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The process of navigating through the landscape of biomedical literature and performing searches or combining them with bioinformatics analyses can be daunting, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related repositories. Herein, we present BioTextQuest v2.0, a tool for biomedical literature mining. BioTextQuest v2.0 is an open-source online web portal for document clustering based on sets of selected biomedical terms, offering efficient management of information derived from PubMed abstracts. Employing established machine learning algorithms, the tool facilitates document clustering while allowing users to customize the analysis by selecting terms of interest. BioTextQuest v2.0 streamlines the process of uncovering valuable insights from biomedical research articles, serving as an agent that connects the identification of key terms like genes/proteins, diseases, chemicals, Gene Ontology (GO) terms, functions, and others through named entity recognition, and their application in biological research. Instead of manually sifting through articles, researchers can enter their PubMed-like query and receive extracted information in two user-friendly formats, tables and word clouds, simplifying the comprehension of key findings. The latest update of BioTextQuest leverages the EXTRACT named entity recognition tagger, enhancing its ability to pinpoint various biological entities within text. BioTextQuest v2.0 acts as a research assistant, significantly reducing the time and effort required for researchers to identify and present relevant information from the biomedical literature.

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.

The objective of this study was to evaluate the effect of modified atmosphere packaging (MAP) on fish skin, gills and intestines bacterial microbiome.Whole gilthead seabream was packed aerobically or under modified atmospheres (60% CO2, 30% N2, 10% O2) and stored isothermally at 0 °C. Next Generation Sequencing (NGS) analysis was applied for the characterization of fish microbiome on fish skin, gills, and intestines initially (time of packaging, 1 day after harvesting) and after 10 days of isothermal storage at 0 °C. NGS results indicated statistically significant differences in families’ richness and diversity in the tested fish tissues between aerobic and MAP packaging during storage at 0 °C. The most persistent bacteria were Proteobacteria for both packaging types. For fish skin microbiota, the initially prevailing families were Comamonadaceae, Enterobacteriaceae and Moraxellaceae while in the intestines Comamonadaceae, Anaplasmataceae, Bacillaceae and Enterobacteriaceae were the dominant bacteria. At the end of storage period, the fish microbiota was dominated by psychotropic and psychrophilic families (Pseudoalteromonadaceae, Psychromonadaceae, and Shewanellaceae), while families such as Comamonadaceae were persistent under MAP conditions. By 8 days of fish storage at 0 °C, ΜΑΡ samples exhibited higher sensory scorings than the respective aerobically stored fish, indicating better retention of fish freshness and fish quality attributes under MAP. Based on the results of the study, MAP modified significantly the microbiological status and extended the shelf life of fish. NGS was a powerful tool that provided a more complete assessment compared to a culture-based analysis.

Orf8, one of the most puzzling genes in the SARS lineage of coronaviruses, marks a unique and striking difference in genome organization between SARS-CoV-2 and SARS-CoV-1. Here, using sequence comparisons, we unequivocally reveal the distant sequence similarities between SARS-CoV-2 Orf8 with its SARS-CoV-1 counterparts and the X4-like genes of coronaviruses, including its highly divergent "paralog" gene Orf7a, whose product is a potential immune antagonist of known structure. Supervised sequence space walks unravel identity levels that drop below 10% and yet exhibit subtle conservation patterns in this novel superfamily, characterized by an immunoglobulin-like beta sandwich topology. We document the high accuracy of the sequence space walk process in detail and characterize the subgroups of the superfamily in sequence space by systematic annotation of gene and taxon groups. While SARS-CoV-1 Orf7a and Orf8 genes are most similar to bat virus sequences, their SARS-CoV-2 counterparts are closer to pangolin virus homologs, reflecting the fine structure of conservation patterns within the SARS-CoV-2 genomes. The divergence between Orf7a and Orf8 is exceptionally idiosyncratic, since Orf7a is more constrained, whereas Orf8 is subject to rampant change, a peculiar feature that may be related to hitherto-unknown viral infection strategies. Despite their common origin, the Orf7a and Orf8 protein families exhibit different modes of evolutionary trajectories within the coronavirus lineage, which might be partly attributable to their complex interactions with the mammalian host cell, reflected by a multitude of functional associations of Orf8 in SARS-CoV-2 compared to a very small number of interactions discovered for Orf7a.IMPORTANCE Orf8 is one of the most puzzling genes in the SARS lineage of coronaviruses, including SARS-CoV-2. Using sophisticated sequence comparisons, we confirm its origins from Orf7a, another gene in the lineage that appears as more conserved, compared to Orf8. Orf7a is a potential immune antagonist of known structure, while a deletion of Orf8 was shown to decrease the severity of the infection in a cohort study. The subtle sequence similarities imply that Orf8 has the same immunoglobulin-like fold as Orf7a, confirmed by structure determination. We characterize the subgroups of this superfamily and demonstrate the highly idiosyncratic divergence patterns during the evolution of the virus.

