Biology subjects

Nejdl, W.

Publications and source records attributed to Nejdl, W..

4 recordsLinked to original sources

bioRxiv2026

THEOBROMA: an aggregated open database of 1.13 million natural products with per-compound license auditing, three-tier classification, and stereochemistry-aware deduplication

AbstractNatural products remain one of the most productive sources of pharmacologically active compounds for drug discovery. Open aggregators attribute licenses at database granularity, and the consequences of that choice have become tangible as the field grows. A recent relicensing event in one constituent source (the September 2024 transition of the Natural Products Atlas to CC BY-NC 4.0) demonstrates how database-level licensing propagates across an aggregate and motivates the per-compound audit framework presented here. The same peer cohort separately leaves classification provenance and stereoisomer-family relations coarser than either layer warrants. THEOBROMA, accessible at https://theobroma.l3s.uni-hannover.de, integrates 1,132,805 natural products from 29 open sources under a per-compound license audit that resolves each compounds license tier across all attesting sources under a most-restrictive-wins rule, identifying 900,103 compounds (79.5%) under open-use licenses and providing the per-source attestation chain and resolved tier through a dedicated audit endpoint and a query- time license filter. A three-tier classification labels all but 21 compounds by the provenance of the assignment (55.3% curated source labels, 18.2% NPClassifier tool assignments, 26.5% model-inferred). Deduplication at the full 27-character InChIKey retains 1,132,805 entries across 486,032 connectivity families, exposed via a dedicated /api/stereoisomers/ endpoint and a radial-family display. Per-compound license provenance is the primary differentiator, while classification stratification and stereoisomer-family exposure add finer-grained access to two related axes, supporting license-compatible virtual screening and isomer-specific bioactivity analysis at corpus scale. As an evolving open resource, THEOBROMA pairs continuous pipeline maintenance with interactive geographic, taxonomic, and chemical-space exploration. Graphical AbstractTHEOBROMA integrates 1,132,805 natural products from 29 open sources across three axes. A per-compound license audit resolves each compound to the most restrictive tier attested by any source, giving 891,860 permissive open, 225,536 non-commercial, 8,243 public domain and 7,166 unspecified, so that 900,103 compounds (79.5%) are open-licensed. A three-tier classification records the provenance of each label as source-supplied (55.3%), tool-assigned (18.2%), or model-inferred (26.5%), leaving 21 compounds unclassified. Deduplication at the full 27-character InChIKey retains the stereochemical and protonation variants that 14-character truncation would erase, illustrated by the (E,E)- and (Z,E)-curcumin pair shown. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/731585v2_ufig1.gif" ALT="Figure 1000"> View larger version (15K): org.highwire.dtl.DTLVardef@d5e1daorg.highwire.dtl.DTLVardef@1ded69eorg.highwire.dtl.DTLVardef@dc576corg.highwire.dtl.DTLVardef@1ef94b8_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

High-resolution taxonomic profiling and metatranscriptomics identify microbial, biochemical, host and ecological factors in peri-implant disease

Biofilm-associated diseases like peri-implant mucositis (PIM) and peri-implantitis (PI) are significant clinical challenges affecting millions of dental implant patients globally. Although studies have described the role of microbial, host, or environmental factors in disease development, their complex interplay, particularly during dysbiosis remains poorly understood. This cross-sectional study characterized the microbiome composition and metatranscriptomes of 125 peri-implant biofilms from 48 individuals uncovering molecular signatures linked to peri-implant health (PIH), PIM, and PI. Distinct variations were observed in biofilm amount, composition, activity, phage populations and host response. Biofilms were categorized into four community types (CTs) based on the bacterial transcriptional activity: one linked to PIH, one to PI, and two to PIM. PIH and PIM were primarily characterized by aerotolerant taxa with increased anabolic processes, while PI was dominated by obligate anaerobes with complex biofilm morphology, and heightened catabolic activity and virulence. PIM samples, relative to PIH were characterized by biofilm expansion with minimal functional changes, except for the Neisseria-rich PIM subtype showing higher pyruvate and lipoic acid metabolism. The phagome mirrored the bacterial compositional variations across disease states. Furthermore, human transcriptome responses varied indicating increased keratinization in PIH, enhanced expression of ribosome components in PIM, and inflammatory signaling and hypoxia in PI. Additionally, we identified complex species-enzyme, phage-bacteria, and host-microbe associations within the peri-implant ecosystem. Our integrative multi-omics approach provides a comprehensive view of microbial, biochemical, host, and ecological factors associated with dysbiosis, offering novel insights into peri-implant disease dynamics. ImportancePeri-implant mucositis and peri-implantitis are highly prevalent inflammatory conditions that compromise the long-term survival and success of dental implants, yet their underlying biological mechanisms are largely unresolved. While next-generation sequencing has advanced our understanding of microbial composition across health and peri-implant diseases, it falls short of capturing microbial activity and the broader molecular context of peri-implant dysbiosis. Metatranscriptomics overcomes this limitation by profiling actively transcribed genes within the biofilm, offering direct insights into microbial community functions. In this study, we integrated full-length 16S rRNA gene amplicon sequencing with metatranscriptomic profiling to simultaneously assess microbial taxonomy, functional activity, phage dynamics, and host gene expression in peri-implant biofilms. Importantly, we provide a systems-level view and report previously undescribed associations between different molecular signatures in peri-implant ecosystem.

