Biology subjects

Schmitz, J. E.

Publications and source records attributed to Schmitz, J. E..

12 recordsLinked to original sources

bioRxiv2026

High-performance xenograft read-sorting for DNA methylation analysis

Xenograft sorting tools separate sequenced DNA reads by species. We have enhanced the computational method and tool Xengsort to enable efficient epigenetic profiling of patient-derived xenograft models; it now supports cytosine-converted whole genome or reduced-representation data. Benchmarking Xengsort against several established methods (ICRG, bamcmp, XenofilteR) revealed consistently high accuracy across tools, but Xengsort outperformed the other tools in computational efficiency due to its alignment-free design.

bioinformatics↗

bioRxiv2026

DivQuant: Estimation of Species Richness and Entropy from Small Samples

Estimating diversity properties of discrete distributions from a small observed sample is a fundamental problem in algorithmic statistics that has applications in many fields, in particular bioinformatics, but also in ecology or linguistics. The two most common diversity measures are the number of distinct elements in a multiset, also referred to as "species richness" in ecology or "alpha diversity" in microbial analysis, and the Shannon entropy, also referred to as "evenness". Estimating these properties from a small sample is particularly challenging for distributions with many rare elements. Thus, many estimators have been proposed in the past that, in practice, work well for different types of distributions. We present DivQuant, an optimization-based, extrapolating richness and entropy estimator with three contributions. First, we formulate the upsampling problem as a convex quadratic program with a Neyman{chi} 2 objective. Unlike the linear program of its predecessor RichnEst, DivQuant admits confidence intervals via{chi} 2 test inversion that are empirically well-calibrated. Second, we replace RichnEsts fixed-threshold fingerprint truncation with the rare/abundant fingerprint split of Valiant and Valiant, which strongly reduces problem size and preserves enough degrees of freedom for the confidence-interval program to remain valid and feasible. Third, we plug the optimal population fingerprint returned by the program into Shannons entropy formula to obtain an entropy estimate. DivQuant attains close-to-nominal 95% confidence intervals in essentially all tested regimes, including six simulated distribution families, Tara Oceans microbiome data, and 10X Genomics scRNA-seq data, while competing state-of-the-art methods (RichnEst, iNext, PreSeq) miss the true richness in up to 80% of instances, well above the nominal 5%. In addition, DivQuant outperforms classical asymptotic entropy estimators (Miller-Madow, CAE) and the extrapolating iNext estimator. Running times remain competitive, with DivQuant typically completing in seconds. DivQuant is available as a command-line tool at https://gitlab.com/rahmannlab/divquant. 2012 ACM Subject ClassificationMathematics of computing[->] Probability and statistics; Mathematics of computing[->] Linear programming; Mathematics of computing[->] Quadratic programming; Applied computing[->] Bioinformatics

bioinformatics↗

bioRxiv2026

PanXpress: Gene expression quantification with a pan-transcriptomic gapped k-mer index

MotivationMost existing workflows for quantifying bacterial gene expression from RNA-seq data rely on mapping reads to a (single) reference transcriptome, typically ignoring strain-level variation. When samples contain unknown or mixed strains, these workflows may introduce reference bias and fail to accurately capture strain-specific gene expression. Pan-transcriptomic approaches address this issue by using pan-transcriptomes as references, but existing solutions require multiple steps for pan-transcriptome construction, indexing, and expression quantification. ResultsWe introduce PanXpress, a unified framework for bacterial pantranscriptomics that performs pantranscriptome construction and indexing directly from genomic FASTA and GFF annotation files, alignment-free mapping of reads to genes from FASTQ samples, and gene expression quantification. The index, a multi-way Cuckoo hash table storing gapped k-mers with associated genes, preserves diversity on the k-mer level. Using simulated RNA-seq data from a mixture of Pseudomonas aeruginosa strains, PanXpress achieves mapping recall comparable to alignment-based methods such as Bowtie2 with higher precision and obtains accurate gene expression and log fold change estimates. On real P. aeruginosa RNA-seq data, using PanXpress pantranscriptomic reference increases the proportion of mapped reads and discovered expressed genes. The index of PanXpress is smaller than that of other tools and it provides faster analysis with consistent results, compared to other tools (Salmon, Kallisto, Bowtie2). PanXpress is thus an accurate and efficient method for bacterial gene expression analysis in complex samples. AvailabilityPanXpress is available at https://gitlab.com/rahmannlab/panxpress. Contactsven.rahmann@uni-saarland.de

bioinformatics↗

bioRxiv2025

Error Correction Algorithms for Efficient Gene ExpressionQuantification in Single Cell Transcriptomics

