Biology subjects

van Geest, G.

Publications and source records attributed to van Geest, G..

7 recordsLinked to original sources

bioRxiv2026

Characterization of heterogeneous bioinformatics training needs in the AI era

Bioinformatics training providers serve heterogeneous audiences, a challenge sharpened by fast-evolving technologies such as generative artificial intelligence (AI). Analyzing training-needs surveys in aggregate can mask this heterogeneity. We present a network-based clustering protocol that groups respondents by the similarity of their Likert-type topic ratings: respondents form a Spearman correlation network, and community detection groups them into learner profiles with shared training needs, independent of demographics. We demonstrate the protocol on the 2026 SIB Swiss Institute of Bioinformatics (SIB) training needs survey (n=260), covering topics, barriers, and formats. We identified four interpretable learner profiles: AI and data, infectious disease genomics, functional genomics, and biochemistry, imaging & AI. Each had distinct topic priorities, barriers, and format preferences that an aggregate analysis hides. For example, LLM-based knowledge extraction ranked 2nd overall but only 16th in the infectious disease genomics profile, whereas phylogenetics ranked 17th overall but first there. Across profiles, AI/LLM skills and reproducible data practices were in high demand, whereas AI-assisted learning was, unexpectedly, the least preferred format. Because the protocol requires only a shared Likert scale, it is reusable for other training-needs assessments; the implementation is openly available. Profile-based curricula can target each group's topics, formats, and barriers.

bioinformatics↗

bioRxiv2026

Community needs for FAIR pathogen data

BackgroundDatasets related to infectious diseases are essential for public health decision-making, yet their reuse remains limited by persistent barriers to data sharing and integration. Achieving data that are Findable, Accessible, Interoperable, and Reusable (FAIR) is widely recognized as essential for accelerating scientific discovery and enabling coordinated responses to emerging threats, but the needs of the global pathogen data community have not been systematically characterized. AimThis study, conducted by the Pathogen Data Network (PDN), aims to identify infrastructural and educational priorities among stakeholders working with infectious disease-related data in order to guide community-responsive support for data sharing and interoperability. MethodsA cross-sectional stakeholder survey was disseminated to a well-defined expert population within PDN networks and via open professional channels. A total of 136 responses from researchers, healthcare professionals, bioinformaticians, and educators were analyzed descriptively to identify prioritized barriers, training needs, and preferred support mechanisms. ResultsRespondents consistently identified structural constraints as the primary impediments to effective data use, including limited funding (74%), data-aggregation challenges (68%), and a shortage of skilled personnel (52%). Respondents identified bioinformatics for infectious disease research (68%) as the highest priority for training, followed by guidance on using the integrated pathogen data and tools portal provided by the PDN, the Pathogens Portal (51%). The Pathogens Portal was also ranked as the most essential PDN resource (72%). Preferred training formats included virtual short courses (68%) and webinars (66%). Notably, while researchers emphasized technical subjects like machine learning, educators prioritized foundational case studies. ConclusionThese findings provide an evidence-based diagnostic of community needs and suggest that barriers to FAIR pathogen data are predominantly systemic rather than purely technological. The survey framework and openly available dataset offer a reusable template for assessing needs in other communities and regions. By aligning training, infrastructure development, and outreach with empirically identified priorities, organizations supporting infectious disease research can strengthen the interoperability and reuse of data and establish a benchmark for future community-driven improvements.

scientific communication and education↗

bioRxiv2024

Using Glittr.org to find, compare and re-use online training materials

A wealth of excellent training and educational materials for the computational life sciences are scattered around the Internet, but they can be hard to find. Many materials reside in public Git repositories that are hosted on platforms such as GitHub and GitLab. Glittr.org is a manually curated database of Git repositories, which enables users to find educational materials that would otherwise be hard to identify. With the application, users can search and compare educational materials based on topic and author, but also on engagement metrics such as stargazers (bookmarks) and recency (days since last commit). Glittr.org currently contains 664 entries, which are assigned to six different categories within the domain of computational life sciences. By analysing the database, we reveal insights in the availability of materials per topic, collaboration patterns of developers, and licensing practices. This knowledge helps to understand in which areas open educational materials are scant, the importance of Git for collaboration on educational materials and how licensing can be improved to enhance sharing and reuse. Taken together, we show that Glittr.org contains a wealth of connected and openly available metadata. Therefore, it enhances adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) principles, which benefits learners, teachers and trainers in the entire life sciences community and beyond.

