Tools   Select Another Resource Report Type

https://www.volumegraphics.com/de/produkte/vgstudio.html

Software tool for analysis and processing of µCT data, e.g. compiling virtual 3D models of morphological structures in site by Volume Graphics GmbH. Used for visualization of CT data.

Proper citation: VG Studio MAX (RRID:SCR_017997) Copy   

•   Source: SciCrunch Registry

https://www.libreoffice.org/

Open source software application with data analysis tools and spreadsheet templates used to manage, process and visualize data. LibreOffice Calc is spreadsheet component of LibreOffice software package developed by Document Foundation.

Proper citation: LibreOfficeCalc (RRID:SCR_018013) Copy   

•   Source: SciCrunch Registry

https://github.com/shanemomara/omaraneurolab/tree/master/NeuroChaT

Software open source python toolbox to analyse neuronal signals recorded in vivo in freely behaving animal, with particular emphasis on spatial coding. Can be used as application programming interface, or as general user interface, and is designed to help simplify adoption of standardised analyses for behavioural neurophysiology and facilitate open data sharing and collaboration between laboratories.

Proper citation: NeuroChaT (RRID:SCR_018020) Copy   

•   Source: SciCrunch Registry

https://github.com/hariszaf/pema

Software as flexible pipeline for environmental DNA metabarcoding analysis of 16S/18S rRNA, ITS and COI marker genes. Performs reads’ pre-processing, clustering to (M)OTUs and taxonomy assignment for 16S rRNA and COI marker gene data. Allows users to explore alternative algorithms for specific steps of pipeline without need of complete re-execution.

Proper citation: PEMA (RRID:SCR_017676) Copy   

•   Source: SciCrunch Registry

http://dichroweb.cryst.bbk.ac.uk/html/home.shtml

Web server for analysis of protein circular dichroism spectra. Provides access to circular dichroism secondary structure calculation algorithms and reference databases. Used in analysis of protein secondary structures.

Proper citation: DichroWeb (RRID:SCR_018125) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/cnv-webstore

Integrated platform to analyse, store, visualise and interpret CopyNumber Variation data. Analysis is supported for Illumina data, all CNV-reports and raw data can be imported after third-party analysis. Platform to streamline processing and downstream interpretation of microarray data in clinical context. Analysis tools include CNV analysis, parent of origin and uniparental disomy detection. Interpretation tools include data visualisation, gene prioritisation, automated PubMed searching, linking data to several genome browsers and annotation of CNVs based on several public databases.

Proper citation: CNV webstore (RRID:SCR_018007) Copy   

•   Source: SciCrunch Registry

https://www.mbfbioscience.com/microdynamix

Software tool for dendritic spine analysis that enables to visualize and quantify spine morphology over time. After loading image data acquired at different time points from in vivo and in vitro imaging sessions, automatically aligns images in 3D, then reconstructs dendritic branches, detects dendritic spines, and identifies important metrics such as length, thickness, and overall number, for accurate quantitative comparison.

Proper citation: MicroDynamix (RRID:SCR_017671) Copy   

•   Source: SciCrunch Registry

https://www.ebi.ac.uk/covid-19

EMBL-EBI portal to enable researchers to upload, access and analyse COVID-19 related reference data and specialist datasets submitted to EMBL-EBI and other major centers for biomedical data. Used to facilitate data sharing and analysis to accelerate coronavirus research. The aim of the COVID-19 Data Portal is to facilitate data sharing and analysis, and to accelerate coronavirus research. EMBL-EBI and partners have set up the COVID-19 Data Portal, which will bring together relevant datasets submitted to EMBL-EBI and other major centres for biomedical data. The aim is to facilitate data sharing and analysis, and to accelerate coronavirus research. The COVID-19 Data Portal will enable researchers to upload, access and analyse COVID-19 related reference data and specialist datasets. The COVID-19 Data Portal will be the primary entry point into the functions of a wider project, the European COVID-19 Data Platform.

Proper citation: EMBL-EBI COVID-19 Portal (RRID:SCR_018337) Copy   

•   Source: SciCrunch Registry

https://viewer.cytosplore.org/

Web based interactive visual analysis system for exploration of single cell data published in Allen Cell Types Database and for number of single cell data resources of Brain Initiative Single Cell Network. Allows interactive exploration of hierarchies of cell types, visualization of transcriptome wide gene expression in combination with metadata of individual cells, performing differential analyses and statistics between manual selections of cells, or between pre-defined clusters throughout cellular hierarchy and more.

