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https://sanger-pathogens.github.io/Roary/

Software tool for rapid large scale prokaryote pan genome analysis. Builds large scale pan genomes, identifying core and accessory genes. Makes construction of pan genome of thousands of prokaryote samples on standard desktop without compromising on accuracy of results. Not intended for meta genomics or for comparing extremely diverse sets of genomes.

Proper citation: Roary (RRID:SCR_018172) Copy   

•   Source: SciCrunch Registry

https://github.com/linnarsson-lab/cytograph

Software multistage analysis pipeline which progressively discovers cell types or states while mitigating impact of technical artifacts.Used for single cell analysis.

Proper citation: Cytograph (RRID:SCR_023101) Copy   

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http://www.biomart.org

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 4,2023.Platform provides free software and data services to international scientific community in order to foster scientific collaboration and facilitate scientific discovery process. Project adheres to open source philosophy that promotes collaboration and code reuse.

Proper citation: BioMart Project (RRID:SCR_002987) Copy   

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http://flybrain.stanford.edu/

Project content including raw image data, neuronal tracings, image registration tools and analysis scripts covering three manuscripts: Comprehensive Maps of DrosophilaHigher Olfactory Centres : Spatially Segregated Fruit and Pheromone Representation which uses single cell labeling and image registration to describe the organization of the higher olfactory centers of Drosophila; Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe which uses single cell labeling to describe the organization of the antennal lobe local interneurons; and Sexual Dimorphism in the Fly Brain which uses clonal analysis and image registration to identify a large number of sex differences in the brain and VNC of Drosophila. Data * Raw Data of Reference Brain (pic, amira) (both seed and average) * Label field of LH and MB calyx and surfaces for these structures * Label field of neuropil of Reference Brain * Traces (before and after registration). Neurolucida, SWC and AmiraMesh lineset. * MB and LH Density Data for different classes of neuron. In R format and as separate amira files. * Registration files for all brains used in the study * MBLH confocal images for all brains actually used in the study (Biorad pic format) * Sample confocal images for antennal lobe of every PN class * Confocal stacks of GABA stained ventral PNs Programs * ImageJ plugins (Biorad reader /writer/Amira reader/writer/IGS raw Reader) * Binary of registration, warp and gregxform (macosx only, others on request) * Simple GUI for registration tools (macosx only at present) * R analysis/visualization functions * Amira Script to show examples of neuronal classes The website is a collaboration between the labs of Greg Jefferis and Liqun Luo and has been built by Chris Potter and Greg Jefferis. The core Image Registration tools were created by Torsten Rohlfing and Calvin Maurer.

Proper citation: Flybrain at Stanford (RRID:SCR_001877) Copy   

•   Source: SciCrunch Registry

http://www.ensembl.org/

Collection of genome databases for vertebrates and other eukaryotic species with DNA and protein sequence search capabilities. Used to automatically annotate genome, integrate this annotation with other available biological data and make data publicly available via web. Ensembl tools include BLAST, BLAT, BioMart and the Variant Effect Predictor (VEP) for all supported species.

Proper citation: Ensembl (RRID:SCR_002344) Copy   

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http://www.openmicroscopy.org/site/support/ome-model/ome-tiff/

A standardized file format for multidimensional microscopy image data. OME-TIFF maximizes the respective strengths of OME-XML and TIFF. It takes advantage of the rich metadata defined in OME-XML while retaining the pixel structure in multi-page TIF format for compatibility with many image-processing applications. An OME-TIFF dataset has the following characteristics: * Image planes are stored within one multi-page TIFF file, or across multiple TIFF files. Any image organization is feasible. * A complete OME-XML metadata block describing the dataset is embedded in each TIFF file's header. Thus, even if some of the TIFF files in a dataset are misplaced, the metadata remains intact. * The OME-XML metadata block may contain anything allowed in a standard OME-XML file. * OME-TIFF uses the standard TIFF mechanism for storing one or more image planes in each of the constituent files, instead of encoding pixels as base64 chunks within the XML. Since TIFF is an image format, it makes sense to only use OME-TIFF as opposed to OME-XML, when there is at least one image plane.

