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https://sites.google.com/site/jpopgen/dbNSFP

A database for functional prediction and annotation of all potential non-synonymous single-nucleotide variants (nsSNVs) in the human genome. Version 2.0 is based on the Gencode release 9 / Ensembl version 64 and includes a total of 87,347,043 nsSNVs and 2,270,742 essential splice site SNVs. It compiles prediction scores from six prediction algorithms (SIFT, Polyphen2, LRT, MutationTaster, MutationAssessor and FATHMM), three conservation scores (PhyloP, GERP++ and SiPhy) and other related information including allele frequencies observed in the 1000 Genomes Project phase 1 data and the NHLBI Exome Sequencing Project, various gene IDs from different databases, functional descriptions of genes, gene expression and gene interaction information, etc. Some dbNSFP contents (may not be up-to-date though) can also be accessed through variant tools, ANNOVAR, KGGSeq, UCSC Genome Browser''s Variant Annotation Integrator, Ensembl Variant Effect Predictor and HGMD.

Proper citation: dbNSFP (RRID:SCR_005178) Copy   

•   Source: SciCrunch Registry

http://mlstoslo.uio.no/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11,2023. SuperCAT hosts typing databases for the Bacillus cereus group of bacteria. The databases contain MultiLocus Sequence Typing (MLST), MultiLocus Enzyme Electrophoresis (MLEE), and Amplified Fragment Length Polymorphism (AFLP) phylogenetic data. multilocus, sequence, Bacillus cereus, bacteria, Genomics, non-vertebrate, taxonomy, identification

Proper citation: SuperCAT (RRID:SCR_004882) Copy   

•   Source: SciCrunch Registry

http://scienceblogs.com/channel/medicine/

ScienceBlogs posts about Medicine & Health.

Proper citation: ScienceBlogs: Medicine and Health (RRID:SCR_005176) Copy   

•   Source: SciCrunch Registry

http://pluto3.nci.nih.gov/tissue/default.cfm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. The Specimen Resource Locator is a database to help researchers locate human specimens (tissue, serum, DNA/RNA, other specimens) for cancer research. It includes tissue banks and tissue procurement systems with access to normal, benign, precancerous and cancerous human tissue from a variety of organs. Researchers specify the types of specimens, number of cases, preservation methods and associated data they require. The Locator will then search the database and return a list of tissue resources most likely to meet their requirements. When no match is obtained, the researcher is referred to the NCI Tissue Expediter ([email protected]). The Tissue expediter is a scientist who can help researchers identify appropriate resources and/or appropriate collaborators.

Proper citation: NCI Specimen Resource Locator (RRID:SCR_004754) Copy   

•   Source: SciCrunch Registry

http://www.guardian.co.uk/science

Latest science news, comment, analysis and features from guardian.co.uk, the world''s leading liberal voice.

Proper citation: The Guardian: Science (RRID:SCR_005166) Copy   

•   Source: SciCrunch Registry

http://blogs.scientificamerican.com/observations/

From the editors and reporters of Scientific American, this blog delivers commentary, opinion and analysis on the latest developments in science and technology and their influence on society and policy. From reasoned arguments and cultural critiques to personal and skeptical takes on interesting science news, you''ll find a wide range of scientifically relevant insights here.

Proper citation: Scientific American Observations (RRID:SCR_005195) Copy   

•   Source: SciCrunch Registry

http://solgenomics.net/

A clade oriented, community curated database containing genomic, genetic, phenotypic and taxonomic information for plant genomes. Genomic information is presented in a comparative format and tied to important plant model species such as Arabidopsis. SGN provides tools such as: BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an intron detection tool, an advanced Alignment Analyzer, and a browser for phylogenetic trees. The SGN code and database are developed as an open source project, and is based on database schemas developed by the GMOD project and SGN-specific extensions.

