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In the majority of downstream analysis pipelines for single-cell RNA sequencing (scRNA-seq), techniques like dimensionality reduction and feature selection are employed to address the problem of high-dimensional nature of the data. These approaches involve mapping the data onto a lower-dimensional space, eliminating less informative genes, and pinpointing the most pertinent features. This process ultimately leads to a reduction in the number of dimensions used for downstream analysis, which in turn speeds up the computation of large-scale scRNA-seq data. Most approaches are directed to isolate from biological background the genes characterizing different cells and or the condition under study by establishing lists of differentially expressed or coexpressed genes. Herein, we present scRNA-Explorer an open-source online tool for simplified and rapid scRNA-seq analysis designed with the end user in mind. scRNA-Explorer utilizes: (i) Filtering out uninformative cells in an interactive manner via a web interface, (ii) Gene correlation analysis coupled with an extra step of evaluating the biological importance of these correlations, and (iii) Gene enrichment analysis of correlated genes in order to find gene implication in specific functions. We developed a pipeline to address the above problem. The scRNA-Explorer pipeline allows users to interrogate in an interactive manner scRNA-sequencing data sets to explore via gene expression correlations possible function(s) of a gene of interest. scRNA-Explorer can be accessed at https://bioinformatics.med.uoc.gr/shinyapps/app/scrnaexplorer.

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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.

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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.

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(1) Background: Chronic inflammation and suboptimal immune responses to vaccinations are considered to be aspects of immune dysregulation in patients that are undergoing dialysis. The present study aimed to evaluate immune responses in hemodialysis (HD) and online hemodiafiltration (OL-HDF) patients to a seasonal inactivated quadrivalent influenza vaccine (IQIV). (2) Methods: We enrolled 172 chronic dialysis patients (87 on HD and 85 on OL-HDF) and 18 control subjects without chronic kidney disease in a prospective, cross-sectional cohort study. Participants were vaccinated with a seasonal IQIV, and antibody titers using the hemagglutination inhibition (HI) assay were determined before vaccination (month 0) and 1, 3, and 6 months thereafter. Demographics and inflammatory markers (CRP, IL-6, IL-1ß) were recorded at month 0. The primary endpoints were the rates of seroresponse (SR), defined as a four-fold increase in the HI titer, and seroprotection (SP), defined as HI titer ≥ 1/40 throughout the study period. Statistical analyses were conducted in R (version 3.6.3) statistical software. The differences between groups were analyzed using chi-square and t-test analyses for dichotomous and continuous variables, respectively. To identify independent determinants of SR and SP, generalized linear models were built with response or protection per virus strain as the dependent variable and group, age, sex, time (month 0, 1, 3, 6), diabetes, IL-6, dialysis vintage, HD access, and HDF volume as independent explanatory variables. (3) Results: SR and SP rates were similar between control subjects, and dialysis patients were not affected by dialysis modality. SP rates were high (> 70%) at the beginning of the study and practically reached 100% after vaccination in all study groups. These results applied to all four virus strains that were included in the IQIV. IL-6 levels significantly differed between study groups, with HD patients displaying the highest values, but this did not affect SP rates. (4) Conclusions: Dialysis patients respond to influenza immunization adequately and similarly to the general population. Thus, annual vaccination policies should be encouraged in dialysis units. OL-HDF reduces chronic inflammation; however, this has no impact on SR rates.

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Pancreatic ductal adenocarcinoma (PDAC), the second most prevalent gastrointestinal malignancy and the most common type of pancreatic cancer is linked with poor prognosis and, eventually, with high mortality rates. Early detection is seldom, while tumor heterogeneity and microarchitectural alterations benefit PDAC resistance to conventional therapeutics. Although emerging evidence suggest the core role of cancer stem cells (CSCs) in PDAC aggressiveness, unique stem signatures are poorly available, thus limiting the efforts of anti-CSC-targeted therapy. Herein, we report the findings of the first genome-wide analyses of mRNA/lncRNA transcriptome profiling and co-expression networks in PDAC cell line-derived CD133+/CD44+ cells, which were shown to bear a CSC-like phenotype in vitro and in vivo. Compared to CD133-/CD44- cells, the CD133+/CD44+ population demonstrated significant expression differences in both transcript pools. Using emerging bioinformatic tools, we performed lncRNA target coding gene prediction analysis, which revealed significant Gene Ontology (GO), pathway, and network enrichments in many dyregulated lncRNA nearby (cis or trans) mRNAs, with reported involvement in the regulation of CSC phenotype and functions. In this context, the construction of lncRNA/mRNA networks by ingenuity platforms identified the lncRNAs ATF2, CHEK1, DCAF8, and PAX8 to interact with "hub" SC-associated mRNAs. In addition, the expressions of the above lncRNAs retrieved by TCGA-normalized RNAseq gene expression data of PAAD were significantly correlated with clinicopathological features of PDAC, including tumor grade and stage, nodal metastasis, and overall survival. Overall, our findings shed light on the identification of CSC-specific lncRNA signatures with potential prognostic and therapeutic significance in PDAC.

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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.

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Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic ß-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature's applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.

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Bottom-up proteomics analyses have been proved over the last years to be a powerful tool in the characterization of the proteome and are crucial for understanding cellular and organism behaviour. Through differential proteomic analysis researchers can shed light on groups of proteins or individual proteins that play key roles in certain, normal or pathological conditions. However, several tools for the analysis of such complex datasets are powerful, but hard-to-use with steep learning curves. In addition, some other tools are easy to use, but are weak in terms of analytical power. Previously, we have introduced ProteoSign, a powerful, yet user-friendly open-source online platform for protein differential expression/abundance analysis designed with the end-proteomics user in mind. Part of Proteosign's power stems from the utilization of the well-established Linear Models For Microarray Data (LIMMA) methodology. Here, we present a substantial upgrade of this computational resource, called ProteoSign v2, where we introduce major improvements, also based on user feedback. The new version offers more plot options, supports additional experimental designs, analyzes updated input datasets and performs a gene enrichment analysis of the differentially expressed proteins. We also introduce the deployment of the Docker technology and significantly increase the speed of a full analysis. ProteoSign v2 is available at http://bioinformatics.med.uoc.gr/ProteoSign.

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ETS2 repressor factor (ERF) haploinsufficiency causes late-onset craniosynostosis (CRS) (OMIM entry 600775; CRS4) in humans, while in mice Erf insufficiency also leads to a similar multisuture synostosis phenotype preceded by mildly reduced calvarium ossification. However, neither the cell types affected nor the effects per se have been identified so far. Here, we establish an ex vivo system for the expansion of suture-derived mesenchymal stem and progenitor cells (sdMSCs) and analyze the role of Erf levels in their differentiation. Cellular data suggest that Erf insufficiency specifically decreases osteogenic differentiation of sdMSCs, resulting in the initially delayed mineralization of the calvarium. Transcriptome analysis indicates that Erf is required for efficient osteogenic lineage commitment of sdMSCs. Elevated retinoic acid catabolism due to increased levels of the cytochrome P450 superfamily member Cyp26b1 as a result of decreased Erf levels appears to be the underlying mechanism leading to defective differentiation. Exogenous addition of retinoic acid can rescue the osteogenic differentiation defect, suggesting that Erf affects cranial bone mineralization during skull development through retinoic acid gradient regulation.

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