Since its first release in 2013, the sequence read trimming tool AlienTrimmer has been continuously improved, for use in various practical cases. In this context, the growing popularity of nanopore sequencing has made it necessary to update AlienTrimmer to deal with such long reads. New features were then implemented while guaranteeing that AlienTrimmer can still run fast. As illustrated in this note, the last release of AlienTrimmer can now filter out troublesome bases (i.e., low-quality and/or exogenous bases) from nanopore read ends with both first-rate accuracy and speed. Availability: the source code of AlienTrimmer is available at https://gitlab.pasteur.fr/GIPhy/AlienTrimmer
Prof. Amos Bairoch (1957–2025) was one of the most influential and transformative figures in modern bioinformatics, a scientist whose vision reshaped how biological information is curated, accessed, and understood. As the creator of Swiss-Prot, UniProtKB/Swiss-Prot, PROSITE, ENZYME, and several other foundational databases, he defined the gold standard for accuracy, manual curation, and open accessibility in protein knowledgebases. His work enabled generations of researchers and laid the groundwork for breakthroughs across genomics, proteomics, biotechnology, precision medicine, and systems biology.A professor at the University of Geneva and a driving force at the Swiss Institute of Bioinformatics (SIB), Prof. Bairoch dedicated his life to building high-quality, sustainable, community-oriented resources that serve millions of users worldwide. He was not only an exceptional innovator but also a generous mentor and a passionate advocate for open science, training and inspiring countless scientists who continue to carry forward his vision.The EMBnet community mourns his sudden passing with profound sadness. Amos was a colleague, collaborator, and friend whose impact on our field—and on global Life Sciences—cannot be overstated. His legacy endures in the resources he created, the standards he established, and the many people whose careers he touched. We honour his memory with deep respect, gratitude, and heartfelt sorrow.
This article addresses the genetic and genomic determinants that influence susceptibility or resistance to human infections, whether caused by bacteria, viruses, fungi, parasites or even prions. A large number of genes are involved in susceptibility or resistance, and this is because Homo sapiens sapiens evolutionarily acquired key DNA modifications adaptable to defence as a result of the interaction of infectious agents with their genetic properties, natural selection and mutations. This article provides a description of the methods used in the investigation of this evolution and mutual host-infectious agent adaptation. The genes involved in susceptibility and resistance are presented, as well as the polymorphic variants that provide greater or lesser reaction capacity. The high number of genes, which produce specific defence proteins, have intricate relationships among them. Relationships among these genes have been analysed. A list of variants, and even mutations of the 70 to 153 proteins/genes that are involved in the immune response to infectious diseases has been compiled. Additionally, we designed a network of physical interactions as predictable, with BioGrid data and enrichment data obtained by the g:Profiler platform, finding 57 of the 70 genes with protein-to-protein interactions. The network detected is composed of 1,049 interacting genes (in total 1,106 genes and 1,910 interactions). This analysis shows the importance of immunity genes in the defence against infectious agents, as well as the effects of the genes involved such as HLA, immunoglobulins, interleukins, immune cells, among others. Based on the functional analysis of genes involved in susceptibility and resistance to infections, we compiled a list of genetic disorders that increase an individual’s risk of developing infectious diseases. It is known that infectious diseases manifest general clinical characteristics, which are a good guide to suspect a disease. Furthermore, we describe general clinical signs that may suggest underlying genetic immunodeficiency and highlight specific infectious diseases—such as HIV/AIDS, amebiasis, malaria, Chagas disease, tuberculosis, and COVID-19—where genetic susceptibility factors have been well-characterised.
The spread of pathogens by animals is a serious issue around the world that causes a severe threat to human health. Feral pigeons (Columba livia forma domestica) that live in urban areas are zoonotic carriers of various pathogens that can be transmitted to humans by faecal contamination. This study aimed to detect the presence of bacterial, viral, and specifically fungal pathogens in pigeon faeces based on the World Health Organization's (WHO) priority pathogen list published in 2022. Fresh faecal samples were collected at Uppsala, Central Station, and Svandammen, the pigeons' most relevant gathering spots and feeding sites. Genomic DNA was directly extracted from these samples, and ScilifeLab performed High throughput sequencing through Oxford Nanopore Technologies(ONT) (PromethION). Metagenomic analysis revealed that most of the critically prioritized viral and bacterial pathogens listed by WHO were present in pigeon faeces. Regarding fungal pathogens, which were the main objective of this study, samples from both studied locations contained all critical, high and, medium-important fungal pathogens published in the WHO list, such as Aspergillus fumigatus, Candida albicans, Candida auris, Cryptococcus neoformans, Nakaseomyces glabratus, Candida tropicalis, and Cryptococcus gattii. These fungal pathogens pose the risk of invasive fungal diseases and severe infections in low-immunity individuals and vulnerable populations. The findings indicate the importance of conducting further research to comprehensively understand potential exposure to feral pigeons. Furthermore, keeping pigeons away from sensitive areas, such as hospitals, and implementing measures to control pigeon populations can significantly decrease the spread of pathogens.
