scholarly journals ShinyGO: a graphical enrichment tool for ani-mals and plants

2018 ◽  
Author(s):  
Steven Xijin Ge ◽  
Dongmin Jung
Keyword(s):  
Web Application ◽  
Enrichment Analysis ◽  
Supplementary Information ◽  
Annotation Database ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Graphical Visualization ◽  
Supplementary Material ◽  
Pathway Diagrams ◽  
Program Interface

AbstractMotivationGene lists are routinely produced from various genome-wide studies. Enrichment analysis can link these gene lists with underlying molecular pathways by using functional categories such as gene ontology (GO).ResultsTo complement existing tools, we developed ShinyGO with several features: (1) large annotation database from GO and many other sources for over 200 plant and animal species, (2) graphical visualization of enrichment results and gene characteristics, and (3) application program interface (API) access to KEGG and STRING for the retrieval of pathway diagrams and protein-protein interaction networks. ShinyGO is an intuitive, graphical web application that can help researchers gain actionable insights from gene lists.Availabilityhttp://ge-lab.org/go/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals ShinyGO: a graphical gene-set enrichment tool for animals and plants

2019 ◽  
Vol 36 (8) ◽  
pp. 2628-2629 ◽  
Author(s):  
Steven Xijin Ge ◽  
Dongmin Jung ◽  
Runan Yao
Keyword(s):  
Web Application ◽  
Bacterial Species ◽  
Enrichment Analysis ◽  
Supplementary Information ◽  
Annotation Database ◽  
Protein Protein Interaction ◽  
Gene Sets ◽  
Graphical Visualization ◽  
Pathway Diagrams ◽  
Program Interface

Abstract Motivation Gene lists are routinely produced from various omic studies. Enrichment analysis can link these gene lists with underlying molecular pathways and functional categories such as gene ontology (GO) and other databases. Results To complement existing tools, we developed ShinyGO based on a large annotation database derived from Ensembl and STRING-db for 59 plant, 256 animal, 115 archeal and 1678 bacterial species. ShinyGO’s novel features include graphical visualization of enrichment results and gene characteristics, and application program interface access to KEGG and STRING for the retrieval of pathway diagrams and protein–protein interaction networks. ShinyGO is an intuitive, graphical web application that can help researchers gain actionable insights from gene-sets. Availability and implementation http://ge-lab.org/go/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PathScore: a web tool for identifying altered pathways in cancer data

2016 ◽  
Author(s):  
Stephen G. Gaffney ◽  
Jeffrey P. Townsend
Keyword(s):  
Web Application ◽  
Somatic Mutations ◽  
Supplementary Information ◽  
Web Tool ◽  
Cancer Data ◽  
Link Type ◽  
Novel Approach ◽  
Supplementary Material ◽  
User Friendly ◽  
Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

scholarly journals emeraLD: Rapid Linkage Disequilibrium Estimation with Massive Data Sets

2018 ◽  
Author(s):  
Corbin Quick ◽  
Christian Fuchsberger ◽  
Daniel Taliun ◽  
Gonçalo Abecasis ◽  
Michael Boehnke ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Association Studies ◽  
Random Access ◽  
Supplementary Information ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Wide Range ◽  
Supplementary Material

AbstractSummaryEstimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies (GWAS). Large genetic data sets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD orders of magnitude faster than existing tools.Availability and ImplementationemeraLD is implemented in C++, and is open source under GPLv3. Source code, documentation, an R interface, and utilities for analysis of summary statistics are freely available at http://github.com/statgen/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Network controllability enrichment analysis reveals that SARS-CoV-2 infection tends to target indispensable nodes of a directed human protein-protein interaction network

2021 ◽  
Author(s):  
Ho-Joon Lee
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
A Genome ◽  
Human Proteins ◽  
Protein Protein Interaction Network ◽  
Network Controllability