Proper citation: SGN (RRID:SCR_004933) Copy   

•   Source: SciCrunch Registry

http://spliceinfo.mbc.nctu.edu.tw/

A database of mRNA alternative splicing in the human genome. Within it, several modes of mRNA alternative splicing, such as exon skipping, alternative 5''-splicing sites, alternative 3''-splicing sites and mutually exclusive exons are computationally derived and extracted. Finally, for each type of alternative splicing, the flanking intronic sequences are collected and then exploited by motif discovery tools. The tissue-specific information and gene functionalities that correspond to the selected regions are also considered. The database provides a means of investigating alternative splicing and can be used for identifying alternative splicing - related motifs, such as the exonic splicing enhancer (ESE), the exonic splicing silencer (ESS) and other intronic splicing motifs.

Proper citation: SpliceInfo (RRID:SCR_005100) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-6/search?q=*&l=

A virtual database currently indexing authoritative information on disease and treatment options from NINDS Disorder List and PubMed Health.

Proper citation: Integrated Disease (RRID:SCR_004892) Copy   

•   Source: SciCrunch Registry

http://exon.cshl.org/cgi-bin/atprobe/atprobe.pl

Arabidopsis thaliana promoter binding element database that focuses on specific binding elements on known genes, found with experimental methods.

Proper citation: AtProbe (RRID:SCR_005412) Copy   

•   Source: SciCrunch Registry

http://cgi-www.daimi.au.dk/cgi-chili/datfap/frontdoor.py

A database of transcription factors from 13 plant species, and PCR primers for around 90% of them.

Proper citation: DATFAP (RRID:SCR_005413) Copy   

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http://bioinfozen.uncc.edu/tfindit/

A database and web service for structural bioinformatics studies of transcription factor (TF)-DNA interactions. Various datasets can be generated based on one or more search options specified by users.

Proper citation: TFinDIT (RRID:SCR_005411) Copy   

•   Source: SciCrunch Registry

http://www.dbs.ifi.lmu.de/~bundschu/LHGDN.html

A text mining derived database with focus on extracting and classifying gene-disease associations with respect to several biomolecular conditions. It uses a machine learning based algorithm to extract semantic gene-disease relations from a textual source of interest. The semantic gene-disease relations were extracted with F-measures of 78. More specifically, the textual source utilized here originates from Entrez Gene''''s GeneRIF (Gene Reference Into Function) database (Mitchell, et al., 2003). LHGDN was created based on a GeneRIF version from March 31st, 2009, consisting of 414241 phrases. These phrases were further restricted to the organism Homo sapiens, which resulted in a total of 178004 phrases. We benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph. We extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining.

Proper citation: Literature-derived human gene-disease network (RRID:SCR_005653) Copy   

•   Source: SciCrunch Registry

http://www.phenologs.org/

Database for identifying orthologous phenotypes (phenologs). Mapping between genotype and phenotype is often non-obvious, complicating prediction of genes underlying specific phenotypes. This problem can be addressed through comparative analyses of phenotypes. We define phenologs based upon overlapping sets of orthologous genes associated with each phenotype. Comparisons of >189,000 human, mouse, yeast, and worm gene-phenotype associations reveal many significant phenologs, including novel non-obvious human disease models. For example, phenologs suggest a yeast model for mammalian angiogenesis defects and an invertebrate model for vertebrate neural tube birth defects. Phenologs thus create a rich framework for comparing mutational phenotypes, identify adaptive reuse of gene systems, and suggest new disease genes. To search for phenologs, go to the basic search page and enter a list of genes in the box provided, using Entrez gene identifiers for mouse/human genes, locus ids for yeast (e.g., YHR200W), or sequence names for worm (e.g., B0205.3). It is expected that this list of genes will all be associated with a particular system, trait, mutational phenotype, or disease. The search will return all identified model organism/human mutational phenotypes that show any overlap with the input set of the genes, ranked according to their hypergeometric probability scores. Clicking on a particular phenolog will result in a list of genes associated with the phenotype, from which potential new candidate genes can identified. Currently known phenotypes in the database are available from the link labeled ''Find phenotypes'', where the associated gene can be submitted as queries, or alternately, can be searched directly from the link provided.

