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http://research.nhgri.nih.gov/skippy/index.shtml

A Web-based tool that allows users to input a set of exonic variants to score them for a number of features (such as changes in splicing regulatory elements) that have been shown to be predictive of known genome variations that cause exon skipping or activation of ectopic splice sites. In this way, variants can be either prioritized for further splicing-based functional analysis or the results can be used as further genomic evidence in cases in which the causative variant is already known.

Proper citation: SKIPPY (RRID:SCR_005430) Copy   

•   Source: SciCrunch Registry

http://mirdsnp.ccr.buffalo.edu/

A database of manually curated dSNPs on the 3''UTRs of human genes from available publications in PubMed. The advanced web interface allows users to perform proximity searches between miRNA target sites and dSNPs by gene name, miRbase ID, target prediction algorithm, disease, and any nucleotide distance between dSNPs and miRNA target sites. The web interface displays detailed sequence views showing the relationship between dSNPs, miRNA target sites, and SNPs. An interactive visualization tool shows the chromosomal distribution of dSNPs, miRNA target sites (from TargetScan), and SNPs. miRdSNP provides a comprehensive data source of dSNPs and robust tools to capture their spacial relationship with miRNA target sites on the 3''UTRs of human genes. miRdSNP enables researchers to further explore the molecular mechanism of gene dysregulation for dSNPs at posttranscriptional level.

Proper citation: miRdSNP (RRID:SCR_005303) Copy   

•   Source: SciCrunch Registry

http://www.neuroepigenomics.org/methylomedb/

A database containing genome-wide brain DNA methylation profiles for human and mouse brains. The DNA methylation profiles were generated by Methylation Mapping Analysis by Paired-end Sequencing (Methyl-MAPS) method and analyzed by Methyl-Analyzer software package. The methylation profiles cover over 80% CpG dinucleotides in human and mouse brains in single-CpG resolution. The integrated genome browser (modified from UCSC Genome Browser allows users to browse DNA methylation profiles in specific genomic loci, to search specific methylation patterns, and to compare methylation patterns between individual samples. Two species were included in the Brain Methylome Database: human and mouse. Human postmortem brain samples were obtained from three distinct cortical regions, i.e., dorsal lateral prefrontal cortex (dlPFC), ventral prefrontal cortex (vPFC), and auditory cortex (AC). Human samples were selected from our postmortem brain collection with extensive neuropathological and psychopathological data, as well as brain toxicology reports. The Department of Psychiatry of Columbia University and the New York State Psychiatric Institute have assembled this brain collection, where a validated psychological autopsy method is used to generate Axis I and II DSM IV diagnoses and data are obtained on developmental history, history of psychiatric illness and treatment, and family history for each subject. The mouse sample (strain 129S6/SvEv) DNA was collected from the entire left cerebral hemisphere. The three human brain regions were selected because they have been implicated in the neuropathology of depression and schizophrenia. Within each cortical region, both disease and non-psychiatric samples have been profiled (matching subjects by age and sex in each group). Such careful matching of subjects allows one to perform a wide range of queries with the ability to characterize methylation features in non-psychiatric controls, as well as detect differentially methylated domains or features between disease and non-psychiatric samples. A total of 14 non-psychiatric, 9 schizophrenic, and 6 depression methylation profiles are included in the database.

Proper citation: MethylomeDB (RRID:SCR_005583) Copy   

•   Source: SciCrunch Registry

http://www.unihi.org

A database of human molecular interaction networks that integrates human protein-protein and transcriptional regulatory interactions from 15 distinct resources and aims to give direct and easy access to the integrated data set and to enable users to perform network-based investigations. The database includes tools (i) to search for molecular interaction partners of query genes or proteins in the integrated dataset, (ii) to inspect the origin, evidence and functional annotation of retrieved proteins and interactions, (iii) to visualize and adjust the resulting interaction network, (iv) to filter interactions based on method of derivation, evidence and type of experiment as well as based on gene expression data or gene lists and (v) to analyze the functional composition of interaction networks.

