Expression signature of human endogenous retroviruses in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) is one of the most diagnosed forms of leukemia worldwide and it is usually classified into two forms: indolent and aggressive. These two forms are characterized by distinct molecular features that drive different responses to treatment and clinical outcomes. In this context, a better understanding of the molecular landscape of the CLL forms may potentially lead to the development of new drugs or the identification of novel biomarkers. Human endogenous retroviruses (HERVs) are a class of transposable elements that have been associated with the development of different human cancers, including different forms of leukemias. However, no studies about HERVs in CLL have ever been reported so far. Here, we present the first locus-specific profiling of HERV expression in both the aggressive and indolent forms of CLL. Our analyses revealed several dysregulations in HERV expression occurring in CLL and some of them were specific for either the aggressive or indolent form of CLL. Such results were also validated by analyzing an external cohort of CLL patients and by RT-qPCR. Moreover, in silico analyses have shown relevant signaling pathways associated with them suggesting a potential involvement of the dysregulated HERVs in these pathways and consequently in CLL development.

NetMe 2.0: a web-based platform for extracting and modeling knowledge from biomedical literature as a labeled graph.

MOTIVATION: The rapid increase of bio-medical literature makes it harder and harder for scientists to keep pace with the discoveries on which they build their studies. Therefore, computational tools have become more widespread, among which network analysis plays a crucial role in several life-science contexts. Nevertheless, building correct and complete networks about some user-defined biomedical topics on top of the available literature is still challenging. RESULTS: We introduce NetMe 2.0, a web-based platform that automatically extracts relevant biomedical entities and their relations from a set of input texts-i.e. in the form of full-text or abstract of PubMed Central's papers, free texts, or PDFs uploaded by users-and models them as a BioMedical Knowledge Graph (BKG). NetMe 2.0 also implements an innovative Retrieval Augmented Generation module (Graph-RAG) that works on top of the relationships modeled by the BKG and allows the distilling of well-formed sentences that explain their content. The experimental results show that NetMe 2.0 can infer comprehensive and reliable biological networks with significant Precision-Recall metrics when compared to state-of-the-art approaches. AVAILABILITY AND IMPLEMENTATION: https://netme.click/.

Virus finding tools: current solutions and limitations.

MOTIVATION: The study of the Human Virome remains challenging nowadays. Viral metagenomics, through high-throughput sequencing data, is the best choice for virus discovery. The metagenomics approach is culture-independent and sequence-independent, helping search for either known or novel viruses. Though it is estimated that more than 40% of the viruses found in metagenomics analysis are not recognizable, we decided to analyze several tools to identify and discover viruses in RNA-seq samples. RESULTS: We have analyzed eight Virus Tools for the identification of viruses in RNA-seq data. These tools were compared using a synthetic dataset of 30 viruses and a real one. Our analysis shows that no tool succeeds in recognizing all the viruses in the datasets. So we can conclude that each of these tools has pros and cons, and their choice depends on the application domain. AVAILABILITY: Synthetic data used through the review and raw results of their analysis can be found at https://zenodo.org/record/6426147. FASTQ files of real data can be found in GEO (https://www.ncbi.nlm.nih.gov/gds) or ENA (https://www.ebi.ac.uk/ena/browser/home). Raw results of their analysis can be downloaded from https://zenodo.org/record/6425917.

DEGGs: an R package with shiny app for the identification of differentially expressed gene-gene interactions in high-throughput sequencing data.

SUMMARY: The discovery of differential gene-gene correlations across phenotypical groups can help identify the activation/deactivation of critical biological processes underlying specific conditions. The presented R package, provided with a count and design matrix, extract networks of group-specific interactions that can be interactively explored through a shiny user-friendly interface. For each gene-gene link, differential statistical significance is provided through robust linear regression with an interaction term. AVAILABILITY AND IMPLEMENTATION: DEGGs is implemented in R and available on GitHub at https://github.com/elisabettasciacca/DEGGs. The package is also under submission on Bioconductor.

A benchmarking of pipelines for detecting ncRNAs from RNA-Seq data.

Next-Generation Sequencing (NGS) is a high-throughput technology widely applied to genome sequencing and transcriptome profiling. RNA-Seq uses NGS to reveal RNA identities and quantities in a given sample. However, it produces a huge amount of raw data that need to be preprocessed with fast and effective computational methods. RNA-Seq can look at different populations of RNAs, including ncRNAs. Indeed, in the last few years, several ncRNAs pipelines have been developed for ncRNAs analysis from RNA-Seq experiments. In this paper, we analyze eight recent pipelines (iSmaRT, iSRAP, miARma-Seq, Oasis 2, SPORTS1.0, sRNAnalyzer, sRNApipe, sRNA workbench) which allows the analysis not only of single specific classes of ncRNAs but also of more than one ncRNA classes. Our systematic performance evaluation aims at guiding users to select the appropriate pipeline for processing each ncRNA class, focusing on three key points: (i) accuracy in ncRNAs identification, (ii) accuracy in read count estimation and (iii) deployment and ease of use.

