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http://www.project-redcap.org/

Web application that allows users to build and manage online surveys and databases. Using REDCap's stream-lined process for rapidly developing projects, you may create and design projects using 1) the online method from your web browser using the Online Designer; and/or 2) the offline method by constructing a "data dictionary" template file in Microsoft Excel, which can be later uploaded into REDCap. Both surveys and databases (or a mixture of the two) can be built using these methods. REDCap provides audit trails for tracking data manipulation and user activity, as well as automated export procedures for seamless data downloads to Excel, PDF, and common statistical packages (SPSS, SAS, Stata, R). Also included are a built-in project calendar, a scheduling module, ad hoc reporting tools, and advanced features, such as branching logic, file uploading, and calculated fields. REDCap has a quick and easy software installation process, so that you can get REDCap running and fully functional in a matter of minutes. Several language translations have already been compiled for REDCap (e.g. Chinese, French, German, Portuguese), and it is anticipated that other languages will be available in full versions of REDCap soon. The REDCap Shared Library is a repository for REDCap data collection instruments and forms that can be downloaded and used by researchers at REDCap partner institutions.

Proper citation: REDCap (RRID:SCR_003445) Copy   

•   Source: SciCrunch Registry

http://rulai.cshl.edu/tools/ESE

A web-based analysis service for identifying exonic splicing enhancers in eukaryotic genes. ESEfinder accept sequences in the FASTA format. A typical mammalian gene is composed of several relatively short exons that are interrupted by much longer introns. To generate correct mature mRNAs, the exons must be identified and joined together precisely and efficiently, in a process that requires the coordinated action of five small nuclear (sn)RNAs (U1, U2, U4, U5 and U6) and more than 60 polypeptides. The inaccurate recognition of exon/intron boundaries or the failure to remove an intron generates aberrant mRNAs that are either unstable or code for defective or deleterious protein isoforms. Exonic enhancers are thought to serve as binding sites for specific serine/arginine-rich (SR) proteins, a family of structurally related and highly conserved splicing factors characterized by one or two RNA-recognition motifs (RRM) and by a distinctive C-terminal domain highly enriched in RS dipeptides (the RS domain). The RRMs mediate sequence-specific binding to the RNA, and so determine substrate specificity, whereas the RS domain appears to be involved mainly in protein-protein interactions. SR proteins bound to ESEs can promote exon definition by directly recruiting the splicing machinery through their RS domain and/or by antagonizing the action of nearby silencer elements. Sponsors: ESEfinder is supported by the Cold Spring Harbor Laboratory.

Proper citation: Exonic Splicing Enhancer Finder (RRID:SCR_002835) Copy   

•   Source: SciCrunch Registry

http://bisearch.enzim.hu

BiSearch is a primer-design algorithm for DNA sequences. It may be used for both bisulfite converted as well as for original not modified sequences. You can search various genomes with the designed primers to avoid non-specific PCR products by our fast ePCR method. This is especially recommended when primers are designed to amplify the highly redundant bisulfite treated sequences. It has the unique property of analyzing the primer pairs for mispriming sites on the bisulfite-treated genome and determines potential non-specific amplification products with a new search algorithm. The options of primer-design and analysis for mispriming sites can be used sequentially or separately, both on bisulfite-treated and untreated sequences. In silico and in vitro tests of the software suggest that new PCR strategies may increase the efficiency of the amplification.

Proper citation: BiSearch: Primer Design and Search Tool (RRID:SCR_002980) Copy   

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http://romi.bu.edu/elisa/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. ELISA is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function neighborhoods. The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). It introduces a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind.