Public-domain availability for bioinformatics software resources is a key requirement that ensures long-term permanence and methodological reproducibility for research and development across the life sciences. These issues are particularly critical for widely used, efficient, and well-proven methods, especially those developed in research settings that often face funding discontinuities. We re-launch a range of established software components for computational genomics, as legacy version 1.0.1, suitable for sequence matching, masking, searching, clustering and visualization for protein family discovery, annotation and functional characterization on a genome scale. These applications are made available online as open source and include MagicMatch, GeneCAST, support scripts for CoGenT-like sequence collections, GeneRAGE and DifFuse, supported by centrally administered bioinformatics infrastructure funding. The toolkit may also be conceived as a flexible genome comparison software pipeline that supports research in this domain. We illustrate basic use by examples and pictorial representations of the registered tools, which are further described with appropriate documentation files in the corresponding GitHub release.

We review the establishment of computational biology in Greece and Cyprus from its inception to date and issue recommendations for future development. We compare output to other countries of similar geography, economy, and size—based on publication counts recorded in the literature—and predict future growth based on those counts as well as national priority areas. Our analysis may be pertinent to wider national or regional communities with challenges and opportunities emerging from the rapid expansion of the field and related industries. Our recommendations suggest a 2-fold growth margin for the 2 countries, as a realistic expectation for further expansion of the field and the development of a credible roadmap of national priorities, both in terms of research and infrastructure funding.

There is significant interest in the response of the mammalian brain to low-dose ionizing radiation (LDIR), mainly examined by gene or protein expression, with applications in radiation safety on Earth, the atmosphere and outer space. Potential associations of molecular-level responses with sensory or cognitive defects and neurodegenerative diseases are currently under investigation. Previously, we have described a light-weight approach for the storage, analysis and distribution of relevant datasets, with the platform BRIDE. We have re-implemented the platform as BRIDE v2 on the cloud, using the bioinformatics infrastructure ELIXIR. We connected the annotated list of 3174 unique gene records with modern omics resources for downstream computational analysis. BRIDE v2 is a cloud-based platform with capabilities that enable researchers to extract, analyze, visualize as well as export the gene collection. The resource is freely available online at .

Background: The evolutionary rate of disordered protein regions varies greatly due to the lack of structural constraints. So far, few studies have investigated the presence/absence patterns of compositional bias, indicative of disorder, across phylogenies in conjunction with human disease. In this study, we report a genome-wide analysis of compositional bias association with disease in human proteins and their taxonomic distribution. Methods: The human genome protein set provided by the Ensembl database was annotated and analysed with respect to both disease associations and the detection of compositional bias. The Uniprot Reference Proteome dataset, containing 11297 proteomes was used as target dataset for the comparative genomics of a well-defined subset of the Human Genome, including 100 characteristic, compositionally biased proteins, some linked to disease. Results: Cross-evaluation of compositional bias and disease-association in the human genome reveals a significant bias towards biased regions in disease-associated genes, with charged, hydrophilic amino acids appearing as over-represented. The phylogenetic profiling of 17 disease-associated, proteins with compositional bias across 11297 proteomes captures characteristic taxonomic distribution patterns. Conclusions: This is the first time that a combined genome-wide analysis of compositional bias, disease-association and taxonomic distribution of human proteins is reported, covering structural, functional, and evolutionary properties. The reported framework can form the basis for large-scale, follow-up projects, encompassing the entire human genome and all known gene-disease associations.

