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http://www.comparative-legumes.org/

LIS is a publicly accessible legume resource that integrates genetic and molecular data from multiple legume species and enables cross-species genomic, transcript and map comparisons. The intent of the LIS is to help researchers leverage data-rich model plants to fill knowledge gaps across crop plant species and provide the ability to traverse between interrelated data types. LIS, a component of the Model Plant Initiative (MPI), is being developed as part of a cooperative research agreement between the National Center for Genome Resources (NCGR) and the USDA Agricultural Research Service (ARS).

Proper citation: Legume Information System (RRID:SCR_007761) Copy   

•   Source: SciCrunch Registry

http://supfam.org/SUPERFAMILY/

SUPERFAMILY is a database of structural and functional protein annotations for all completely sequenced organisms. The SUPERFAMILY annotation is based on a collection of hidden Markov models, which represent structural protein domains at the SCOP superfamily level. A superfamily groups together domains which have an evolutionary relationship. The annotation is produced by scanning protein sequences from over 1,700 completely sequenced genomes against the hidden Markov models.

Proper citation: SUPERFAMILY (RRID:SCR_007952) Copy   

•   Source: SciCrunch Registry

http://stitch.embl.de

Database to explore known and predicted interactions of chemicals and proteins. It integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug-target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. The database contains interaction information for over 68,000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database.

Proper citation: Search Tool for Interactions of Chemicals (RRID:SCR_007947) Copy   

•   Source: SciCrunch Registry

http://www.bioinfodatabase.com/pint/

A protein-protein interactions thermodynamic database which contains data of several thermodynamic parameters along with sequence and structural information experimental conditions and literature information. Each entry contains numerical data for features of the interacting proteins such as the free energy change, dissociation constant, association constant, enthalpy change, and heat capacity change. PINT includes: the name and source of the proteins involved in binding, SWISS-PROT and Protein Data Bank (PDB) codes, secondary structure and solvent accessibility of residues at mutant positions, measuring methods, and experimental conditions such as buffers, ions and additives, and literature information. PINT is cross-linked with other related databases such as PIR, SWISS-PROT, PDB and the NCBI PUBMED literature database.

Proper citation: PINT (RRID:SCR_007856) Copy   

•   Source: SciCrunch Registry

http://polya.umdnj.edu/

A database of mRNA polyadenylation sites. PolyA_DB version 1 contains human and mouse poly(A) sites that are mapped by cDNA/EST sequences. PolyA_DB version 2 contains poly(A) sites in human, mouse, rat, chicken and zebrafish that are mapped by cDNA/EST and Trace sequences. Sequence alignments between orthologous sites are available. PolyA_SVM predicts poly(A) sites using 15 cis elements identified for human poly(A) sites.

Proper citation: PolyA DB (RRID:SCR_007867) Copy   

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http://pirnabank.ibab.ac.in/

A web analysis system and resource, which provides comprehensive information on piRNAs in the widely studied mammals. It compiles all the possible clusters of piRNAs and also depicts piRNAs along with the associated genomic elements like genes and repeats on a genome wide map. piRNABank mainly provides data onnamely Human, Mouse, Rat, Zebrafish, Platypus and a fruit fly, Drosophila.Search options have been designed to query and obtain useful data from this online resource. It also facilitates abstraction of sequences and structural features from piRNA data. piRNABank provides the following features: * Simple search * Search piRNA clusters * Search homologous piRNAs * piRNA visualization map * Analysis tools, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: piRNABank (RRID:SCR_007858) Copy   

•   Source: SciCrunch Registry

http://www.brainresource.com/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 10th,2023. Commercial provider of cognitive assessments, including their proprietary database, the Brain Resource International Database (BRID) that allows users to quantify individual differences in brain function, compare individual performance against peers, and provide a robust frame of reference for clinical assessment and treatment decisions. Database provides evidence for brain behavior connection so important to reliably enabling optimal solutions for mental health and wellbeing. It powers all Brain Resource products.

Proper citation: Brain Resource (RRID:SCR_006172) Copy   

•   Source: SciCrunch Registry

http://www.ch.embnet.org/software/COILS_form.html

COILS is a program that compares a sequence to a database of known parallel two-stranded coiled-coils and derives a similarity score. By comparing this score to the distribution of scores in globular and coiled-coil proteins, the program then calculates the probability that the sequence will adopt a coiled-coil conformation.

