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http://www.nmrfam.wisc.edu/

Provides access and developes NMR technology to advance range of applications and improves the efficiency, rigor and reproducibility of NMR data acquisition and analysis. Houses NMR spectrometers equipped with state-of-the-art probe technology and protocols to support acquisition of high-quality data. Spectrometers range from 500 MHz to 1100 MHz. Service is tailored to the needs of individual users and projects. Provides training and advice on experimental design, best practices for data acquisition, and data analysis. Experienced staff support users with training opportunities including workshops, video tutorials and protocols.

Proper citation: National Magnetic Resonance Facility at Madison (RRID:SCR_001449) Copy   

•   Source: SciCrunch Registry

http://biology.hunter.cuny.edu/index.php?option=com_content&view=article&id=138&Itemid=117

Facility which provides instruments and instrument resources for analyzing DNA, RNA, protein, and radio-labeled substances.

Proper citation: Hunter Genomic Facility (RRID:SCR_001983) Copy   

•   Source: SciCrunch Registry

http://www.exactantigen.com

Database of hundreds of thousands of products submitted by reagent provider partners, and millions of webpages selected from reagent suppliers. All are organized according to genes, species, and reagent types (antibodies, recombinant proteins, ELISA, siRNA, cDNA clones, biochemicals, and others).

Proper citation: Labome (RRID:SCR_007384) Copy   

•   Source: SciCrunch Registry

http://noble.gs.washington.edu/proj/sdp-svm/

A statistical framework for genomic data fusion is a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins--membrane proteins and ribosomal proteins--performs significantly better than the same algorithm trained on any single type of data. Matlab code to center a kernel matrix and Matlab code for normalization are available.

Proper citation: A statistical framework for genomic data fusion (RRID:SCR_007219) Copy   

•   Source: SciCrunch Registry

http://wiki.c2b2.columbia.edu/califanolab/index.php/BCellInteractome.htm

A network of protein-protein, protein-DNA and modulatory interactions in human B cells. The network contains known interactions (reported in public databases) and predicted interactions by a Bayesian evidence integration framework which integrates a variety of generic and context specific experimental clues about protein-protein and protein-DNA interactions with inferences from different reverse engineering algorithms, such as GeneWays and ARACNE. Modulatory interactions are predicted by the MINDY, an algorithm for the prediction of modulators of transcriptional interactions (please refer to the publication section for more information). The BCI can be downloaded as one tab delimited file containing the complete network (BCI.txt) with each type of interaction explicitly defined.

Proper citation: B Cell Interactome (RRID:SCR_008655) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/guide/sitemap/

The National Center for Biotechnology Information''s listing of resources. Sort by alphabetical character, Databases, Downloads, Submissions, Tools and How-To; or by Topic: Chemicals & Bioassays; Data & Software; DNA & RNA; Domains & Structures; Genes & Expression; Genetics & Medicine; Genomes & Maps; Homology; Literature; Proteins; Sequence Analysis; Taxonomy; Training & Tutorials; Variation.

Proper citation: NCBI Resource List (RRID:SCR_005628) Copy   

•   Source: SciCrunch Registry

https://www.synapse.org/#!Synapse:syn4921369/wiki/235539

Portal of PsychENCODE Consortium to study role of rare genetic variants involved in several psychiatric disorders. Database of regulatory elements, epigenetic modifications, RNA and protein in brain.

Proper citation: PsychENCODE Knowledge Portal (RRID:SCR_017500) Copy   

•   Source: SciCrunch Registry

https://github.com/TransDecoder/TransDecoder

Software tool to identify candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to genome using Tophat and Cufflinks.Starts from FASTA or GFF file. Can scan and retain open reading frames (ORFs) for homology to known proteins by using BlastP or Pfam search and incorporate results into obtained selection. Predictions can then be visualized by using genome browser such as IGV.

Proper citation: TransDecoder (RRID:SCR_017647) Copy   

•   Source: SciCrunch Registry

http://bioinf.uab.es/aggrescan/

Web-based tool for identifying hot spots of aggregation in polypeptides. Aggrescan uses an aggregation-propensity scale for natural amino acids derived from in vivo experiments and on the assumption that short and specific sequence stretches modulate protein aggregation. The algorithm is shown to identify a series of protein fragments involved in the aggregation of disease-related proteins and to predict the effect of genetic mutations on their deposition propensities. It also provides new insights into the differential aggregation properties displayed by globular proteins, natively unfolded polypeptides, amyloidogenic proteins and proteins found in bacterial inclusion bodies.