Plant-pathogen interactions activate molecular activities wherein the host defends the pathogen while the pathogen tries to suppress the plant response. Small RNAs (sRNAs) mediate major mechanisms, including post-transcriptional gene silencing, histone modification and DNA methylation by which plants respond to the presence of pathogens. Genome-wide profiling of host and pathogen sRNAs is therefore pivotal to uncovering the mechanisms underlying the host-pathogen interaction and mechanisms for host resistance. sRNA high throughput sequencing (HTS) data analysis often involves multiple stages/tools. Most necessary tools are accessible only through the command line, making it challenging for those without a high level of Unix/Linux skills. Furthermore, installation of some of these tools may become difficult due to dependencies and software version compatibility. We have developed an integrated open-source pipeline, Ds-Seq, for end-to-end in silico analysis of sRNA HTS data with improved reproducibility. The pipeline combines in-house scripts and public tools in a shell script, which can be invoked with a single command. The pipeline's usefulness has been demonstrated with testing on publicly available and published data from independent sRNA-seq datasets of host-pathogen interaction studies. Ds-Seq is available on GitHub, while a Docker image can be obtained from the Docker hub.Availability: Ds-Seq is freely available from the GitHub repository at https://github.com/CEPHAS-01/small-RNASeq.ngs and Docker hub with ID cephas/ds-seq (https://hub.docker.com/r/cephas/ds-seq).
Bioinformatics and artificial intelligence (AI) have emerged as transformative tools in modern medicine, revolutionizing the landscape of medical diagnosis and treatment. Herein, we provide an overview of the synergistic relationship between bioinformatics and AI, elucidating their pivotal roles in deciphering complex biological data and advancing precision medicine and in particular endocrinology. We explore various applications of bioinformatics and AI in medical research, including genomic analysis, drug discovery, disease diagnosis, and personalized treatment strategies. Additionally, we discuss challenges and future directions in leveraging these technologies to enhance healthcare outcomes.
Breast milk, often referred to as "liquid gold," is a complex biofluid that provides essential nutrients, immune factors, and developmental cues for newborns. Recent advancements in the field of exosome research have shed light on the critical role of exosomes in breast milk. Exosomes are nanosized vesicles that carry bioactive molecules, including proteins, lipids, nucleic acids, and miRNAs. These tiny messengers play a vital role in intercellular communication and are now being recognized as key players in infant health and development. This paper explores the emerging field of milk exosomics, emphasizing the potential of exosome fingerprinting to uncover valuable insights into the composition and function of breast milk. By deciphering the exosomal cargo, we can gain a deeper understanding of how breast milk influences neonatal health and may even pave the way for personalized nutrition strategies.
Extracellular vesicles (EVs) are a heterogeneous group of lipid-bound vesicles released by cells into the extracellular space. EVs are an important mediator of intercellular communications and carry a wide variety of molecules that exert a biological function, such as lipids, nucleic acids, proteins, ions, and adenosine triphosphate (ATP). Extracellular vesicles are classified into microvesicles, exosomes, and apoptotic bodies depending on their biogenesis and size. Exosomes are spherical lipid-bilayer vesicles with a diameter of about 40 to 100 nm. Exosomes originate from intracellular endosomal compartments, while microvesicles originated directly from a cell’s plasma membrane and apoptotic bodies originate from cells undergoing apoptosis and are released via outward blebbing and fragmentation of the plasma membrane. Specifically, exosomes have garnered great attention since they display great potential as both biomarkers and carriers of therapeutic molecules.
