Biology subjects

Dilthey, A. T.

Publications and source records attributed to Dilthey, A. T..

13 recordsLinked to original sources

bioRxiv2026

KIR*BLOOM: Accurate KIR genotyping using a new copy number-aware integrated genotype likelihood framework

Killer-cell immunoglobulin-like receptor (KIR) genes, key modulators of natural killer (NK) cell activity, play critical roles in immune response and disease susceptibility. Accurate KIR genotyping from short-read sequencing data remains challenging because of high sequence similarity among genes, extensive copy number variation, and substantial allelic diversity. Here, we present KIR*BLOOM, a likelihood-based approach for KIR genotyping from short-read data that models read depth and sequencing error across alternative genotype configurations. KIR*BLOOM first identifies KIR-relevant read pairs, maps them to a KIR allele database, and reduces the candidate allele space by excluding alleles unlikely to be present. It then infers gene copy number and selects alleles under the inferred copy-number constraints. Finally, variant calling is used to refine CDS sequences and identify potential novel alleles. We evaluated performance on 45 whole-genome sequencing samples with haplotype-resolved assemblies from the HPRC or HGSVC, using Immuannot-derived annotations as ground truth. KIR*BLOOM achieved 99.85% precision, 99.92% recall, and a Jaccard index of 99.77% for copy-number inference. At five-digit allele resolution, it achieved 92.73% precision, 92.69% recall, and an 87.29% Jaccard index, outperforming T1K, GraphKIR, and Geny. Together, these results demonstrate that KIR*BLOOM enables highly accurate KIR genotyping from short-read sequencing data.

bioinformatics↗

bioRxiv2025

Intra-Strain Genetic Heterogeneity in Toxoplasma gondii ME49: Oxford Nanopore Long-Read Sequencing Reveals Copy Number Variation in the ROP8-ROP2A Locus

BackgroundToxoplasma gondii is an important pathogen and model organism for studying mechanisms of immune evasion and defense. Within the same strain, model organisms are typically assumed to be isogenic; for T. gondii, within-strain genetic divergence has been detected based on phenotypic changes and older molecular techniques but not characterized at the genomic level. We therefore used Oxford Nanopore long-read sequencing to characterize three independently maintained T. gondii ME49 isolates: 2015T and 2020T (obtained from ATCC and propagated in cell culture), and 2000B (propagated in mice). ResultsWe de novo assembled a new T. gondii ME49 reference genome and, using state of-the-art variant calling combined with pangenomic genotyping, detected variants between the sequenced isolates. Our new reference genome exceeded existing reference genomes in continuity (NG50 = 6.68 Mb versus 1.2 Mb in RefSeq) and structural accuracy, resolving all chromosomes except for a single break in the ribosomal DNA region. For isolates 2000B and 2020T, we identified 106 and 128 variants, respectively, across a final call set of 79 SNVs, 93 INDELs, and five structural variants; 18 small non-synonymous variants included genes associated with T. gondii life cycle (AP2X-8) and virulence in vivo (6-phosphogluconate dehydrogenase). A 13 kb expansion in the ROP8-ROP2A virulence locus increased the copy number of ROP2A-ROP8 genes in isolates 2000B and 2020T from three to six. ConclusionsWe provide an improved T. gondii ME49 reference genome and demonstrate the potentially confounding effect of intra-strain genetic heterogeneity, highlighting the need for continuous genomic monitoring for long-term genetic identity.

genomics↗

bioRxiv2024

Integer programming framework for pangenome-based genome inference

Affordable genotyping methods are essential in genomics. Commonly used genotyping methods primarily support single nucleotide variants and short indels but neglect structural variants. Additionally, accuracy of read alignments to a reference genome is unreliable in highly polymorphic and repetitive regions, further impacting genotyping performance. Recent works highlight the advantage of haplotype-resolved pangenome graphs in addressing these challenges. Building on these developments, we propose a rigorous alignment-free genotyping framework. Our formulation seeks a path through the pangenome graph that maximizes the matches between the path and substrings of sequencing reads (e.g., k-mers) while minimizing recombination events (haplotype switches) along the path. We prove that this problem is NP-Hard and develop efficient integer-programming solutions. We benchmarked the algorithm using downsampled short-read datasets from homozygous human cell lines with coverage ranging from 0.1x to 10x. Our algorithm accurately estimates complete major histocompatibility complex (MHC) haplotype sequences with small edit distances from the ground-truth sequences, providing a significant advantage over existing methods on low-coverage inputs. Although our algorithm is designed for haploid samples, we discuss future extensions to diploid samples. Implementationhttps://github.com/at-cg/PHI

bioinformatics↗

bioRxiv2024

Lightweight taxonomic profiling of long-read sequenced metagenomes with Lemur and Magnet

