Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Cross-ID: An Interactive Platform for in-depth Analysis and Visualization of Complex XL-MS Driven Protein Interaction Networks

ABSTRACT: Software platform dedicated to XL-MS interactive data analysis and visualization

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Bos Taurus (bovine)

TISSUE(S): Heart

SUBMITTER: Oleg Klykov

LAB HEAD: Prof. dr. Albert J. R. Heck

PROVIDER: PXD011077 | Pride | 2019-01-02

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	CSMs_Output.txt	Txt
	PKA_TMT_DSSO_SCX_frac01-6.raw	Raw
	PKA_TMT_DSSO_SCX_frac10.raw	Raw
	PKA_TMT_DSSO_SCX_frac11.raw	Raw
	PKA_TMT_DSSO_SCX_frac12.raw	Raw

Items per page:

1 - 5 of 12

Publications

Cross-ID: Analysis and Visualization of Complex XL-MS-Driven Protein Interaction Networks.

de Graaf Sebastiaan C SC Klykov Oleg O van den Toorn Henk H Scheltema Richard A RA

Journal of proteome research 20190117 2

Protein interactions enable much more complex behavior than the sum of the individual protein parts would suggest and represents a level of biological complexity requiring full understanding when unravelling cellular processes. Cross-linking mass spectrometry has emerged as an attractive approach to study these interactions, and recent advances in mass spectrometry and data analysis software have enabled the identification of thousands of cross-links from a single experiment. The resulting data ...[more]

PMID: 30575379

Similar Datasets

Cross-ID: Analysis and Visualization of Complex XL-MS-Driven Protein Interaction Networks.

Project description:Protein interactions enable much more complex behavior than the sum of the individual protein parts would suggest and represents a level of biological complexity requiring full understanding when unravelling cellular processes. Cross-linking mass spectrometry has emerged as an attractive approach to study these interactions, and recent advances in mass spectrometry and data analysis software have enabled the identification of thousands of cross-links from a single experiment. The resulting data complexity is, however, difficult to understand and requires interactive software tools. Even though solutions are available, these represent an agglomerate of possibilities, and each features its own input format, often forcing manual conversion. Here we present Cross-ID, a visualization platform that links directly into the output of XlinkX for Proteome Discoverer but also plays well with other platforms by supporting a user-controllable text-file importer. The platform includes features like grouping, spectral viewer, gene ontology (GO) enrichment, post-translational modification (PTM) visualization, domains and secondary structure mapping, data set comparison, previsualization overlap check, and more. Validation of detected cross-links is available for proteins and complexes with known structure or for protein complexes through the DisVis online platform ( http://milou.science.uu.nl/cgi/services/DISVIS/disvis/ ). Graphs are exportable in PDF format, and data sets can be exported in tab-separated text files for evaluation through other software.

| S-EPMC6407916 | biostudies-literature

PIVOT: platform for interactive analysis and visualization of transcriptomics data.

Project description:BACKGROUND:Many R packages have been developed for transcriptome analysis but their use often requires familiarity with R and integrating results of different packages requires scripts to wrangle the datatypes. Furthermore, exploratory data analyses often generate multiple derived datasets such as data subsets or data transformations, which can be difficult to track. RESULTS:Here we present PIVOT, an R-based platform that wraps open source transcriptome analysis packages with a uniform user interface and graphical data management that allows non-programmers to interactively explore transcriptomics data. PIVOT supports more than 40 popular open source packages for transcriptome analysis and provides an extensive set of tools for statistical data manipulations. A graph-based visual interface is used to represent the links between derived datasets, allowing easy tracking of data versions. PIVOT further supports automatic report generation, publication-quality plots, and program/data state saving, such that all analysis can be saved, shared and reproduced. CONCLUSIONS:PIVOT will allow researchers with broad background to easily access sophisticated transcriptome analysis tools and interactively explore transcriptome datasets.

| S-EPMC5756333 | biostudies-literature

Combenefit: an interactive platform for the analysis and visualization of drug combinations.

Project description:Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies.We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data.Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/).Giovanni.DiVeroli@cruk.cam.ac.ukSupplementary data are available at Bioinformatics online.

| S-EPMC5018366 | biostudies-other

BALSAM-An Interactive Online Platform for Breath Analysis, Visualization and Classification.

