Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

ABSTRACT: Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page.The web-interface is accessible via http://cgenome.net/calypso/ . The software is programmed in Java, PERL and R and the source code is available from Zenodo ( https://zenodo.org/record/50931 ). The software is freely available for non-commercial users.l.krause@uq.edu.au.Supplementary data are available at Bioinformatics online.

SUBMITTER: Zakrzewski M

PROVIDER: S-EPMC5408814 | biostudies-other | 2017 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

Zakrzewski Martha M Proietti Carla C Ellis Jonathan J JJ Hasan Shihab S Brion Marie-Jo MJ Berger Bernard B Krause Lutz L

Bioinformatics (Oxford, England) 20170301 5

Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehen ...[more]

PMID: 28025202

Similar Datasets

BRepertoire: a user-friendly web server for analysing antibody repertoire data.

Project description:Antibody repertoire analysis by high throughput sequencing is now widely used, but a persisting challenge is enabling immunologists to explore their data to discover discriminating repertoire features for their own particular investigations. Computational methods are necessary for large-scale evaluation of antibody properties. We have developed BRepertoire, a suite of user-friendly web-based software tools for large-scale statistical analyses of repertoire data. The software is able to use data preprocessed by IMGT, and performs statistical and comparative analyses with versatile plotting options. BRepertoire has been designed to operate in various modes, for example analysing sequence-specific V(D)J gene usage, discerning physico-chemical properties of the CDR regions and clustering of clonotypes. Those analyses are performed on the fly by a number of R packages and are deployed by a shiny web platform. The user can download the analysed data in different table formats and save the generated plots as image files ready for publication. We believe BRepertoire to be a versatile analytical tool that complements experimental studies of immune repertoires. To illustrate the server's functionality, we show use cases including differential gene usage in a vaccination dataset and analysis of CDR3H properties in old and young individuals. The server is accessible under http://mabra.biomed.kcl.ac.uk/BRepertoire.

| S-EPMC6031031 | biostudies-literature

BAGEL4: a user-friendly web server to thoroughly mine RiPPs and bacteriocins.

Project description:Interest in secondary metabolites such as RiPPs (ribosomally synthesized and posttranslationally modified peptides) is increasing worldwide. To facilitate the research in this field we have updated our mining web server. BAGEL4 is faster than its predecessor and is now fully independent from ORF-calling. Gene clusters of interest are discovered using the core-peptide database and/or through HMM motifs that are present in associated context genes. The databases used for mining have been updated and extended with literature references and links to UniProt and NCBI. Additionally, we have included automated promoter and terminator prediction and the option to upload RNA expression data, which can be displayed along with the identified clusters. Further improvements include the annotation of the context genes, which is now based on a fast blast against the prokaryote part of the UniRef90 database, and the improved web-BLAST feature that dynamically loads structural data such as internal cross-linking from UniProt. Overall BAGEL4 provides the user with more information through a user-friendly web-interface which simplifies data evaluation. BAGEL4 is freely accessible at http://bagel4.molgenrug.nl.

| S-EPMC6030817 | biostudies-literature

DAnIEL: A User-Friendly Web Server for Fungal ITS Amplicon Sequencing Data.

Project description:Trillions of microbes representing all kingdoms of life are resident in, and on, humans holding essential roles for the host development and physiology. The last decade over a dozen online tools and servers, accessible via public domain, have been developed for the analysis of bacterial sequences; however, the analysis of fungi is still in its infancy. Here, we present a web server dedicated to the comprehensive analysis of the human mycobiome for (i) translating raw sequencing reads to data tables and high-standard figures, (ii) integrating statistical analysis and machine learning with a manually curated relational database and (iii) comparing the user's uploaded datasets with publicly available from the Sequence Read Archive. Using 1,266 publicly available Internal transcribed spacers (ITS) samples, we demonstrated the utility of DAnIEL web server on large scale datasets and show the differences in fungal communities between human skin and soil sites.