Publication interests should not limit access to public data

Malignant gliomas constitute a complex disease phenotype that demands optimum decision-making as they are highly heterogeneous. Such inter-individual variability also renders optimum patient stratification extremely difficult. microRNA (hsa-miR-20a, hsa-miR-21, hsa-miR-21) expression levels were determined by RT-qPCR, upon FFPE tissue sample collection of glioblastoma multiforme patients (n = 37). In silico validation was then performed through discriminant analysis. Immunohistochemistry images from biopsy material were utilized by a hybrid deep learning system to further cross validate the distinctive capability of patient risk groups. Our standard-of-care treated patient cohort demonstrates no age- or sex- dependence. The expression values of the 3-miRNA signature between the low- (OS > 12 months) and high-risk (OS < 12 months) groups yield a p-value of 12 months) vs. high-risk (OS < 12 months) glioblastoma multiforme patients. Our 3-microRNA signature (hsa-miR-20a, hsa-miR-21, hsa-miR-10a) may further empower glioblastoma multiforme prognostic evaluation in clinical practice and enrich drug repurposing pipelines.

Background: The evolutionary rate of disordered proteins varies greatly due to the lack of structural constraints. So far, few studies have investigated the presence/absence patterns of intrinsically disordered regions (IDRs) across phylogenies in conjunction with human disease. In this study, we report a genome-wide analysis of compositional bias association with disease in human proteins and their taxonomic distribution. Methods: The human genome protein set provided by the Ensembl database was annotated and analysed with respect to both disease associations and the detection of compositional bias. The Uniprot Reference Proteome dataset, containing 11297 proteomes was used as target dataset for the comparative genomics of a well-defined subset of the Human Genome, including 100 characteristic, compositionally biased proteins, some linked to disease. Results: Cross-evaluation of compositional bias and disease-association in the human genome reveals a significant bias towards low complexity regions in disease-associated genes, with charged, hydrophilic amino acids appearing as over-represented. The phylogenetic profiling of 17 disease-associated, low complexity proteins across 11297 proteomes captures characteristic taxonomic distribution patterns. Conclusions: This is the first time that a combined genome-wide analysis of low complexity, disease-association and taxonomic distribution of human proteins is reported, covering structural, functional, and evolutionary properties. The reported framework can form the basis for large-scale, follow-up projects, encompassing the entire human genome and all known gene-disease associations.

Simple Summary: Prognosis, early detection, and relapse identification are jointly crucial factors for effective lung cancer treatment. Blood-based liquid biopsy, a non-invasive method, can provide solutions to the deadlocks in the management of this widespread cancer type by eliciting biomarkers. In this research, an integrated approach is developed, which combines the use of databases and experimental validation to find adequate biomarkers in non-small-cell lung cancer. The arising results, concerning miR-29a-3p in exosomes and lncRNA H19 in cfRNA, widen the horizons for identifying and exploiting promising biomarkers in non-small-cell lung cancer. Background and Objective: Lung cancer, the second most prevalent cancer globally, poses significant challenges in early detection and prognostic assessment. Despite advancements in targeted therapies and immunotherapy, the timely identification of relapse remains elusive. Blood-based liquid biopsy biomarkers, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating-free RNAs (cfRNAs), and extracellular vesicles (EVs)/exosomes, offer promise for non-invasive monitoring. Methods: We employ a comprehensive approach integrating miRNA/lncRNA/metabolomic datasets, following a mixed-methods content analysis, to identify candidate biomarkers in NSCLC. NSCLC-associated miRNA/gene/lncRNA associations were linked to in silico-derived molecular pathways. Results: For data validation, mass spectrometry-based untargeted metabolomics of plasma EVs highlighted miRNA/lncRNA/metabotypes, linking "glycerophospholipid metabolism" to lncRNA H19 and "alanine, aspartate and glutamate metabolism" to miR-29a-3p. Prognostic significance was established for miR-29a-3p, showing lower expression in NSCLC patients with disease progression compared to stable disease (p = 0.004). Kaplan–Meier survival analysis indicated that patients with miR-29a-3p under-expression had significantly shorter overall survival (OS) (p = 0.038). Despite the expression of lncRNA H19 in plasma EVs being undetected, its expression in plasma cfRNAs correlated significantly with disease progression (p = 0.035). Conclusions: Herein, we showcase the potential of plasma EV-derived miR-29a-3p as a prognostic biomarker and underscore the intricate interplay of miRNAs, lncRNAs, and metabolites in NSCLC biology. Our findings offer new insights and avenues for further exploration, contributing to the ongoing quest for effective biomarkers in early-stage NSCLC. [ABSTRACT FROM AUTHOR]

