Prediction and Ranking of Biomarkers Using multiple UniReD.

Protein-protein interactions (PPIs) are of key importance for understanding how cells and organisms function. Thus, in recent decades, many approaches have been developed for the identification and discovery of such interactions. These approaches addressed the problem of PPI identification either by an experimental point of view or by a computational one. Here, we present an updated version of UniReD, a computational prediction tool which takes advantage of biomedical literature aiming to extract documented, already published protein associations and predict undocumented ones. The usefulness of this computational tool has been previously evaluated by experimentally validating predicted interactions and by benchmarking it against public databases of experimentally validated PPIs. In its updated form, UniReD allows the user to provide a list of proteins of known implication in, e.g., a particular disease, as well as another list of proteins that are potentially associated with the proteins of the first list. UniReD then automatically analyzes both lists and ranks the proteins of the second list by their association with the proteins of the first list, thus serving as a potential biomarker discovery/validation tool.

Immune response (IgG) following full inoculation with BNT162b2 COVID­19 mRNA among healthcare professionals.

Soon after the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) pandemic in December, 2019, numerous research teams, assisted by vast capital investments, achieved vaccine development in a fraction of time. However, almost 8 months following the initiation of the European vaccination programme, the need for prospective monitoring of the vaccine­induced immune response, its determinants and related side­effects remains a priority. The present study aimed to quantify the immune response following full vaccination with the BNT162b2 coronavirus disease 2019 (COVID­19) mRNA vaccine by measuring the levels of immunoglobulin G (IgG) titers in healthcare professionals. Moreover, common side­effects and factors associated with IgG titers were identified. For this purpose, blood samples from 517 individuals were obtained and analysed. Blood sampling was performed at a mean period of 69.0±23.5 days following the second dose of the vaccine. SARS­CoV­2 IgG titers had an overall mean value of 4.23±2.76. Females had higher titers than males (4.44±2.70 and 3.89 ±2.84, respectively; P=0.007), while non­smokers had higher titers than smokers (4.48±2.79 and 3.80±2.64, respectively; P=0.003). An older age was also associated with lower antibody titers (P<0.001). Moreover, the six most prevalent adverse effects were pain at the injection site (72.1%), generalized fatigue (40.5%), malaise (36.3%), myalgia (31,0%), headache (25.8%) and dizziness/weakness (21.6%). The present study demonstrated that the immune response after receiving the BNT162b2 COVID­19 mRNA vaccine is dependent on various modifiable and non­modifiable factors. Overall, the findings of the present study highlight two key aspects of the vaccination programs: First, the need for prospective immunosurveillance studies in order to estimate the duration of immunity, and second, the need to identify those individuals who are at a greater risk of developing low IgG titers in order to evaluate the need for a third dose of the vaccine.

NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks.

OBJECTIVE: Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. RESULTS: Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .

ProteoSign: an end-user online differential proteomics statistical analysis platform.

Profiling of proteome dynamics is crucial for understanding cellular behavior in response to intrinsic and extrinsic stimuli and maintenance of homeostasis. Over the last 20 years, mass spectrometry (MS) has emerged as the most powerful tool for large-scale identification and characterization of proteins. Bottom-up proteomics, the most common MS-based proteomics approach, has always been challenging in terms of data management, processing, analysis and visualization, with modern instruments capable of producing several gigabytes of data out of a single experiment. Here, we present ProteoSign, a freely available web application, dedicated in allowing users to perform proteomics differential expression/abundance analysis in a user-friendly and self-explanatory way. Although several non-commercial standalone tools have been developed for post-quantification statistical analysis of proteomics data, most of them are not end-user appealing as they often require very stringent installation of programming environments, third-party software packages and sometimes further scripting or computer programming. To avoid this bottleneck, we have developed a user-friendly software platform accessible via a web interface in order to enable proteomics laboratories and core facilities to statistically analyse quantitative proteomics data sets in a resource-efficient manner. ProteoSign is available at http://bioinformatics.med.uoc.gr/ProteoSign and the source code at https://github.com/yorgodillo/ProteoSign.

Genome urbanization: clusters of topologically co-regulated genes delineate functional compartments in the genome of Saccharomyces cerevisiae.

The eukaryotic genome evolves under the dual constraint of maintaining coordinated gene transcription and performing effective DNA replication and cell division, the coupling of which brings about inevitable DNA topological tension. DNA supercoiling is resolved and, in some cases, even harnessed by the genome through the function of DNA topoisomerases, as has been shown in the concurrent transcriptional activation and suppression of genes upon transient deactivation of topoisomerase II (topoII). By analyzing a genome-wide transcription run-on experiment upon thermal inactivation of topoII in Saccharomyces cerevisiae we were able to define 116 gene clusters of consistent response (either positive or negative) to topological stress. A comprehensive analysis of these topologically co-regulated gene clusters reveals pronounced preferences regarding their functional, regulatory and structural attributes. Genes that negatively respond to topological stress, are positioned in gene-dense pericentromeric regions, are more conserved and associated to essential functions, while upregulated gene clusters are preferentially located in the gene-sparse nuclear periphery, associated with secondary functions and under complex regulatory control. We propose that genome architecture evolves with a core of essential genes occupying a compact genomic 'old town', whereas more recently acquired, condition-specific genes tend to be located in a more spacious 'suburban' genomic periphery.