Proper citation: Unified Human Interactome (RRID:SCR_005805) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/funcassociate/

A web-based tool that accepts as input a list of genes, and returns a list of GO attributes that are over- (or under-) represented among the genes in the input list. Only those over- (or under-) representations that are statistically significant, after correcting for multiple hypotheses testing, are reported. Currently 37 organisms are supported. In addition to the input list of genes, users may specify a) whether this list should be regarded as ordered or unordered; b) the universe of genes to be considered by FuncAssociate; c) whether to report over-, or under-represented attributes, or both; and d) the p-value cutoff. A new version of FuncAssociate supports a wider range of naming schemes for input genes, and uses more frequently updated GO associations. However, some features of the original version, such as sorting by LOD or the option to see the gene-attribute table, are not yet implemented. Platform: Online tool

Proper citation: FuncAssociate: The Gene Set Functionator (RRID:SCR_005768) Copy   

•   Source: SciCrunch Registry

http://snps-and-go.biocomp.unibo.it/snps-and-go/

A server for the prediction of single point protein mutations likely to be involved in the insurgence of diseases in humans.

Proper citation: SNPsandGO (RRID:SCR_005788) Copy   

•   Source: SciCrunch Registry

http://malacards.org

An integrated database of human maladies and their annotations, modeled on the architecture and richness of the popular GeneCards database of human genes. The database contains 17,705 diseases, consolidated from 28 sources.

Proper citation: MalaCards (RRID:SCR_005817) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-5/search?q=*

A virtual database currently indexing clinical trials databases including EU Clinical Trials Register and Clinicaltrials.gov.

Proper citation: Integrated Clinical Trials (RRID:SCR_005969) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/depressiondatabase/

The Major Depressive Disorder Neuroimaging Database (MaND) contains information of 225 studies which have investigated brain structure (using MRI and CT scans) in patients with major depressive disorder compared to a control group. 143 studies and 63 brain structures are included in the meta-analysis. The database and meta-analysis are contained in an Excel spreadsheet file which may be freely downloaded from this website.

Proper citation: Major depressive disorder neuroimaging database (RRID:SCR_005835) Copy   

•   Source: SciCrunch Registry

http://www.btool.org/ADGO2

A web-based tool that provides composite interpretations for microarray data comparing two sample groups as well as lists of genes from diverse sources of biological information. It provides multiple gene set analysis methods for microarray inputs as well as enrichment analyses for lists of genes. It screens redundant composite annotations when generating and prioritizing them. It also incorporates union and subtracted sets as well as intersection sets. Users can upload their gene sets (e.g. predicted miRNA targets) to generate and analyze new composite sets.

Proper citation: ADGO (RRID:SCR_006343) Copy   

•   Source: SciCrunch Registry

https://open.med.harvard.edu/display/SHRINE/Community

Software providing a scalable query and aggregation mechanism that enables federated queries across many independently operated patient databases. This platform enables clinical researchers to solve the problem of identifying sufficient numbers of patients to include in their studies by querying across distributed hospital electronic medical record systems. Through the use of a federated network protocol, SHRINE allows investigators to see limited data about patients meeting their study criteria without compromising patient privacy. This software should greatly enable population-based research, assessment of potential clinical trials cohorts, and hypothesis formation for followup study by combining the EHR assets across the hospital system. In order to obtain the maximum number of cases representing the study population, it is useful to aggregate patient facts across as many sites as possible. Cutting across institutional boundaries necessitates that each hospital IRB remain in control, and that their local authority is recognized for each and every request for patient data. The independence, ownership, and legal responsibilities of hospitals predetermines a decentralized technical approach, such as a federated query over locally controlled databases. The application comes with the SHRINE Core Ontology but it can be used with any ontology, even one that is disease specific. The Core Ontology is designed to enable the widest range of studies possible using facts gathered in the EMR during routine patient care. SHRINE allows multiple ontologies to be used for different research purposes on the same installed systems.

Proper citation: SHRINE (RRID:SCR_006293) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-7/search?q=*

Virtual database currently indexing interaction between genes and diseases from Online Mendelian Inheritance in Man (OMIM) and Comparative Toxicogenomics Database (CTD).