microbiology↗

bioRxiv2021

GenOtoScope: Towards automating ACMG classification of variants associated with congenital hearing loss

Since next-generation sequencing (NGS) has become widely available, large gene panels containing up to several hundred genes can be sequenced cost-efficiently. However, the interpretation of the often large numbers of sequence variants detected when using NGS is laborious, prone to errors and often not comparable across laboratories. To overcome this challenge, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) introduced standards and guidelines for the interpretation of sequencing variants. Further gene- and disease-specific refinements regarding hereditary hearing loss have been developed since then. With more than 200 genes associated with hearing disorders, the manual inspection of possible causative variants is especially difficult and time consuming. We developed an open-source bioinformatics tool GenOtoScope, which automates all ACMG/AMP criteria that can be assessed without further individual patient information or human curator investigation, including the refined loss of function criterion ("PVS1"). Two types of interfaces are provided: (i) a command line application to classify sequence variants in batches for a set of patients and (ii) a user-friendly website to classify single variants. We compared the performance of our tool with two other variant classification tools using two hearing loss data sets, which were manually annotated either by the ClinGen Hearing Loss Gene Curation Expert Panel or the diagnostics unit of our human genetics department. GenOtoScope achieved the best average accuracy and precision for both data sets. Compared to the second-best tool, GenOtoScope improved accuracy metric by 25.75% and 4.57% and precision metric by 52.11% and 12.13% on the two data sets respectively. The web interface is freely accessible. The command line application along with all source code, documentation and example outputs can be found via the project GitHub page. Author summaryNew high-throughput sequencing technologies can produce massive amounts of information and are utilized by laboratories to explain the often complex genetic aetiology of hereditary diseases. The most common sensory disease, hearing loss, is often hereditary and has a high impact on a patients every-day life. To use these sequencing technologies effectively, software tools were developed that can aid researchers interpreting genetic data by semi-automatically classifying the biologic (and thus potentially medical) impact of detected variants (the alterations of the patients genome compared to the human reference genome). The available genetic variant classification tools are either not designed specifically for the interpretation of variants detected in subjects with hearing loss or they do not allow researchers to use them for batch classification of all variants detected, e. g. in a study group. To address this drawback, we developed GenOtoScope, an open-source tool that automates the pathogenicity classification of variants potentially associated with congenital hearing loss. GenOtoScope can be applied for the automatic classification of all variants detected in a set of probands.

bioinformatics↗

bioRxiv2020

dom2vec: Assessable domain embeddings and their use for protein prediction tasks

MotivationWord embedding approaches have revolutionized Natural Language Processing NLP research. These approaches aim to map words to a low-dimensional vector space in which words with similar linguistic features are close in the vector space. These NLP approaches also preserve local linguistic features, such as analogy. Embedding-based approaches have also been developed for proteins. To date, such approaches treat amino acids as words, and proteins are treated as sentences of amino acids. These approaches have been evaluated either qualitatively, via visual inspection of the embedding space, or extrinsically, via performance on a downstream task. However, it is difficult to directly assess the intrinsic quality of the learned embeddings. ResultsIn this paper, we introduce dom2vec, an approach for learning protein domain embeddings. We also present four intrinsic evaluation strategies which directly assess the quality of protein domain embeddings. We leverage the hierarchy relationship of InterPro domains, known secondary structure classes, Enzyme Commission class information, and Gene Ontology annotations in these assessments. These evaluations allow us to assess the quality of learned embeddings independently of a particular downstream task. Importantly, allow us to draw an analog between the local linguistic features in nature languages and the domain structure and function information in domain architectures, thus providing data-driven insights into the context found in the language of domain architectures. We also show that dom2vec embeddings outperform, or are comparable with, state-of-the-art approaches on downstream tasks. AvailabilityThe protein domain embeddings vectors and the entire code to reproduce the results are available at https://github.com/damianosmel/dom2vec. Contactmelidis@l3s.uni-hannover.de

bioinformatics↗