Technological advances in single-cell RNA sequencing (scRNA-seq) allow us to sequence the transcriptomes of thousands of single cells in parallel, resulting in massive amounts of raw sequence data that must be processed efficiently to obtain a genes x cells expression matrix. In droplet-based scRNA-seq protocols, the sequenced mRNA molecules are tagged with a cell-specific barcode and a unique molecular identifier (UMI) within each cell. Both barcodes and UMIs may contain errors from production, amplification, or sequencing. Correcting and resolving such errors before further processing yields more reliable data and more accurate expression measurements. We propose algorithmic advancements for barcode correction, read-to-gene mapping, and UMI resolution, which we combine into a new method called O_SCPLOWARCANEC_SCPLOW for efficient gene expression quantification from scRNA-seq data. We additionally provide an implementation as a workflow-friendly command-line tool, also called O_SCPLOWARCANEC_SCPLOW. This work builds on the recently published Fourway method to efficiently discover DNA k-mers with a Hamming distance of 1, speeding up barcode correction and UMI resolution and allowing for distinguishing k-mers into weakly and strongly unique ones during read-to-gene mapping. As a side result of separate interest, we show that for the mapping step, it suffices to store three genes per k-mer in order to cover almost all of the genes almost completely, thus avoiding arbitrarily large color sets in the colored De Bruijn graph index. As a result, O_SCPLOWARCANEC_SCPLOW is faster than existing methods while producing very similar results, as demonstrated in a comparison with CO_SCPLOWELLC_SCPLOWRO_SCPLOWANGERC_SCPLOW, KO_SCPLOWALLISTOC_SCPLOWO_SCPCAP|C_SCPCAPO_SCPLOWBUSTOOLSC_SCPLOW and AO_SCPLOWLEVINC_SCPLOWO_SCPCAP-C_SCPCAPO_SCPLOWFRYC_SCPLOW. O_SCPLOWARCANEC_SCPLOW is available via GitLab (https://gitlab.com/rahmannlab/arcane).

bioinformatics↗

bioRxiv2025

Unmasking Pathogen Traits for Chronic Colonization in Neurogenic Bladder Patients

Individuals with neurogenic bladder are particularly susceptible to both chronic bacterial colonization of the bladder and urinary tract infections (UTIs). Neurogenic bladder can arise from a variety of diseases such as diabetes, spinal cord injuries, and spina bifida. To study the ecological and evolutionary dynamics of the microbiome in neurogenic bladder, we developed a longitudinal cohort of 77 children and young adults with spina bifida from two medical centers. We used enhanced urine culture, 16S rRNA sequencing, and whole genome sequencing to characterize the microbial composition of urine and fecal samples. In addition to prospective sample collection, we retrieved prior bacterial isolates from enrolled patients from Vanderbilts clinical microbial biobank, MicroVU. This allowed us to compare bacterial isolates from the same patients over a period of five years. Urine samples were characterized by high abundance of urinary pathogens, such as E. coli and Klebsiella. From longitudinal isolates from individual patients, we identified two common patterns of urinary tract colonization. We observed either the rapid cycling of strains and/or species, often following antibiotic treatment, or we observed the persistence of a single strain across timepoints. Neither persistence of a strain nor colonization with a new strain or species was associated with increased antibiotic resistance. Rather, in paired longitudinally collected strains from the same patients, mutations were identified in genes that code for cell envelope components associated with immune or phage evasion. Experimental testing revealed that O-antigen/LPS biosynthesis mutations confer protection from the immune system while altering susceptibility to phage predation, reflecting a fitness trade-off. We argue that this unparalleled cohort offers the opportunity to identify mechanisms of bacterial adaptation to the urinary tract that can be exploited in future therapeutic approaches.

microbiology↗

bioRxiv2025

Integrated flexible DNA methylation-chromatin segmentation modelingenhances epigenomic state annotation

DNA methylation and histone modifications together shape the cell-type-specific epigenomic landscape. To enable a more comprehensive genome-wide annotation, we developed EpiSegMixMeth (ESMM), the first truly integrative segmentation model combining chromatin marks and DNA methylation. ESMM extends hidden Markov models with flexible read count distributions and state duration modeling. Applied to 154 high-quality human epigenomes from the IHEC EpiAtlas, ESMM substantially improves the annotation of broad heterochromatic regions-covering over 60% of the genome, that are frequently missed by chromatin-only models. Additionally, it precisely defines the boundaries of narrow regulatory elements and resolves local chromatin state transitions during cell differentiation. Notably, we demonstrate that DNA methylation can substitute for missing repressive histone marks in segmentation, ensuring robust annotation across diverse cell types. In memory B-cell development, ESMM reveals fine-scale chromatin state shifts that align with 3D genome architecture changes. Our results highlight the power of integrating DNA methylation into genome segmentation and provide a valuable resource for dissecting cell-type-specific epigenomic regulation.

