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A large collection of tools for basic and advanced analyses of nucleotide and protein sequences. The tools are wrapped into a common user interface making handling, storage, retrieval and viewing the results easy and logical. A seqtools project can accommodate many thousand sequences making unattended batch analyses like database searching at NCBI painless with the robust search engine included in seqtools.
Proper citation: SEQtools (RRID:SCR_008579) Copy
https://bioinf.eva.mpg.de/patman/
Software that searches for short patterns in large DNA databases, allowing for approximate matches., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: PatMaN (RRID:SCR_011821) Copy
Software for searching DNA sequence databases for RNA structure and sequence similarities.
Proper citation: Infernal (RRID:SCR_011809) Copy
GIRI is a private, non-profit research institution founded in 1994. Our mission is to understand biological processes which alter the genetic makeup of different organisms, as a basis for potential gene therapy and genome engineering techniques. We pursue and promote original peer-reviewed, public domain research on genetic information (DNA sequence data), as well as dissemination of databases and computer software related to this research. Our research is heavily based on computer power and human skills different from those applied in classical molecular biology and genetics.
Proper citation: Genetic Information Research Institute (RRID:SCR_012762) Copy
http://www.nbrc.nite.go.jp/e/index.html
Collection of microbial resources and perform taxonomic characterization of individual microorganisms such as bacteria including actinomycetes and archaea, yeasts, fungi, algaes, bacteriophages and DNA resources for academic research and industrial applications. NBRC is a member of WFCC, OECD Global BRC Network, ACM and JSCC. They are certified by quality management system ISO 9001. To provide attractive biological resources with useful information attached, they actively collect potentially useful biological resources (microorganisms and cloned genes) and distributes them to promote basic research as well as industrial applications. At the Biological Resource Center, they explore, isolate and identify microorganisms from various natural environments and at the same time accept scientifically and industrially useful microorganisms from researchers in academic and industrial sectors. The microbial DNA library constructed at the Biotechnology Development Center is also part of their collection. To improve and expand the collection, new methodologies for the isolation, identification and preservation of microorganisms and DNA will be investigated and developed so as to provide biological resources of higher quality. Their resources serve, for example, as the standard for determining antimicrobial activity, in aseptic tests as well as for the production of pharmaceutical substances and will be constantly reinforced for wider distribution to researchers in academia and industries. Please refer to the catalog shown at the NBRC website for details.
Proper citation: NBRC (RRID:SCR_002660) Copy
Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
Proper citation: The Cancer Genome Atlas (RRID:SCR_003193) Copy
Commercial antibody vendor which supplies antibodies and other products to life science researchers.
Proper citation: Novus Biologicals (RRID:SCR_004286) Copy
A multi-site, multi-disciplinary undertaking with the overall goals of characterizing the familial transmission of alcoholism and related phenotypes and identifying susceptibility genes using genetic linkage. The study is being coordinated by the SUNY Health Science Center at Brooklyn (HSCB) under the leadership of Henri Begleiter. The study was initially funded by the National Institute of Alcohol Abuse and Alcoholism (NIAAA) in 1989. Additional useful information at http://www.niaaa.nih.gov/ResearchInformation/ExtramuralResearch/SharedResources/projcoga.htm
Proper citation: Collaborative Study on the Genetics of Alcoholism (RRID:SCR_013395) Copy
Collection of human embryonic and fetal material (Tissue and RNA) ranging from 3 to 20 weeks of development available to the international scientific community. Material can either be sent to registered users or our In House Gene Expression Service (IHGES) can carry out projects on user''''s behalf, providing high quality images and interpretation of gene expression patterns. Gene expression data emerging from HDBR material is added to our gene expression database which is accessible via our HUDSEN (Human Developmental Studies Network) website. A significant proportion of the material has been cytogenetically karyotyped, and normal karyotyped material is provided for research.
