scholarly journals Differential Expression Gene Explorer (DrEdGE): a tool for generating interactive online visualizations of gene expression datasets

2020 ◽  
Vol 36 (8) ◽  
pp. 2581-2583 ◽  
Author(s):  
Sophia C Tintori ◽  
Patrick Golden ◽  
Bob Goldstein
Keyword(s):  
Gene Expression ◽  
Caenorhabditis Elegans ◽  
Differential Expression ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Online Data ◽  
Web Based ◽  
Neuronal Tissue ◽  
Differential Expression Gene ◽  
Data Visualizations

Abstract Summary Differential Expression Gene Explorer (DrEdGE) is a web-based tool that guides genomicists through easily creating interactive online data visualizations, which colleagues can query according to their own conditions to discover genes, samples or patterns of interest. We demonstrate DrEdGE’s features with three example websites generated from publicly available datasets—human neuronal tissue, mouse embryonic tissue and Caenorhabditis elegans whole embryos. DrEdGE increases the utility of large genomics datasets by removing technical obstacles to independent exploration. Availability and implementation Freely available at http://dredge.bio.unc.edu. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Differential Expression Gene Explorer (DrEdGE): A tool for generating interactive online data visualizations for exploration of quantitative transcript abundance datasets

2019 ◽  
Author(s):  
Sophia C. Tintori ◽  
Patrick Golden ◽  
Bob Goldstein
Keyword(s):  
Data Sharing ◽  
Transcript Abundance ◽  
Rna Seq ◽  
Online Data ◽  
Web Based ◽  
Neuronal Tissue ◽  
C Elegans ◽  
Differential Expression Gene ◽  
Public Data ◽  
Data Visualizations

AbstractAs the scientific community becomes increasingly interested in data sharing, there is a growing need for tools that facilitate the querying of public data. Mining of RNA-seq datasets, for example, has value to many biomedical researchers, yet is often effectively inaccessible to non-genomicist experts, even when the raw data are available. Here we present DrEdGE (dredge.bio.unc.edu), a free Web-based tool that facilitates data sharing between genomicists and their colleagues. The DrEdGE software guides genomicists through easily creating interactive online data visualizations, which colleagues can then explore and query according to their own conditions to discover genes, samples, or patterns of interest. We demonstrate DrEdGE’s features with three example websites we generated from publicly available datasets—human neuronal tissue, mouse embryonic tissue, and a C. elegans embryonic series. DrEdGE increases the utility of large genomics datasets by removing the technical obstacles that prevent interested parties from exploring the data independently.

WormExp: a web-based application for a Caenorhabditis elegans-specific gene expression enrichment analysis

2015 ◽  
Vol 32 (6) ◽  
pp. 943-945 ◽  
Author(s):  
Wentao Yang ◽  
Katja Dierking ◽  
Hinrich Schulenburg
Keyword(s):  
Gene Expression ◽  
Caenorhabditis Elegans ◽  
Enrichment Analysis ◽  
Supplementary Information ◽  
Specific Gene ◽  
Data Sets ◽  
Complete Collection ◽  
Web Based ◽  
Expression Of Genes ◽  
Gene Sets

Abstract Motivation: A particular challenge of the current omics age is to make sense of the inferred differential expression of genes and proteins. The most common approach is to perform a gene ontology (GO) enrichment analysis, thereby relying on a database that has been extracted from a variety of organisms and that can therefore only yield reliable information on evolutionary conserved functions. Results: We here present a web-based application for a taxon-specific gene set exploration and enrichment analysis, which is expected to yield novel functional insights into newly determined gene sets. The approach is based on the complete collection of curated high-throughput gene expression data sets for the model nematode Caenorhabditis elegans, including 1786 gene sets from more than 350 studies. Availability and implementation: WormExp is available at http://wormexp.zoologie.uni-kiel.de. Contacts: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals ccNetViz: a WebGL-based JavaScript library for visualization of large networks

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  
Keyword(s):  
Systems Biology ◽  
Complex Networks ◽  
Open Source ◽  
High Speed ◽  
A Priori ◽  
Supplementary Information ◽  
Network Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Epidemiological modeling in StochSS Live!

2021 ◽  
Author(s):  
Richard Jiang ◽  
Bruno Jacob ◽  
Matthew Geiger ◽  
Sean Matthew ◽  
Bryan Rumsey ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Epidemiological Model ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Based ◽  
Epidemiological Modeling ◽  
Modeling Simulation ◽  
Wide Range ◽  
Biochemical Systems

Abstract Summary We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results. Availability and implementation StochSS Live! is freely available at https://live.stochss.org/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BIOLITMAP: a web-based geolocated, temporal and thematic visualization of the evolution of bioinformatics publications

2018 ◽  
Vol 35 (14) ◽  
pp. 2518-2520
Author(s):  
Adrián Bazaga ◽  
Alfonso Valencia ◽  
María- JoséRementeria
Keyword(s):  
General Public ◽  
Fast Growth ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Based ◽  
Research Publications

Abstract Motivation The fast growth of bioinformatics adds a significant difficulty to assess the contribution, geographical and thematic distribution of the research publications. Results To help researchers, grant agencies and general public to assess the progress in bioinformatics, we have developed BIOLITMAP, a web-based geolocation system that allows an easy and sensible exploration of the publications by institution, year and topic. Availability and implementation BIOLITMAP is available at http://socialanalytics.bsc.es/biolitmap and the sources have been deposited at https://github.com/inab/BIOLITMAP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals INDRA-IPM: interactive pathway modeling using natural language with automated assembly

