scholarly journals eUTOPIA: Solution for omics data preprocessing and analysis

2018 ◽  
Author(s):  
Veer Singh Marwah ◽  
Giovanni Scala ◽  
Pia Anneli Sofia Kinaret ◽  
Angela Serra ◽  
Harri Alenius ◽  
...  
Keyword(s):  
State Of The Art ◽  
Community Resources ◽  
Omics Data ◽  
Graphical Interface ◽  
R Shiny ◽  
Negative Effect ◽  
Integrated Software ◽  
Computational Skill ◽  
Processing Steps ◽  
Shiny Application

AbstractApplication of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R that have become standard for high-throughput analytics. However, there is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results, which is computational skill neutral. We propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application. eUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods. eUTOPIA is free for academic use and can be obtained from the GitHub repository https://github.com/Greco-Lab/eUTOPIA.

scholarly journals Quickomics: exploring omics data in an intuitive, interactive and informative manner

2021 ◽  
Author(s):  
Benbo Gao ◽  
Jing Zhu ◽  
Soumya Negi ◽  
Xinmin Zhang ◽  
Stefka Gyoneva ◽  
...  
Keyword(s):  
Modular Design ◽  
Functional Module ◽  
Supplementary Information ◽  
Data Sets ◽  
Omics Data ◽  
Proteomics Data ◽  
Primary Analysis ◽  
Link Type ◽  
R Shiny ◽  
Advanced Analysis

AbstractSummaryWe developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics data and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customized configurations and generates both interactive and publication-ready high-resolution plots to uncover biological insights from data. The modular design makes the tool extensible with ease.AvailabilityResearchers can experience the functionalities with their own data or demo RNA-Seq and proteomics data sets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/[email protected], [email protected] informationSupplementary materials are available at https://bit.ly/37HP17g.

Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations

2020 ◽  
Author(s):  
Minsik Oh ◽  
Sungjoon Park ◽  
Sun Kim ◽  
Heejoon Chae
Keyword(s):  
Machine Learning ◽  
Gene Regulation ◽  
State Of The Art ◽  
Patient Specific ◽  
Specific Gene ◽  
Omics Data ◽  
Gene Expressions ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Disease Subtype

Abstract Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.

scholarly journals Improved Pre-miRNA Classification by Reducing the Effect of Class Imbalance

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yingli Zhong ◽  
Ping Xuan ◽  
Ke Han ◽  
Weiping Zhang ◽  
Jianzhong Li
Keyword(s):  
Cross Validation ◽  
State Of The Art ◽  
Class Imbalance ◽  
Superior Performance ◽  
Biological Processes ◽  
Classification Models ◽  
New Classification ◽  
Negative Effect ◽  
Human Animal ◽  
Species Specific

MicroRNAs (miRNAs) play important roles in the diverse biological processes of animals and plants. Although the prediction methods based on machine learning can identify nonhomologous and species-specific miRNAs, they suffered from severe class imbalance on real and pseudo pre-miRNAs. We propose a pre-miRNA classification method based on cost-sensitive ensemble learning and refer to it as MiRNAClassify. Through a series of iterations, the information of all the positive and negative samples is completely exploited. In each iteration, a new classification instance is trained by the equal number of positive and negative samples. In this way, the negative effect of class imbalance is efficiently relieved. The new instance primarily focuses on those samples that are easy to be misclassified. In addition, the positive samples are assigned higher cost weight than the negative samples. MiRNAClassify is compared with several state-of-the-art methods and some well-known classification models by testing the datasets about human, animal, and plant. The result of cross validation indicates that MiRNAClassify significantly outperforms other methods and models. In addition, the newly added pre-miRNAs are used to further evaluate the ability of these methods to discover novel pre-miRNAs. MiRNAClassify still achieves consistently superior performance and can discover more pre-miRNAs.

scholarly journals Single Cell Viewer (SCV): An interactive visualization data portal for single cell RNA sequence data

2019 ◽  
Author(s):  
Shuoguo Wang ◽  
Constance Brett ◽  
Mohan Bolisetty ◽  
Ryan Golhar ◽  
Isaac Neuhaus ◽  
...  
Keyword(s):  
Single Cell ◽  
Sequence Data ◽  
Single Cells ◽  
Link Type ◽  
Technological Advances ◽  
R Shiny ◽  
Data Volume ◽  
Exploratory Data ◽  
Cell Data ◽  
Shiny Application