Proper citation: Phenologs (RRID:SCR_005529) Copy   

•   Source: SciCrunch Registry

http://blog.ketyov.com/

Bradley Voytek''''s blog is where he tries out new ideas. He will often be wrong, but that''''s the point. He is a Neuroscientist studying human cognition, neuroplasticity, and brain computer interfacing. Into really geeky stuff. World zombie neuroscience expert. Also runs brainSCANr.com with his wife, Jessica.

Proper citation: Oscillatory Thoughts (RRID:SCR_005481) Copy   

•   Source: SciCrunch Registry

http://www.youtube.com/user/WholeBrainCatalog?feature=autoshare

Videos uploaded to YouTube by the Whole Brain Catalog.

Proper citation: WholeBrainCatalog's Channel - YouTube (RRID:SCR_005436) Copy   

•   Source: SciCrunch Registry

http://highwire.stanford.edu/

Service that partners with independent scholarly publishers, societies, associations, and university presses to facilitate the digital dissemination of 1779 journals, reference works, books, and proceedings. It also offers a complete manuscript submission, tracking, peer review, and publishing system for journal editors.

Proper citation: HighWire (RRID:SCR_005316) Copy   

•   Source: SciCrunch Registry

http://med.emory.edu/ADRC/research/core_neurology_database.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 9, 2025. A database which retains extensive clinical information about study subjects recruited by the Alzheimer's Disease Research Center Clinical Core, as well as other individuals with neurological diseases. In addition to clinical information, the database has basic demographics, medical history (including risk factors such as smoking), and a detailed family history from all subjects. Some entries have neuropsychological measures. Users can access a Summary Database which contains the most commonly requested variables. A data dictionary describing the variables in the Summary Database is available.

Proper citation: Emory Neurology Database (RRID:SCR_005277) Copy   

•   Source: SciCrunch Registry

http://maize.tigr.org

A database of maize genomic sequences, searchable by BLAST, by repeat sequences, and sequence name, gene name, locus, or other landmark. TIGR is a member of the Consortium for Maize Genomics. The Consortium received a funding award from the National Science Foundation in September 2002, to evaluate two gene-enrichment techniques, methylation filtration and high Cot selection, to sequence the maize 'genespace'. Draft assemblies of 287 maize BAC clones selected by the maize community and the Consortium were also produced in the Consortium project. We have recently developed an improved version of the TIGR annotation pipeline optimized for maize genomic assemblies. The latest maize genomic assemblies obtained by gene-enrichment (AZM5) and the 287 maize draft BAC assemblies have been annotated using the improved pipeline. Gene model annotation and functional annotation can be accessed via the TIGR maize BLAST server or the TIGR maize gbrowse display. The first version of the Maize Repeat Database contained 485 characterized maize repeat sequences from the TIGR Cereal Repeat Database. To these we added repetitive sequences downloaded from GenBank and a file of retrotransposon sequences compiled by Phillip SanMiguel (Purdue University). In addition we searched our maize genomic assemblies (AZMs) to identify new repeats. Any sequence within an AZM that showed at least 80% identity over a minimum stretch of 100 bp with an entry in the TIGR Cereal Repeat Database was coded accordingly and added to the Maize Repeat Database.

Proper citation: TIGR Maize database (RRID:SCR_005431) Copy   

•   Source: SciCrunch Registry

http://rnp.uthscsa.edu/rnp/tmRDB/tmRDB.html

The tmRDB is a tool in the study of the structures and functions of the tmRNA (earlier called 10S RNA). As the name implies, tmRNA has properties of tRNA and mRNA combined in a single molecule. The tmRDB provides aligned, annotated and phylogenetically ordered tmRNA sequences. The alignments of the sequences represent conserved secondary structure elements where each base pair is proven by comparative sequence analysis. Where possible, we established direct links to primary sources. We acknowledge support provided by the National Institutes of Health and the Danish Technical Research Council. tRNA, mRNA, trans-translation, rescue, ribosome, broken mRNA, bacteria, mitochondria chloroplasts, cyanelles, bacteriphage, phylogenetic

Proper citation: tmRNA Database (RRID:SCR_005540) Copy   

•   Source: SciCrunch Registry