| S-EPMC8416086 | biostudies-literature

spatialGE is a User-Friendly Web Application that Facilitates Spatial Transcriptomics Data Analysis

Project description:Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, the advanced data analysis and programming skills required can hinder researchers from realizing of the full potential of ST. To address this, we developed spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provided a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enabled comparative analysis among samples and supported various ST technologies. The utility of spatialGE was demonstrated through its application in studying the tumor microenvironment of two data sets: 10X Visium samples from a cohort of melanoma metastasis and Nanostring CosMx fields of vision from a cohort of Merkel cell carcinoma samples. These results support the ability of spatialGE to identify spatial gene expression patterns that provide valuable insights into the tumor microenvironment and highlight its utility in democratizing ST data analysis for the wider scientific community.

2024-11-21 | GSE271422 | GEO

patcHwork: a user-friendly pH sensitivity analysis web server for protein sequences and structures.

Project description:pH regulates protein function and interactions by altering the charge of individual residues causing loss or gain of intramolecular noncovalent bonds, which may lead to structural rearrangements. While tools to analyze residue-specific charge distribution of proteins at a given pH exist, currently no tool is available to investigate noncovalent bond changes at two different pH values. To make protein pH sensitivity analysis more accessible, we developed patcHwork, a web server that combines the identification of amino acids undergoing a charge shift with the determination of affected noncovalent bonds at two user-defined pH values. At the sequence-only level, patcHwork applies the Henderson-Hasselbalch equation to determine pH-sensitive residues. When the 3D protein structure is available, patcHwork can be employed to gain mechanistic understanding of the effect of pH. This is achieved using the PDB2PQR and PROPKA tools and noncovalent bond determination algorithms. A user-friendly interface allows visualizing pH-sensitive residues, affected salt bridges, hydrogen bonds and aromatic (pi-pi and cation-pi) interactions. patcHwork can be used to identify patches, a new concept we propose of pH-sensitive residues in close proximity on the protein, which may have a major impact on function. We demonstrate the attractiveness of patcHwork studying experimentally investigated pH-sensitive proteins (https://patchwork.biologie.uni-freiburg.de/).

| S-EPMC9252814 | biostudies-literature

VSTH: a user-friendly web server for structure-based virtual screening on Tianhe-2.

Project description:SummaryVSTH is a user-friendly web server with the complete workflow for virtual screening. By self-customized visualization software, users can interactively prepare protein files, set docking sites as well as view binding conformers in a target protein in a few clicks. We provide serval purchasable ligand libraries for selection. And, we integrate six open-source docking programs as computing engine, or as conformational sampling tools for DLIGAND2. Users can select various docking methods simultaneously and personalize computing parameters. After docking processing, user can filter docking conformations by ranked scores, or cluster-based molecular similarity to find highly populated clusters of low-energy conformations.Availability and implementationThe VSTH web server is free and open to all users at https://matgen.nscc-gz.cn/VirtualScreening.html.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9805582 | biostudies-literature

TRUFA: A User-Friendly Web Server for de novo RNA-seq Analysis Using Cluster Computing.

Project description:Application of next-generation sequencing (NGS) methods for transcriptome analysis (RNA-seq) has become increasingly accessible in recent years and are of great interest to many biological disciplines including, eg, evolutionary biology, ecology, biomedicine, and computational biology. Although virtually any research group can now obtain RNA-seq data, only a few have the bioinformatics knowledge and computation facilities required for transcriptome analysis. Here, we present TRUFA (TRanscriptome User-Friendly Analysis), an open informatics platform offering a web-based interface that generates the outputs commonly used in de novo RNA-seq analysis and comparative transcriptomics. TRUFA provides a comprehensive service that allows performing dynamically raw read cleaning, transcript assembly, annotation, and expression quantification. Due to the computationally intensive nature of such analyses, TRUFA is highly parallelized and benefits from accessing high-performance computing resources. The complete TRUFA pipeline was validated using four previously published transcriptomic data sets. TRUFA's results for the example datasets showed globally similar results when comparing with the original studies, and performed particularly better when analyzing the green tea dataset. The platform permits analyzing RNA-seq data in a fast, robust, and user-friendly manner. Accounts on TRUFA are provided freely upon request at https://trufa.ifca.es.

| S-EPMC4444131 | biostudies-literature

Mining, visualizing and comparing multidimensional biomolecular data using the Genomics Data Miner (GMine) Web-Server.

