with much denser, complex variation than regions covered by previous benchmarks. These methods demonstrate a path towards future diploid assembly-based benchmarks for other complex regions of the genome.

genomics↗

bioRxiv2019

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

Genome graphs can represent genetic variation and sequence uncertainty. Aligning sequences to genome graphs is key to many applications, including error correction, genome assembly, and genotyping of variants in a pan-genome graph. Yet, so far this step is often prohibitively slow. We present GraphAligner, a tool for aligning long reads to genome graphs. Compared to state-of-the-art tools, GraphAligner is 12x faster and uses 5x less memory, making it as efficient as aligning reads to linear reference genomes. When employing GraphAligner for error correction, we find it to be almost 3x more accurate and over 15x faster than extant tools.\n\nAvailability Package managerhttps://anaconda.org/bioconda/graphaligner and source code: https://github.com/maickrau/GraphAligner

bioinformatics↗

bioRxiv2019

Efficient chromosome-scale haplotype-resolved assembly of human genomes

Haplotype-resolved or phased sequence assembly provides a complete picture of genomes and complex genetic variations. However, current phased assembly algorithms either fail to generate chromosome-scale phasing or require pedigree information, which limits their application. We present a method that leverages long accurate reads and long-range conformation data for single individuals to generate chromosome-scale phased assembly within a day. Applied to three public human genomes, PGP1, HG002 and NA12878, our method produced haplotype-resolved assemblies with contig NG50 up to 25 Mb and phased [~]99.5% of heterozygous sites to 98-99% accuracy, outperforming other approaches in terms of both contiguity and phasing completeness. We demonstrate the importance of chromosome-scale phased assemblies to discover structural variants (SVs), including thousands of new transposon insertions, and of highly polymorphic and medically important regions such as HLA and KIR. Our improved method will enable high-quality precision medicine and facilitate new studies of individual haplotype variation and population diversity.

bioinformatics↗

bioRxiv2019

Enhancing sensitivity and controlling false discovery rate in somatic indel discovery

As witnessed by various population-scale cancer genome sequencing projects, accurate discovery of somatic variants has become of central importance in modern cancer research. However, count statistics on somatic insertions and deletions (indels) discovered so far point out that large amounts of discoveries must have been missed. The reason is that the combination of uncertainties relating to, for example, gap and alignment ambiguities, twilight zone indels, cancer heterogeneity, sample purity, sampling and strand bias are hard to accurately quantify. Here, a unifying statistical model is provided whose dependency structures enable to accurately quantify all inherent uncertainties in short time. As major consequence, false discovery rate (FDR) in somatic indel discovery can now be controlled at utmost accuracy. As demonstrated on simulated and real data, this enables to dramatically increase the amount of true discoveries while safely suppressing the FDR. Specifically supported by workflow design, our approach can be integrated as a post-processing step in large-scale projects.\n\nThe software is publicly available at https://varlociraptor.github.io and can be easily installed via Bioconda1 [Gruning et al., 2018].

bioinformatics↗

bioRxiv2019

Efficient de novo assembly of eleven human genomes using PromethION sequencing and a novel nanopore toolkit

Present workflows for producing human genome assemblies from long-read technologies have cost and production time bottlenecks that prohibit efficient scaling to large cohorts. We demonstrate an optimized PromethION nanopore sequencing method for eleven human genomes. The sequencing, performed on one machine in nine days, achieved an average 63x coverage, 42 Kb read N50, 90% median read identity and 6.5x coverage in 100 Kb+ reads using just three flow cells per sample. To assemble these data we introduce new computational tools: Shasta - a de novo long read assembler, and MarginPolish & HELEN - a suite of nanopore assembly polishing algorithms. On a single commercial compute node Shasta can produce a complete human genome assembly in under six hours, and MarginPolish & HELEN can polish the result in just over a day, achieving 99.9% identity (QV30) for haploid samples from nanopore reads alone. We evaluate assembly performance for diploid, haploid and trio-binned human samples in terms of accuracy, cost, and time and demonstrate improvements relative to current state-of-the-art methods in all areas. We further show that addition of proximity ligation (Hi-C) sequencing yields near chromosome-level scaffolds for all eleven genomes.

bioinformatics↗