Biology subjects

Wolff, J.

Publications and source records attributed to Wolff, J..

11 recordsLinked to original sources

bioRxiv2026

vardictcpp: A C++ reimplementation of VarDict variant caller

Genomic variant calling is a central computational task in disease research and diagnostics, where somatic mutations such as JAK2 V617F serve as diagnostic markers in myeloid neoplasms. VarDict, originally written in Perl, and its reimplementation VarDictJava are widely used variant callers in the bioinformatics community. The Perl implementation requires run times of up to several hours; VarDictJava reduces this to a few minutes but can consume large amounts of main memory, which limits the number of jobs that can be run in parallel on a given machine. We present vardictcpp, a functionally identical reimplementation of VarDictJava in C++17. vardictcpp is approximately four times faster in single-core execution and 9-11 times faster using eight cores, while requiring up to 17 times less main memory. Variant calls are identical to those of VarDictJava on the evaluated whole-exome samples. The reduced memory footprint allows more concurrent jobs on existing hardware and thereby increases sample throughput, which is particularly relevant for clinical sequencing laboratories with fixed compute resources. vardictcpp is implemented in C++17 and freely available at https://github.com/MHH-Bioinformatics-Hematology/vardictcpp

bioinformatics↗

bioRxiv2026

Hi-cGAN: Prediction of Hi-C interaction matrices with conditional generative adversarial networks

Background: The three-dimensional organization of the genome is a fundamental aspect of its function and regulation. High-throughput chromosome conformation capture techniques, such as Hi-C, have revolutionized our understanding of spatial genome organization. However, 3C-based methods are resource-intensive and technically demanding. This has driven the development of computational approaches for predicting Hi-C interaction matrices. Hi-cGAN, a novel approach based on conditional generative adversarial networks, offers a computational alternative to extensive wet-lab work by predicting Hi-C interaction matrices. This computational approach contributes to a broader exploration and understanding of genome architecture. Findings: The network pairs a convolutional generator with a convolutional discriminator, evaluated across bin sizes, inputs and cell types. It predicts a whole genome as a cool file at bin sizes from 2 to 25 kb, where Akita, C.Origami and Epiphany emit fixed windows of 1 Mb, 2 Mb and 990 kb. With the input chosen on a validation chromosome, agreement approaches Epiphany's and stays below the sequence-based C.Origami and Akita: over Akita's 411 held-out windows the mean correlation is 0.238 against 0.506. Boundary and loop calls agree less closely, placing the maps at the domain scale. Conclusions: Chromatin factor occupancy determines a substantial part of contact structure, and two tracks capture most of it. The most informative track depends on the resolution: CTCF and the cohesin subunits at 5 to 10 kb, active histone marks at 25 kb. Transfer to an unseen cell type costs about 0.12 SCC, and which method leads depends on the measure.

bioinformatics↗

bioRxiv2026

Variation in infant subcortical brain development from 6 to 12 months in Down syndrome

IntroductionDown syndrome (DS), arising from Trisomy 21, is the most common genetic condition associated with intellectual disability. While smaller total brain volumes have been consistently observed in DS, no longitudinal neuroimaging studies have examined volumetric brain development in DS during infancy, a period of rapid neural growth when interventions may have the greatest impact. MethodHigh-resolution T1- and T2-weighted images were acquired during natural sleep in a multisite longitudinal cohort of 44 infants with DS and 39 control infants without DS at ages 6 and 12 months. Neuroimaging data were harmonized to reduce batch effects, and a novel deep-learning, repeated-measures segmentation approach was applied to optimize neuroanatomical segmentations. Total intracranial volume (ICV) and bilateral absolute subcortical volumes (amygdala, caudate, hippocampus, pallidum, putamen, thalamus) were first directly compared in infants with and without DS at 6 and 12 months. Hierarchical linear modeling (HLM) evaluated longitudinal group differences for each structure, accounting for sex, gestational age, and laterality. Subcortical group differences estimated by HLM were also compared to group differences in total ICV. ResultsICV in infants with DS was lower than controls at 6 months (12.6%; p<.001) and 12 months (16.3%; p<.001). Subcortical structures displayed a range of lower volumes (6.9%-13.1%; ps[≤].003) in infants with DS, although the caudate and putamen were exceptions. Caudate volumes were on average lower in DS but not significantly different from controls, while putamen volumes were on average higher in DS but not significantly different from controls, except for the right putamen, which was significantly larger (5.3%; p=.018) at 6 months. In HLM, ICV and all subcortical structures showed slower growth in DS from 6 to 12 months, except for the amygdala and putamen, which displayed similar growth rates to controls. DS-associated reductions in subcortical volumes were similar in magnitude to ICV, although 12-month caudate and 6- and 12-month putamen volumes were enlarged relative to ICV. ConclusionInfants with DS exhibited substantially reduced ICV and widespread reductions in subcortical volumes and growth from 6-12 months. Across a range of volumetric differences, findings were most distinct in the basal ganglia, for which volume reductions were attenuated in the caudate, while the putamen was uniquely enlarged with comparable growth to controls. These observations support early regional specificity in the neural impact of Trisomy 21 and underscore the utility of infant neuroimaging to inform biologically based interventions and clinical trial readiness in DS.