bioinformatics↗

bioRxiv2025

Cleanifier: A fast and lightweight k-mer based tool to remove contamination in microbial sequence data

MotivationThe first step when working with DNA sequence data of human-derived microbiomes is usually to remove human contamination for two reasons. First, many countries have strict privacy and data protection guidelines for human sequence data, so microbiome data containing partly human data cannot be easily further processed or published. Second, human contamination may cause problems in downstream analysis steps, such as genome assembly and binning. For large-scale metagenomics projects, fast and accurate removal of human contamination is hence critical. ResultsWe introduce Cleanifier, a fast and memory frugal alignment-free tool for detecting and removing human contamination based on gapped k-mers, or spaced seeds. Cleanifier uses a pangenome index of all human gapped k-mers, but the creation and use of alternative references is also possible. Reads are filtered based on the gapped k-mers present in the index. Cleanifier supports two filtering modes: one that queries all gapped k-mers and one that queries only a sample of them. A comparison of Cleanifier with other state-of-the-art tools shows that our sampling mode makes Cleanifier the fastest method with comparable accuracy. Because we store the gapped k-mers in a probabilistic Cuckoo filter, Cleanifier has similar memory requirements to methods that use a minimizer index. At the same time, Cleanifier is more flexible, because it can use different sampling methods on the same index. Availability and ImplementationThe Cleanifier tool is available via gitlab (https://gitlab.com/rahmannlab/cleanifier), PyPi (https://pypi.org/project/cleanifier/) and Bioconda (https://anaconda.org/bioconda/cleanifier). The pre-computed human pangenome index is available for download at https://doi.org/10.5281/zenodo.15639519. Contactrahmann@cs.uni-saarland.de

bioinformatics↗

bioRxiv2024

In depth sequencing of a serially sampled household cohort reveals the within-host dynamics of Omicron SARS-CoV-2 and rare selection of novel spike variants

SARS-CoV-2 has undergone repeated and rapid evolution to circumvent host immunity. However, outside of prolonged infections in immunocompromised hosts, within-host positive selection has rarely been detected. The low diversity within-hosts and strong genetic linkage among genomic sites make accurately detecting positive selection difficult. Longitudinal sampling is a powerful method for detecting selection that has seldom been used for SARS-CoV-2. Here we combine longitudinal sampling with replicate sequencing to increase the accuracy of and lower the threshold for variant calling. We sequenced 577 specimens from 105 individuals from a household cohort primarily during the BA.1/BA.2 variant period. There was extremely low diversity and a low rate of divergence. Specimens had 0-12 intrahost single nucleotide variants (iSNV) at >0.5% frequency, and the majority of the iSNV were at frequencies <2%. Within-host dynamics were dominated by genetic drift and purifying selection. Positive selection was rare but highly concentrated in spike. Two individuals with BA.1 infections had S:371F, a lineage defining substitution for BA.2. A Wright Fisher Approximate Bayesian Computational model identified positive selection at 14 loci with 7 in spike, including S:448 and S:339. We also detected significant genetic hitchhiking between synonymous changes and nonsynonymous iSNV under selection. The detectable immune-mediated selection may be caused by the relatively narrow antibody repertoire in individuals during the early Omicron phase of the SARS-CoV-2 pandemic. As both the virus and population immunity evolve, understanding the corresponding shifts in SARS-CoV-2 within-host dynamics will be important.

microbiology↗

bioRxiv2024

A Comprehensive Review and Evaluation of Species Richness Estimation

MotivationThe statistical problem of estimating the total number of distinct species in a population (or distinct elements in a multiset), given only a small sample, occurs in various areas, ranging from the unseen species problem in ecology to estimating the diversity of immune repertoires. Accurately estimating the true richness from very small samples is challenging, in particular for highly diverse populations with many rare species. Depending on the application, different estimation strategies have been proposed that incorporate explicit or implicit assumptions about either the species distribution or about the sampling process. These methods are scattered across the literature, and an extensive overview of their assumptions, methodology and performance is currently lacking. ResultsWe comprehensively review and evaluate a variety of existing methods on real and simulated data with different compositions of rare and abundant elements. Our evaluation shows that, depending on species composition, different methods provide the most accurate richness estimates. Simpler methods, like the Chao 1 and Chiu estimators, yield accurate predictions for many of the tested species compositions, but tend to underestimate the true richness for heterogeneous populations and small (containing 1% to 5% of the population) samples. When the population size is known, upsampling estimators such as PreSeq and RichnEst often yield more accurate results. Availability and implementationSource code for data simulation and richness estimation is available at https://gitlab.com/rahmannlab/speciesrichness.

