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http://www.ncbi.nlm.nih.gov/sra

Repository of raw sequencing data from next generation of sequencing platforms including including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, Complete Genomics, and Pacific Biosciences SMRT. In addition to raw sequence data, SRA now stores alignment information in form of read placements on reference sequence. Data submissions are welcome. Archive of high throughput sequencing data,part of international partnership of archives (INSDC) at NCBI, European Bioinformatics Institute and DNA Database of Japan. Data submitted to any of this three organizations are shared among them.

Proper citation: NCBI Sequence Read Archive (SRA) (RRID:SCR_004891) Copy   

•   Source: SciCrunch Registry

http://www.anatomyatlases.org/

An anatomy digital health sciences library to educate patients, healthcare providers, and students in a free and anonymous manner while using current, authoritative, trustworthy health information. Anatomy Atlases addresses the continuum of anatomy education and may be of use primarily to three distinct populations. It is written for and intended primarily for use by Medical Students, Residents, Fellows, or Attending Physicians studying anatomy. Other Health Care Providers studying anatomy should find it useful. Finally, Patients (including patient''s family members or friends) may find it helpful. Anatomy Textbooks and Anatomy Atlases: * Atlas of Human Anatomy * Atlas of Human Anatomy in Cross Section * Illustrated Encyclopedia of Human Anatomic Variation * Atlas of Microscopic Anatomy - A Functional Approach: Companion to Histology and Neuroanatomy: Second Edition * Anatomy of First Aid - A Case Study Approach * Lessons From a Bone Box Lessons From a Bone Box

Proper citation: Anatomy Atlases (RRID:SCR_004888) Copy   

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http://www.uiowa.edu/

Public research university in Iowa City, Iowa. Founded in 1847, it is the oldest and the second-largest university in the state.

Proper citation: University of Iowa; Iowa; USA (RRID:SCR_005011) Copy   

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http://www.mycobank.org/

Database documenting mycological nomenclatural novelties (new names and combinations) and associated data, for example descriptions and illustrations. The nomenclatural novelties will each be allocated a unique MycoBank number that can be cited in the publication where the nomenclatural novelty is introduced. These numbers will also be used by the nomenclatural database Index Fungorum, with which MycoBank is associated and will also serve as Life Science Identifiers (LSIDs). Nomenclatural experts will be available to check the validity, legitimacy and linguistic correctness of the proposed names in order to avoid nomenclatural errors; however, no censorship whatsoever, (nomenclatural or taxonomic) will be exerted by MycoBank. Deposited names will remain -when desired- strictly confidential until after publication, and will then be accessible through MycoBank, Index Fungorum, GBIF and other international biodiversity initiatives, where they will further be linked to other databases to realize a species bank that eventually will link all databases of life. MycoBank will (when applicable) provide onward links to other databases containing, for example, living cultures, DNA data, reference specimens and pleomorphic names linked to the same holomorph. Authors intending to publish nomenclatural novelties are encouraged to contribute to this new initiative. For the moment 2 search engines are available from the MycoBank website. The first one permits to search for fungal names (at any rank level), the authority or the MycoBank unique number. The second is dedicated to bibliographic queries related to fungal name''''s publications. MycoBank users willing to deposit their data will have to register so that they willbe able to contact the depositor for specific information (e.g. MycoBank number, possible points of attention regarding the name, actual publication, etc), and to avoid fake entries.