Proper citation: COILS: Prediction of Coiled Coil Regions in Proteins (RRID:SCR_008440) Copy   

•   Source: SciCrunch Registry

http://www.ini.uzh.ch/~acardona/trakem2.html

An ImageJ plugin for morphological data mining, three-dimensional modeling and image stitching, registration, editing and annotation. Two independent modalities exist: either XML-based projects, working directly with the file system, or database-based projects, working on top of a local or remote PostgreSQL database. What can you do with it? * Semantic segmentation editor: order segmentations in tree hierarchies, whose template is exportable for reuse in other, comparable projects. * Model, visualize and export 3D. * Work from your laptop on your huge, remote image storage. * Work with an endless number of images, limited only by the hard drive capacity. Dozens of formats supported thanks to LOCI Bioformats and ImageJ. * Import stacks and even entire grids (montages) of images, automatically stitch them together and homogenize their histograms for best montaging quality. * Add layers conveniently. A layer represents, for example, one 50 nm section (for TEM) or a confocal section. Each layer has its own Z coordinate and thickness, and contains images, labels, areas, nodes of 3d skeletons, profiles... * Insert layer sets into layers: so your electron microscopy serial sections can live inside your optical microscopy sections. * Run any ImageJ plugin on any image. * Measure everything: areas, volumes, pixel intensities, etc. using both built-in data structures and segmentation types, and standard ImageJ ROIs. And with double dissectors! * Visualize RGB color channels changing the opacity of each on the fly, non-destructively. * Annotate images non-destructively with floating text labels, which you can rotate/scale on the fly and display in any color. * Montage/register/stitch/blend images manually with transparencies, semiautomatically, or fully automatically within and across sections, with translation, rigid, similarity and affine models with automatically extracted SIFT features. * Correct the lens distortion present in the images, like those generated in transmission electron microscopy. * Add alpha masks to images using ROIs, for example to split images in two or more parts, or to remove the borders of an image or collection of images. * Model neuronal arbors with 3D skeletons (with areas or radiuses), and synapses with connectors. * Undo all steps. And much more...

Proper citation: TrakEM2 (RRID:SCR_008954) Copy   

•   Source: SciCrunch Registry

http://www.ntnu.edu/hunt

International biobank storing whole blood and DNA from 200,000 individuals, serum and plasma samples from more than 100,000 individuals as well as urine, RNA tubes, cells, buffy coat and Na-heparin tubes for environmental analysis for as many as 50,000 individuals. All bio-specimens from the HUNT surveys are collected, processed and stored at the HUNT Biobank in Levanger. The National CONOR Biobank is located on the same site, where it serves as a central research repository for DNA samples from all the largest Norwegian health surveys. These make up the Cohorts of Norway (CONOR), which include samples from more than 200,000 individuals. * HUNT 1 was carried out in 1984-1986 to establish the health history of 75,000 people. * HUNT 2, carried out in 1995-1997, focused on the evolution of the health history of 74,000 people. This included blood sample collection from 65,000 people. The data that accompany biospecimens in the biobank are stored in secured computer systems that run complex database management and analysis software. * HUNT 3 was completed in June 2008. 93,210 people were invited to participate in the study, and as of the 6th of June, 2008, 48,289 people participated (52% participation rate). The data, collected by means of questionnaires, interviews, clinical examinations and collection of blood and urine samples, will be ready for analysis in January 2009. * Young-HUNT is the adolescent part of HUNT including participants aged 13-19 years. Young-HUNT1 (1995-97) was conducted as part of HUNT2, 9141 adolescents participated (90% response rate). Young-HUNT2 (2000-01) was a follow-up study of Young-HUNT1, 2400 students participated in both studies (77% of the invited). Young-HUNT3 (2006-08) was a new cross-sectional study as part of HUNT3. This time 8677 adolescents participated (87% response rate). Data collection included self-reported questionnaires, structured interviews, clinical measurements and, in Young-HUNT3, buccal smears. All institutions with research expertise can apply for access to analyze HUNT data. Projects must have recommendations from The Regional Committee for Medical Research in Norway (REK) and be registered with The Norwegian Social Science Data Services (NSD).

