Biology subjects

Kafkas, S.

Publications and source records attributed to Kafkas, S..

11 recordsLinked to original sources

bioRxiv2025

Genomic surveillance of Methicillin Resistant Staphylococcus aureus in Saudi Arabia.

Methicillin-resistant Staphylococcus aureus (MRSA) surveillance in regions with mass gatherings presents unique challenges for public health systems. Saudi Arabia, hosting millions of pilgrims annually, provides a distinctive setting for studying how human mobility shapes bacterial populations, yet comprehensive genomic surveillance data from this region remains limited. Here, we present an integrated analysis of S. aureus isolates collected across seven Saudi Arabian regions, combining whole-genome sequencing with extensive antimicrobial susceptibility testing and standardized metadata following FAIR data principles. Our analysis revealed striking differences between pilgrimage and non-pilgrimage cities. Pilgrimage cities showed significantly higher genetic diversity and antimicrobial resistance rates, harboring numerous international strains including recognized clones from diverse geographic origins. Reported lineage dynamics is changing, expanding toward community clones. While genomic prediction of antimicrobial resistance showed high accuracy for some antibiotics, particularly beta-lactams, with varying performance for others, highlighting the necessity for phenotypic testing in clinical settings. Our findings demonstrate how mass gatherings drive bacterial population structures and emphasize the importance of integrated surveillance approaches in regions with significant global connectivity and travel. ImportanceGenomic data enables the tracking of pathogens by revealing clonal expansions within populations and identifying successful lineages. However, comprehensive national-level data from Saudi Arabia remains limited on a large scale. The adoption of FAIR principles and reproducible workflows ensures robust, consistent analysis, fostering effective data sharing. The OneHealth approachs success depends on the integration and collaboration across diverse domains in todays digital landscape.

genomics↗

bioRxiv2025

NanoDesigner: Resolving the complex-CDR interdependency with iterative refinement

Camelid heavy-chain only antibodies consist of two heavy chains and single variable domains (VHHs), which retain antigen-binding functionality even when isolated. The term "nanobody" is now more generally used for describing small, single-domain antibodies. Several antibody generative models have been developed for the sequence and structure co-design of the complementary-determining regions (CDRs) based on the binding interface with a target antigen. However, these models are not tailored for nanobodies and are often constrained by their reliance on experimentally determined antigen-antibody structures, which are labor-intensive to obtain. Here, we introduce NanoDesigner, a tool for nanobody design and optimization based on generative AI methods. NanoDesigner integrates key stages -- structure prediction, docking, CDR generation, and side-chain packing -- into an iterative framework based on an expectation maximization (EM) algorithm. The algorithm effectively tackles an interdependency challenge where accurate docking presupposes a priori knowledge of the CDR conformation, while effective CDR generation relies on accurate docking outputs to guide its design. NanoDesigner approximately doubles the success rate of de novo nanobody designs through continuous refinement of docking and CDR generation.

bioinformatics↗

bioRxiv2023

BORD: A Biomedical Ontology based method for concept Recognition using Distant supervision: Application to Phenotypes and Diseases

MotivationConcept recognition in biomedical text is an important yet challenging task. The two main approaches to recognize concepts in text are dictionary-based approaches and supervised machine learning approaches. While dictionary-based approaches fail in recognising new concepts and variations of existing concepts, supervised methods require sufficiently large annotated datasets which are expensive to obtain. Methods based on distant supervision have been developed to use machine learning without large annotated corpora. However, for biomedical concept recognition, these approaches do not yet exploit the context in which a concept occurs in literature, and they do not make use of prior knowledge about dependencies between concepts. ResultsWe developed BORD, a Biomedical Ontology-based method for concept Recognition using Distant supervision. BORD utilises context from corpora which are lexically annotated using labels and synonyms from the classes of a biomedical ontology for model training. Furthermore, BORD utilises the ontology hierarchy for normalising the recognised mentions to their concept identifiers. We show how our method improves the performance of state of the art methods for recognising disease and phenotype concepts in biomedical literature. Our method is generic, does not require manually annotated corpora, and is robust to identify mentions of ontology classes in text. Moreover, to the best of our knowledge, this is the first approach utilising the ontology hierarchy for concept recognition. AvailabilityBORD is publicly available from https://github.com/bio-ontology-research-group/BORD Contactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗

bioRxiv2022

Genome assembly and association tests identify interacting loci associated with vigor, precocity, and sex in interspecific pistachio rootstocks