Proper citation: Human Developmental Biology Resource (RRID:SCR_006326) Copy
An observational longitudinal clinical study partnership to identify and validate biomarkers of Parkinson disease (PD) progression and provide easy and open web-based access to the comprehensive set of correlated clinical data and biospecimens, information, and biosamples acquired from PD and age and gender matched healthy control subjects to the research community. The data and specimens have been collected in a standardized manner under strict protocols and includes clinical (demographic, motor and non-motor, cognitive and neurobehavioral), imaging (raw and processed MRI, SPECT and DAT), and blood chemistry and hematology subject assessments and biospecimen inventories (serum, plasma, whole blood, CSF, DNA, RNA and urine). All data are de-identified to protect patient privacy. PPMI will be carried out over five years at 21 clinical sites in the United States and Europe and requires the participation of 400 Parkinson's patients and 200 control participants. The PPMI database provides researchers with access to correlated clinical and imaging data, along with annotated biospecimens, all available within an open access system that encourages data sharing (http://www.ppmi-info.org/access-data-specimens/). The website hosts an Ongoing Analysis section to keep the scientific community apprised of analyses being completed, in hopes of stimulating collaborations between researchers who are using PPMI data and specimens.
Proper citation: Parkinson's Progression Markers Initiative (RRID:SCR_006431) Copy
http://www.framinghamheartstudy.org/
A longitudinal, epidemiologic study to identify the common risk factors or characteristics that contribute to cardiovascular disease by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms or suffered a heart attack or stroke. Since that time the FHS has studied three generations of participants resulting in biological specimens and data from nearly 15,000 participants. Since 1994, two groups from minority populations, including related individuals have been added to the FHS. FHS welcomes proposals from outside investigators for data and biospecimens. The researchers recruited 5,209 men and women between the ages of 30 and 62 from the town of Framingham, Massachusetts, and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Since 1948, the subjects have continued to return to the study every two years for a detailed medical history, physical examination, and laboratory tests, and in 1971, the Study enrolled a second generation - 5,124 of the original participants'''' adult children and their spouses - to participate in similar examinations. In 1994, the need to establish a new study reflecting a more diverse community of Framingham was recognized, and the first Omni cohort of the Framingham Heart Study was enrolled. In April 2002 the Study entered a new phase, the enrollment of a third generation of participants, the grandchildren of the Original Cohort. In 2003, a second group of Omni participants was enrolled. Over the years, careful monitoring of the Framingham Study population has led to the identification of major CVD risk factors, as well as valuable information on the effects of these factors such as blood pressure, blood triglyceride and cholesterol levels, age, gender, and psychosocial issues. Risk factors for other physiological conditions such as dementia have been and continue to be investigated. In addition, the relationships between physical traits and genetic patterns are being studied. FHS clinical and research data is stored in the dbGaP and NHLBI Repository repositories and may be accessed by application. Please check the following repositories before applying for data through FHS. Investigators seeking data that is not available through dbGaP or BioLINCC or seeking biological specimens may submit a proposal through the FHS web-based research application. The FHS data repository may be accessed through this FHS website, under the For Researchers link, then Description of Data, in order to determine if and how the desired data is stored. Proposals may involve the use of existing data, the collection of new data, either directly from participants or from previously collected samples, images, or other materials (e.g., medical records). The FHS Repository also has biological specimens available for genetic and non-genetic research proposals. Specimens include urine, blood and blood products, as well as DNA.
Proper citation: Framingham Heart Study (RRID:SCR_008963) Copy
Software to simplify the processing of large rRNA sequence libraries (including single-strand and paired-end reads) obtained through high-throughput sequencing technology. Tools for assembly, quality filtering, taxonomy based analysis and taxonomy independent analysis tools, and tools to convert the data to formats suitable for common ecological and statistical packages are available. For extremely large datasets, command line tools are available.