Proper citation: MycoBank (RRID:SCR_004950) Copy   

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http://brainliner.jp

Portal and tools for sharing and editing neurophysiological and behavioral data for brain-machine interface research. Users can search for existing data or login with their Google, Facebook, or Twitter account and upload new data. Their main focus is on supporting brain-machine interface research, so we encourage users to not just provide recordings of brain activity data, but also information about stimuli, etc., so that statistical relationships can be found between stimuli and/or subject behavior and brain activity. The Matlab tools are for writing, reading, and converting Neuroshare files, the common file format. A free, open source desktop tool for editing neurophysiological data for brain-machine interface research is also available: https://github.com/ATR-DNI/BrainLiner Since data formats aren''''t standardized between programs and researchers, data and analysis programs for data cannot be easily shared. Neuroshare was selected as the common file format. Neuroshare can contain several types of neurophysiological data because of its high flexibility, including analog time-series data and neuronal spike timing. Some applications have plug-ins or libraries available that can read Neuroshare format files, thus making Neuroshare somewhat readily usable. Neuroshare can contain several types of neurophysiological data, but there were no easy tools to convert data into the Neuroshare format, so they made and are providing a Neuroshare Converter Library and Simple Converter using the library. In future work they will make and provide many more useful tools for data sharing. Shared experiments include: EMG signal, Takemiya Exp, Reconstruct (Visual image reconstruction from human brain activity using a combination of multi-scale local image decoders), SPIKE data, Speech Imagery Dataset (Single-trial classification of vowel speech imagery using common spatial patterns), Functional Multineuron Calcium Imaging (fMCI), Rock-paper-scissors (The data was obtained from subject while he make finger-form of rock/paper/scissors). They also have a page at https://www.facebook.com/brainliner where you can contact us

Proper citation: BrainLiner (RRID:SCR_004951) Copy   

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http://pythia.sourceforge.net/

Pythia is an open source thermodynamically oriented primer design python module. Pythia can be used in two ways. 1. Executable binaries only: under windows with cygwin and python 2.5 (built with mingw, that comes with the cygwin release). These executables allow the user to index DNA files for primer specificity search, design one primer pair per region, and tile regions with PCR amplicons. 2. A python module: under windows with cygwin, python2.5, numpy, swig, and mingw, or under linux with python2.4 or later, numpy, and swig (everything but numpy should be pre-installed on a normal linux system). The module gets you everything that the binaries get you, in a more pythonic framework. This package also includes modules for computing DNA binding and folding energies using the partition function approach with publicly available thermodynamic data. Usage documentation is in the downloads.

Proper citation: Pythia (RRID:SCR_004952) Copy   

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http://bioinformatics.rutgers.edu/Software/SLiQ/

Software for simple linear inequalities based Mate-Pair reads filtering and scaffolding. A set of simple linear inequalities (SLIQ) derived from the geometry of contigs on the line that can be used to predict the relative positions and orientations of contigs from individual mate pair reads and thus produce a contig digraph. The SLIQ inequalities can also filter out unreliable mate pairs and can be used as a pre-processing step for any scaffolding algorithm. This tool filters mate pairs and then produces a Directed Contig Graph (contig diGraph). Also provided is a Naive scaffolder that can then produce scaffolds out of the contig diGraph.

Proper citation: SLIQ (RRID:SCR_005003) Copy   

•   Source: SciCrunch Registry

http://pdbml.pdb.org/

Markup Language that provides a representation of PDB data in XML format. The description of this format is provided in XML schema of the PDB Exchange Data Dictionary. This schema is produced by direct translation of the mmCIF format PDB Exchange Data Dictionary Other data dictionaries used by the PDB have been electronically translated into XML/XSD schemas and these are also presented in the list below. * PDBML data files are provided in three forms: ** fully marked-up files, ** files without atom records ** files with a more space efficient encoding of atom records * Data files in PDBML format can be downloaded from the RCSB PDB website or by ftp. * Software tools for manipulating PDB data in XML format are available.

