Research Output and International Cooperation Among Countries During the COVID-19 Pandemic: Scientometric Analysis

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has instigated immediate and massive worldwide research efforts. Rapid publication of research data may be desirable but also carries the risk of quality loss. Objective: This analysis aimed to correlate the severity of the COVID-19 outbreak with its related scientific output per country. Methods: All articles related to the COVID-19 pandemic were retrieved from Web of Science and analyzed using the web application SciPE (science performance evaluation), allowing for large data scientometric analyses of the global geographical distribution of scientific output. Results: A total of 7185 publications, including 2592 articles, 2091 editorial materials, 2528 early access papers, 1479 letters, 633 reviews, and other contributions were extracted. The top 3 countries involved in COVID-19 research were the United States, China, and Italy. The confirmed COVID-19 cases or deaths per region correlated with scientific research output. The United States was most active in terms of collaborative efforts, sharing a significant amount of manuscript authorships with the United Kingdom, China, and Italy. The United States was China’s most frequent collaborative partner, followed by the United Kingdom. Conclusions: The COVID-19 research landscape is rapidly developing and is driven by countries with a generally strong prepandemic research output but is also significantly affected by countries with a high prevalence of COVID-19 cases. Our findings indicate that the United States is leading international collaborative efforts

miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems

Gene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs. Here, we present miEAA 2.0, supporting miRNA input from ten frequently investigated organisms. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miRBase miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualizations such as heatmaps and running sum curves with background distributions. We demonstrate the new features with case studies for human kidney cancer, a biomarker study on Parkinson’s disease from the PPMI cohort, and a mouse model for breast cancer. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2

GeneTrail 3: advanced high-throughput enrichment analysis

We present GeneTrail 3, a major extension of our web service GeneTrail that offers rich functionality for the identification, analysis, and visualization of deregulated biological processes. Our web service provides a comprehensive collection of biological processes and signaling pathways for 12 model organisms that can be analyzed with a powerful framework for enrichment and network analysis of transcriptomic, miRNomic, proteomic, and genomic data sets. Moreover, GeneTrail offers novel workflows for the analysis of epigenetic marks, time series experiments, and single cell data. We demonstrate the capabilities of our web service in two case-studies, which highlight that GeneTrail is well equipped for uncovering complex molecular mechanisms. GeneTrail is freely accessible at: http://genetrail.bioinf.uni-sb.de

CoolMPS: evaluation of antibody labeling based massively parallel non-coding RNA sequencing

Results of massive parallel sequencing-by-synthesis vary depending on the sequencing approach. CoolMPS™ is a new sequencing chemistry that incorporates bases by labeled antibodies. To evaluate the performance, we sequenced 240 human non-coding RNA samples (dementia patients and controls) with and without CoolMPS. The Q30 value as indicator of the per base sequencing quality increased from 91.8 to 94%. The higher quality was reached across the whole read length. Likewise, the percentage of reads mapping to the human genome increased from 84.9 to 86.2%. For both technologies, we computed similar distributions between different RNA classes (miRNA, piRNA, tRNA, snoRNA and yRNA) and within the classes. While standard sequencing-by-synthesis allowed to recover more annotated miRNAs, CoolMPS yielded more novel miRNAs. The correlation between the two methods was 0.97. Evaluating the diagnostic performance, we observed lower minimal P-values for CoolMPS (adjusted P-value of 0.0006 versus 0.0004) and larger effect sizes (Cohen's d of 0.878 versus 0.9). Validating 19 miRNAs resulted in a correlation of 0.852 between CoolMPS and reverse transcriptase-quantitative polymerase chain reaction. Comparison to data generated with Illumina technology confirmed a known shift in the overall RNA composition. With CoolMPS we evaluated a novel sequencing-by-synthesis technology showing high performance for the analysis of non-coding RNAs

ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification

Motivation: Breast cancer is the second leading cause of cancer death among women. Tumors, even of the same histopathological subtype, exhibit a high genotypic diversity that impedes therapy stratification and that hence must be accounted for in the treatment decision-making process. Results: Here, we present ClinOmicsTrailbc, a comprehensive visual analytics tool for breast cancer decision support that provides a holistic assessment of standard-of-care targeted drugs, candidates for drug repositioning and immunotherapeutic approaches. To this end, our tool analyzes and visualizes clinical markers and (epi-)genomics and transcriptomics datasets to identify and evaluate the tumor’s main driver mutations, the tumor mutational burden, activity patterns of core cancerrelevant pathways, drug-specific biomarkers, the status of molecular drug targets and pharmacogenomic influences. In order to demonstrate ClinOmicsTrailbc’s rich functionality, we present three case studies highlighting various ways in which ClinOmicsTrailbc can support breast cancer precision medicine. ClinOmicsTrailbc is a powerful integrated visual analytics tool for breast cancer research in general and for therapy stratification in particular, assisting oncologists to find the best possible treatment options for their breast cancer patients based on actionable, evidence-based results. Availability and implementation: ClinOmicsTrailbc can be freely accessed at https://clinomicstrail. bioinf.uni-sb.de

