Biology subjects

Klau, G. W.

Publications and source records attributed to Klau, G. W..

15 recordsLinked to original sources

bioRxiv2026

Barcode Crosstalk in ONT Multiplex Sequencing: Quantification and Mitigation Strategies

Multiplexing samples in long-read sequencing with Oxford Nanopore Next Generation Sequencing Technology (ONT) by ligating specific native barcodes to individual DNA samples enables significant increases of high throughput sequencing combined with a significant reduction of sequencing costs. However, this advantage carries the risk of barcode misassignment / crosstalk. Employing ONT multiplex sequencing with samples, we observed misassigned barcodes so called barcode crosstalk, after ONT library preparation according to the standard protocol, particularly in samples with low input DNA concentrations. We assumed that these barcode misassignments are largely due to misligation of remaining native barcodes during subsequent the subsequent sequencing adapter ligation. To systematically investigate and quantify barcode crosstalk, genomic DNA (gDNA) from four bacterial type strains with different DNA input concentrations was prepared using three protocols for library preparation: the Nanopore standard protocol (protocol A: version valid until July 2, 2025) the new Nanopore protocol (protocol B: version from July 2, 2025), and an in house protocol with pooling of the barcoded samples only after the sequencing adapter ligation step (protocol C: in house). All samples were sequenced on a Nanopore PromethIon device. The results clearly showed that the use of protocol A resulted in a pronounced barcode crosstalk especially detectable in samples with low DNA input concentrations (up to 2.4% misassigned reads). The ONT adjustment in protocol B (altered washing buffer vs. protocol A) significantly alleviated the barcode crosstalk to below 0.01%, whereas protocol C eliminated barcode crosstalk virtually completely. These observations emphasize that sequencing results obtained with older ONT native barcoding protocol variants should be critically reviewed. The newer ONT barcoding protocol is preferable for sequencing, but it does not completely eliminate the barcode crosstalk effect. In conclusion, for low DNA input and high accuracy sequencing, protocol C is recommended.

molecular biology↗

bioRxiv2026

POTTR: Identifying Recurrent Trajectories in Evolutionary and Developmental Processes using Posets

Multiple biological processes, including cancer evolution and organismal development, are described as a sequence of events with a temporal ordering. While cancer evolves independently in each patient, DNA sequencing studies have shown that in some cancers different patients share specific orders of mutations and these correlate with distinct morphology, drug response, and treatment outcomes. Several methods have been developed to identify such recurrent trajectories of genetic events from phylogenetic trees, but this is complicated by high intra- and inter-tumor heterogeneity as well as uncertainty in the inferred tumor phylogenies including the ambiguous orders between some mutations. We formalize the problem of finding recurrent mutation trajectories using a novel framework of incomplete partially ordered sets (posets), which generalize representations used in previous works and explicitly account for the uncertainty in tumor phylogenies. We define the problem of identifying the largest recurrent trajectories shared in at least k input phylogenies as the maximum k-common induced incomplete subposet (MkCIIS) problem, which we show is NP-hard. We present a combinatorial algorithm, POsets for Temporal Trajectory Resolution (POTTR), to solve the MkCIIS problem using a conflict graph that models recurrent trajectories as independent sets. Thereby we identify maximum recurrent trajectories while resolving multiple sources of uncertainty, like mutation clusters, in the phylogenetic data. We apply POTTR to TRACERx non-small cell lung cancer bulk sequencing and acute myeloid leukemia single-cell sequencing data and through resolution of mutation clusters discover previously unreported trajectories of high statistical significance. On lineage tracing data of an in vitro embryoid model, POTTR identifies conserved differentiation routes across biological replicates and how these routes change in response to chemical perturbations.

bioinformatics↗

bioRxiv2025

Sequence-to-graph alignment based copy number calling using a network flow formulation

Variation of sequence copy number (CN) between individuals can be associated with phenotypical differences. Consequently, CN calling is an important step for disease association and identification, as well as for genome assembly validation. Traditionally, CN calling is done by mapping sequencing reads to a linear reference genome and estimating the CN from the observed read depth. This approach, however, is significantly hampered by sequences and rearrangements not present in a linear reference genome; at the same time simple CN prediction for individual graph nodes does not make use of the graph topology and can lead to inconsistent results. To address these issues, we propose Floco, a method for CN calling with respect to a genome graph using a network flow formulation. Given a graph and alignments against that graph, we calculate raw CN probabilities for every graph node based on the Negative Binomial distribution and the base pair coverage across the node, and then use integer linear programming to compute the CN flow through the whole graph. We tested this approach on 15 aligned datasets, involving three different graphs, as well as HiFi and ONT sequencing reads and linear assemblies split into reads. These results demonstrate that the addition of the network flow formulation increases the accuracy of CN predictions by up to 43% when compared with read depth based estimation alone. Additionally, we observed that concordance between predictions from the three different sequence sources was able to reach 93.2%. Floco fills a gap in CN calling tools specifically designed for genome graphs.