The advent of long-read sequencing of microbiomes necessitates the development of new taxonomic profilers tailored to long-read shotgun metagenomic datasets. Here, we introduce Lemur and Magnet, a pair of tools optimized for lightweight and accurate taxonomic profiling for long-read shotgun metagenomic datasets. Lemur is a marker-gene-based method that leverages an EM algorithm to reduce false positive calls while preserving true positives; Magnet is a whole-genome read-mapping-based method that provides detailed presence and absence calls for bacterial genomes. We demonstrate that Lemur and Magnet can run in minutes to hours on a laptop with 32 GB of RAM, even for large inputs, a crucial feature given the portability of long-read sequencing machines. Furthermore, the marker gene database used by Lemur is only 4 GB and contains information from over 300,000 RefSeq genomes. Lemur and Magnet are open-source and available at https://github.com/treangenlab/lemur and https://github.com/treangenlab/magnet.

bioinformatics↗

bioRxiv2024

Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny

The SARS-CoV-2 genome occupies a unique place in infection biology - it is the most highly sequenced genome on earth (making up over 20% of public sequencing datasets) with fine scale information on sampling date and geography, and has been subject to unprecedented intense analysis. As a result, these phylogenetic data are an incredibly valuable resource for science and public health. However, the vast majority of the data was sequenced by tiling amplicons across the full genome, with amplicon schemes that changed over the pandemic as mutations in the viral genome interacted with primer binding sites. In combination with the disparate set of genome assembly workflows and lack of consistent quality control (QC) processes, the current genomes have many systematic errors that have evolved with the virus and amplicon schemes. These errors have significant impacts on the phylogeny, and therefore over the last few years, many thousands of hours of researchers time has been spent in "eyeballing" trees, looking for artefacts, and then patching the tree. Given the huge value of this dataset, we therefore set out to reprocess the complete set of public raw sequence data in a rigorous amplicon-aware manner, and build a cleaner phylogeny. Here we provide a global tree of 4,471,579 samples, built from a consistently assembled set of high quality consensus sequences from all available public data as of June 2024, viewable at https://viridian.taxonium.org. Each genome was constructed using a novel assembly tool called Viridian (https://github.com/iqbal-lab-org/viridian), developed specifically to process amplicon sequence data, eliminating artefactual errors and mask the genome at low quality positions. We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny. We hope the tree, consensus sequences and Viridian will be a valuable resource for researchers.

bioinformatics↗

bioRxiv2023

MetaGut: Insights into gut microbiomes in stem cell transplantation by comprehensive shotgun long-read sequencing

The gut microbiome is a diverse ecosystem, dominated by bacteria; however, fungi, phages/viruses, archaea, and protozoa are also important members of the gut microbiota. Up to recently, exploration of taxonomic compositions beyond bacteria as well as an understanding of the interaction between the bacteriome with the other members was limited due to 16S rDNA sequencing. Here, we developed MetaGut, a method enabling the simultaneous interrogation of the gut microbiome (bacteriome, mycobiome, archaeome, eukaryome, DNA virome) and of antibiotic resistance genes based on optimized long-read shotgun metagenomics protocols and custom bioinformatics. Using MetaGut we investigated the longitudinal composition of the gut microbiome in an exploratory clinical study in patients undergoing allogeneic hematopoietic stem cell transplantation (alloHSCT; n = 31). Pre-transplantation microbiomes exhibited a 3-cluster structure, associated with Bacteroides/Phocaeicola, mixed composition and Enterococcus abundances. MetaGut revealed substantial inter-individual and temporal variabilities of microbial domain compositions, human DNA, and antibiotic resistance genes during the course of alloHSCT. Interestingly, viruses and fungi accounted for substantial proportions of microbiome content in individual samples (up to >50% and >20%, respectively). After leukopenia, strains were stable or newly acquired. Our results demonstrate the disruptive effect of alloHSCT on the gut microbiome and pave the way for future studies based on long-read metagenomics.

bioinformatics↗

bioRxiv2023

HOGVAX: Exploiting Peptide Overlaps to Maximize Population Coverage in Vaccine Design with Application to SARS-CoV-2

Peptide vaccines present a safe and cost-efficient alternative to traditional vaccines. Their efficacy depends on the peptides included in the vaccine and the ability of major histocompatibility complex (MHC) molecules to bind and present these peptides. Due to the high diversity of MHC alleles, their diverging peptide binding specificities, and physical constraints on the maximum length of peptide vaccine constructs, choosing a set of peptides that effectively achieve immunization across a large proportion of the population is challenging. Here, we present HOGVAX, a combinatorial optimization approach to select peptides that maximize population coverage. The key idea behind HOGVAX is to exploit overlaps between peptide sequences to include a large number of peptides in limited space and thereby also cover rare MHC alleles. We formalize the vaccine design task as a theoretical problem, which we call the Maximum Scoring k-Superstring Problem (MSKS). We show that MSKS is NP-hard, reformulate it into a graph problem using the hierarchical overlap graph (HOG), and present a haplotype-aware variant of MSKS to take linkage disequilibrium between MHC loci into account. We give an integer linear programming formulation for the graph problem and provide an open source implementation. We demonstrate on a SARS-CoV-2 case study that HOGVAX-designed vaccine formulations contain significantly more peptides than vaccine sequences built from concatenated peptides. We predict over 98% population coverage and high numbers of per-individual presented peptides, leading to robust immunity against new pathogens or viral variants.