neuroscience↗

bioRxiv2026

Visual Cortical Response Variability in Infants at High Familial Likelihood for Autism

Visual processing undergoes rapid development in the first year of life, supporting the emergence of higher-order cognitive, language, and motor functions. Visual evoked potentials (VEPs) provide a non-invasive measure of visual system maturation that may shed light on heterogeneous developmental trajectories among infants at high familial likelihood for autism. Infants with an older sibling with autism spectrum disorder (N = 177 at 6 months; N = 132 at 12 months) participated in the Infant Brain Imaging Study-Early Prediction (IBIS-EP) study. Pattern-reversal VEPs were recorded at 6 and 12 months, and developmental skills were assessed at 24 months using the Bayley Scales of Infant and Toddler Development (Bayley-4). VEP components (P1 and N1) were characterized by their amplitude and latency, as well as trial-to-trial variability in these measures. Associations with 24-month cognitive, language, and motor scores were examined using general linear models controlling for age, site, sex, and trial count. Robust VEPs were observed at both time points, with age-appropriate morphology and expected developmental changes, including decreases in P1 latency and amplitude from 6 to 12 months. Greater trial-to-trial variability in P1 latency at both time points was associated with higher cognitive and language scores at 24 months. In contrast, conventional measures of mean P1 latency and amplitude were not associated with developmental outcomes. These findings suggest that temporal variability in early visual responses may index adaptive sensory-circuit flexibility during a period of rapid experience-dependent development. VEP response-timing variability may therefore provide an early mechanistic marker of sensory-circuit organization relevant to later developmental trajectories. Research HighlightsO_LIGreater trial-to-trial variability in visual cortical response timing was associated with higher cognitive and language scores in infants at high familial likelihood for autism. C_LIO_LIConventional average VEP measures were unrelated to outcomes, suggesting temporal variability may more sensitively capture relevant neural-circuit differences in this population. C_LIO_LIGreater variability in early visual responses may reflect adaptive sensory-circuit flexibility during a critical period of neurodevelopment. C_LIO_LIVEP response-timing measures provide an early mechanistic window into sensory-circuit organization and its relation to later developmental trajectories. C_LI

neuroscience↗

bioRxiv2025

Landscape context drives within-species variation in dispersal kernels and limits transferability

Dispersal is a key ecological trait that ensures connectivity, gene flow, and range dynamics, yet empirical information about the dispersal process remains scarce. While interspecific differences have been synthesized, intraspecific variation driven by individual movement and landscape context remains poorly understood. Here, we use the individual-based modelling platform RangeShifter to simulate movement trajectories of four animal species, from insects to mammals, across neutral landscapes varying in habitat amount and fragmentation. From these simulations we derived dispersal kernels using five probability density functions and analysed their response to landscape context. Across species, log-normal (fat-tailed) kernels consistently best described dispersal, but both median and long-distance dispersal varied strongly with habitat configuration. Habitat loss consistently reduced abundances, whereas fragmentation per se more strongly shaped dispersal kernels, providing mechanistic insight into a central debate in landscape ecology. Our results highlight that dispersal kernels do not transfer reliably across environments and should be treated as emergent, context-dependent properties. Progress in predicting species responses to global change will require empirical studies explicitly integrated with mechanistic modelling to uncover generalizable links between dispersal, traits, and landscape context.

ecology↗

bioRxiv2024

HIV-1 neutralizing antibodies in SHIV-infected macaques recapitulate structurally divergent modes of human V2 apex recognition with a single D gene

Broadly neutralizing antibodies targeting the V2 apex of the HIV-1 envelope trimer are among the most common specificities elicited in HIV-1-infected humans and simian-human immunodeficiency virus (SHIV)-infected macaques. To gain insight into the prevalent induction of these antibodies, we isolated and characterized 11 V2 apex-directed neutralizing antibody lineages from SHIV-infected rhesus macaques. Remarkably, all SHIV-induced V2 apex lineages were derived from reading frame two of the rhesus DH3-15*01 gene. Cryo-EM structures of envelope trimers in complex with antibodies from nine rhesus lineages revealed modes of recognition that mimicked three canonical human V2 apex-recognition modes. Notably, amino acids encoded by DH3-15*01 played divergent structural roles, inserting into a hole at the trimer apex, H-bonding to an exposed strand, or forming part of a loop scaffold. Overall, we identify a DH3-15*01-signature for rhesus V2 apex broadly neutralizing antibodies and show that highly selected genetic elements can play multiple roles in antigen recognition. HighlightsO_LIIsolated 11 V2 apex-targeted HIV-neutralizing lineages from 10 SHIV-infected Indian-origin rhesus macaques C_LIO_LICryo-EM structures of Fab-Env complexes for nine rhesus lineages reveal modes of recognition that mimic three modes of human V2 apex antibody recognition C_LIO_LIAll SHIV-elicited V2 apex lineages, including two others previously published, derive from the same DH3-15*01 gene utilizing reading frame two C_LIO_LIThe DH3-15*01 gene in reading frame two provides a necessary, but not sufficient, signature for V2 apex-directed broadly neutralizing antibodies C_LIO_LIStructural roles played by DH3-15*01-encoded amino acids differed substantially in different lineages, even for those with the same recognition mode C_LIO_LIPropose that the anionic, aromatic, and extended character of DH3-15*01 in reading frame two provides a selective advantage for V2 apex recognition compared to B cells derived from other D genes in the naive rhesus repertoire C_LIO_LIDemonstrate that highly selected genetic elements can play multiple roles in antigen recognition, providing a structural means to enhance recognition diversity C_LI