Proper citation: Aggrescan: The Hot Spot Finder (RRID:SCR_008403) Copy   

•   Source: SciCrunch Registry

http://caps.ncbs.res.in/imotdb/

Comprehensive collection of spatially interacting motifs in proteins. Interacting motif database lists interacting motifs that are identified for all structural entries in PDB. Conserved patterns or finger prints are identified for individual structural entries and also grouped together for reporting common motifs shared among all superfamily members.

Proper citation: Database of Spatially Interacting Motifs in Proteins (RRID:SCR_007735) Copy   

•   Source: SciCrunch Registry

http://wwwmgs.bionet.nsc.ru/mgs/programs/panalyst/

WebProAnalyst provides web-accessible analysis for scanning the quantitative structure-activity relationships in protein families. It searches for a sequence region, whose substitutions are correlated with variations in the activities of a homologous protein set, the so-called activity modulating sites. WebProAnalyst allows users to search for the key physicochemical characteristics of the sites that affect the changes in protein activities. It enables the building of multiple linear regression and neural networks models that relate these characteristics to protein activities. WebProAnalyst implements multiple linear regression analysis, back propagation neural networks and the Structure-Activity Correlation/Determination Coefficient (SACC/SADC). A back propagation neural network is implemented as a two-layered network, one layer as input, the other as output (Rumelhart et al, 1986). WebProAnalyst uses alignment of amino acid sequences and data on protein activity (pK, Km, ED50, among others). The input data are the numerical values for the physicochemical characteristics of a site in the multiple alignment given by a slide window. The output data are the predicted activity values. The current version of WebProAnalyst handles a single activity for a single protein. The SACC/SADC may be defined as an estimate of the strongest multiple correlation between the physicochemical characteristics of a site in a multiple alignment and protein activities. The SACC/SADC coefficient makes possible the calculation of the possible highest correlation achievable for the quantitative relationship between the physicochemical properties of sites and protein activities. The SACC/SADC is a convenient means for an arrangement of positions by their functional significance. WebProAnalyst outputs a list of multiple alignment positions, the respective correlation values, also regression analysis parameters for the relationships between the amino acid physicochemical characteristics at these positions and the protein activity values.

Proper citation: Webproanalyst (RRID:SCR_008348) Copy   

•   Source: SciCrunch Registry

http://www.glycosciences.de/tools/carp/

Service that generates Ramachandran-like plots of carbohydrate linkage torsions in pdb-files. The Ramachandran Plot, where backbone torsion angles are plotted against each other, is a frequently used tool to evaluate the quality of a protein 3D structure. For carbohydrate structures, linkage torsions can be evaluated in a similar way. Preferred Phi/Psi values of the torsion angles of glycosidic bonds depend strongly on the types of monosaccharides involved in the linkage, the kind of linkage (1-3, 1-4, etc) as well as the degree of branching of the structure. CARP analyses carbohydrate data given in PDB files using the pdb2linucs algorithm. For each different linkage type a separate plot is generated. The user can choose between two sources for plot background information for comparison: data obtained from PDB provided by GlyTorsion or from GlycoMapsDB. GlycoMapsDB provides calculated conformational maps, which show energetically preferred regions for a specific linkage, while PDB data are based on experimentally solved structures. For seldom occuring linkages, however, PDB data are often rare, so maybe not sufficient background information for comparison will be available from this source., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CARP (RRID:SCR_009021) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/sss/wublast/

Tool to find regions of sequence similarity within selected protein databases quickly, with minimum loss of sensitivity.

Proper citation: WU-BLAST (RRID:SCR_011824) Copy   

•   Source: SciCrunch Registry

http://www.mrmatlas.org/

Resource of targeted proteomics assays to detect and quantify proteins in complex proteome digests by mass spectrometry. Used to quantify the complete human proteome.

Proper citation: SRMAtlas (RRID:SCR_016996) Copy   

•   Source: SciCrunch Registry

http://www.cdtdb.brain.riken.jp/CDT/Top.jsp

Transcriptomic information (spatiotemporal gene expression profile data) on the postnatal cerebellar development of mice (C57B/6J & ICR). It is a tool for mining cerebellar genes and gene expression, and provides a portal to relevant bioinformatics links. The mouse cerebellar circuit develops through a series of cellular and morphological events, including neuronal proliferation and migration, axonogenesis, dendritogenesis, and synaptogenesis, all within three weeks after birth, and each event is controlled by a specific gene group whose expression profile must be encoded in the genome. To elucidate the genetic basis of cerebellar circuit development, CDT-DB analyzes spatiotemporal gene expression by using in situ hybridization (ISH) for cellular resolution and by using fluorescence differential display and microarrays (GeneChip) for developmental time series resolution. The CDT-DB not only provides a cross-search function for large amounts of experimental data (ISH brain images, GeneChip graph, RT-PCR gel images), but also includes a portal function by which all registered genes have been provided with hyperlinks to websites of many relevant bioinformatics regarding gene ontology, genome, proteins, pathways, cell functions, and publications. Thus, the CDT-DB is a useful tool for mining potentially important genes based on characteristic expression profiles in particular cell types or during a particular time window in developing mouse brains.

