ORSO (Online Resource for Social Omics): A data-driven social network connecting scientists to genomics datasets.

PLoS Comput Biol

Office of Scientific Computing, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, United States of America.

Published: January 2020

High-throughput sequencing has become ubiquitous in biomedical sciences. As new technologies emerge and sequencing costs decline, the diversity and volume of available data increases exponentially, and successfully navigating the data becomes more challenging. Though datasets are often hosted by public repositories, scientists must rely on inconsistent annotation to identify and interpret meaningful data. Moreover, the experimental heterogeneity and wide-ranging quality of high-throughput biological data means that even data with desired cell lines, tissue types, or molecular targets may not be readily interpretable or integrated. We have developed ORSO (Online Resource for Social Omics) as an easy-to-use web application to connect life scientists with genomics data. In ORSO, users interact within a data-driven social network, where they can favorite datasets and follow other users. In addition to more than 30,000 datasets hosted from major biomedical consortia, users may contribute their own data to ORSO, facilitating its discovery by other users. Leveraging user interactions, ORSO provides a novel recommendation system to automatically connect users with hosted data. In addition to social interactions, the recommendation system considers primary read coverage information and annotated metadata. Similarities used by the recommendation system are presented by ORSO in a graph display, allowing exploration of dataset associations. The topology of the network graph reflects established biology, with samples from related systems grouped together. We tested the recommendation system using an RNA-seq time course dataset from differentiation of embryonic stem cells to cardiomyocytes. The ORSO recommendation system correctly predicted early data point sources as embryonic stem cells and late data point sources as heart and muscle samples, resulting in recommendation of related datasets. By connecting scientists with relevant data, ORSO provides a critical new service that facilitates wide-ranging research interests.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001987PMC
http://dx.doi.org/10.1371/journal.pcbi.1007571DOI Listing

Publication Analysis

Top Keywords

recommendation system
20
data orso
12
data
11
orso
8
orso online
8
online resource
8
resource social
8
social omics
8
data-driven social
8
social network
8

Similar Publications

A historical perspective of more than one hundred years of influenza surveillance in New York State demonstrates the progression from anecdotes and case counts to next-generation sequencing and electronic database management, greatly improving pandemic preparedness and response. Here, we determined if influenza virologic surveillance at the New York State public health laboratory (NYS PHL) tests sufficient specimen numbers within preferred confidence limits to assess situational awareness and detect novel viruses that pose a pandemic risk. To this end, we analyzed retrospective electronic data on laboratory test results for the influenza seasons 1997-1998 to 2021-2022 according to sample sizes recommended in the Influenza Virologic Surveillance Right Size Roadmap issued by the Association of Public Health Laboratories and Centers for Disease Control and Prevention.

View Article and Find Full Text PDF

Background: In recent decades, the number of immunocompromised patients (ICPs) has increased significantly. ICPs have an impaired immune system, making them susceptible to complicated infections. To protect them from infections, ICPs are eligible to receive several medically indicated vaccines.

View Article and Find Full Text PDF

The growing prevalence of cybersecurity threats is a significant concern for railway systems, which rely on an extensive network of onboard and trackside sensors. These threats have the potential to compromise the safety of railway operations and the integrity of the railway infrastructure itself. This paper aims to examine the current cybersecurity measures in use, identify the key vulnerabilities that they address, and propose solutions for enhancing the security of railway infrastructures.

View Article and Find Full Text PDF

Prehospital medical care is a major challenge for both civilian and military situations as resources are limited, yet critical triage and treatment decisions must be rapidly made. Prehospital medicine is further complicated during mass casualty situations or remote applications that require more extensive medical treatments to be monitored. It is anticipated on the future battlefield where air superiority will be contested that prolonged field care will extend to as much 72 h in a prehospital environment.

View Article and Find Full Text PDF

Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition.

Sensors (Basel)

December 2024

Nokia Bell Labs, 1082 Budapest, Hungary.

Human action recognition using WiFi channel state information (CSI) has gained attention due to its non-intrusive nature and potential applications in healthcare, smart environments, and security. However, the reliability of methods developed for CSI-based action recognition is often contingent on the quality of the datasets and evaluation protocols used. In this paper, we uncovered a critical data leakage issue, which arises from improper data partitioning, in a widely used WiFi CSI benchmark dataset.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!