Project description:The field of breath analysis lacks a fully automated analysis platform that enforces machine learning good practice and enables clinicians and clinical researchers to rapidly and reproducibly discover metabolite patterns in diseases. We present BALSAM-a comprehensive web-platform to simplify and automate this process, offering features for preprocessing, peak detection, feature extraction, visualization and pattern discovery. Our main focus is on data from multi-capillary-column ion-mobility-spectrometry. While not limited to breath data, BALSAM was developed to increase consistency and robustness in the data analysis process of breath samples, aiming to expand the array of low cost molecular diagnostics in clinics. Our platform is freely available as a web-service and in form of a publicly available docker container.

| S-EPMC7601018 | biostudies-literature

SNPitty: An intuitive web-application for interactive B-allele frequency and copy-number visualization of next generation sequencing data.

Project description:Development of the web-application SNPitty that allows interactive visualization and interrogation of Variant Call Format (VCF) files by utilizing B-allele frequencies (BAF) of single nucleotide polymorphisms (SNPs), coverage metrics and copy-numbers.

2017-12-13 | PRJEB21914 | EVA

Visual Omics Explorer (VOE): a cross-platform portal for interactive data visualization.

Project description:MotivationGiven the abundance of genome sequencing and omics data, an opprtunity and challenge in bioinformatics relates to data mining and visualization. The majority of current bioinformatics visualizations are implemented either as multi-tier web server applications that require significant maintenance effort, or as client software that presumes technical expertise for installation. Here we present the Visual Omics Explorer (VOE), a cross-platform data visualization portal that is implemented using only HTML and Javascript code. VOE is a standalone software that can be loaded offline on the web browser from a local copy of the code, or over the internet without any dependency other than distributing the code through a file sharing service. VOE can interactively display genomics, transcriptomics, epigenomics and metagenomics data stored either locally or retrieved from cloud storage services, and runs on both desktop computers and mobile devices.Availability and implementationVOE is accessible at http://bcil.github.io/VOE/ CONTACT: agbiotec@gmail.comSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC5860494 | biostudies-literature

CPA: a web-based platform for consensus pathway analysis and interactive visualization.

Project description:In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.

| S-EPMC8262702 | biostudies-literature

Oviz-Bio: a web-based platform for interactive cancer genomics data visualization.

Project description:Genetics data visualization plays an important role in the sharing of knowledge from cancer genome research. Many types of visualization are widely used, most of which are static and require sufficient coding experience to create. Here, we present Oviz-Bio, a web-based platform that provides interactive and real-time visualizations of cancer genomics data. Researchers can interactively explore visual outputs and export high-quality diagrams. Oviz-Bio supports a diverse range of visualizations on common cancer mutation types, including annotation and signatures of small scale mutations, haplotype view and focal clusters of copy number variations, split-reads alignment and heatmap view of structural variations, transcript junction of fusion genes and genomic hotspot of oncovirus integrations. Furthermore, Oviz-Bio allows landscape view to investigate multi-layered data in samples cohort. All Oviz-Bio visual applications are freely available at https://bio.oviz.org/.

| S-EPMC7319551 | biostudies-literature

PILGRM: an interactive data-driven discovery platform for expert biologists.

Project description:PILGRM (the platform for interactive learning by genomics results mining) puts advanced supervised analysis techniques applied to enormous gene expression compendia into the hands of bench biologists. This flexible system empowers its users to answer diverse biological questions that are often outside of the scope of common databases in a data-driven manner. This capability allows domain experts to quickly and easily generate hypotheses about biological processes, tissues or diseases of interest. Specifically PILGRM helps biologists generate these hypotheses by analyzing the expression levels of known relevant genes in large compendia of microarray data. Because PILGRM is data-driven, it complements a user's knowledge and literature analysis with mining of diverse functional genomic data, thereby generating novel predictions that can drive experimental follow-up. This server is free, does not require registration and is available for use at http://pilgrm.princeton.edu.

| S-EPMC3125802 | biostudies-literature

Interactive effects of arsenic and a high-fat diet on hepatic gene expression

Project description:Chronic exposure to inorganic arsenic (iAs) or a high-fat diet (HFD) can produce liver injury. However, the interactive molecular biological effects and mechanism of iAs and HFD are as of yet unclear. We used microarrays to detail the interactive effects of arsenic and a high-fat diet on hepatic gene expression. The C57BL/6 Mice fed low-fat diet (LFD) or HFD were exposed to 3 mg/L iAs or deionized water for 10 weeks. Then, hepatic RNA were extraction and hybridization on Affymetrix microarrays. Differentially expressed genes in LFD+As, HFD, and HFD+As groups compared to LFD group were identified, and interactive molecular biological effects and mechanism of iAs and HFD were discussed.

2013-11-30 | E-GEOD-52702 | biostudies-arrayexpress

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