AbstractMotivationThanks to technological advances made in the last few years, we are now able to study transcriptomes from thousands of single cells. These have been applied widely to study various aspects of Biology. Nevertheless, comprehending and inferring meaningful biological insights from these large datasets is still a challenge. Although tools are being developed to deal with the data complexity and data volume, we do not have yet an effective visualizations and comparative analysis tools to realize the full value of these datasets.ResultsIn order to address this gap, we implemented a single cell data visualization portal called Single Cell Viewer (SCV). SCV is an R shiny application that offers users rich visualization and exploratory data analysis options for single cell datasets.AvailabilitySource code for the application is available online at GitHub (http://www.github.com/neuhausi/single-cell-viewer) and there is a hosted exploration application using the same example dataset as this publication at http://periscopeapps.org/[email protected]; [email protected]

scholarly journals UCSCXenaShiny: An R Package for Exploring and Analyzing UCSC Xena Public Datasets in Web Browser

2020 ◽  
Author(s):  
Shixiang Wang ◽  
Yi Xiong ◽  
Kai Gu ◽  
Longfei Zhao ◽  
Yin Li ◽  
...  
Keyword(s):  
R Package ◽  
Data Availability ◽  
Analysis Tool ◽  
Omics Data ◽  
Analysis Framework ◽  
Web Browser ◽  
Research Opportunities ◽  
Public Projects ◽  
R Shiny ◽  
Public Datasets

Motivation: UCSC Xena platform provides huge amounts of processed cancer omics data from big public projects like TCGA or individual reserach groups for enabling unprecedented research opportunities. In 2019, we developed UCSCXenaTools, an R package for retrieval of UCSC Xena data. However, an easier dataset exploration and analysis tool is still lack, especially for researchers without programming experience. Results: We develop UCSCXenaShiny, an R Shiny package to quickly explore, download all datasets from UCSC Xena data hubs. In addiction, a module based analysis framework is constructed to analyze and visualize data. Availability: https://github.com/openbiox/UCSCXenaShiny or https://cran.r-project.org/package=UCSCXenaShiny.

scholarly journals metaCOVID: An R-Shiny application for living meta-analyses of COVID-19 trials

2021 ◽  
Author(s):  
Theodoros Evrenoglou ◽  
Isabelle Boutron ◽  
Anna Chaimani
Keyword(s):  
Evidence Synthesis ◽  
Source Code ◽  
Decision Makers ◽  
Sensitivity Analyses ◽  
Preventive Interventions ◽  
Primary Interest ◽  
Link Type ◽  
R Shiny ◽  
Meta Analyses ◽  
Shiny Application

Abstract“Living” evidence synthesis is of primary interest for decision-makers to overcome the COVID-19 pandemic. The COVID-NMA provides open-access living meta-analyses assessing different therapeutic and preventive interventions. Data are posted on a platform (https://covid-nma.com/) and analyses are updated every week. However, guideline developers and other stakeholders also need to investigate the data and perform their own analyses. This requires resources, time, statistical expertise, and software knowledge. To assist them, we created the “metaCOVID” application which, based on automation processes, facilitates the fast exploration of the data and the conduct of analyses tailored to end-users needs. metaCOVID has been created in R and is freely available as an R-Shiny application. The application conducts living meta-analyses for every outcome. Several options are available for subgroup and sensitivity analyses. The results are presented in downloadable forest plots. metaCOVID is freely available from https://covid-nma.com/metacovid/ and the source code from https://github.com/TEvrenoglou/metaCovid.

scholarly journals Effects of COVID-19 on trade flows: Measuring their impact through government policy responses

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258356
Author(s):  
Javier Barbero ◽  
Juan José de Lucio ◽  
Ernesto Rodríguez-Crespo
Keyword(s):  
Bilateral Trade ◽  
State Of The Art ◽  
Negative Impact ◽  
Trade Agreements ◽  
Trade Flows ◽  
Policy Responses ◽  
Negative Effect ◽  
Bilateral Trade Flows ◽  
Gravity Model Of Trade ◽  
The Impact

This paper examines the impact of COVID-19 on bilateral trade flows using a state-of-the-art gravity model of trade. Using the monthly trade data of 68 countries exporting across 222 destinations between January 2019 and October 2020, our results are threefold. First, we find a greater negative impact of COVID-19 on bilateral trade for those countries that were members of regional trade agreements before the pandemic. Second, we find that the impact of COVID-19 is negative and significant when we consider indicators related to governmental actions. Finally, this negative effect is more intense when exporter and importer country share identical income levels. In the latter case, the highest negative impact is found for exports between high-income countries.

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