Common diseases alter the physiological age-related blood microRNA profile

Aging is a key risk factor for chronic diseases of the elderly. MicroRNAs regulate post-transcriptional gene silencing through base-pair binding on their target mRNAs. We identified nonlinear changes in age-related microRNAs by analyzing whole blood from 1334 healthy individuals. We observed a larger influence of the age as compared to the sex and provide evidence for a shift to the 5' mature form of miRNAs in healthy aging. The addition of 3059 diseased patients uncovered pan-disease and disease-specific alterations in aging profiles. Disease biomarker sets for all diseases were different between young and old patients. Computational deconvolution of whole-blood miRNAs into blood cell types suggests that cell intrinsic gene expression changes may impart greater significance than cell abundance changes to the whole blood miRNA profile. Altogether, these data provide a foundation for understanding the relationship between healthy aging and disease, and for the development of age-specific disease biomarkers

Deep sequencing of sncRNAs reveals hallmarks and regulatory modules of the transcriptome during Parkinson’s disease progression

Noncoding RNAs have diagnostic and prognostic importance in Parkinson’s disease (PD). We studied circulating small non coding RNAs (sncRNAs) in two large-scale longitudinal PD cohorts (Parkinson’s Progression Markers Initiative (PPMI) and Luxembourg Parkinson’s Study (NCER-PD)) and modeled their impact on the transcriptome. Sequencing of sncRNAs in 5,450 blood samples of 1,614 individuals in PPMI yielded 323 billion reads, most of which mapped to microRNAs but covered also other RNA classes such as piwi-interacting RNAs, ribosomal RNAs and small nucleolar RNAs. Dysregulated microRNAs associated with disease and disease progression occur in two distinct waves in the third and seventh decade of life. Originating predominantly from immune cells, they resemble a systemic inflammation response and mitochondrial dysfunction, two hall marks of PD. Profiling 1,553 samples from 1,024 individuals in the NCER-PD cohort validated biomarkers and main findings by an independent technology. Finally, network analysis of sncRNA and transcriptome sequencing from PPMI identified regulatory modules emerging in patients with progressing P

Deep sncRNA-seq of the PPMI cohort to study Parkinson’s disease progression

Coding and non-coding RNAs have diagnostic and prognostic importance in Parkinson’s diseases (PD). We studied circulating small non-coding RNAs (sncRNAs) in 7, 003 samples from two longitudinal PD cohorts (Parkinson’s Progression Marker Initiative (PPMI) and Luxembourg Parkinson’s Study (NCER-PD)) and modelled their influence on the transcriptome. First, we sequenced sncRNAs in 5, 450 blood samples of 1, 614 individuals in PPMI. The majority of 323 billion reads (59 million reads per sample) mapped to miRNAs. Other covered RNA classes include piRNAs, rRNAs, snoRNAs, tRNAs, scaRNAs, and snRNAs. De-regulated miRNAs were associated with the disease and disease progression and occur in two distinct waves in the third and seventh decade of live. Originating mostly from a characteristic set of immune cells they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. By profiling 1, 553 samples from 1, 024 individuals in the NCER-PD cohort using an independent technology, we validate relevant findings from the sequencing study. Finally, network analysis of sncRNAs and transcriptome sequencing of the original cohort identified regulatory modules emerging in progressing PD patients.Competing Interest StatementThe authors have declared no competing interest

Common diseases alter the physiological age-related blood microRNA profile.

Aging is a key risk factor for chronic diseases of the elderly. MicroRNAs regulate post-transcriptional gene silencing through base-pair binding on their target mRNAs. We identified nonlinear changes in age-related microRNAs by analyzing whole blood from 1334 healthy individuals. We observed a larger influence of the age as compared to the sex and provide evidence for a shift to the 5' mature form of miRNAs in healthy aging. The addition of 3059 diseased patients uncovered pan-disease and disease-specific alterations in aging profiles. Disease biomarker sets for all diseases were different between young and old patients. Computational deconvolution of whole-blood miRNAs into blood cell types suggests that cell intrinsic gene expression changes may impart greater significance than cell abundance changes to the whole blood miRNA profile. Altogether, these data provide a foundation for understanding the relationship between healthy aging and disease, and for the development of age-specific disease biomarkers