Diabetes mellitus affects more than 400 million people worldwide, and the incidence of disease is rising. Current anti-hyperglycemic agents share major drawbacks, such as hypoglycemia and low potency due to a lack of target specificity. Drug repurposing accelerates drug research and development pipelines and empowers chemical space enrichment. Herein, we propose a data-driven approach towards drug repurposing in diabetes mellitus by integrating heterogeneous biomedical data in a unified knowledge graph. Through extensive data mining in public repositories, diabetes-related multimodal data have been retrieved. Several data analysis techniques were employed to extract information and define semantic associations, followed by data parsing and, next, descriptive statistics, regression, and cluster analysis. Biomedical entity recognition and negation detection were performed by natural language processing. Predefined biological ontologies served as reference endpoints for class definition upon data integration. Graph analytics were performed, and drug–drug, protein–protein, drug–protein, and drug–disease interactions were established. A majority vote-based machine learning framework for the prediction of human cytochrome P450 inhibitors was also integrated into the proposed enhanced knowledge graph analysis that facilitates data-driven ranking for drug repurposing candidates in diabetes mellitus. The presented method yields a ranked list of repurposing candidates.

Developing predictive methods to identify patients with high risk of severe COVID-19 disease is of crucial importance. Authors show here that by measuring anti-SARS-CoV-2 antibody and cytokine levels at the time of hospital admission and integrating the data by unsupervised hierarchical clustering/machine learning, it is possible to predict unfavourable outcome.

Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein-protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL's scalability to cluster large datasets still remains a bottleneck due to high running times and memory demands. Here, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ∼70 million nodes with ∼68 billion edges in ∼2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.

Summary: The MetaSUB Consortium, founded in 2015, is a global consortium with an interdisciplinary team of clinicians, scientists, bioinformaticians, engineers, and designers, with members from more than 100 countries across the globe. This network has continually collected samples from urban and rural sites including subways and transit systems, sewage systems, hospitals, and other environmental sampling. These collections have been ongoing since 2015 and have continued when possible, even throughout the COVID-19 pandemic. The consortium has optimized their workflow for the collection, isolation, and sequencing of DNA and RNA collected from these various sites and processing them for metagenomics analysis, including the identification of SARS-CoV-2 and its variants. Here, the Consortium describes its foundations, and its ongoing work to expand on this network and to focus its scope on the mapping, annotation, and prediction of emerging pathogens, mapping microbial evolution and antibiotic resistance, and the discovery of novel organisms and biosynthetic gene clusters.

The genome of SARS-CoV-2, the coronavirus responsible for the Covid-19 pandemic, encodes a number of accessory genes. The longest accessory gene, Orf3a, plays important roles in the virus lifecycle indicated by experimental findings, known polymorphisms, its evolutionary trajectory and a distinct three-dimensional fold. Here we show that supervised, sensitive database searches with Orf3a detect weak, yet significant and highly specific similarities to the M proteins of coronaviruses. The similarity profiles can be used to derive low-resolution three-dimensional models for M proteins based on Orf3a as a structural template. The models also explain the emergence of Orf3a from M proteins and suggest a recent origin across the coronavirus lineage, enunciated by its restricted phylogenetic distribution. This study provides evidence for the common origin of M and Orf3a families and proposes for the first time a working model for the structure of the universally distributed M proteins in coronaviruses, consistent with the properties of both protein families.

The accumulation of cell biomass is associated with dramatically increased bioenergetic and biosynthetic demand. Metabolic reprogramming, once thought as an epiphenomenon, currently relates to disease progression, also in response to extracellular fate-decisive signals. Glioblastoma multiforme patients often suffer misdiagnosis, short survival time, low quality of life, and poor disease management options. Today, tumor genetic testing and histological analysis guide diagnosis and treatment. We and others appreciate that metabolites complement translational biomarkers and molecular signatures in disease profiling and phenotyping. Herein, we coupled a mixed-methods content analysis to a mass spectrometry-based untargeted metabolomic analysis on plasma samples from glioblastoma multiforme patients to delineate the role of metabolic remodeling in biological plasticity and, hence, disease severity. Following data processing and analysis, we established a bioenergetic profile coordinated by the mitochondrial function and redox state, lipids, and energy substrates. Our findings show that epigenetic modulators are key players in glioblastoma multiforme cell metabolism, in particular when microRNAs are considered. We propose that biological plasticity in glioblastoma multiforme is a mechanism of adaptation and resistance to treatment which is eloquently revealed by bioenergetics.