Proper citation: OME-TIFF Format (RRID:SCR_002636) Copy   

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http://hapmap.ncbi.nlm.nih.gov/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A multi-country collaboration among scientists and funding agencies to develop a public resource where genetic similarities and differences in human beings are identified and catalogued. Using this information, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. All of the information generated by the Project will be released into the public domain. Their goal is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. HapMap project related data, software, and documentation include: bulk data on genotypes, frequencies, LD data, phasing data, allocated SNPs, recombination rates and hotspots, SNP assays, Perlegen amplicons, raw data, inferred genotypes, and mitochondrial and chrY haplogroups; Generic Genome Browser software; protocols and information on assay design, genotyping and other protocols used in the project; and documentation of samples/individuals and the XML format used in the project.

Proper citation: International HapMap Project (RRID:SCR_002846) Copy   

•   Source: SciCrunch Registry

http://www.genes2cognition.org/db/Search

Database of protein complexes, protocols, mouse lines, and other research products generated from the Genes to Cognition project, a project focused on understanding molecular complexes involved in synaptic transmission in the brain.

Proper citation: Genes to Cognition Database (RRID:SCR_002735) Copy   

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http://www.ebi.ac.uk/goldman-srv/pandit

PANDIT is a collection of multiple sequence alignments and phylogenetic trees covering many common protein domains. It contains: * the seed protein sequence alignments from the Pfam-A (curated families) database (version 17.0) * nucleotide sequence alignments derived from sequences available for the above and using the protein alignments as "templates"; * protein sequence alignments restricted to the family members for which nucleotide sequences are available * inferred phylogenetic trees for each alignment The data in PANDIT and the dataset's development have been frozen owing to a lack of funding support. The existing data, version 17.0 corresponding to Pfam 17.0, remain stable and, we hope, useful. The entire database is also available for download as a flatfile from this website.

Proper citation: PANDIT : Protein and Associated Nucleotide Domains with Inferred Trees (RRID:SCR_003321) Copy   

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http://old.genedb.org/genedb/glossina/

As of 12th March 2009, GeneDB provides access to the transcriptome of the Tsetse fly Glossina morsitans morsitans, the biological vector of African trypanosomiases. The current data set includes: >>7,015 contigs comprised of ESTs from Trypanosoma brucei infected midgut tissue (Lehane et al, Genome Biol. 2003;4(10):R63) >>7,493 contigs comprised of ESTs from salivary gland tissue >>18,404 contigs comprised of EST pooled from a range of different tissue- and developmental stage-specific libraries: head (2,700 ESTs), midgut (21,662 ESTs), reproductive organs (3, 438 ESTs), salivary gland (27,426 ESTs), larvae (2,304 ESTs), pupae (2,304 ESTs), fatbody (20,257 ESTs) (Attardo et al, Insect Molecular Biology 2006, 15(4):411-424), male and female whole bodies (19,968 ESTs). These data include the midgut and salivary gland ESTs used in the library specific clustering for the contig sets listed above. Initial automated annotations of product descriptions were manually revised by participants in two community annotation jamborees held under the auspice of the International Glossina Genome Initiative (IGGI) with funding by TDR. A Glossina morsitans morsitans genome project is currently also underway. To date, 2.4M capillary shotgun reads have been produced and the initial assembly is available to download via the ftp server and for blast analysis.

Proper citation: GeneDB Gmorsitans (RRID:SCR_004310) Copy   

•   Source: SciCrunch Registry

http://supfam.mbu.iisc.ernet.in/index.html

SUPFAM is a database that consists of clusters of potentially related homologous protein domain families, with and without three-dimensional structural information, forming superfamilies. The present release (Release 3.0) of SUPFAM uses homologous families in Pfam (Version 23.0) and SCOP (Release 1.69) which are examples of sequence -alignment and structure classification databases respectively. The two steps involved in setting up of SUPFAM database are * Relating Pfam and SCOP families using a new profile-profile alignment algorithm AlignHUSH. This results in identifying many Pfam families which could be related to a family or superfamily of known structural information. * An all-against-all match among Pfam families with yet unknown structure resulting in identification of related Pfam families forming new potential superfamilies. The SUPFAM database can be used in either the Browse mode or Search mode. In Browse mode you can browse through the Superfamilies, Pfam families or SCOP families. In each of these modes you will be presented with a full list which can be easily browsed. In Search mode, you can search for Pfam families, SCOP families or Superfamilies based on keywords or SCOP/Pfam identifiers of families and superfamilies., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SUPFAM (RRID:SCR_005304) Copy   