PHENSIM: Phenotype Simulator.

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues' physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool's applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach's reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.

aEEG vs cEEG's sensivity for seizure detection in the setting of neonatal intensive care units: A systematic review and meta-analysis.

AIM: Amplitude-integrated electroencephalography (aEEG)'s accuracy compared to conventional electroencephalography (cEEG) has not been fully established. The aim of our study was to conduct a systematic review on the sensitivity of the aEEG for neonatal seizure detection. METHODS: Studies from PubMed and Google Scholar databases comparing recordings of cEEG and aEEG in newborns were included according to the PRISMA method. A quality assessment using the QUADAS-2 tool was provided. A random-effect model was used to account for different sources of variations among studies. Publication biases were represented by a funnel plot, and funnel plot symmetry was assessed. RESULTS: Fourteen studies were reported; sensitivity of each diagnostic tool used (single-channel aEEG, two-channel aEEG, two-channel aEEG plus raw trace EEG) was compared to that of the gold-standard cEEG and to those of the other methods used. Overall sensitivity of the aEEG ranged from 31.25% to 90%. CONCLUSION: Our study provides evidence that sensitivity of aEEG varies significantly and that seizure detection rate is lower than that of cEEG. The two-channel aEEG with raw trace EEG shows a high sensitivity and might represent a valid alternative to the cEEG in the setting of neonatal intensive care units (NICUs).

Computational Methods for Drug Repurposing.

The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.

Pathway Analysis for Cancer Research and Precision Oncology Applications.

With the advent of OMICs technologies, several bioinformatics methods have been developed to infer biological knowledge from such data. Pathway analysis methodologies help integrate multi-OMICs data and find altered function in known metabolic and signaling pathways. As widely known, such alterations promote the cancer cells' progression and the maintenance of the malignant state. In this chapter, we provide (i) a comprehensive description of the primary data sources for omics data, cancer "omics" projects, and precision oncology knowledge bases; (ii) a survey of the main biological pathway databases; (iii) and a global view of the principal pathway analysis tools and methodologies, describing their main characteristics and shortcomings highlighting their potential applications in cancer research and precision oncology.

Computational Resources for the Interpretation of Variations in Cancer.

A broad ecosystem of resources, databases, and systems to analyze cancer variations is present in the literature. These are a strategic element in the interpretation of NGS experiments. However, the intrinsic wealth of data from RNA-seq, ChipSeq, and DNA-seq can be fully exploited only with the proper skill and knowledge. In this chapter, we survey relevant literature concerning databases, annotators, and variant prioritization tools.

Soil and Soilless Tomato Cultivation Promote Different Microbial Communities That Provide New Models for Future Crop Interventions.

The cultivation of soilless tomato in greenhouses has increased considerably, but little is known about the assembly of the root microbiome compared to plants grown in soil. To obtain such information, we constructed an assay in which we traced the bacterial and fungal communities by amplicon-based metagenomics during the cultivation chain from nursery to greenhouse. In the greenhouse, the plants were transplanted either into agricultural soil or into coconut fiber bags (soilless). At the phylum level, bacterial and fungal communities were primarily constituted in all microhabitats by Proteobacteria and Ascomycota, respectively. The results showed that the tomato rhizosphere microbiome was shaped by the substrate or soil in which the plants were grown. The microbiome was different particularly in terms of the bacterial communities. In agriculture, enrichment has been observed in putative biological control bacteria of the genera Pseudomonas and Bacillus and in potential phytopathogenic fungi. Overall, the study describes the different shaping of microbial communities in the two cultivation methods.

Psychological well-being and family distress of Italian caregivers during the COVID-19 outbreak.

The present study aimed to investigate the personal well-being and family distress of Italian caregivers during the lockdown. Five hundred sixty-five family caregivers and 638 age- and sex-matched noncaregivers completed a web-based survey. The following scales were administered to all participants: General Health Questionnaire-12 items (GHQ-12), Insomnia Severity Index (ISI), Brief Resilient Coping Scale (BRCS), and Family Distress Index (FDI). Caregivers were also asked to provide information about their family members with disabilities. Individual and family distress, as well as insomnia, were significantly higher in caregivers than controls. Contrariwise, caregivers reported lower resilience levels. Multiple linear regression showed that distress was higher in caregivers living in Central and Southern Italy. Individual well-being was negatively predicted by low independence measured by the activities of daily living (ADL). Family distress was higher in households of psychiatric patients. Finally, low resilience levels appeared as the strongest predictors of both individual and family distress. The lockdown caused severe distress among caregivers and families of people with disabilities. Support networks for people with disabilities and their families are fundamental to prevent severe consequences from a psychological, social, and economical point of view.