DrugQuest - a text mining workflow for drug association discovery.

BACKGROUND: Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. RESULTS: Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. CONCLUSIONS: DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future.

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.

Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies.

Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of "metagenomics", often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards.

Protein-protein interaction predictions using text mining methods.

It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools.

BioTextQuest(+): a knowledge integration platform for literature mining and concept discovery.

SUMMARY: The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing. AVAILABILITY: The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest. CONTACT: g.pavlopoulos@gmail.com or georgios.pavlopoulos@esat.kuleuven.be SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The chlamydiales pangenome revisited: structural stability and functional coherence.

The entire publicly available set of 37 genome sequences from the bacterial order Chlamydiales has been subjected to comparative analysis in order to reveal the salient features of this pangenome and its evolutionary history. Over 2,000 protein families are detected across multiple species, with a distribution consistent to other studied pangenomes. Of these, there are 180 protein families with multiple members, 312 families with exactly 37 members corresponding to core genes, 428 families with peripheral genes with varying taxonomic distribution and finally 1,125 smaller families. The fact that, even for smaller genomes of Chlamydiales, core genes represent over a quarter of the average protein complement, signifies a certain degree of structural stability, given the wide range of phylogenetic relationships within the group. In addition, the propagation of a corpus of manually curated annotations within the discovered core families reveals key functional properties, reflecting a coherent repertoire of cellular capabilities for Chlamydiales. We further investigate over 2,000 genes without homologs in the pangenome and discover two new protein sequence domains. Our results, supported by the genome-based phylogeny for this group, are fully consistent with previous analyses and current knowledge, and point to future research directions towards a better understanding of the structural and functional properties of Chlamydiales.

BioTextQuest: a web-based biomedical text mining suite for concept discovery.

SUMMARY: BioTextQuest combines automated discovery of significant terms in article clusters with structured knowledge annotation, via Named Entity Recognition services, offering interactive user-friendly visualization. A tag-cloud-based illustration of terms labeling each document cluster are semantically annotated according to the biological entity, and a list of document titles enable users to simultaneously compare terms and documents of each cluster, facilitating concept association and hypothesis generation. BioTextQuest allows customization of analysis parameters, e.g. clustering/stemming algorithms, exclusion of documents/significant terms, to better match the biological question addressed. AVAILABILITY: http://biotextquest.biol.ucy.ac.cy CONTACT: vprobon@ucy.ac.cy; iliopj@med.uoc.gr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene socialization: gene order, GC content and gene silencing in Salmonella.

BACKGROUND: Genes of conserved order in bacterial genomes tend to evolve slower than genes whose order is not conserved. In addition, genes with a GC content lower than the GC content of the resident genome are known to be selectively silenced by the histone-like nucleoid structuring protein (H-NS) in Salmonella. RESULTS: In this study, we use a comparative genomics approach to demonstrate that in Salmonella, genes whose order is not conserved (or genes without homologs) in closely related bacteria possess a significantly lower average GC content in comparison to genes that preserve their relative position in the genome. Moreover, these genes are more frequently targeted by H-NS than genes that have conserved their genomic neighborhood. We also observed that duplicated genes that do not preserve their genomic neighborhood are, on average, under less selective pressure. CONCLUSIONS: We establish a strong association between gene order, GC content and gene silencing in a model bacterial species. This analysis suggests that genes that are not under strong selective pressure (evolve faster than others) in Salmonella tend to accumulate more AT-rich mutations and are eventually silenced by H-NS. Our findings may establish new approaches for a better understanding of bacterial genome evolution and function, using information from functional and comparative genomics.

Lead toxicity update. A brief review.

Lead is a metal which has been associated with human activities for the last 6000 years. In ancient civilizations, uses of lead included the manufacture of kitchen utensils, trays, and other decorative articles. However, lead is also toxic to humans, with the most deleterious effects on the hemopoietic, nervous, reproductive systems and the urinary tract. The main sources of lead exposure are paints, water, food, dust, soil, kitchen utensils, and leaded gasoline. The majority of cases of lead poisoning are due to oral ingestion and absorption through the gut. Lead poisoning in adults occurs more frequently during exposure in the workplace and primarily involves the central nervous system. Symptoms of hemopoietic system involvement include microcytic, hypochromic anemia with basophilic stippling of the erythrocytes. Hyperactivity, anorexia, decreased play activity, low intelligence quotient, and poor school performance have been observed in children with high lead levels. Lead crosses the placenta during pregnancy and has been associated with intrauterine death, prematurity, and low birth weight. In 1991, the Centers for Disease Control and Prevention in the USA redefined elevated blood lead levels as those > or = 10 microg/dl and recommended a new set of guidelines for the treatment of lead levels > or =15 microg/dl.