bioinformatics↗

bioRxiv2022

Complete sequences of six Major Histocompatibility Complex haplotypes, including all the major MHC class II structures

Accurate and comprehensive immunogenetic reference panels are key to the successful implementation of population-scale immunogenomics. The 5Mbp Major Histocompatibility Complex (MHC) is the most polymorphic region of the human genome and associated with multiple immune-mediated diseases, transplant matching and therapy responses. Analysis of MHC genetic variation is severely complicated by complex patterns of sequence variation, linkage disequilibrium and a lack of fully resolved MHC reference haplotypes, increasing the risk of spurious findings on analyzing this medically important region. Integrating Illumina and ultra-long Nanopore sequencing as well as bespoke bioinformatics, we completed five of the alternative MHC reference haplotypes of the current (B38) build of the human reference genome and added one other. The six assembled MHC haplotypes encompass the DR1 and DR4 haplotype structures in addition to the previously completed DR2 and DR3, as well as six distinct classes of the structurally variable C4 region. Analysis of the assembled haplotypes showed that MHC class II sequence structures, including repeat element positions, are generally conserved within the DR haplotype supergroups, and that sequence diversity peaks in three regions around HLA-A, HLA-B+C, and the HLA class II genes. Demonstrating the potential for improved short-read analysis, the number of proper read pairs recruited to the MHC was found to be increased by 0.32% - 0.69% in a 1000 Genomes Project read re-mapping experiment with seven diverse samples. Furthermore, the assembled haplotypes can serve as references for the community and provide the basis of a structurally accurate genotyping graph of the complete MHC region.

genomics↗

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↗

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↗

bioRxiv2018

HLA*PRG:LA - HLA typing from linearly projected graph alignments

SummaryHLA*PRG:LA implements a new graph alignment model for HLA type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data); and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample.\n\nAvailability and ImplementationHLA*PRG:LA is implemented in C++ and Perl and freely available from https://github.com/DiltheyLab/HLA-PRG-LA (GPL v3).\n\nContactalexander.dilthey@med.uni-duesseldorf.de\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2018

Complete assembly of parental haplotypes with trio binning

Reference genome projects have historically selected inbred individuals to minimize heterozygosity and simplify assembly. We challenge this dogma and present a new approach designed specifically for heterozygous genomes. \"Trio binning\" uses short reads from two parental genomes to partition long reads from an offspring into haplotype-specific sets prior to assembly. Each haplotype is then assembled independently, resulting in a complete diploid reconstruction. On a benchmark human trio, this method achieved high accuracy and recovered complex structural variants missed by alternative approaches. To demonstrate its effectiveness on a heterozygous genome, we sequenced an F1 cross between cattle subspecies Bos taurus taurus and Bos taurus indicus, and completely assembled both parental haplotypes with NG50 haplotig sizes >20 Mbp and 99.998% accuracy, surpassing the quality of current cattle reference genomes. We propose trio binning as a new best practice for diploid genome assembly that will enable new studies of haplotype variation and inheritance.

genomics↗

bioRxiv2017

Nanopore sequencing and assembly of a human genome with ultra-long reads

Nanopore sequencing is a promising technique for genome sequencing due to its portability, ability to sequence long reads from single molecules, and to simultaneously assay DNA methylation. However until recently nanopore sequencing has been mainly applied to small genomes, due to the limited output attainable. We present nanopore sequencing and assembly of the GM12878 Utah/Ceph human reference genome generated using the Oxford Nanopore MinION and R9.4 version chemistry. We generated 91.2 Gb of sequence data ([~]30x theoretical coverage) from 39 flowcells. De novo assembly yielded a highly complete and contiguous assembly (NG50 [~]3Mb). We observed considerable variability in homopolymeric tract resolution between different basecallers. The data permitted sensitive detection of both large structural variants and epigenetic modifications. Further we developed a new approach exploiting the long-read capability of this system and found that adding an additional 5x-coverage of ultra-long reads (read N50 of 99.7kb) more than doubled the assembly contiguity. Modelling the repeat structure of the human genome predicts extraordinarily contiguous assemblies may be possible using nanopore reads alone. Portable de novo sequencing of human genomes may be important for rapid point-of-care diagnosis of rare genetic diseases and cancer, and monitoring of cancer progression. The complete dataset including raw signal is available as an Amazon Web Services Open Dataset at: https://github.com/nanopore-wgs-consortium/NA12878.

genomics↗