Understanding the basis of hybrid vigor remains a key question in crop breeding and improvement, especially for rootstock development where F1 hybrids are extensively utilized. Full-sibling UCB-1 F1 seedling rootstocks are widely planted in commercial pistachio orchards that are generated by crossing two highly heterozygous outbreeding parental trees of Pistacia atlantica (female) and P. integerrima (male). This results in extensive phenotypic variability, prompting costly removal of low-yielding small trees. To identify the genetic basis of this variability, we assembled chromosome-scale genome assemblies of the parental trees of UCB-1. We genotyped 960 UCB-1 trees in an experimental orchard for which we also collected multi-year phenotypes. We genotyped an additional 1,358 rootstocks in six commercial pistachio orchards and collected single-year tree size data. Genome-wide single marker association tests identified loci associated with tree size and shape, sex, and precocity. In the experimental orchard, we identified multiple trait-associated loci and a strong candidate for ZZ/ZW sex chromosomes. We found significant marker associations unique to different traits and to early vs. late phenotypic measures of the same trait. We detected two loci strongly associated with rootstock size in commercial orchards. Pseudo-testcross classification of markers demonstrated that the trait-associated alleles for each locus were segregating in the gametes of opposite parents. These two loci interact epistatically to generate the bimodal distribution of tree size with undesirable small trees observed by growers. We identified candidate genes within these regions. These findings provide a foundational resource for marker development and genetic selection of vigorous pistachio UCB-1 rootstock.

genetics↗

bioRxiv2020

Prediction of novel virus-host interactions by integrating clinical symptoms and protein sequences

MotivationInfectious diseases from novel viruses have become a major public health concern. Rapid identification of virus-host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. ResultsWe developed DeepViral, a deep learning based method that predicts protein-protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses. Availabilityhttps://github.com/bio-ontology-research-group/DeepViral Contactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗

bioRxiv2020

DDIEM: Drug Database for Inborn Errors of Metabolism

BackgroundInborn errors of metabolism (IEM) represent a subclass of rare inherited diseases caused by a wide range of defects in metabolic enzymes or their regulation. Of over a thousand characterized IEMs, only about half are understood at the molecular level, and overall the development of treatment and management strategies has proved challenging. An overview of the changing landscape of therapeutic approaches is helpful in assessing strategic patterns in the approach to therapy, but the information is scattered throughout the literature and public data resources. ResultsWe gathered data on therapeutic strategies for 299 diseases into the Drug Database for Inborn Errors of Metabolism (DDIEM). Therapeutic approaches, including both successful and ineffective treatments, were manually classified by their mechanisms of action using a new ontology. ConclusionsWe present a manually curated, ontologically formalized knowledgebase of drugs, therapeutic procedures, and mitigated phenotypes. DDIEM is freely available through a web interface and for download at http://ddiem.phenomebrowser.net.

bioinformatics↗

bioRxiv2018

Phenotypic, functional and taxonomic features predict host-pathogen interactions

MotivationIdentification of host-pathogen interactions (HPIs) can reveal mechanistic insights of infectious diseases for potential treatments and drug discoveries. Current computational methods for the prediction of HPIs often rely on our knowledge on the sequences and functions of pathogen proteins, which is limited for many species, especially for emerging pathogens. Matching the phenotypes elicited by pathogens with phenotypes associated with host proteins might improve the prediction of HPIs. ResultsWe developed an ontology-based machine learning method that predicts potential interaction protein partners for pathogens. Our method exploits information about disease mechanisms through features learned from phenotypic, functional and taxonomic knowledge about pathogens and human proteins. Additionally, by embedding the phenotypic information of the pathogens within a formal representation of pathogen taxonomy, we demonstrate that our model can accurately predict interaction partners for pathogens without known phenotypes, using a combination of their taxonomic relationships with other pathogens and information from ontologies as background knowledge. Our results show that the integration of phenotypic, functional and taxonomic knowledge not only improves the prediction of HPIs, but also enables us to investigate novel pathogens in emerging infectious diseases. Availabilityhttps://github.com/bio-ontology-research-group/hpi-predict Contactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗

bioRxiv2018

PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research

Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate phenotypes with infectious disease. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/, and the data is freely available through a public SPARQL endpoint.

bioinformatics↗

bioRxiv2018

Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

Recent developments in machine learning have lead to a rise of large number of methods for extracting features from structured data. The features are represented as a vectors and may encode for some semantic aspects of data. They can be used in a machine learning models for different tasks or to compute similarities between the entities of the data. SPARQL is a query language for structured data originally developed for querying Resource Description Framework (RDF) data. It has been in use for over a decade as a standardized NoSQL query language. Many different tools have been developed to enable data sharing with SPARQL. For example, SPARQL endpoints make your data interoperable and available to the world. SPARQL queries can be executed across multiple endpoints. We have developed a Vec2SPARQL, which is a general framework for integrating structured data and their vector space representations. Vec2SPARQL allows jointly querying vector functions such as computing similarities (cosine, correlations) or classifications with machine learning models within a single SPARQL query. We demonstrate applications of our approach for biomedical and clinical use cases. Our source code is freely available at https://github.com/bio-ontology-research-group/vec2sparql and we make a Vec2SPARQL endpoint available at http://sparql.bio2vec.net/.

bioinformatics↗

bioRxiv2018

Ontology based mining of pathogen-disease associations from literature

BackgroundInfectious diseases claim millions of lives especially in the developing countries each year, and resistance to drugs is an emerging threat worldwide. Identification of causative pathogens accurately and rapidly plays a key role in the success of treatment. To support infectious disease research and mechanisms of infection, there is a need for an open resource on pathogen-disease associations that can be utilized in computational studies. A large number of pathogen-disease associations is available from the literature in unstructured form and we need automated methods to extract the data.\n\nResultsWe developed a text mining system designed for extracting pathogen-disease relations from literature. Our approach utilizes background knowledge from an ontology and statistical methods for extracting associations between pathogens and diseases. In total, we extracted a total of 3,420 pathogen-disease associations from literature. We integrated our literature-derived associations into a database which links pathogens to their phenotypes for supporting infectious disease research.\n\nConclusionsTo the best of our knowledge, we present the first study focusing on extracting pathogen-disease associations from publications. We believe the text mined data can be utilized as a valuable resource for infectious disease research. All the data is publicly available from https://github.com/bio-ontology-research-group/padimi and through a public SPARQL endpoint from http://patho.phenomebrowser.net/.

bioinformatics↗

bioRxiv2017

Literature Evidence in Open Targets - a target validation platform

BackgroundWe present the Europe PMC literature component of Open Targets - a target validation platform that integrates various evidence to aid drug target identification and validation. The component identifies target-disease associations in documents and ranks the documents based on their confidence from the Europe PMC literature database, by using rules utilising expert-provided heuristic information and serves the platform regularly with the up-to-date data since December, 2015.\n\nResultsCurrently, there are a total number of 1168365 distinct target-disease associations text mined from >26 million PubMed abstracts and >1.2 million Open Access full text articles. Our comparative analyses on the current available evidence data in the platform revealed that 850179 of these associations are exclusively identified by literature mining.\n\nConclusionThis component helps the platforms users by providing the most relevant literature hits for a given target and disease. The text mining evidence along with the other types of evidence can be explored visually through https://www.targetvalidation.org and all the evidence data is available for download in json format from https://www.targetvalidation.org/downloads/data.

bioinformatics↗