Proper citation: Cytosplore Viewer (RRID:SCR_018330) Copy   

•   Source: SciCrunch Registry

http://mole.upol.cz

Interactive web based tool for analyzing biomacromolecular channels, tunnels and pores. Enables two modes of calculation with one dedicated to analysis of channels and another for transmembrane pores. Can use PDB and mmCIF formats. Can analyze biomacromolecular structures stemming from NMR, X-ray and cryo-EM techniques. Interconnected with PDBe, CSA, ChannelsDB, OPM, UniProt to help setup and analysis of acquired results. Provides analytics for detection and structural characterization of channels and information about their physicochemical features.

Proper citation: MOLEonline (RRID:SCR_018314) Copy   

•   Source: SciCrunch Registry

https://pdc.cancer.gov/pdc/

Portal to make cancer related proteomic datasets easily accessible to public. Facilitates multiomic integration in support of precision medicine through interoperability with other resources. Developed to advance our understanding of how proteins help to shape risk, diagnosis, development, progression, and treatment of cancer. One of several repositories within NCI Cancer Research Data Commons which enables researchers to link proteomic data with other data sets (e.g., genomic and imaging data) and to submit, collect, analyze, store, and share data throughout cancer data ecosystem. PDC provides access to highly curated and standardized biospecimen, clinical, and proteomic data, intuitive interface to filter, query, search, visualize and download data and metadata. Provides common data harmonization pipeline to uniformly analyze all PDC data and provides advanced visualization of quantitative information. Cloud based (Amazon Web Services) infrastructure facilitates interoperability with AWS based data analysis tools and platforms natively. Application programming interface (API) provides cloud-agnostic data access and allows third parties to extend functionality beyond PDC. Structured workspace that serves as private user data store and also data submission portal. Distributes controlled access data, such as patient-specific protein fasta sequence databases, with dbGaP authorization and eRA Commons authentication.

Proper citation: Proteomic Data Commons (RRID:SCR_018273) Copy   

•   Source: SciCrunch Registry

http://statistika.mfub.bg.ac.rs/interactive-repeated-experiments-dotplot/

Web tool for creating interactive graphics. Enables authors to create interactive graphics from data obtained with repeated independent experiments. Designed for laboratory studies with repeated experiments.

Proper citation: Interactive Repeated Experiments Dotplot (RRID:SCR_018328) Copy   

•   Source: SciCrunch Registry

https://www.bio.ifi.lmu.de/software/watchdog/index.html

Software workflow management system for automated and distributed analysis of large scale experimental data. Implemented in Java and is thus platform independent.

Proper citation: Watchdog (RRID:SCR_018355) Copy   

•   Source: SciCrunch Registry

http://pepr.cnmcresearch.org/

An experiment in web-database access to large multi-dimensional data sets using a standardized experimental platform to determine if the larger scientific community can be given simple, intuitive, and user-friendly web-based access to large microarray data sets. All data in PEPR is also available via NCBI GEO. The structure and goals of PEPR differ from other mRNA expression profiling databases in a number of important ways. * The experimental platform in PEPR is standardized, and is an Affymetrix - only database. All microarrays available in the PEPR web database should ascribe to quality control and standard operating procedures. A recent publication has described the QC/SOP criteria utilized in PEPR profiles ( The Tumor Analysis Best Practices Working Group 2004 ). * PEPR permits gene-based queries of large Affymetrix array data sets without any specialized software. For example, a number of large time series projects are available within PEPR, containing 40-60 microarrays, yet these can be simply queried via a dynamic web interface with no prior knowledge of microarray data analysis. * Projects in PEPR originate from scientists world-wide, but all data has been generated by the Research Center for Genetic Medicine, Children''''s National Medical Center, Washington DC. Future developments of PEPR will allow remote entry of Affymetrix data ascribing to the same QC/SOP protocols. They have previously described an initial implementation of PEPR, and a dynamic web-queried time series graphical interface ( Chen et al. 2004 ). A publication showing the utility of PEPR for pharmacodynamic data has recently been published ( Almon et al. 2003 ).

Proper citation: Public Expression Profiling Resource (RRID:SCR_007274) Copy   

•   Source: SciCrunch Registry

http://pbil.univ-lyon1.fr/acuts/ACUTS.html

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on August 12, 2014. Database that identifies new regulatory elements in untranslated regions of protein-coding genes (5 prime flanks, 5 prime UTRs, introns, 3 prime UTRs and 3 prime flanks). The analyses is focused on genes from metazoan species (essentially vertebrates, insects and nematodes). Information on highly conserved regions (sequences, alignments, annotations, bibliographic references) are compiled. Currently 176 out of 326 detected highly conserved regions (HCRs) have been analyzed and incorporated in the database. You can also access the list of annotated conserved elements and the list of conserved elements that remain to be processed. Their approach is based on comparative sequence analysis, for the identification of phylogenetic footprints.