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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://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://merops.sanger.ac.uk/

An information resource for peptidases (also termed proteases, proteinases and proteolytic enzymes) and the proteins that inhibit them. The MEROPS database uses an hierarchical, structure-based classification of the peptidases. In this, each peptidase is assigned to a Family on the basis of statistically significant similarities in amino acid sequence, and families that are thought to be homologous are grouped together in a Clan. There is a Summary page for each family and clan, and these have indexes. Each of the Summary pages offers links to supplementary pages. About 3000 individual peptidases and inhibitors are included in the database, and there is a Summary page describing each one. You can navigate to this by any of several routes. There are indexes of Name, MEROPS Identifier and source Organism on the menu bar. Each Summary page describes the classification and nomenclature of the peptidase or inhibitor, and provides links to supplementary pages showing sequence identifiers, the structure if known, literature references and more.

Proper citation: MEROPS (RRID:SCR_007777) Copy   

•   Source: SciCrunch Registry

http://www.ukbiobank.ac.uk/

Biobank provides data collected at Assessment Center and via online questionnaires on participants aged 40-69 years recruited throughout United Kingdom and provides summary information to improve prevention, diagnosis and treatment of serious and life threatening illnesses.

Proper citation: UK Biobank (RRID:SCR_012815) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

https://www.gencodegenes.org

Human and mouse genome annotation project which aims to identify all gene features in the human genome using computational analysis, manual annotation, and experimental validation.

Proper citation: GENCODE (RRID:SCR_014966) Copy   

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https://github.com/JCVenterInstitute/NSForest/releases

Software tool as method that takes cluster results from single cell nuclei RNAseq experiments and generates lists of minimal markers needed to define each cell type cluster. Utilizes random forest of decision trees machine learning approach. Used to determine minimum set of marker genes whose combined expression identified cells of given type with maximum classification accuracy.

Proper citation: NS-Forest (RRID:SCR_018348) Copy   

•   Source: SciCrunch Registry

http://www.mlst.net/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 17, 2022. A nucleotide sequence based approach for the unambiguous characterisation of isolates of bacteria and other organisms via the internet. The aim of MLST is to provide a portable, accurate, and highly discriminating typing system that can be used for most bacteria and some other organisms. It is envisaged that this approach will be particularly helpful for the typing of bacterial pathogens. To achieve this aim we have taken the proven concepts of multilocus enzyme electrophoresis (MLEE) and have adapted them so that alleles at each locus are defined directly, by nucleotide sequencing, rather than indirectly from the electrophoretic moblity of their gene products. MLST was developed in the laboratories of Martin Maiden, Dominique Caugant, Ian Feavers, Mark Achtman and Brian Spratt. This site is hosted at Imperial College with funding from the Wellcome Trust. The location of the subsites for the individual species are shown on their respective front pages.

Proper citation: MLST (RRID:SCR_010245) Copy   

•   Source: SciCrunch Registry

https://opentrials.net/

Database that contains data such as registry entries, portions of regulatory documents describing individual trials, structured data on methods and results, and researchers and papers from and/or related to clinical trials. The initiative aims to locate, match, and share all publicly accessible data and documents, on all trials conducted, on all medicines and other treatments, globally.

Proper citation: Open Trials (RRID:SCR_015570) Copy   

•   Source: SciCrunch Registry

http://geroprotectors.org

Collection of structured and manually curated data of current therapeutic interventions in aging and age-related disease. Describes compounds and mechanisms using multiple chemical and biological databases.

Proper citation: GEROprotectors (RRID:SCR_016737) Copy   

•   Source: SciCrunch Registry