RNAdetector: a free user-friendly stand-alone and cloud-based system for RNA-Seq data analysis.

BACKGROUND: RNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement. RESULTS: Motivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species. CONCLUSIONS: RNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.

miRandola 2017: a curated knowledge base of non-invasive biomarkers.

miRandola (http://mirandola.iit.cnr.it/) is a database of extracellular non-coding RNAs (ncRNAs) that was initially published in 2012, foreseeing the relevance of ncRNAs as non-invasive biomarkers. An increasing amount of experimental evidence shows that ncRNAs are frequently dysregulated in diseases. Further, ncRNAs have been discovered in different extracellular forms, such as exosomes, which circulate in human body fluids. Thus, miRandola 2017 is an effort to update and collect the accumulating information on extracellular ncRNAs that is spread across scientific publications and different databases. Data are manually curated from 314 articles that describe miRNAs, long non-coding RNAs and circular RNAs. Fourteen organisms are now included in the database, and associations of ncRNAs with 25 drugs, 47 sample types and 197 diseases. miRandola also classifies extracellular RNAs based on their extracellular form: Argonaute2 protein, exosome, microvesicle, microparticle, membrane vesicle, high density lipoprotein and circulating. We also implemented a new web interface to improve the user experience.

TACITuS: transcriptomic data collector, integrator, and selector on big data platform.

BACKGROUND: Several large public repositories of microarray datasets and RNA-seq data are available. Two prominent examples include ArrayExpress and NCBI GEO. Unfortunately, there is no easy way to import and manipulate data from such resources, because the data is stored in large files, requiring large bandwidth to download and special purpose data manipulation tools to extract subsets relevant for the specific analysis. RESULTS: TACITuS is a web-based system that supports rapid query access to high-throughput microarray and NGS repositories. The system is equipped with modules capable of managing large files, storing them in a cloud environment and extracting subsets of data in an easy and efficient way. The system also supports the ability to import data into Galaxy for further analysis. CONCLUSIONS: TACITuS automates most of the pre-processing needed to analyze high-throughput microarray and NGS data from large publicly-available repositories. The system implements several modules to manage large files in an easy and efficient way. Furthermore, it is capable deal with Galaxy environment allowing users to analyze data through a user-friendly interface.

A novel computational method for inferring competing endogenous interactions.

Posttranscriptional cross talk and communication between genes mediated by microRNA response element (MREs) yield large regulatory competing endogenous RNA (ceRNA) networks. Their inference may improve the understanding of pathologies and shed new light on biological mechanisms. A variety of RNA: messenger RNA, transcribed pseudogenes, noncoding RNA, circular RNA and proteins related to RNA-induced silencing complex complex interacting with RNA transfer and ribosomal RNA have been experimentally proved to be ceRNAs. We retrace the ceRNA hypothesis of posttranscriptional regulation from its original formulation [Salmena L, Poliseno L, Tay Y, et al. Cell 2011;146:353-8] to the most recent experimental and computational validations. We experimentally analyze the methods in literature [Li J-H, Liu S, Zhou H, et al. Nucleic Acids Res 2013;42:D92-7; Sumazin P, Yang X, Chiu H-S, et al. Cell 2011;147:370-81; Sarver AL, Subramanian S. Bioinformation 2012;8:731-3] comparing them with a general machine learning approach, called ceRNA predIction Algorithm, evaluating the performance in predicting novel MRE-based ceRNAs.

INBIA: a boosting methodology for proteomic network inference.

BACKGROUND: The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing. RESULTS: We have developed a methodology called Inference Network Based on iRefIndex Analysis (INBIA) to accurately correlate proteomic inferred relations to protein-protein interaction (PPI) networks. INBIA makes use of 14 network inference methods on protein expressions related to 16 cancer types. It uses as reference model the iRefIndex human PPI network. Predictions are validated through non-interacting and tissue specific PPI networks resources. The first, Negatome, takes into account likely non-interacting proteins by combining both structure properties and literature mining. The latter, TissueNet and GIANT, report experimentally verified PPIs in more than 50 human tissues. The reliability of the proposed methodology is assessed by comparing INBIA with PERA, a tool which infers protein interaction networks from Pathway Commons, by both functional and topological analysis. CONCLUSION: Results show that INBIA is a valuable approach to predict proteomic interactions in pathological conditions starting from the current knowledge of human protein interactions.

POOLED ESTIMATES OF INCIDENCE OF ENDOPHTHALMITIS AFTER INTRAVITREAL INJECTION OF ANTI-VASCULAR ENDOTHELIAL GROWTH FACTOR AGENTS WITH AND WITHOUT TOPICAL ANTIBIOTIC PROPHYLAXIS.