bioinformatics↗

bioRxiv2025

Anticlustering for Sample Allocation To Minimize Batch Effects

High throughput sequencing is a powerful tool for processing large amounts of DNA and RNA samples in batches. Proper experimental design and statistical methods are required to mitigate systematic technical factors due to differences in batches ("batch effects"), as data variation due to these non-biological factors can mask actual biological differences. We propose using anticlustering as an automated method to assign samples to balanced batches. Anticlustering effectively negates differences in (numeric and/or categorical) covariates among batches, and implements user-defined restrictions on the number of batches, the number of samples per batch, and whether to assign certain samples to the same batch ("must-link constraints"). A simulation study shows that anticlustering is better at achieving balance among batches than previous approaches. An application from the UCSF-Stanford Endometriosis Center for Discovery, Innovation, Training and Community Engagement ("ENACT", https://enactcenter.org/) is presented as a real-life example. In the application, multiple samples provided by an individual had to be processed on the same batch, so that comparisons among different samples of the same patient were not diluted by batch effects. The novel Two Phase Must Link (2PML) anticlustering algorithm realized the must-link restrictions while simultaneously obtaining balance among batches regarding disease stage, menstrual cycle phase, case versus control sample, and clinical site. All methods presented here are accessible via the free and open source R package anticlust (https://cran.r-project.org/package=anticlust). An interactive visualization and web-based batch assignment tool are made available in the Rshiny app "anticlust" (https://anticlust.org/).

bioinformatics↗

bioRxiv2023

CATHI: An interactive platform for comparative genomics and homolog identification

AO_SCPLOWBSTRACTC_SCPLOWBioinformatics has established itself as a central pillar of modern biology. Specifically, comparative genomics enables scientists to study a vast number of genomes efficiently. These comparative analyses shed light on the evolution and potential function of genomes and genes, but are also increasingly used as a key tool for metabolic engineering and synthetic biology by identifying appropriate targets for modification. While numerous sophisticated tools for comparative genomics and homolog identification exist, those tools predominantly target highly skilled bioinformatics users. Consequently, many biologists either defer such analyses to their more versed bioinformatic collaborators or resort to suboptimal tools. Here, we present an intuitive solution available on all major operating systems, easily accessed through common web browsers. CATHI - Comparative Analysis Tool for Homolog Identification - integrates a suite of best-practice bioinformatic tools, encompassing BLAST for homology searches, MAFFT for multiple sequence alignment, FastTree2 for phylogeny reconstruction, and clinker for synteny analysis. Specifically tailored to biologists, CATHI orchestrates predefined settings and automated pipelines, obviating the need for programming expertise. This platform empowers researchers to confidently engage in detailed comparative genomics studies by streamlining the analytical process. The interactive framework provides users with a plethora of options. This includes real-time execution and progress monitoring, facilitates dynamic result tracking, and a set of search functions across NCBI databases like CDD or ProtFam. Users can interactively engage in data exploration, filtering, and visualization through CATHIs intuitive interface. Furthermore, the seamless export of project data in standard formats (FASTA, Newick, CSV, and HTML) facilitates the integration with further third-party tools such as TreeViewer and Jalview. To benchmark CATHI, we revisited the comparative analysis of cyanobacterial circadian clock proteins conducted by Schmelling et al. in 2017, revealing consistent global patterns among identified homologs, while also highlighting individual variations attributed to the expansion of available databases.

bioinformatics↗

bioRxiv2023

Small molecule machine learning: All models are wrong, some may not even be useful

Small molecule machine learning tries to predict chemical, biochemical or biological properties from the structure of a molecule. Applications include prediction of toxicity, ligand binding or retention time. A recent trend is to develop end-to-end models that avoid the explicit integration of domain knowledge via inductive bias. A central assumption in doing so, is that there is no coverage bias in the training and evaluation data, meaning that these data are a representative subset of the true distribution we want to learn. Usually, the domain of applicability is neither considered nor analyzed for such large-scale end-to-end models. Here, we investigate how well certain large-scale datasets from the field cover the space of all known biomolecular structures. Investigation of coverage requires a sensible distance measure between molecular structures. We use a well-known distance measure based on solving the Maximum Common Edge Subgraph (MCES) problem, which agrees well with the chemical intuition of similarity between compounds. Unfortunately, this computational problem is provably hard, severely restricting the use of the corresponding distance measure in large-scale studies. We introduce an exact approach that combines Integer Linear Programming and intricate heuristic bounds to ensure efficient computations and dependable results. We find that several large-scale datasets frequently used in this domain of machine learning are far from a uniform coverage of known biomolecular structures. This severely confines the predictive power of models trained on this data. Next, we propose two further approaches to check if a training dataset differs substantially from the distribution of known biomolecular structures. On the positive side, our methods may allow creators of large-scale datasets to identify regions in molecular structure space where it is advisable to provide additional training data.