immunology↗

bioRxiv2023

Facilitating taxonomy and phylogenetics: An informative and cost-effective protocol integrating long amplicon PCRs and third-generation sequencing

Phylogenetic inference has become a standard technique in integrative taxonomy and systematics, as well as in biogeography and ecology. DNA barcodes are often used for phylogenetic inference, despite being strongly limited due to their low number of informative sites. Also, because current DNA barcodes are based on a fraction of a single, fast-evolving gene, they are highly unsuitable for resolving deeper phylogenetic relationships due to saturation. In recent years, methods that analyse hundreds and thousands of loci at once have improved the resolution of the Tree of Life, but these methods require resources, experience and molecular laboratories that most taxonomists do not have. This paper introduces a PCR-based protocol that produces long amplicons of both slow- and fast-evolving unlinked mitochondrial and nuclear gene regions, which can be sequenced by the affordable and portable ONT MinION platform with low infrastructure or funding requirements. As a proof of concept, we inferred a phylogeny of a sample of 63 spider species from 20 families using our proposed protocol. The results were overall consistent with the results from approaches based on hundreds and thousands of loci, while requiring just a fraction of the cost and labour of such approaches, making our protocol accessible to taxonomists worldwide. HighlightsO_LIDNA barcoding is an invaluable tool for fast and accurate taxonomic classification C_LIO_LIExisting DNA barcodes are still insufficient for obtaining well-supported phylogenies C_LIO_LIWe present a protocol that produces long amplicons of unlinked loci for spiders C_LIO_LIAmplicons are sequenced at very low cost per specimen with ONT MinION C_LIO_LIOur recovered phylogeny is largely consistent with that of high-cost approaches. C_LI

evolutionary biology↗

bioRxiv2020

Reef Cover: a coral reef classification for global habitat mapping from biophysical remote sensing.

Coral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications employed in large-scale reef mapping to date are typically poorly defined, not shared or region-specific. Here we present Reef Cover, a new coral reef geomorphic zone classification, developed to support global-scale coral reef habitat mapping in a transparent and version-based framework. We developed scalable classes by focusing on attributes that can be observed remotely, but whose membership rules also reflect knowledge of reef formation, growth and functioning. Bridging the divide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. We use the Caroline and Mariana Island chains in the Pacific as a case study to demonstrate use of the classification scheme and its scientific and conservation applications. The primary application of Reef Cover is the Allen Coral Atlas global coral reef mapping project, but the system will support bespoke reef mapping conducted at a variety of spatial scales.

systems biology↗

bioRxiv2020

Loop detection using Hi-C data with HiCExplorer

Chromatin loops are an important factor in the structural organization of the genome. The detection of chromatin loops in Hi-C interaction matrices is a challenging and compute intensive task. The presented approach shows a chromatin loop detection algorithm which applies a strict candidate selection based on continuous negative binomial distributions and performs a Wilcoxon rank-sum test to detect enriched Hi-C interactions.

bioinformatics↗

bioRxiv2020

Approximate k-nearest neighbors graph for single-cell Hi-C dimensional reduction with MinHash

Single-cell Hi-C interaction matrices are high dimensional and very sparse. To cluster thousands of single-cell Hi-C interaction matrices they are flattened and compiled into one matrix. This matrix can, depending on the resolution, have a few millions or even billions of features and any computation with it is therefore memory demanding. A common approach to reduce the number of features is to compute a nearest neighbors graph. However, the exact euclidean distance computation is in O(n2) and therefore we present an implementation of an approximate nearest neighbors method based on local sensitive hashing running in O(n). The presented method is able to process a 10kb single-cell Hi-C data set with 2500 cells and needs 53 GB of memory while the exact k-nearest neighbors approach is not computable with 1 TB of memory.

bioinformatics↗

bioRxiv2017

Community-driven data analysis training for biology

The primary problem with the explosion of biomedical datasets is not the data itself, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web-browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences.

bioinformatics↗