Proper citation: Integrated Gene-Disease Interaction (RRID:SCR_006173) Copy   

•   Source: SciCrunch Registry

http://app1.bioinformatics.mdanderson.org/tarhub/_design/basic/index.html

A database of miRNA-mRNA interactions. The interaction data is obtained from various external data sources and in some cases computed in-house by algorithms implemented for miRNA target prediction.

Proper citation: targetHub (RRID:SCR_006522) Copy   

•   Source: SciCrunch Registry

http://www.nlm.nih.gov/medlineplus/druginformation.html

Database of authoritative health information about diseases, conditions, and wellness issues that offers reliable, up-to-date health information for free. It contains the latest treatments, information on drugs and supplements, the meanings of words, and medical videos and illustrations. Links to the latest topic or disease specific medical research or clinical trials are also offered. * MedlinePlus pages contain carefully selected links to Web resources with health information on over 900 topics. ** The MedlinePlus health topic pages include links to current news on the topic and related information. You can also find preformulated searches of the MEDLINE/PubMed database, which allow you to find references to latest health professional articles on your topic. * The A.D.A.M. medical encyclopedia brings health consumers an extensive library of medical images and videos, as well as over 4,000 articles about diseases, tests, symptoms, injuries, and surgeries. * The Merriam-Webster medical dictionary allows you to look up definitions and spellings of medical words. * Drug and supplement information is available from the American Society of Health-System Pharmacists (ASHP) via AHFS Consumer Medication Information, and Natural Medicines Comprehensive Database Consumer Version. ** AHFS Consumer Medication Information provides extensive information about more than 1,000 brand name and generic prescription and over-the-counter drugs, including side effects, precautions and storage for each drug. ** Natural Medicines Comprehensive Database Consumer Version is an evidence-based collection of information on alternative treatments. MedlinePlus has 100 monographs on herbs and supplements. * Interactive tutorials from the Patient Education Institute explain over 165 procedures and conditions in easy-to-read language. An XML File for the MedlinePlus Health Topics is available, http://www.nlm.nih.gov/medlineplus/xmldescription.html. The ontology is available through Bioportal, http://bioportal.bioontology.org/ontologies/MEDLINEPLUS

Proper citation: MedlinePlus (RRID:SCR_006512) Copy   

•   Source: SciCrunch Registry

http://pcidb.russelllab.org/

The database of protein-chemical structural interactions includes all existing 3D structures of complexes of proteins with low molecular weight ligands. When one considers the proteins and chemical vertices of a graph, all these interactions form a network. Biological networks are powerful tools for predicting undocumented relationships between molecules. The underlying principle is that existing interactions between molecules can be used to predict new interactions. For pairs of proteins sharing a common ligand, we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other. The current version includes data from the Protein Data Bank as of August 2011. The database is updated monthly.

Proper citation: ProtChemSI (RRID:SCR_006115) Copy   

•   Source: SciCrunch Registry

http://www.chordomafoundation.org/research/rdrepository.aspx

U-CH1 and U-CH2 are sacral-chordoma derived cell lines created by the lab of Dr. Peter Moeller at the University of Ulm, Germany. The Chordoma Foundation maintains a repository of these cell lines and makes them available to academic and industry investigators. Additionally, Notochordal tissue is available from the Congenital Defects Lab at the University of Washington. (Contact us for more information.) The following data is available: * Xenographs: The U-CH1 cell line forms tumors that resemble chordomas in NSG mice. A protocol for creating a xenograft using U-CH1 was developed by Dr. Adrienne Flanagan. * Tissue Microarrays (sacral, spine, skull base, lumbar, cervical, mobile spine, etc.) * Gene Expression Data: EBI Array Express Experiment E-MEXP-353: transcription profiling of human mesenchymal and some possibly neural crest derived neoplasms using the Affymetrix GeneChip? Human Genome HG-U133A. This data set was generated by the University College London Cancer Institute and contains 96 tissue samples including 4 chordomas. * Comparative Genomic Hybridization Data: Gene Expression Omnibus Series GSE9023: DNA copy number analysis of 21 fresh frozen chordoma biopsies, and the respective relapse in four of them, using 32k and 1Mb array CGH. Cases 1-11 were analyzed using 32k array CGH and male genomic DNA (Promega) was used as reference. Cases 17-26, and the respective relapse in four of these tumors, were analyzed with 1 Mb array CGH, using sex matched controls. All cases showed copy number alterations and primarily deletions of chromosomal regions were found. Particularly, the CDKN2A and CDKN2B loci in 9p21 were homo- or heterozygously lost in 70% of the tumors.