Epigenetics is the study of heritable changes in gene expression that occur without changes to the underlying DNA sequence. Epigenetic modifications can include DNA methylation, histone modifications, and non-coding RNAs, among others. These modifications can influence the expression of genes by altering the way DNA is packaged and accessed by transcriptional machinery, thereby affecting cellular function and behavior. Epigenetic modifications can be influenced by a variety of factors, including environmental exposures, lifestyle factors, and aging, whilst abnormal epigenetic modifications have been implicated in a range of diseases, including cancer, neurodegenerative disorders, and cardiovascular disease. The study of epigenetics has the potential to provide new insights into the mechanisms of disease and could lead to the development of new diagnostic and therapeutic strategies. Exosomes can transfer epigenetic information to recipient cells, thereby influencing various physiological and pathological processes, and the identification of specific epigenetic modifications that are associated with a particular disease could lead to the development of targeted therapies that restore normal gene expression patterns. In recent years, the emerging role of exosomal epigenetics in human breast milk, highlighting its significance in infant nutrition and immune development. Milk exosomes are shown to carry epigenetic regulators, including miRNAs and long non-coding RNAs, which can modulate gene expression in recipient cells. These epigenetic modifications mediated by milk exosomal RNAs have implications for the development of the gastrointestinal tract, immune system, and metabolic processes in infants.
EpiCass and CassavaNet4Dev are collaborative projects funded by the Swedish Research Council between the Swedish University of Agriculture (SLU) and the International Institute of Tropical Agriculture (IITA). The projects aim to investigate the influence of epigenetic changes on agricultural traits such as yield and virus resistance while also providing African students and researchers with advanced bioinformatics training and opportunities to participate in big data analysis events. The first advanced bioinformatics training workshop took place from May 16th to May 18th, 2022, followed by an online mini-symposium titled "Epigenetics and crop improvement" on May 19th. The symposium featured international speakers covering a wide range of topics related to plant epigenetics, cassava viral diseases, and cassava breeding strategies. A new online and on-site teaching concept was developed for the three-day workshop to ensure maximum student participation across Western, Eastern, and Southern Africa. Initially planned in Nigeria, Kenya, Ethiopia, Tanzania, and Zambia, the workshop ultimately focused on Nigeria, Kenya, and Ethiopia due to a lack of qualified candidates in the other countries. Each classroom hosted 20 to 25 students, with at least one bioinformatician present for support. The classrooms were connected via video conferencing, whereas teachers located in different places in Africa and Europe joined the video stream to conduct teaching sessions. The workshop was divided into theoretical classes and hands-on sessions, where participants could run data analysis with support from online teachers and local bioinformaticians. To enable participants to run guided, CPU and RAM-intensive data analysis workflows and overcome local computing and internet access restrictions, a system of virtual machines (VMs) hosted in the cloud was developed. The teaching platform provided teaching and exercise materials to support the use of the VMs. Although some students could not run heavy data analysis workflows due to unforeseen restrictions in the cloud, these issues were solved. All participants had the opportunity to run the analysis steps independently in the cloud using the protocols hosted on the teaching platform.
Financial stress can negatively affect a couple's relationship. The Dyadic Coping Inventory for Financial Stress (DCIFS) instrument assesses the way couples cope with financial stress. This study sought to validate the Dyadic Coping Inventory for Financial Stress (DCIFS) in Greek. The sample included 152 Greek couples (mean age: 42.82 ± 11.94). Confirmatory factor analyses provided support for delegated dyadic coping and evaluation of dyadic coping. Confirmatory Factor Analysis results supported a 33-item version consisting of the following subscales for both men and women: Stress Communication by Oneself and by Partner, Emotion and Problem-Focused Supportive Dyadic Coping (DC) by Oneself and by Partner, Negative DC by Oneself and by Partner, Emotion and Problem-Focused Common DC, and Evaluation of DC. The Dyadic Coping Inventory questionnaire and Perceived Stress Scale were used to assess the criterion validity of DCIFS.