The COVID-19 disease has been a global threat caused by the new coronavirus species, SARS-CoV-2, since early 2020 with an urgent need for therapeutic interventions. In order to provide insight into human proteins targeted by SARS-CoV-2, here we study a directed human protein-protein interaction network (dhPPIN) based on our previous work on network controllability of virus targets. We previously showed that human proteins targeted by viruses tend to be those whose removal in a dhPPIN requires more control of the network dynamics, which were classified as indispensable nodes. In this study we introduce a more comprehensive rank-based enrichment analysis of our previous dhPPIN for SARS-CoV-2 infection and show that SARS-CoV-2 also tends to target indispensable nodes in the dhPPIN using multiple proteomics datasets, supporting validity and generality of controllability analysis of viral infection in humans. Also, we find differential controllability among SARS-CoV-2, SARS-CoV-1, and MERS-CoV from a comparative proteomics study. Moreover, we show functional significance of indispensable nodes by analyzing heterogeneous datasets from a genome-wide CRISPR screening study, a time-course phosphoproteomics study, and a genome-wide association study. Specifically, we identify SARS-CoV-2 ORF3A as most frequently interacting with indispensable proteins in the dhPPIN, which are enriched in TGF-beta signaling and tend to be sources nodes and interact with each other. Finally, we built an integrated network model of ORF3A-interacting indispensable proteins with multiple functional supports to provide hypotheses for experimental validation as well as therapeutic opportunities. Therefore, a sub-network of indispensable proteins targeted by SARS-CoV-2 could serve as a prioritized network of drug targets and a basis for further functional and mechanistic studies from a network controllability perspective.

scholarly journals IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, Mapping and Mining ANalysis

2016 ◽  
Author(s):  
Minoo Ashtinai ◽  
Payman Nickchi ◽  
Soheil Jahangiri-Tazehkand ◽  
Abdollah Safari ◽  
Mehdi Mirzaie ◽  
...  
Keyword(s):  
Protein Function ◽  
Protein Function Prediction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Network ◽  
Supplementary Information ◽  
Protein Protein Interaction ◽  
Link Type ◽  
Supplementary Material ◽  
Protein Protein Interaction Networks

SummaryIMMAN is a software for reconstructing Interolog Protein Network (IPN) by integrating several Protein-protein Interaction Networks (PPIN). Users can unify different PPINs to mine conserved common network among species. IMMAN helps to retrieve IPNs with different degrees of conservation to engage for protein function prediction analysis based on protein networks.AvailabilityIMMAN is freely available at https://bioconductor.org/packages/IMMAN, http://profiles.bs.ipm.ir/softwares/IMMAN/[email protected], [email protected], [email protected] informationSupplementary data are available online.

Varanto: variant enrichment analysis and annotation

2019 ◽  
Vol 35 (17) ◽  
pp. 3154-3156 ◽  
Author(s):  
Oskari Timonen ◽  
Mikko Särkkä ◽  
Tibor Fülöp ◽  
Anton Mattsson ◽  
Juha Kekäläinen ◽  
...  
Keyword(s):  
Association Studies ◽  
Enrichment Analysis ◽  
Genetic Variations ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Shiny App ◽  
Specific Trait ◽  
Diverse Data

Abstract Summary Genome-wide association studies (GWAS) aim to identify associations of genetic variations such as single-nucleotide polymorphisms (SNPs) to a specific trait or a disease. Identifying common themes such as pathways, biological processes and diseases associations is needed to further explore and interpret these results. Varanto is a novel web tool for annotating, visualizing and analyzing human genetic variations using diverse data sources. Varanto can be used to query a set of input variations, retrieve their associated variation and gene level annotations, perform annotation enrichment analysis and visualize the results. Availability and implementation Varanto web app is developed with R and implemented as Shiny app with PostgreSQL database and is freely available at http://bioinformatics.uef.fi/varanto. Source code for the tool is available at https://github.com/oqe/varanto. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals deTS: tissue-specific enrichment analysis to decode tissue specificity

2019 ◽  
Vol 35 (19) ◽  
pp. 3842-3845 ◽  
Author(s):  
Guangsheng Pei ◽  
Yulin Dai ◽  
Zhongming Zhao ◽  
Peilin Jia
Keyword(s):  
Expression Profiles ◽  
Association Studies ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide ◽  
Specific Regulation