Project description:Genomics Data Miner (GMine) is a user-friendly online software that allows non-experts to mine, cluster and compare multidimensional biomolecular datasets. Various powerful visualization techniques are provided, generating high quality figures that can be directly incorporated into scientific publications. Robust and comprehensive analyses are provided via a broad range of data-mining techniques, including univariate and multivariate statistical analysis, supervised learning, correlation networks, clustering and multivariable regression. The software has a focus on multivariate techniques, which can attribute variance in the measurements to multiple explanatory variables and confounders. Various normalization methods are provided. Extensive help pages and a tutorial are available via a wiki server. Using GMine we reanalyzed proteome microarray data of host antibody response against Plasmodium falciparum. Our results support the hypothesis that immunity to malaria is a higher-order phenomenon related to a pattern of responses and not attributable to any single antigen. We also analyzed gene expression across resting and activated T cells, identifying many immune-related genes with differential expression. This highlights both the plasticity of T cells and the operation of a hardwired activation program. These application examples demonstrate that GMine facilitates an accurate and in-depth analysis of complex molecular datasets, including genomics, transcriptomics and proteomics data.

| S-EPMC5138638 | biostudies-literature

ProteomeExpert: a Docker image-based web server for exploring, modeling, visualizing and mining quantitative proteomic datasets.

Project description:SummaryThe rapid progresses of high-throughput sequencing technology-based omics and mass spectrometry-based proteomics, such as data-independent acquisition and its penetration to clinical studies have generated increasing number of proteomic datasets containing hundreds to thousands of samples. To analyze these quantitative proteomic datasets and other omics (e.g. transcriptomics and metabolomics) datasets more efficiently and conveniently, we present a web server-based software tool ProteomeExpert implemented in Docker, which offers various analysis tools for experimental design, data mining, interpretation and visualization of quantitative proteomic datasets. ProteomeExpert can be deployed on an operating system with Docker installed or with R language environment.Availability and implementationThe Docker image of ProteomeExpert is freely available from https://hub.docker.com/r/lifeinfo/proteomeexpert. The source code of ProteomeExpert is also openly accessible at http://www.github.com/guomics-lab/ProteomeExpert/. In addition, a demo server is provided at https://proteomic.shinyapps.io/peserver/.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC8055226 | biostudies-literature

REVERSE: a user-friendly web server for analyzing next-generation sequencing data from in vitro selection/evolution experiments.

Project description:Next-generation sequencing (NGS) enables the identification of functional nucleic acid sequences from in vitro selection/evolution experiments and illuminates the evolutionary process at single-nucleotide resolution. However, analyzing the vast output from NGS can be daunting, especially with limited programming skills. We developed REVERSE (Rapid EValuation of Experimental RNA Selection/Evolution) (https://www.reverseserver.org/), a web server that implements an integrated computational pipeline through a graphical user interface, which performs both pre-processing and detailed sequence level analyses within minutes. Raw FASTQ files are quality-filtered, dereplicated, and trimmed before being analyzed by either of two pipelines. The first pipeline counts, sorts, and tracks enrichment of unique sequences and user-defined sequence motifs. It also identifies mutational intermediates present in the sequence data that connect two input sequences. The second pipeline sorts similar sequences into clusters and tracks enrichment of peak sequences. It also performs nucleotide conservation analysis on the cluster of choice and generates a consensus sequence. Both pipelines generate downloadable spreadsheets and high-resolution figures. Collectively, REVERSE is a one-stop solution for the rapid analysis of NGS data obtained from in vitro selection/evolution experiments that obviates the need for computational expertise.

| S-EPMC9252737 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data