Proper citation: Ancient conserved untranslated sequences (RRID:SCR_008130) Copy   

•   Source: SciCrunch Registry

http://bioinfo-out.curie.fr/ittaca/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 6/12/25. ITTACA is a database created for Integrated Tumor Transcriptome Array and Clinical data Analysis. ITTACA centralizes public datasets containing both gene expression and clinical data and currently focuses on the types of cancer that are of particular interest to the Institut Curie: breast carcinoma, bladder carcinoma, and uveal melanoma. ITTACA is developed by the Institut Curie Bioinformatics group and the Molecular Oncology group of UMR144 CNRS/Institut Curie. A web interface allows users to carry out different class comparison analyses, including comparison of expression distribution profiles, tests for differential expression, patient survival analyses, and users can define their own patient groups according to clinical data or gene expression levels. The different functionalities implemented in ITTACA are: - To test if one or more gene, of your choice, is differentially expressed between two groups of samples exhibiting distinct phenotypes (Student and Wilcoxon tests). - The detection of genes differentially expressed (Significance Analysis of Microarrays) between two groups of samples. - The creation of histograms which represent the expression level according to a clinical parameter for each sample. - The computation of Kaplan Meier survival curves for each group. ITTACA has been developed to be a useful tool for comparing personal results to the existing results in the field of transcriptome studies with microarrays.

Proper citation: Integrated Tumor Transcriptome Array and Clinical data Analysis (RRID:SCR_008182) Copy   

•   Source: SciCrunch Registry

http://www.bh4.org/BH4DatabasesBiodef.asp

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. The BIODEF database have tabulated the most common clinical and laboratory data related to hyperphenylalaninaemia and tetrahydrobiopterin deficiencies. Additionally, there are data regarding treatment, outcome, and DNA analysis. Approximately 2% of newborns with hyperphenylalaninaemia are deficient in tetrahydrobiopterin. Selective screening must be performed in all instances where hyperphenylalaninaemia is detected by neonatal screening. In the last 20 years, 308 patients with tetrahydrobiopterin deficiencies have been recognized as a result of screening carried out, worldwide, in Departments of Paediatrics. Of these 308 patients, 181 suffered from 6-pyruvoyltetrahydropterin synthase deficiency, 92 from dihydropteridine reductase deficiency, 13 from pterin-4a-carbinolamine dehydratase deficiency, 12 from GTP cyclohydrolase I deficiency, and 10 are still unclassified. The BIODEF database have tabulated the most common clinical and laboratory data related to hyperphenylalaninaemia and tetrahydrobiopterin deficiencies. Additionally, there are data regarding treatment, outcome, and DNA analysis. Preliminary evaluation reveals that the degree of hyperphenylalaninaemia can vary from normal to 2500 mumol/L. Analyses of pterins in urine and measurement of dihydropteridine reductase activity from Guthrie cards are absolutely essential tests for accurate diagnosis. There is a regional (demographic) variation in the frequency of tetrahydrobiopterin deficiencies indicating the highest incidence in Saudi Arabia, probably a consequence of the high consanguinity rate.

Proper citation: International Database of Tetrahydrobiopterin Deficiencies (RRID:SCR_008171) Copy   

•   Source: SciCrunch Registry

http://www.bumc.bu.edu/cardiovascularproteomics/cpctools/strap/

Software program that automatically annotates a protein list with information that helps in the meaningful interpretation of data from mass spectrometry and other techniques. It takes protein lists as input, in the form of plain text files, protXML files (usually from the TPP), or Dat files from MASCOT search results. From this, it generates protein annotation tables, and a variety of GO charts to aid individual and differential analysis of proteomics data. It downloads information from mainly the Uniprot and EBI QuickGO databases. STRAP requires Windows XP or higher with at least version 3.5 of the Microsoft .NET Framework installed. Platform: Windows compatible

Proper citation: STRAP (RRID:SCR_005675) Copy   

•   Source: SciCrunch Registry

http://cellprofiler.org

Software tool to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. It counts cells and also measures the size, shape, intensity and texture of every cell (and every labeled subcellular compartment) in every image. It was designed for high throughput screening but can perform automated image analysis for images from time-lapse movies and low-throughput experiments. CellProfiler has an increasing number of algorithms to identify and measure properties of neuronal cell types.

Proper citation: CellProfiler Image Analysis Software (RRID:SCR_007358) Copy   

•   Source: SciCrunch Registry

http://www.gepas.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. An integrated packages of tools for microarray data analysis. GEPAS provides a web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-color microarray experiments and other preprocessing options), to the final step of the functional profiling of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), which include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management.

Proper citation: Gene Expression Profile Analysis Suite (RRID:SCR_008341) Copy   

•   Source: SciCrunch Registry