Abstract Motivation Diseases and traits are under dynamic tissue-specific regulation. However, heterogeneous tissues are often collected in biomedical studies, which reduce the power in the identification of disease-associated variants and gene expression profiles. Results We present deTS, an R package, to conduct tissue-specific enrichment analysis with two built-in reference panels. Statistical methods are developed and implemented for detecting tissue-specific genes and for enrichment test of different forms of query data. Our applications using multi-trait genome-wide association studies data and cancer expression data showed that deTS could effectively identify the most relevant tissues for each query trait or sample, providing insights for future studies. Availability and implementation https://github.com/bsml320/deTS and CRAN https://cran.r-project.org/web/packages/deTS/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Drug Targetor: a web interface to investigate the human druggome for over 500 phenotypes

2018 ◽  
Vol 35 (14) ◽  
pp. 2515-2517 ◽  
Author(s):  
Héléna A Gaspar ◽  
Christopher Hübel ◽  
Gerome Breen
Keyword(s):  
Web Application ◽  
Drug Target ◽  
Association Studies ◽  
Source Code ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Web Interface ◽  
Genome Wide ◽  
Therapeutic Mechanisms

Abstract Summary Results from hundreds of genome-wide association studies (GWAS) are now freely available and offer a catalogue of the association between phenotypes across medicine with variants in the genome. With the aim of using this data to better understand therapeutic mechanisms, we have developed Drug Targetor, a web interface that allows the generation and exploration of drug–target networks of hundreds of phenotypes using GWAS data. Drug Targetor networks consist of drug and target nodes ordered by genetic association and connected by drug–target or drug–gene relationship. We show that Drug Targetor can help prioritize drugs, targets and drug–target interactions for a specific phenotype based on genetic evidence. Availability and implementation Drug Targetor v1.21 is a web application freely available online at drugtargetor.com and under MIT licence. The source code can be found at https://github.com/hagax8/drugtargetor. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RAD: a web application to identify region associated differentially expressed genes

2021 ◽  
Author(s):  
Yixin Guo ◽  
Ziwei Xue ◽  
Ruihong Yuan ◽  
Jingyi Jessica Li ◽  
William A Pastor ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Web Application ◽  
Distal Region ◽  
Differentially Expressed ◽  
Supplementary Information ◽  
Regulatory Function ◽  
Genome Wide ◽  
Wide Scale ◽  
Genomic Regions ◽  
User Friendly

Abstract Summary With the advance of genomic sequencing techniques, chromatin accessible regions, transcription factor binding sites and epigenetic modifications can be identified at genome-wide scale. Conventional analyses focus on the gene regulation at proximal regions; however, distal regions are usually less focused, largely due to the lack of reliable tools to link these regions to coding genes. In this study, we introduce RAD (Region Associated Differentially expressed genes), a user-friendly web tool to identify both proximal and distal region associated differentially expressed genes (DEGs). With DEGs and genomic regions of interest (gROI) as input, RAD maps the up- and down-regulated genes associated with any gROI and helps researchers to infer the regulatory function of these regions based on the distance of gROI to differentially expressed genes. RAD includes visualization of the results and statistical inference for significance. Availability and implementation RAD is implemented with Python 3.7 and run on a Nginx server. RAD is freely available at https://labw.org/rad as online web service. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts

2017 ◽  
Author(s):  
Chris Chatzinakos ◽  
Donghyung Lee ◽  
Bradley T Webb ◽  
Vladimir I Vladimirov ◽  
Kenneth S Kendler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Supplementary Information ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Expression Studies ◽  
Genome Wide ◽  
Gene Level ◽  
Supplementary Material ◽  
Gene Expression Studies

AbstractMotivationTo increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on i) summary statistics from genome-wide association studies (GWAS) and ii) linkage disequilibrium (LD) patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals.ResultsWe propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses i) cis-eQTL SNPs from the latest expression studies and ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and ii), to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of [email protected] informationSupplementary material is available at Bioinformatics online.

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