Systematic physical activity (PA) is crucial in preventing illnesses that can become life-threatening, such as colon and breast cancer, heart disease and ischemic stroke, cardio-respiratory disease, type II diabetes, and depression. Many theory–based interventions have been applied to achieve positive outcomes in an individual's behavioural change and the ability to engage in systematic PA. This systematic review investigates the influence of the Transtheoretical model of behaviour (TTM) and the theory of planned behaviour (TPB) on PA. A substantial search in Science Direct, Wiley Online Library databases and PubMed was performed to obtain articles about the topic. Data exportation was possible after the reviewers applied exclusion–inclusion criteria to estimate evidence quality. Empirical evidence was assessed with the CONSORT checklist to appraise the risk of bias. The primary search identified 195 studies. Of those, ten original studies were comprised. All studies indicated a positive influence of TPB and TTM on physical activity in non–health and healthy populations. In particular, it was found to have an impact on energy expenditure, balance and body strength. Theory-based interventions are notably effective in promoting physical activity behaviour. Researchers and health professionals must select and utilise interventions based on the above mentioned theories and aim to enhance PA behavioural change on individual and interpersonal factors. Although the positive outcomes of theory-based interventions on PA behaviour, it is necessary for further research to be conducted.
Recently, there has been a growing interest in bioinformatics toward the adoption of increasingly complex machine learning models for the analysis of next-generation sequencing data with the goal of disease subtyping (i.e., patient stratification based on molecular features) or risk-based classification for specific endpoints, such as survival. With gene-expression data, a common approach consists in characterising the emerging groups by exploiting a differential expression analysis, which selects relevant gene sets coupled with pathway enrichment analysis, providing an insight into the underlying biological processes. However, when non-linear machine learning models are involved, differential expression analysis could be limiting since patient groupings identified by the model could be based on a set of genes that are hidden to differential expression due to its linear nature, affecting subsequent biological characterisation and validation. The aim of this study is to provide a proof-of-concept example demonstrating such a limitation. Moreover, we suggest that this issue could be overcome by the adoption of the innovative paradigm of eXplainable Artificial Intelligence, which consists in building an additional explainer to get a trustworthy interpretation of the model outputs and building a reliable set of genes characterising each group, preserving also non-linear relations, to be used for downstream analysis and validation.
Many people are worried about their social appearance. The fear of negative evaluation and judgment regarding one's look in social circumstances is referred to as social appearance anxiety. Social appearance anxiety belongs to social anxiety. The aim of the present study was to validate the Social Appearance Anxiety Scale (SAAS) in the Greek language and to examine its psychometric properties. An online survey was conducted in a Greek population sample of adolescents and young adults aged 18 to 35 years. The survey instruments included the Social Appearance Anxiety Scale, the Social Physique Anxiety Scale (SPAS), 2 subscales of Multidimensional Body-Self Relations Questionnaire Appearance Scale (MBSRQ), the Appearance Schemas Inventory-Revised Scale (ASI-R) and the Depression Anxiety Stress Scale (DASS). A total of 429 respondents participated in this research. The statistical analysis showed that the Greek version of the SAAS has good psychometric properties. The internal consistency of questions within the SAAS was 0.942. Positive correlations were found between SAAS and SPAS, the overweight preoccupation subscale of MBSRQ, the ASI-R and the DASS, while negative correlations were observed between SAAS and the appearance evaluation subscale of MBSRQ and age. The results of this study suggest that the Greek version of SAAS can be used as a reliable and valid instrument in the Greek population.
Healthy eating has gained ground in people's daily lives in modern society. However, an overwhelming preoccupation with healthy eating can lead to a pathological form setting the ground for orthorexia nervosa. This study aimed to validate the Greek version of the Eating Habits Questionnaire (EHQ) in adults 18 to 65 years old. The EHQ evaluates orthorexia nervosa traits. An online survey was conducted among adults of the general Greek population by administrating a battery of self-report instruments. The IPIP Big-Five personality questionnaire, Beck's Depression Inventory, the Obsessive-Compulsive Inventory-Revised, the Bulimic Investigatory Test, the Edinburg BITE, and the Eating Attitudes Test-13 were used. Internal consistency, test-retest reliability, and convergent and criterion validity were examined. A total of 551 adults (92.2% females) voluntarily participated in the study. Results suggest that the Greek version of the instrument has good psychometric properties. Analysis revealed a 3-factor model explaining 48.20% of the total variance. Cronbach's alphas ranged between 0.80 to 0.82, indicating good internal consistency. The test-retest reliability analysis showed no statistically significant difference between the measurements of the first and the post-2 weeks. Correlations with other eating disorder-related constructs were found to be weak to moderate. Body mass index was not significantly correlated with neither of the three EHQ subscales. The Greek version of EHQ is a robust instrument that could be used in clinical practice and research in the field of eating disorders in Greece.