Proper citation: Hunt Biobank (RRID:SCR_010626) Copy   

•   Source: SciCrunch Registry

https://www.broadinstitute.org/ccle/

A collaborative project between the Broad Institute and the Novartis Institutes for Biomedical Research and its Genomics Institute of the Novartis Research Foundation, with the goal of conducting a detailed genetic and pharmacologic characterization of a large panel of human cancer models. The CCLE also works to develop integrated computational analyses that link distinct pharmacologic vulnerabilities to genomic patterns and to translate cell line integrative genomics into cancer patient stratification. The CCLE provides public access to genomic data, analysis and visualization for about 1000 cell lines.

Proper citation: Cancer Cell Line Encyclopedia (RRID:SCR_013836) Copy   

•   Source: SciCrunch Registry

http://sideeffects.embl.de/

Database containing information on marketed medicines and their recorded adverse drug reactions. The information is extracted from public documents and package inserts. The available information include side effect frequency, drug and side effect classifications as well as links to further information, for example drug-target relations. The SIDER Side Effect Resource represents an effort to aggregate dispersed public information on side effects. To our knowledge, no such resource exist in machine-readable form despite the importance of research on drugs and their effects. The creation of this resource was motivated by the many requests for data that we received related to our paper (Campillos, Kuhn et al., Science, 2008, 321(5886):263-6.) on the utilization of side effects for drug target prediction. Inclusion of side effects as readouts for drug treatment should have many applications and we hope to be able to enhance the respective research with this resource. You may browse the drugs by name, browse the side effects by name, download the current version of SIDER, or use the search interface.

Proper citation: SIDER (RRID:SCR_004321) Copy   

•   Source: SciCrunch Registry

http://exac.broadinstitute.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. An aggregated data platform for genome sequencing data created by a coalition of investigators seeking to aggregate and harmonize exome sequencing data from a variety of large-scale sequencing projects, and to make summary data available for the wider scientific community. The data set provided on this website spans 61,486 unrelated individuals sequenced as part of various disease-specific and population genetic studies. They have removed individuals affected by severe pediatric disease, so this data set should serve as a useful reference set of allele frequencies for severe disease studies. All of the raw data from these projects have been reprocessed through the same pipeline, and jointly variant-called to increase consistency across projects. They ask that you not publish global (genome-wide) analyses of these data until after the ExAC flagship paper has been published, estimated to be in early 2015. If you''re uncertain which category your analyses fall into, please email them. The aggregation and release of summary data from the exomes collected by the Exome Aggregation Consortium has been approved by the Partners IRB (protocol 2013P001477, Genomic approaches to gene discovery in rare neuromuscular diseases).

Proper citation: ExAc (RRID:SCR_004068) Copy   

•   Source: SciCrunch Registry

http://www.yeastgenome.org/

A curated database that provides comprehensive integrated biological information for Saccharomyces cerevisiae along with search and analysis tools to explore these data. SGD allows researchers to discover functional relationships between sequence and gene products in fungi and higher organisms. The SGD also maintains the S. cerevisiae Gene Name Registry, a complete list of all gene names used in S. cerevisiae which includes a set of general guidelines to gene naming. Protein Page provides basic protein information calculated from the predicted sequence and contains links to a variety of secondary structure and tertiary structure resources. Yeast Biochemical Pathways allows users to view and search for biochemical reactions and pathways that occur in S. cerevisiae as well as map expression data onto the biochemical pathways. Literature citations are provided where available.

Proper citation: SGD (RRID:SCR_004694) Copy   

•   Source: SciCrunch Registry

http://pfam.xfam.org/

A database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).