PURPOSE: To assess the effect of topical antibiotic prophylaxis on postoperative endophthalmitis after intravitreal injection of anti-vascular endothelial growth factor agents. METHODS: A systematic literature search was performed from inception to March 2016 using PubMed, Medline, Web of Science, Embase, and the Cochrane Library, to identify articles that reported cases of endophthalmitis after intravitreal injection of anti-vascular endothelial growth factor agents. We used a pooled analysis to estimate the incidence of cases of endophthalmitis who developed after injections performed with and without topical antibiotic prophylaxis. We used regression analysis to explore the effects of study characteristics on heterogeneity. RESULTS: From our search of electronic databases, we identified and screened 4,561 unique records. We judged 60 articles to have reported findings for cohorts of patients who met our inclusion criteria, (12 arms of randomized clinical trials, 11 prospective cohort studies, and 37 retrospective cohort studies), which included 244 cases of endophthalmitis and 639,391 intravitreal injections of anti-vascular endothelial growth factor agents. The final pooled estimate endophthalmitis proportions were 9/10,000 (95% confidence interval, 7/10,000-12/10,000) in the antibiotic-treated group and 3/10,000 (95% confidence interval, 2/10,000-5/10,000) in the untreated group. The estimated incidence of endophthalmitis with topical antibiotic prophylaxis was approximated three times the incidence without prophylaxis. Random effects regression showed that none of the study characteristics significantly affected the effect size in either group. CONCLUSION: Topical antibiotic after intravitreal injection of anti-vascular endothelial growth factor agents is associated with a higher risk of endophthalmitis.

Introduction of gluten, HLA status, and the risk of celiac disease in children.

BACKGROUND: The relationship between the risk of celiac disease and both the age at which gluten is introduced to a child's diet and a child's early dietary pattern is unclear. METHODS: We randomly assigned 832 newborns who had a first-degree relative with celiac disease to the introduction of dietary gluten at 6 months (group A) or 12 months (group B). The HLA genotype was determined at 15 months of age, and serologic screening for celiac disease was evaluated at 15, 24, and 36 months and at 5, 8, and 10 years. Patients with positive serologic findings underwent intestinal biopsies. The primary outcome was the prevalence of celiac disease autoimmunity and of overt celiac disease among the children at 5 years of age. RESULTS: Of the 707 participants who remained in the trial at 36 months, 553 had a standard-risk or high-risk HLA genotype and completed the study. At 2 years of age, significantly higher proportions of children in group A than in group B had celiac disease autoimmunity (16% vs. 7%, P=0.002) and overt celiac disease (12% vs. 5%, P=0.01). At 5 years of age, the between-group differences were no longer significant for autoimmunity (21% in group A and 20% in group B, P=0.59) or overt disease (16% and 16%, P=0.78 by the log-rank test). At 10 years, the risk of celiac disease autoimmunity was far higher among children with high-risk HLA than among those with standard-risk HLA (38% vs. 19%, P=0.001), as was the risk of overt celiac disease (26% vs. 16%, P=0.05). Other variables, including breast-feeding, were not associated with the development of celiac disease. CONCLUSIONS: Neither the delayed introduction of gluten nor breast-feeding modified the risk of celiac disease among at-risk infants, although the later introduction of gluten was associated with a delayed onset of disease. A high-risk HLA genotype was an important predictor of disease. (Funded by the Fondazione Celiachia of the Italian Society for Celiac Disease; CELIPREV ClinicalTrials.gov number, NCT00639444.).

APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks.

MOTIVATION: Biological network querying is a problem requiring a considerable computational effort to be solved. Given a target and a query network, it aims to find occurrences of the query in the target by considering topological and node similarities (i.e. mismatches between nodes, edges, or node labels). Querying tools that deal with similarities are crucial in biological network analysis because they provide meaningful results also in case of noisy data. In addition, as the size of available networks increases steadily, existing algorithms and tools are becoming unsuitable. This is rising new challenges for the design of more efficient and accurate solutions. RESULTS: This paper presents APPAGATO, a stochastic and parallel algorithm to find approximate occurrences of a query network in biological networks. APPAGATO handles node, edge and node label mismatches. Thanks to its randomic and parallel nature, it applies to large networks and, compared with existing tools, it provides higher performance as well as statistically significant more accurate results. Tests have been performed on protein-protein interaction networks annotated with synthetic and real gene ontology terms. Case studies have been done by querying protein complexes among different species and tissues. AVAILABILITY AND IMPLEMENTATION: APPAGATO has been developed on top of CUDA-C ++ Toolkit 7.0 framework. The software is available online http://profs.sci.univr.it/∼bombieri/APPAGATO CONTACT: rosalba.giugno@univr.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.