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

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↗

bioRxiv2021

Rhizobiales commensal bacteria promote Arabidopsis thaliana root growth via host sulfated peptide pathway

Root-associated commensal bacteria that belong to the order Rhizobiales, which also contains symbiotic and pathogenic bacteria, promote primary root growth of Arabidopsis thaliana. However, the molecular mechanism underlying this root growth promotion (RGP) activity remained unclear. Here, we conducted a transcriptomic analysis of A. thaliana roots inoculated with root-associated commensal bacteria of Rhizobiales and sister lineages and revealed common and strain/lineage-specific transcriptional response, possibly mediated by WRKY and ANAC family of transcription factors. We showed that the observed common response was also partly triggered by a wide range of non-pathogenic bacteria, fungi, and a multikingdom synthetic community (SynCom). This response was characterized by a down-regulation of genes related to intracellular redox regulation, suggesting distinctive redox status between pathogenic and non-pathogenic interactions. By integrating with developmental and cell biological experiments, we identified a crucial role of TYROSYLPROTEIN SULFOTRANSFERASE (TPST) in Rhizobiales RGP. Conversely, none of the known TPST-dependent sulfated peptide pathways appeared to be required for this activity, suggesting an unidentified component in the protein sulfation pathway targeted by Rhizobiales RGP. Finally, we show that TPST is needed for RGP exerted by Rhizobiales but not Pseudomonadales isolates, delineating lineage-specific mechanisms to manipulate host root development.

plant biology↗

bioRxiv2021

Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted heart

In the adult heart, the epicardium becomes activated after injury, contributing to cardiac healing by secretion of paracrine factors. Here we analyzed by single-cell RNA sequencing combined with RNA in situ hybridization and lineage tracing of WT1+ cells the cellular composition, location, and hierarchy of epicardial stromal cells (EpiSC) in comparison to activated myocardial fibroblasts/stromal cells in infarcted mouse hearts. We identified 11 transcriptionally distinct EpiSC populations, that can be classified in three groups each containing a cluster of proliferating cells. Two groups expressed cardiac specification makers and sarcomeric proteins suggestive of cardiomyogenic potential. Transcripts of HIF-1 and HIF-responsive genes were enriched in EpiSC consistent with an epicardial hypoxic niche. Expression of paracrine factors was not limited to WT1+ cells but was a general feature of activated cardiac stromal cells. Our findings provide the cellular framework by which myocardial ischemia may trigger in EpiSC the formation of cardioprotective/regenerative responses.

cell biology↗

bioRxiv2020

Integrating genetic dependencies and genomic alterations across pathways and cancer types

Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive characterization of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce SuperDendrix, an algorithm to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genetic alterations and cell-type-specific markers. Application of SuperDendrix to CRISPR-Cas9 loss-of-function screens from 554 cancer cell lines reveals a landscape of associations between differential dependencies and genomic alterations across multiple cancer pathways in different combinations of cancer types. We find that these associations respect the position and type of interactions within pathways with increased dependencies on downstream activators of pathways, such as NFE2L2 and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues.

cancer biology↗

bioRxiv2020

The Homo-Edit Distance Problem

We consider the homo-edit distance problem, which is the minimum number of homo-deletions or homo-insertions to convert one string into another. A homo-insertion is the insertion of a string of equal characters into another string, while a homo-deletion is the inverse operation. We show how to compute the homo-edit distance of two strings in polynomial time: We first demonstrate that the problem is equivalent to computing a common subsequence of the two input strings with a minimum number of homo-deletions and then present a dynamic programming solution for the reformulated problem. 2012 ACM Subject ClassificationApplied computing [->] Bioinformatics; Applied computing [->] Molecular sequence analysis; Theory of computation [->] Dynamic programming

bioinformatics↗

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↗

bioRxiv2016

WhatsHap: fast and accurate read-based phasing

Read-based phasing allows to reconstruct the haplotypes of a sample purely from sequencing reads. While phasing is an important step for answering questions about population genetics, compound heterozygosity, and to aid in clinical decision making, there has been a lack of accurate, usable and standards-based software.\n\nWhatsHap is a production-ready tool for highly accurate read-based phasing. It was designed from the beginning to leverage third-generation sequencing technologies, whose long reads can span many variants and are therefore ideal for phasing. WhatsHap works also well with second-generation data, is easy to use and will phase not only SNVs, but also indels and other variants. It is unique in its ability to combine read-based with pedigree-based phasing, allowing to further improve accuracy if multiple related samples are provided.

bioinformatics↗