Proper citation: Cerebellar Development Transcriptome Database (RRID:SCR_013096) Copy   

•   Source: SciCrunch Registry

http://emboss.sourceforge.net/apps/cvs/embassy/index.html#DOMSEARCH

Source code for EMBOSS commands to search for protein domains. Its functions include removing redundant and fragment sequences from DHF files, generating PSI-BLAST hits (DHF file) from a DAF file, removing ambiguous classified sequences from DHF files, and generating DHF files from keyword search of UniProt.

Proper citation: Embassy-domsearch (RRID:SCR_016086) Copy   

•   Source: SciCrunch Registry

http://specimencentral.com/

World's open biospecimen research database where biobanks and biomedical researchers meet to exchange human biospecimen needs and supply: whole blood, serum, plasma, solid tissue samples and more. The connection is accelerated so researchers save valuable time and money and tissue banks utilize inventory. The pace of specimen procurement remains unacceptably slow to the biomedical research community. Specimen Central is the foremost global resource to aid biomedical researchers in expediting their search for high quality human biospecimens, tissues, samples and specimens. They facilitate your search for blood, whole blood, buccal swab, DNA, RNA, protein, cell lines, plasma, serum, RBC, white cells, buffy coat, fluid, marrow, urine, stem cells, and solid tissue such as tumor, tumor and biopsy materials spanning all manner of common and rare pathologies and indications including Alzheimer's, basal cell carcinoma, bladder cancer, bone cancer, brain cancer, breast cancer, cerebrospinal fluid, amniotic fluid, colorectal cancer, colon cancer, hodgkins and non-hodgkins lymphoma, kidney/renal cancer, leukemia, liver cancer, lung cancer, melanoma, multiple sclerosis, myeloma neuroblastoma, neurodegenerative diseases, ovarian cancer, pancreatic cancer, prostate cancer, urinary cancer. This includes adult and pediatric indications. Specimen Central users specify a number of variables in their Specimen Requests, including preparation, preservation and handling requirements such as cryo-preserved, FFPE (Formalin-fixed paraffin-embedded), formalin, frozen, refrigerated, OCT, snap frozen, paraffin block, fresh, prospective, autopsy or cadaveric, etc. Many users require clinically annotated date associated with their specimens, as well as documentation of IRB or ethics committee approval and informed consents. For Researchers Most specimen databases require researchers to waste time and effort entering lengthy registrations and search queries that yield poor results, if anything. Specimen Central solves this problem by having tissue banks search for you. From years to months, months to weeks, and weeks to days, Specimen Central seeks to reduce delays and costs in the research & development life cycle by expediting connections between demand and supply. For Biobanks The capital costs of maintaining a biobank infrastructure are substantial and growing. Biobanks use Specimen Central as a marketing tool to augment their business development efforts. By routinely checking Specimen Central's Specimen Requests, biobanks can uncover market demand for their inventories and develop new connections and revenue streams to defray costs. Specimen Central supplements - not displaces - the efforts of your sales representatives, agents, brokers and commercial partners.

Proper citation: SpecimenCentral.com (RRID:SCR_003536) Copy   

•   Source: SciCrunch Registry

http://interactome.org/index.php/Main_Page

This Wiki page provides information about Interactome of various species. An interactome of a species provides an important clues on how to interpret metabolic pathways of constituent enzymes and global protein network, which facilitates in turn to understand the mechanism responsible for the cellular functions.

Proper citation: Interactome Wiki (RRID:SCR_000750) Copy   

•   Source: SciCrunch Registry

http://www.proteomexchange.org

A data repository for proteomic data sets. The ProteomeExchange consortium, as a whole, aims to provide a coordinated submission of MS proteomics data to the main existing proteomics repositories, as well as to encourage optimal data dissemination. ProteomeXchange provides access to a number of public databases, and users can access and submit data sets to the consortium's PRIDE database and PASSEL/PeptideAtlas.

Proper citation: ProteomeXchange (RRID:SCR_004055) Copy   

•   Source: SciCrunch Registry

http://statalign.github.io/

Software package for Bayesian analysis of protein, DNA and RNA sequences. It utilizes multiple alignments, phylogenetic trees and evolutionary parameters to quantify uncertainty in these analyses. It is written in Java.

Proper citation: StatAlign (RRID:SCR_001892) Copy   

•   Source: SciCrunch Registry