Proper citation: Evolutionary Lineage Inferred from Structural Analysis (RRID:SCR_002343) Copy   

•   Source: SciCrunch Registry

http://babelomics.bioinfo.cipf.es

An integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Version 4 of Babelomics integrates primary (normalization, calls, etc.) and secondary (signatures, predictors, associations, TDTs, clustering, etc.) analysis tools within an environment that allows relating genomic data and/or interpreting them by means of different functional enrichment or gene set methods. Such interpretation is made not only using functional definitions (GO, KEGG, Biocarta, etc.) but also regulatory information (from Transfac, Jaspar, etc.) and other levels of regulation such as miRNA-mediated interference, protein-protein interactions, text-mining module definitions and the possibility of producing de novo annotations through the Blast2GO system . Babelomics has been extensively re-engineered and now it includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. In this release GEPAS and Babelomics have integrated into a unique web application with many new features and improvements: * Data input: import and quality control for the most common microarray formats * Normalization and base calling: for the most common expression, tiling and SNP microarrays (Affymetrix and Agilent). * Transcriptomics: diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and time-series analysis. * Genotyping: stratification analysis, association, TDT. * Functional profiling: functional enrichment and gene set enrichment analysis with functional terms (GO, KEGG, Biocarta, etc.), regulatory (Transfac, Jaspar, miRNAs, etc.), text-mining, derived bioentities, protein-protein interaction analysis. * Integrative analysis: Different variables can be related to each other (e.g. gene expression to gnomic copy number) and the results subjected to functional analysis. Platform: Online tool

Proper citation: Babelomics (RRID:SCR_002969) Copy   

•   Source: SciCrunch Registry

http://sonorus.princeton.edu/hefalmp/

HEFalMp (Human Experimental/FunctionAL MaPper) is a tool developed by Curtis Huttenhower in Olga Troyanskaya's lab at Princeton University. It was created to allow interactive exploration of functional maps. Functional mapping analyzes portions of these networks related to user-specified groups of genes and biological processes and displays the results as probabilities (for individual genes), functional association p-values (for groups of genes), or graphically (as an interaction network). HEFalMp contains information from roughly 15,000 microarray conditions, over 15,000 publications on genetic and physical protein interactions, and several types of DNA and protein sequence analyses and allows the exploration of over 200 H. sapiens process-specific functional relationship networks, including a global, process-independent network capturing the most general functional relationships. Looking to download functional maps? Keep an eye on the bottom of each page of results: every functional map of any kind is generated with a Download link at the bottom right. Most functional maps are provided as tab-delimited text to simplify downstream processing; graphical interaction networks are provided as Support Vector Graphics files, which can be viewed using the Adobe Viewer, any recent version of Firefox, or the excellent open source Inkscape tool.

Proper citation: Human Experimental/FunctionAL MaPper: Providing Functional Maps of the Human Genome (RRID:SCR_003506) Copy   

•   Source: SciCrunch Registry

http://www.tmanavigator.org/

A free web-based service open to all users for analysis of tissue microarray (TMA) data and related information, accommodating categorical, semi-continuous and continuous expression scores. There is no login requirement.

Proper citation: TMA Navigator (RRID:SCR_005599) Copy   

•   Source: SciCrunch Registry

http://biit.cs.ut.ee/graphweb/

GraphWeb allows the detection of modules from biological, heterogeneous and multi-species networks, and the interpretation of detected modules using Gene Ontology, cis-regulatory motifs and biological pathways. GraphWeb is a public web server for graph-based analysis of biological networks that: * analyses directed and undirected, weighted and unweighted heterogeneous networks of genes, proteins and microarray probesets for many eukaryotic genomes; * integrates multiple diverse datasets into global networks; * incorporates multispecies data using gene orthology mapping; * filters nodes and edges based on dataset support, edge weight and node annotation; * detects gene modules from networks using a collection of algorithms; * interprets discovered modules using Gene Ontology, pathways, and cis-regulatory motifs. Platform: Online tool

Proper citation: GraphWeb (RRID:SCR_005746) Copy   

•   Source: SciCrunch Registry

http://www.animalgenome.org/bioinfo/tools/catego/

CateGOrizer takes batch input of GO term IDs in a list format or unformatted plain text file, allows users to choose one of the available classifications such as GO_slim, GOA, EGAD, MGI_GO_slim, GO-ROOT, or a self-defined classification list, find its parental branch and performs an accumulative classification count, and returns the results in a sorted table of counts, percentages, and a pie chart (if it takes longer than standard time out period, it will email the user with a URL link to the results). This tool is comprised with a set of perl CGI programs coupled with a MySQL DBMS that stores the GO terms DAG data. Platform: Online tool

Proper citation: CateGOrizer (RRID:SCR_005737) 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.nitrc.org/projects/jhucis_pedatlas/

Anatomical atlases constructed by Computational Anatomy of Johns Hopkins University for analysis of shape vectors. The atlases were generated from segmented hippocampal and amygdala structures in acquired populations of children, adolescents and young adults in neuroimaging studies of major depression disorder (MDD) at Washington University at St Louis.