Proper citation: Pfam (RRID:SCR_004726) Copy   

•   Source: SciCrunch Registry

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://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://www.knockoutmouse.org/

Database of the international consortium working together to mutate all protein-coding genes in the mouse using a combination of gene trapping and gene targeting in C57BL/6 mouse embryonic stem (ES) cells. Detailed information on targeted genes is available. The IKMC includes the following programs: * Knockout Mouse Project (KOMP) (USA) ** CSD, a collaborative team at the Children''''s Hospital Oakland Research Institute (CHORI), the Wellcome Trust Sanger Institute and the University of California at Davis School of Veterinary Medicine , led by Pieter deJong, Ph.D., CHORI, along with K. C. Kent Lloyd, D.V.M., Ph.D., UC Davis; and Allan Bradley, Ph.D. FRS, and William Skarnes, Ph.D., at the Wellcome Trust Sanger Institute. ** Regeneron, a team at the VelociGene division of Regeneron Pharmaceuticals, Inc., led by David Valenzuela, Ph.D. and George D. Yancopoulos, M.D., Ph.D. * European Conditional Mouse Mutagenesis Program (EUCOMM) (Europe) * North American Conditional Mouse Mutagenesis Project (NorCOMM) (Canada) * Texas A&M Institute for Genomic Medicine (TIGM) (USA) Products (vectors, mice, ES cell lines) may be ordered from the above programs.

Proper citation: International Knockout Mouse Consortium (RRID:SCR_005574) Copy   

•   Source: SciCrunch Registry

http://swissregulon.unibas.ch/fcgi/sr/swissregulon

A database of genome-wide annotations of regulatory sites. The predictions are based on Bayesian probabilistic analysis of a combination of input information including: * Experimentally determined binding sites reported in the literature. * Known sequence-specificities of transcription factors. * ChIP-chip and ChIP-seq data. * Alignments of orthologous non-coding regions. Predictions were made using the PhyloGibbs, MotEvo, IRUS and ISMARA algorithms developed in their group, depending on the data available for each organism. Annotations can be viewed in a Gbrowse genome browser and can also be downloaded in flat file format.

Proper citation: SwissRegulon (RRID:SCR_005333) Copy   

•   Source: SciCrunch Registry

http://plantta.jcvi.org/

The TIGR database is a collection of plant transcript sequences. Transcript assemblies are searchable using BLAST and accession number. The construction of plant transcript assemblies (TAs) is similar to the TIGR gene indices. The sequences that are used to build the plant TAs are expressed transcripts collected from dbEST (ESTs) and the NCBI GenBank nucleotide database (full length and partial cDNAs). "Virtual" transcript sequences derived from whole genome annotation projects are not included. All plant species for which more than 1,000 ESTs or cDNA sequences are available are included in this project. TAs are clustered and assembled using the TGICL tool (Pertea et al., 2003), Megablast (Zhang et al., 2000) and the CAP3 assembler (Huang and Madan, 1999). TGICL is a wrapper script which invokes Megablast and CAP3. Sequences are initially clustered based on an all-against-all comparisons using Megablast. The initial clusters are assembled to generate consensus sequences using CAP3. Assembly criteria include a 50 bp minimum match, 95% minimum identity in the overlap region and 20 bp maximum unmatched overhangs. Any EST/cDNA sequences that are not assembled into TAs are included as singletons. All singletons retain their GenBank accession numbers as identifiers. Plant TA identifiers are of the form TAnumber_taxonID, where number is a unique numerical identifier of the transcript assembly and taxonID represents the NCBI taxon id. In order to provide annotation for the TAs, each TA/singleton was aligned to the UniProt Uniref database. For release 1 TAs, a masked version of the Uniref90 database was used. For release 2 and onwards, a masked version of the UniRef100 database is used. Alignments were required to have at least 20% identity and 20% coverage. The annotation for the protein with the best alignment to each TA or singleton was used as the annotation for that sequence. Additionally, the relative orientation of each TA/singleton to the best matching protein sequence was used to determine the orientation of each TA/singleton. Some sequences did not have alignments to the protein database that met our quality criteria, and those sequences have neither annotation nor orientation assignments. The release number for the plant TAs refers to the release version for a particular species. For the initial build, all TA sets are of version 1. Subsequent TA updates for new releases will be carried out when the percentage increase of the EST and cDNA counts exceeds 10% of the previous release and when the increase contains more than 1,000 new sequences. New releases will also include additional plant species with more than 1,000 EST or cDNA sequences that have become publicly available.

Proper citation: TIGR Plant Transcript Assembly database (RRID:SCR_005470) Copy   

•   Source: SciCrunch Registry