Exosomes are under intense study as a promising means for drug or biomarker discovery, primarily due to their implication in intercellular communication and the emergence of disease states and their potential as a cutting-edge, natural delivery system at a nanoscale level. The proteins and nucleic acid cargo of exosomes has been at the center of exosome bioinformatic analysis in the context of health and disease, towards the hunt for novel biomarkers and diagnostics. However, the exosomal lipid composition has been emerging as an interesting target of study as well. Exosomes derived from different sources exhibit enrichment of specific lipid classes and various lipid compositions under different physiological conditions. Therefore, there is a mounting need for exosome lipidomic studies to build the foundation for novel therapeutic studies that use exosomal components. Bioinformatic pipelines are under development to efficiently identify, quantify and elucidate the exosomal lipids and their roles in disease. Cutting-edge bioinformatic tools, such as LipidXplorer, LUX Score, and LipidHome allow the execution of essential analyses such as shotgun lipidomics, and the detection of systematic differences in lipid composition and metadata processing. In the case of pancreatic cancer, an admittedly prevalent and life-threatening disease, these tools have yielded novel exosome lipid biomarkers. Furthermore, bioinformatic platforms such as “Lipidomics Informatics for Life-Science” enable fast and integrated access to these pipelines in a user-friendly manner. Modern bioinformatic methods facilitate the processing of exosome lipidomics
Bioinformatics has, among others, the issue of solving complex computational problems with vast amounts of sequencing data. Recently, a new computing architecture, the annealing machine, has emerged that applies to actual problems and is available for practical use. This novel architecture can solve discrete optimisation problems by replacing algorithms designed under the von Neumann architecture. To perform computations on the annealing machine, quadratic unconstrained binary optimisation (QUBO) formulations should be constructed and optimised according to the application. In this study, we developed an algorithm under the annealing machine architecture to solve sequence alignment problems, a known fundamental process widely used in genetic analysis, such as mutation detection and genome assembly. We constructed a QUBO formulation based on dynamic programming to solve a pairwise sequence alignment and derived its general form. We compared with conventional methods to solve 40 bp of pairwise alignment problem. Our implementation, named qualign, solved sequence alignment problems with accuracy comparable to that of conventional methods. Although a small pairwise alignment was solved owing to the limited memory size of this method, this is the first step of the application of annealing machines. We showed that our QUBO formulation solved the sequencing alignment problem. In the future, increasing the memory size of annealing machine will allow annealing machines to impact a wide range of bioinformatics applications positively.Availability: the source code of qualign is available at https://github.com/ymatsumoto/qualign
Sequence variants of human mitochondrial DNA (mt DNA) have been implicated in a variety of disorders and conditions. Massive parallel sequencing is becoming increasingly popular due to its efficiency and cost-effectiveness. In relation to acquiring significant sequence information like levels of heteroplasmy in mt DNA, it offers a marked improvement compared to previous methods used. Here we describe a variant calling pipeline for human mitochondrial DNA using Next Generation Sequencing (NGS) data obtained by enriching the sample only for mitochondria prior to sequencing.
Yellow Fever is a viral hemorrhagic disease that is transmitted mainly through arthropods with high mortality rates. Yellow Fever Virus (YFV) is an enveloped positive sense single-stranded RNA virus, member of the Flaviviridae family and the Flavivirus genus, and is endemic in countries of Africa and South America. However, recent cases of infection in North America, Asia and Europe are highlighting the potential risk of an outbreak with no effective treatment available and the urgent need to develop potent antiviral agents against the YFV. In this direction a range of specific modulators were designed and in silico evaluated in an effort to hinder the enzymatic activity of the YFV helicase as a prominent pharmacological target. Following a structure-based rational drug design pipeline, a phylogenetic analysis of Flaviviridae viruses and an in-depth evolutionary study on the Yellow Fever Virus helicase has provided invaluable insights into structural conservation and structural elements and features that are vital for the viral helicase function. Using comparative modelling and molecular dynamics simulations the YFV helicase-ssRNA complex was established, and the specific molecular interactions and physicochemical properties of the complex could be analyzed and used towards the designing and elucidation of a specific YFV 3D pharmacophore model. A high throughput virtual screening simulation was conducted to assess a set of in-house maintained low molecular weight compounds as bioactive inhibitors of the YFV helicase enzyme. The in-silico study described herein, could pave the way towards the designing and more efficient screening of potential novel modulator compounds against the YFV as well as attest and designate the NS3 helicase as an antiviral pharmacological target of uttermost value and potential.