bioinformatics↗

bioRxiv2022

Molecular characterization of cell types in the squid Loligo vulgaris

Cephalopods have long been getting a lot of attention for their fascinating behavioral abilities and for the complexity of their nervous systems that set them apart from other mollusks. Because of the great evolutionary distance that separates vertebrates from mollusks, it is evident that higher cognitive features have evolved independently in this clade although they sometimes resemble cognitive functions of vertebrates. Alongside their complex behavioral abilities, cephalopods have evolved specialized cells and tissues, such as the chromatophores for camouflage or suckers to grasp prey. Gaining a better understanding of the biology of various species of cephalopods can significantly improve our knowledge of how these animals evolved and better identify the mechanisms that drive the astonishing faculties of their nervous systems. In this study, we performed single-cell transcriptomics of whole heads of Loligo vulgaris pre-hatchlings. We characterized the different cell types in the head of these animals and explored the expression patterns of core cell type markers by hybridization chain reaction. We were able to thoroughly describe some major components of the squid nervous that play important roles for the maintenance, development and sensory function in the nervous system of these animals.

developmental biology↗

bioRxiv2021

Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR

MotivationThe investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialised tools for such analyses. Moreover, deciphering meiotic processes at higher ploidy levels is not straightforward, but is necessary to understand the reproductive dynamics of these species, or uncover potential barriers to their genetic improvement. ResultsHere we present polyqtlR, a novel software tool to facilitate such analyses in (auto)polyploid crops. It performs QTL interval mapping in F1 populations of outcrossing polyploids of any ploidy level using identity-by-descent (IBD) probabilities. The allelic composition of discovered QTL can be explored, enabling favourable alleles to be identified and tracked in the population. Visualisation tools within the package facilitate this process, and options to include genetic co-factors and experimental factors are included. Detailed information on polyploid meiosis including prediction of multivalent pairing structures, detection of preferential chromosomal pairing and location of double reduction events can be performed. Availability and implementationpolyqtlR is freely available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polyqtlR. Contactpeter.bourke@wur.nl or chris.maliepaard@wur.nl Supplementary informationSupplementary data are available

genetics↗

bioRxiv2020

CD8+ T cell priming in oral mucosa-draining lymph nodes supports systemic immunity

The gastrointestinal (GI) tract constitutes an essential barrier against ingested pathogens. While immune reactions are well-studied in the lower GI tract, it remains unclear how adaptive immune responses are initiated during microbial challenge of the oral mucosa, the primary site of pathogen encounter in the upper GI tract. Here, we identify mandibular lymph nodes (mandLN) as sentinel lymphoid organs that collect orally administered Listeria monocytogenes (Lm), leading to local CD8+ T cell activation. In contrast to CD8+ T effector cells (TEFF) generated in mesenteric lymph nodes, mandLN CD8+ TEFF lacked a gut-seeking phenotype but contributed to systemic host protection. Accordingly, mandLN stromal and dendritic cells expressed low levels of enzymes required for gut homing imprinting. Our findings extend the concept of regional specialization of immune responses along the length of the GI tract, with mandLN acting as oral lymph-draining counterparts of intestinal lymph-draining LN of the lower GI tract. SummaryListeria monocytogenes ingestion leads to priming of cytotoxic T cells in oral mucosa draining mandibular lymph nodes, which contribute to systemic host protection.

immunology↗

bioRxiv2017

polymapR: linkage analysis and genetic map construction from F1 populations of outcrossing polyploids

MotivationPolyploid species carry more than two copies of each chromosome, a condition found in many of the worlds most important crops. Genetic mapping in polyploids is more complex than in diploid species, resulting in a lack of available software tools. These are needed if we are to realise all the opportunities offered by modern genotyping platforms for genetic research and breeding in polyploid crops.\n\nResultspolymapR is an R package for genetic linkage analysis and integrated genetic map construction from bi-parental populations of outcrossing autopolyploids. It can currently analyse triploid, tetraploid and hexaploid marker datasets and is applicable to various crops including potato, leek, alfalfa, blueberry, chrysanthemum, sweet potato or kiwifruit. It can detect, estimate and correct for preferential chromosome pairing, and has been tested on high-density marker datasets from potato, rose and chrysanthemum, generating high-density integrated linkage maps in all of these crops.\n\nAvailability and ImplementationpolymapR is freely available under the general public license from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/packages=polymapR.\n\nContactChris Maliepaard chris.maliepaard@wur.nl or Roeland E. Voorrips roeland.voorrips@wur.nl

genetics↗