Proper citation: Protein Data Bank Markup Language (RRID:SCR_005085) Copy   

•   Source: SciCrunch Registry

http://cortexassembler.sourceforge.net/index_cortex_var.html

A tool for genome assembly and variation analysis from sequence data. You can use it to discover and genotype variants on single or multiple haploid or diploid samples. If you have multiple samples, you can use Cortex to look specifically for variants that distinguish one set of samples (eg phenotype=X, cases, parents, tumour) from another set of samples (eg phenotype=Y, controls, child, normal). cortex_var features * Variant discovery by de novo assembly - no reference genome required * Supports multicoloured de Bruijn graphs - have multiple samples loaded into the same graph in different colours, and find variants that distinguish them. * Capable of calling SNPs, indels, inversions, complex variants, small haplotypes * Extremely accurate variant calling - see our paper for base-pair-resolution validation of entire alleles (rather than just breakpoints) of SNPs, indels and complex variants by comparison with fully sequenced (and finished) fosmids - a level of validation beyond that demanded of any other variant caller we are aware of - currently cortex_var is the most accurate variant caller for indels and complex variants. * Capable of aligning a reference genome to a graph and using that to call variants * Support for comparing cases/controls or phenotyped strains * Typical memory use: 1 high coverage human in under 80Gb of RAM, 1000 yeasts in under 64Gb RAM, 10 humans in under 256 Gb RAM

Proper citation: cortex var (RRID:SCR_005081) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/thornton-srv/databases/archschema/

ArchSchema is a java webstart application that generates dynamic plots of related Pfam domain architectures. The protein sequences having each architecture can be displayed on the plot and separately listed. Where there is 3D structural information in the PDB, the relevant PDB codes can be shown on the plot. Sequences can be be filtered by organism, or the output can be limited to just those protein sequences for which there is structural information in the PDB. Search by UniProt sequence id, or by Pfam domain id. Red underlines indicate the extent to which 3D structures of the domains and architectures are available in the PDB. Left-clicking on a node shows a panel containing information about the constituent domains, the protein sequences having the given architecture, and any sequences that have whole or partial structures in the PDB. You can display protein sequence (or, alternatively, the protein structures) associated with each architecture. You can download ArchSchema to run locally from your own machine. Note, however, you only download the code and not the data. Thus you will need to be connected to the Internet whenever you perform a search from within ArchSchema. The search initiates a call to the EBI which returns the data to ArchSchema for graphing.

Proper citation: ArchSchema (RRID:SCR_004947) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/ega/

Web service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects. The repository allows you to explore datasets from numerous genotype experiments, supplied by a range of data providers. The EGA''s role is to provide secure access to the data that otherwise could not be distributed to the research community. The EGA contains exclusive data collected from individuals whose consent agreements authorize data release only for specific research use or to bona fide researchers. Strict protocols govern how information is managed, stored and distributed by the EGA project. As an example, only members of the EGA team are allowed to process data in a secure computing facility. Once processed, all data are encrypted for dissemination and the encryption keys are delivered offline. The EGA also supports data access only for the consortium members prior to publication.

Proper citation: European Genome phenome Archive (RRID:SCR_004944) Copy   

•   Source: SciCrunch Registry

http://www.appliedbiosystems.com/absite/us/en/home.html

An Antibody supplier

Proper citation: Applied Biosystems (RRID:SCR_005039) Copy   

•   Source: SciCrunch Registry

http://www.physics.rutgers.edu/~anirvans/SOPRA/

Software tool to exploit the mate pair/paired-end information for assembly of short reads from high throughput sequencing platforms, e.g. Illumina and SOLiD.

Proper citation: SOPRA (RRID:SCR_005035) Copy   

•   Source: SciCrunch Registry

https://www.bioworlde.com/

An Antibody supplier

Proper citation: Bioworld Technology (RRID:SCR_005036) Copy   

•   Source: SciCrunch Registry

http://openneuro.org

Open platform for analyzing and sharing neuroimaging data from human brain imaging research studies. Brain Imaging Data Structure ( BIDS) compliant database. Formerly known as OpenfMRI. Data archives to hold magnetic resonance imaging data. Platform for sharing MRI, MEG, EEG, iEEG, and ECoG data.

Proper citation: OpenNeuro (RRID:SCR_005031) Copy   

•   Source: SciCrunch Registry

http://dermatlas.med.jhmi.edu/derm/

Database of dermatology cases and browsable by diagnosis, category or body site with 12,176 images, 583 contributors and dermatology links. You may retrieve images using any diagnosis, disease category, body site, pigmentation, image contributor, patient age, image name, and/or key words. You are welcome submit images or to download images for lectures and other teaching purposes - or with permission for other uses. Additionally, you may search DermAtlas from your website. Add YOUR Link On the DermAtlas'''' Add a Link Page you can associate your link with as many diagnoses as you like. Case submission If you have a high quality image that you would like to submit to DermAtlas, submit the requested information, and upload the image. The data and image will automatically be sent to the editors for review. You will be notified within one week of submission of images. In order for an image to be considered for inclusion into this collection, consent must be obtained from the patient or his/her legal guardian. Contributors are solely responsible for obtaining consent.