bioinformatics↗

bioRxiv2023

EpiSegMix: A Flexible Distribution Hidden Markov Model with Duration Modeling for Chromatin State Discovery

MotivationAutomated chromatin segmentation based on ChIP-seq data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality. ResultsWe introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with two existing tools, ChromHMM, Segway and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states. Availability and implementationSource code: https://gitlab.com/rahmannlab/episegmix.

bioinformatics↗

bioRxiv2021

BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data

Bioinformatic analysis--such as genome assembly quality assessment, alignment summary statistics, relative synonymous codon usage, paired-end aware quality trimming and filtering of sequencing reads, file format conversion, and processing and analysis--is integrated into diverse disciplines in the biological sciences. Several command-line pieces of software have been developed to conduct some of these individual analyses; however, the lack of a unified toolkit that conducts all these analyses can be a barrier in workflows. To address this obstacle, we introduce BioKIT, a versatile toolkit for the UNIX shell environment with 40 functions, several of which were community-sourced, that conduct routine and novel processing and analysis of genome assemblies, multiple sequence alignments, coding sequences, sequencing data, and more. To demonstrate the utility of BioKIT, we assessed the quality and characteristics of 901 eukaryotic genome assemblies, calculated alignment summary statistics for 10 phylogenomic data matrices, determined relative synonymous codon usage across 171 fungal genomes including those that use alternative genetic codes, and demonstrate that a novel metric, gene-wise relative synonymous codon usage, can accurately estimate gene-wise codon optimization. BioKIT will be helpful in facilitating and streamlining sequence analysis workflows. BioKIT is freely available under the MIT license from GitHub (https://github.com/JLSteenwyk/BioKIT), PyPi (https://pypi.org/project/jlsteenwykbiokit/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/jlsteenwyk-biokit). Documentation, user tutorials, and instructions for requesting new features are available online (https://jlsteenwyk.com/BioKIT).

bioinformatics↗

bioRxiv2021

Redox-mediated inactivation of the transcriptional repressor C3600 makes uropathogenic Escherichia coli exquisitely resistant to reactive chlorine species

The ability to overcome stressful environments is critical for pathogen survival in the host. One challenge for bacteria is the exposure to reactive chlorine species (RCS), which are generated by innate immune cells as critical part of the oxidative burst. Hypochlorous acid (HOCl) is the most potent antimicrobial RCS and associated with extensive macromolecular damage in the phagocytized pathogen. However, bacteria have evolved defense strategies to alleviate the effects of HOCl-mediated damage. Among these are RCS-sensing transcriptional regulators that control the expression of HOCl-protective genes under non- and HOCl stress. Uropathogenic Escherichia coli (UPEC), the major causative agent of urinary tract infections (UTIs), is particularly exposed to infiltrating neutrophils during pathogenesis, however, their responses to and defenses of HOCl are still completely unexplored. Here, we present evidence that UPEC strains tolerate higher levels of HOCl and are better protected from neutrophil-mediated killing compared to other E. coli. Transcriptomic analysis of HOCl-stressed UPEC revealed the upregulation of an operon consisting of three genes, one of which encodes the transcriptional regulator C3600. We identified C3600 as a HOCl-sensing transcriptional repressor, which, under non-stress conditions, is bound to the operator and represses the expression of its target genes. During HOCl exposure, however, the repressor forms reversible intermolecular disulfide bonds and dissociates from the DNA resulting in the de-repression of the operon. Deletion of one of the target genes renders UPEC significantly more susceptible to HOCl indicating that the HOCl-mediated induction of the regulon plays a major role for UPECs HOCl resistance. IMPORTANCEHow do pathogens deal with antimicrobial oxidants produced by the innate immune system during infection? Uropathogenic Escherichia coli (UPEC), the most common etiological agent of urinary tract infections (UTIs), is particularly exposed to infiltrating neutrophils and, therefore, must counter elevated levels of the antimicrobial oxidant HOCl to establish infection. Our study provides fundamentally new insights into a defense mechanism that enables UPEC to fend off the toxic effects of HOCl stress. Intriguingly, the defense system is predominantly found in UPEC and absent in non-invasive enteropathogenic E. coli. Our data suggest that expression of the target gene c3601 is exclusively responsible for UPECs increased HOCl tolerance in culture and therefore potentially contributes to UPECs survival during phagocytosis. Thus, this novel HOCl stress defense system could potentially serve as an attractive drug target to increase the bodys own capacity to fight UTIs.

microbiology↗