Proper citation: Atlases of amygdala and hippocampus for pediatric populations (RRID:SCR_014085) Copy   

•   Source: SciCrunch Registry

http://hb.flatironinstitute.org/

Formerly known as GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues), HumanBase applies machine learning algorithms to learn biological associations from massive genomic data collections. These integrative analyses reach beyond existing "biological knowledge" represented in the literature to identify novel, data-driven associations.

Proper citation: HumanBase (RRID:SCR_016145) Copy   

•   Source: SciCrunch Registry

https://github.com/stamatak/ExaML

Source code for large-scale phylogenetic analyses on whole-transcriptome and whole-genome alignments using supercomputers.

Proper citation: Examl (RRID:SCR_016087) Copy   

•   Source: SciCrunch Registry

http://www.scienceexchange.com/facilities/dna-microarray-core-facility-ualbany

Core provides microarray services for Affymetrix GeneChip arrays, Agilent microarrays, NimbleGen microarrays and custom-produced spotted cDNA microarrays. Projects developed through DNA Microarray Center have made use of arrays from variety of genomes, eukaryotic, prokaryotic, and plant. Core services includes RNA/DNA isolation, gene expression, miRNA, Chip-chip, Rip-chip and DNA methylation services. Provides bioinformatics tools for further analysis of results of expression experiments.

Proper citation: University at Albany Center for Functional Genomics DNA Microarray Core Facility (RRID:SCR_012502) Copy   

•   Source: SciCrunch Registry

https://www.mcgill.ca/cian/

Core facility at Biology Department in McGill Faculty of Science. Expertise in Light Microscopy and Image Analysis. Provides light microscopes, ranging from Point Scanning and Spinning Disc Confocals to Multi-Photon, TIRF, Light Sheet and Super-Resolution microscopes. Provides services in Automation/High throughput screening (liquid handler, pinning robot), Protein expression and antibody production. Users get training.

Proper citation: McGill Cell Imaging and Analysis Network Core Facility (RRID:SCR_012623) Copy   

•   Source: SciCrunch Registry

http://diabetesresearch.med.umich.edu/Core_MCDTR_Methods.php

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on November 6,2024. Research core that works to assist investigators in designing studies, collecting data, and analyzing data to examine questions related to the causes, prevention, and control of diabetes, its complications, and comorbidities.

Proper citation: Michigan Center for Diabetes Translational Research Methods and Measurements Core (RRID:SCR_015188) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/mpr/lung

Data set of a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, researchers analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct sub-classes of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.

Proper citation: Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Sub-classes (RRID:SCR_003010) Copy   

•   Source: SciCrunch Registry

http://www.eecs.qmul.ac.uk/mmv/datasets/deap/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on December 12,2025. Multimodal dataset for emotion analysis using EEG, Physiological and Video Signals of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection was used, utilizing retrieval by affective tags from the last.fm website, video highlight detection and an online assessment tool. The dataset is made publicly available and other researchers are encouraged to use it for testing their own affective state estimation methods.

Proper citation: DEAPdataset (RRID:SCR_001586) Copy   

•   Source: SciCrunch Registry

http://howard.eagle-i.net/i/00000134-a517-1426-bf4c-ca4080000000

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 27,2023. Core for bioinformatics consultation and software access. Laboratory of Molecular Computations and Bioinformatics (LMCB) is a resource facility dedicated to the support of computational biomedical research at Howard University. Provides molecular modeling, molecular dynamics, bioinformatics, and computational quantum chemistry capabilities and support to a variety of research projects at Howard University.

Proper citation: Howard University Center for Computational Biology and Bioinformatics Core Facility (RRID:SCR_009864) Copy   

•   Source: SciCrunch Registry