Proper citation: DermAtlas. (RRID:SCR_004977) Copy   

•   Source: SciCrunch Registry

http://www.tnp.pitt.edu/pages/donationfrm_mb.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 19,2024. Brain tissue donation is a valuable contribution to mental health research. It enables scientists to investigate how the normal brain works, and how the brain is disturbed when it is affected by schizophrenia, depression, bipolar (manic depressive) disease or other related disorders. The Department of Psychiatry at the University of Pittsburgh has established a brain tissue bank to which brain tissue can be donated at no expense. The gift of brain tissue enables scientists to conduct research designed to understand causes, to develop new treatments, and ultimately to find cures for diseases that affect the brain. Brain tissue donation is a gift that makes it possible for researchers to study various types of mental disorders. Donations of brain tissue from individuals without these disorders are also needed to establish comparisons with brain samples from individuals who have these disorders. Any legally competent adult or guardian may indicate during life their interest in donating brain tissue after death. Next-of-kin either of healthy individuals or of those with psychiatric disorders may give consent to donate brain tissue following the death of a loved one. Brain tissue is removed during autopsy at a morgue or hospital and is transported to the University of Pittsburgh Medical Center for examination and study.

Proper citation: University of Pittsburgh Brain Tissue Donation Program (RRID:SCR_005028) Copy   

•   Source: SciCrunch Registry

http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/sspacev12/

A stand-alone software program for scaffolding pre-assembled contigs using paired-read data. Main features are: a short runtime, multiple library input of paired-end and/or mate pair datasets and possible contig extension with unmapped sequence reads.

Proper citation: SSPACE (RRID:SCR_005056) Copy   

•   Source: SciCrunch Registry

http://meringlab.org/software/hpc-clust/

A set of tools designed to cluster large numbers (>1 million) of pre-aligned nucleotide sequences. It performs the clustering of sequences using the Hierarchical Clustering Algorithm (HCA). There are currently three different cluster metrics implemented: single-linkage, complete-linkage, and average-linkage. In addition, there are currently four sequence distance functions implemented, these are: identity (gap-gap counting as match), nogap (gap-gap being ignored), nogap-single (like nogap, but consecutive gap-nogap''s count as a single mismatch), tamura (distance is calculated with the knowledge that transitions are more likely than transversions). One advantage that HCA has over other algorithms is that instead of producing only the clustering at a given threshold, it produces the set of merges occuring at each threshold. With this approach, the clusters can afterwards very quickly be reported for every arbitrary threshold with little extra computation. This approach also allows the plotting of the variation of number of clusters with clustering threshold without requiring the clustering to be run for each threshold independently. Another feature of the way HPC-CLUST is implemented is that the single-, complete-, and average-linkage clusterings can be computed in a single run with little overhead.

Proper citation: HPC-CLUST (RRID:SCR_005052) Copy   

•   Source: SciCrunch Registry

http://www.openphacts.org/

Project that developed an open access discovery platform, called Open Pharmacological Space (OPS), via a semantic web approach, integrating pharmacological data from a variety of information resources and tools and services to question this integrated data to support pharmacological research. The project is based upon the assimilation of data already stored as triples, in the form subject-predicate-object. The software and data are available for download and local installation, under an open source and open access model. Tools and services are provided to query and visualize this data, and a sustainability plan will be in place, continuing the operation of the Open PHACTS Discovery Platform after the project funding ends. Throughout the project, a series of recommendations will be developed in conjunction with the community, building on open standards, to ensure wide applicability of the approaches used for integration of data.

Proper citation: Open PHACTS (RRID:SCR_005050) Copy   

•   Source: SciCrunch Registry