Proper citation: Chordoma Foundation Biobank (RRID:SCR_006425) Copy   

•   Source: SciCrunch Registry

http://www.mappd.org/

A database of experimental behavioral data from > 170 aphasia patients who exhibited language impairments secondary to chronic left hemisphere stroke. The core of the database is individual-trial performance data from picture naming. Picture naming is a primary test of lexical processing. The task taps a critical juncture in the language system because naming mediates between high-level conceptual and syntactic processing and low-level phonological processing. Difficulty in this task is present to varying degrees in nearly all aphasic individuals. This site allows researchers to search through naming data from over 170 patients. Searches can narrow in on data subsets based on patient characteristics (e.g. time since aphasia onset, clinical diagnosis), stimulus characteristics (e.g. semantic category, lexical frequency) and task performance (e.g. error type). The data available on this site can be used to test hypotheses about naming impairment and aphasic impairment generally. Once the basic analysis tools of the site are exhausted, users can export the raw data for further analysis and visualization. The web database represents years of data collection. Most were recruited to the research program at Moss Rehabilitation Research Institute (MRRI).

Proper citation: Moss Aphasia Psycholinguistics Project Database (RRID:SCR_006265) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/books/NBK1116/

Provides clinically relevant and medically actionable information for inherited conditions in standardized journal-style format, covering diagnosis, management, and genetic counseling for patients and their families. Searchable book of expert-authored, peer-reviewed disease descriptions presented in standardized format and focused on clinically relevant and medically actionable information on diagnosis, management, and genetic counseling of patients and families with specific inherited conditions.

Proper citation: GeneReviews (RRID:SCR_006560) Copy   

•   Source: SciCrunch Registry

http://decipher.sanger.ac.uk/

Interactive database which incorporates a suite of tools designed to aid the interpretation of submicroscopic chromosomal imbalance. Used to enhance clinical diagnosis by retrieving information from bioinformatics resources relevant to the imbalance found in the patient. Contributing to the DECIPHER database is a Consortium, comprising an international community of academic departments of clinical genetics. Each center maintains control of its own patient data (which are password protected within the center''''s own DECIPHER project) until patient consent is given to allow anonymous genomic and phenotypic data to become freely viewable within Ensembl and other genome browsers. Once data are shared, consortium members are able to gain access to the patient report and contact each other to discuss patients of mutual interest, thus facilitating the delineation of new microdeletion and microduplication syndromes.

Proper citation: DECIPHER (RRID:SCR_006552) Copy   

•   Source: SciCrunch Registry

http://biogps.org/

An extensible and customizable gene annotation portal that emphasizes community extensibility and user customizability. It is a complete resource for learning about gene and protein function. Community extensibility reflects a belief that any BioGPS user should be able to add new content to BioGPS using the simple plugin interface, completely independently of the core developer team. User customizability recognizes that not all users are interested in the same set of gene annotation data, so the gene report layouts enable each user to define the information that is most relevant to them. Currently, BioGPS supports eight species: Human (Homo sapiens), Mouse (Mus musculus), Rat (Rattus norvegicus), Fruitfly (Drosophila melanogaster), Nematode (Caenorhabditis elegans), Zebrafish (Danio rerio), Thale-cress (Arabidopsis thaliana), Frog (Xenopus tropicalis), and Pig (Sus scrofa). BioGPS presents data in an ortholog-centric format, which allows users to display mouse plugins next to human ones. Our data for defining orthologs comes from NCBI's HomoloGene database.

Proper citation: BioGPS: The Gene Portal Hub (RRID:SCR_006433) Copy   

•   Source: SciCrunch Registry