2019 ◽  
Vol 35 (21) ◽  
pp. 4501-4503 ◽  
Author(s):  
Petar V Todorov ◽  
Benjamin M Gyori ◽  
John A Bachman ◽  
Peter K Sorger
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Web Service ◽  
Language Processing ◽  
Source Code ◽  
Supplementary Information ◽  
Expression Data ◽  
Supplementary Data ◽  
Automated Assembly ◽  
Web Based

Abstract Summary INDRA-IPM (Interactive Pathway Map) is a web-based pathway map modeling tool that combines natural language processing with automated model assembly and visualization. INDRA-IPM contextualizes models with expression data and exports them to standard formats. Availability and implementation INDRA-IPM is available at: http://pathwaymap.indra.bio. Source code is available at http://github.com/sorgerlab/indra_pathway_map. The underlying web service API is available at http://api.indra.bio:8000. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Accurate detection of short and long active ORFs using Ribo-seq data

2019 ◽  
Vol 36 (7) ◽  
pp. 2053-2059 ◽  
Author(s):  
Saket Choudhary ◽  
Wenzheng Li ◽  
Andrew D. Smith
Keyword(s):  
Caenorhabditis Elegans ◽  
Deep Sequencing ◽  
Open Reading Frames ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Accurate Detection ◽  
Short Orfs ◽  
Coding Potential ◽  
Reading Frames

Abstract Motivation Ribo-seq, a technique for deep-sequencing ribosome-protected mRNA fragments, has enabled transcriptome-wide monitoring of translation in vivo. It has opened avenues for re-evaluating the coding potential of open reading frames (ORFs), including many short ORFs that were previously presumed to be non-translating. However, the detection of translating ORFs, specifically short ORFs, from Ribo-seq data, remains challenging due to its high heterogeneity and noise. Results We present ribotricer, a method for detecting actively translating ORFs by directly leveraging the three-nucleotide periodicity of Ribo-seq data. Ribotricer demonstrates higher accuracy and robustness compared with other methods at detecting actively translating ORFs including short ORFs on multiple published datasets across species inclusive of Arabidopsis, Caenorhabditis elegans, Drosophila, human, mouse, rat, yeast and zebrafish. Availability and implementation Ribotricer is available at https://github.com/smithlabcode/ribotricer. All analysis scripts and results are available at https://github.com/smithlabcode/ribotricer-results. Supplementary information Supplementary data are available at Bioinformatics online.

SolupHred: a server to predict the pH-dependent aggregation of intrinsically disordered proteins

2020 ◽  
Author(s):  
Carlos Pintado ◽  
Jaime Santos ◽  
Valentín Iglesias ◽  
Salvador Ventura
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Solution Ph ◽  
Predictive Tool ◽  
Web Based ◽  
Intrinsically Disordered ◽  
Dependent Protein ◽  
Ph Dependent

Abstract Summary Polypeptides are exposed to changing environmental conditions that modulate their intrinsic aggregation propensities. Intrinsically disordered proteins (IDPs) constitutively expose their aggregation determinants to the solvent, thus being especially sensitive to its fluctuations. However, solvent conditions are often disregarded in computational aggregation predictors. We recently developed a phenomenological model to predict IDPs' solubility as a function of the solution pH, which is based on the assumption that both protein lipophilicity and charge depend on this parameter. The model anticipated solubility changes in different IDPs accurately. In this application note, we present SolupHred, a web-based interface that implements the aforementioned theoretical framework into a predictive tool able to compute IDPs aggregation propensities as a function of pH. SolupHred is the first dedicated software for the prediction of pH-dependent protein aggregation. Availability and implementation The SolupHred web server is freely available for academic users at: https://ppmclab.pythonanywhere.com/SolupHred. It is platform-independent and does not require previous registration. Supplementary information Supplementary data are available at Bioinformatics online.

Analysis of variance when both input and output sets are high-dimensional

2020 ◽  
Author(s):  
Gustavo de los Campos ◽  
Torsten Pook ◽  
Agustin Gonzalez-Raymundez ◽  
Henner Simianer ◽  
George Mias ◽  
...  
Keyword(s):  
Gene Expression ◽  
Linear Span ◽  
Copy Number Variants ◽  
Real Data ◽  
Supplementary Information ◽  
High Dimensional ◽  
Supplementary Data ◽  
Random Effects Models ◽  
Input And Output ◽  
Data Layers

AbstractMotivationModern genomic data sets often involve multiple data-layers (e.g., DNA-sequence, gene expression), each of which itself can be high-dimensional. The biological processes underlying these data-layers can lead to intricate multivariate association patterns.ResultsWe propose and evaluate two methods for analysis variance when both input and output sets are high-dimensional. Our approach uses random effects models to estimate the proportion of variance of vectors in the linear span of the output set that can be explained by regression on the input set. We consider a method based on orthogonal basis (Eigen-ANOVA) and one that uses random vectors (Monte Carlo ANOVA, MC-ANOVA) in the linear span of the output set. We used simulations to assess the bias and variance of each of the methods, and to compare it with that of the Partial Least Squares (PLS)–an approach commonly used in multivariate-high-dimensional regressions. The MC-ANOVA method gave nearly unbiased estimates in all the simulation scenarios considered. Estimates produced by Eigen-ANOVA and PLS had noticeable biases. Finally, we demonstrate insight that can be obtained with the of MC-ANOVA and Eigen-ANOVA by applying these two methods to the study of multi-locus linkage disequilibrium in chicken genomes and to the assessment of inter-dependencies between gene expression, methylation and copy-number-variants in data from breast cancer tumors.AvailabilityThe Supplementary data includes an R-implementation of each of the proposed methods as well as the scripts used in simulations and in the real-data [email protected] informationSupplementary data are available at Bioinformatics online.

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