Proper citation: RDPipeline (RRID:SCR_001192) Copy
http://www.cs.cmu.edu/~jernst/stem/
The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.) Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Proper citation: Short Time-series Expression Miner (STEM) (RRID:SCR_005016) Copy
http://www.bumc.bu.edu/cardiovascularproteomics/cpctools/strap/
Software program that automatically annotates a protein list with information that helps in the meaningful interpretation of data from mass spectrometry and other techniques. It takes protein lists as input, in the form of plain text files, protXML files (usually from the TPP), or Dat files from MASCOT search results. From this, it generates protein annotation tables, and a variety of GO charts to aid individual and differential analysis of proteomics data. It downloads information from mainly the Uniprot and EBI QuickGO databases. STRAP requires Windows XP or higher with at least version 3.5 of the Microsoft .NET Framework installed. Platform: Windows compatible
Proper citation: STRAP (RRID:SCR_005675) Copy
A standalone Java application with a GUI (graphical user interface) for editing genome annotations. Like GBrowse, it allows users to scroll and zoom in on areas of interest in a sequence; authorized users can edit annotations and write the changes back to the underlying database. Apollo can run off GFF3 or a Chado database, and it can also integrate with remote services, such as BLAST and Primer BLAST analyses.
Proper citation: Apollo (RRID:SCR_001936) Copy
http://genapha.icapture.ubc.ca/PathTutorial/
Web application to investigate gene-gene interactions in genetic association studies designed to: 1. Interface your SNP data with biological information from several online bioinformatics databases. 2. Generate biologically plausible hypotheses for testing gene-gene interactions. 3. Select a subset of SNPs and conduct SNP-SNP interaction tests. 4. Store analysis results. 5. Explore analysis results through interactive plots and summary tables. (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: PATH (RRID:SCR_009042) Copy
http://david.abcc.ncifcrf.gov/content.jsp?file=/ease/ease1.htm&type=1
Windows(c) desktop software application, customizable and standalone, that facilitates the biological interpretation of gene lists derived from the results of microarray, proteomic, and SAGE experiments. Provides statistical methods for discovering enriched biological themes within gene lists, generates gene annotation tables, and enables automated linking to online analysis tools. Offers statistical models to deal with multi-test comparison problem. Platform: Windows compatible
Proper citation: EASE: the Expression Analysis Systematic Explorer (RRID:SCR_013361) Copy
https://compumedicsneuroscan.com/products/by-name/curry/
Processing software for multimodal neuroimaging centered on combining functional data such as EEG and MEG with imaging data from MRI and CT to optimize source reconstruction. They are now combining Curry's strength with the acquisition and signal processing features of the SCAN software for a comprehensive EEG acquisition, data analysis, source localization and source imaging package.
Proper citation: CURRY (RRID:SCR_009546) Copy
V-Cell is a remote user modeling and simulation environment utilizing Java''s Remote Method Invocation (RMI). The biologically oriented user interface allows experimentalists to create models, define cellular geometry, specify simulations and analyze the simulation results. The results are run and stored on a remote server and can be reviewed in the software and/or exported in a variety of popular formats. The design of the biological to mathematical mapping allows for separate use of biological and math components, and includes automatic mathematical simplification using pseudo-steady approximations and mass conservation relationships. This allows for direct specification of mathematical problems, performing simulations and analysis on those systems. The stand alone mathematics user interface is also a powerful tool for modeling reaction-diffusion systems. A transparent general purpose solver is used to translate the initial biological description into a set of concise mathematical problems. The solver is transparent to the average user, but is accessible to the theorist as the Math Editor component. The software is composed of three main components: 1. The modeling framework represents the physiological models of the Virtual Cell and allows for persistence and database support. 2. The mathematics framework transparently solves an important class of mathematical problems encountered in the cellular modeling. 3. The WWW accessible graphical user interface provides access to the technology mentioned above. The user interface has been developed using Java 2 Applets.
Proper citation: Virtual Cell at the National Resource for Cell Analysis and Modeling (RRID:SCR_007421) Copy
http://fmri.wfubmc.edu/software/PickAtlas
A software toolbox that provides a method for generating Region of Interest (ROI) masks based on the Talairach Daemon database. The atlases include Brodmann area, Lobar, Hemisphere, Anatomic Label (gyral anatomy), and Tissue type. The atlases have been extended to the vertex in MNI space, and corrected for the precentral gyrus anomaly. Additional atlases (including non-human atlases) can be added without difficulty., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: WFU PickAtlas (RRID:SCR_007378) Copy
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