Concept: Uploading and downloading
We present VISual Plotting Interface for Genetics (visPIG; http://vispig.icr.ac.uk), a web application to produce multi-track, multi-scale, multi-region plots of genetic data. visPIG has been designed to allow users not well versed with mathematical software packages and/or programming languages such as R , Matlab®, Python, etc., to integrate data from multiple sources for interpretation and to easily create publication-ready figures. While web tools such as the UCSC Genome Browser  or the WashU Epigenome Browser  allow custom data uploads, such tools are primarily designed for data exploration. This is also true for the desktop-run Integrative Genomics Viewer (IGV) ,. Other locally run data visualisation software such as Circos  require significant computer skills of the user. The visPIG web application is a menu-based interface that allows users to upload custom data tracks and set track-specific parameters. Figures can be downloaded as PDF or PNG files. For sensitive data, the underlying R  code can also be downloaded and run locally. visPIG is multi-track: it can display many different data types (e.g association, functional annotation, intensity, interaction, heat map data,…). It also allows annotation of genes and other custom features in the plotted region(s). Data tracks can be plotted individually or on a single figure. visPIG is multi-region: it supports plotting multiple regions, be they kilo- or megabases apart or even on different chromosomes. Finally, visPIG is multi-scale: a sub-region of particular interest can be ‘zoomed’ in. We describe the various features of visPIG and illustrate its utility with examples. visPIG is freely available through http://vispig.icr.ac.uk under a GNU General Public License (GPLv3).
Different approaches can be chosen to quantify the impact and merits of scientific oncology publications. These include source of publication (including journal reputation and impact factor), whether or not articles are cited by others, and access/download figures. When relying on citation counts, one needs to obtain access to citation databases and has to consider that results differ from one database to another. Accumulation of citations takes time and their dynamics might differ from journal to journal and topic to topic. Therefore, we wanted to evaluate the correlation between citation and download figures, hypothesising that articles with fewer downloads also accumulate fewer citations. Typically, publishers provide download figures together with the article. We extracted and analysed the 50 most viewed articles from 5 different open access oncology journals. For each of the 5 journals and also all journals combined, correlation between number of accesses and citations was limited (r = 0.01-0.30). Considerable variations were also observed when analyses were restricted to specific article types such as reviews only (r = 0.21) or case reports only (r = 0.53). Even if year of publication was taken into account, high correlation coefficients were the exception from the rule. In conclusion, downloads are not a universal surrogate for citation figures.
Ever growing interest in microRNAs has immensely populated the number of resources and research papers devoted to the field and, as a result, it becomes more and more demanding to find miRNA data of interest. To mitigate this problem, we created miRNEST database (http://mirnest.amu.edu.pl), an integrative microRNAs resource. In its updated version, named miRNEST 2.0, the database is complemented with our extensive miRNA predictions from deep sequencing libraries, data from plant degradome analyses, results of pre-miRNA classification with HuntMi and miRNA splice sites information. We also added download and upload options and improved the user interface to make it easier to browse through miRNA records.
STITCH is a database of protein-chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions. Available at http://stitch.embl.de, the resulting interaction network includes 390 000 chemicals and 3.6 million proteins from 1133 organisms. Compared with the previous version, the number of high-confidence protein-chemical interactions in human has increased by 45%, to 367 000. In this version, we added features for users to upload their own data to STITCH in the form of internal identifiers, chemical structures or quantitative data. For example, a user can now upload a spreadsheet with screening hits to easily check which interactions are already known. To increase the coverage of STITCH, we expanded the text mining to include full-text articles and added a prediction method based on chemical structures. We further changed our scheme for transferring interactions between species to rely on orthology rather than protein similarity. This improves the performance within protein families, where scores are now transferred only to orthologous proteins, but not to paralogous proteins. STITCH can be accessed with a web-interface, an API and downloadable files.
Recent rapid advances in high-throughput, next-generation sequencing (NGS) technologies have promoted mitochondrial genome studies in the fields of human evolution, medical genetics, and forensic casework. However, scientists unfamiliar with computer programming often find it difficult to handle the massive volumes of data that are generated by NGS. To address this limitation, we developed MitoSuite, a user-friendly graphical tool for analysis of data from high-throughput sequencing of the human mitochondrial genome. MitoSuite generates a visual report on NGS data with simple mouse operations. Moreover, it analyzes high-coverage sequencing data but runs on a stand-alone computer, without the need for file upload. Therefore, MitoSuite offers outstanding usability for handling massive NGS data, and is ideal for evolutionary, clinical, and forensic studies on the human mitochondrial genome variations. It is freely available for download from the website https://mitosuite.com.
The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu/) is a web-based application that integrates relevant data, analysis and visualization, allowing users to easily discover and share their research observations. Users can explore the relationship between genomic alterations and phenotypes by visualizing various -omic data alongside clinical and phenotypic features, such as age, subtype classifications and genomic biomarkers. The Cancer Genomics Browser currently hosts 575 public datasets from genome-wide analyses of over 227 000 samples, including datasets from TCGA, CCLE, Connectivity Map and TARGET. Users can download and upload clinical data, generate Kaplan-Meier plots dynamically, export data directly to Galaxy for analysis, plus generate URL bookmarks of specific views of the data to share with others.
A number of different molecular interactions data download formats now exist, designed to allow access to these valuable data by diverse user groups. These formats include the PSI-XML and MITAB standard interchange formats developed by Molecular Interaction workgroup of the HUPO-PSI in addition to other, use-specific downloads produced by other resources. The onus is currently on the user to ensure that a piece of software is capable of read/writing all necessary versions of each format. This problem may increase, as data providers strive to meet ever more sophisticated user demands and data types.
The impact of scholarly output is typically measured by the number of citations and, more recently, downloads. Newer metrics have been developed to reflect digital dissemination of knowledge such as the Altmetric and Mendeley reader scores. This article examines the relationship among citations, download rates, Altmetric scores, and Mendeley reader scores in Plastic and Reconstructive Surgery.
The Vaccine Adverse Event Reporting System (VAERS), co-managed by CDC and the Food and Drug Administration (FDA), is the national postmarketing safety monitoring system that accepts reports about adverse events that occur after administration of U.S.-licensed vaccines (1,2). On June 30, 2017, CDC and FDA implemented a revised reporting form and a new process for submitting reports to VAERS. Persons reporting adverse events are now able to use the VAERS 2.0 online reporting tool to submit reports directly online; alternatively, they may download and complete the writable and savable VAERS 2.0 form and submit it using an electronic document upload feature.
The abstract has a lot of special characters, so we will upload a separate file.