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http://gusevlab.org/projects/germline/

Software application for discovering long shared segments of Identity by Descent (IBD) between pairs of individuals in a large population. It takes as input genotype or haplotype marker data for individuals (as well as an optional known pedigree) and generates a list of all pairwise segmental sharing., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GERMLINE (RRID:SCR_001720) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/VennDiagram/

Software providing a set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.

Proper citation: VennDiagram (RRID:SCR_002414) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/rft_fdr/

So far there is a lack for Random Field Theory (RFT) -based multiple comparison correction for surfaces generated in Freesurfer software package. This set of Matlab-based functions can be used for that purpose. They are based on Worsley?s SurfStat toolbox. You also need to have installed Freesurfer software package and included the Freesurfer?s matlab subdirectory in the Matlab?s search path. In addition, this tool implements the RFT-FDR hierarchical correction that can be used for optimizing the amount of smoothing in cortical thickness analyses (Neuroimage 52, 158-171).

Proper citation: RFT FDR (RRID:SCR_002533) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/science/tools/olorin

An interactive filtering tool for next generation sequencing data coming from the study of large complex disease pedigrees. It integrates gene flow output from Merlin and next generation sequencing data. Users can interactively filter and prioritize variants based on haplotype sharing across different sets of selected individuals and allele frequency in reference datasets. (entry from Genetic Analysis Software)

Proper citation: OLORIN (RRID:SCR_002015) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/nirx2nirs/

A matlab script which takes near-infrared spectroscopy data recorded by NIRx system(s) and converts it to a .nirs file format for use with the HOMER2 NIRS processing pacakge.

Proper citation: NIRx2nirs: A NIRx to .nirs data converter (RRID:SCR_002492) Copy   

•   Source: SciCrunch Registry

https://sites.pitt.edu/~weeks/docs/SimIBD.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Software application (entry from Genetic Analysis Software)

Proper citation: SIMIBD (RRID:SCR_002094) Copy   

•   Source: SciCrunch Registry

http://gmt.genome.wustl.edu/packages/breakdancer/

A Perl/C++ software package that provides genome-wide detection of structural variants from next generation paired-end sequencing reads. BreakDancerMax predicts five types of structural variants: insertions, deletions, inversions, inter- and intra-chromosomal translocations from next-generation short paired-end sequencing reads using read pairs that are mapped with unexpected separation distances or orientation. (entry from Genetic Analysis Software)

Proper citation: BREAKDANCER (RRID:SCR_001799) Copy   

•   Source: SciCrunch Registry

http://haplopainter.sourceforge.net/

A pedigree drawing program, suitable in processing haplotype outputs from GENEHUNTER, ALLEGRO, MERLIN, and SIMWALK (entry from Genetic Analysis Software)

Proper citation: HAPLOPAINTER (RRID:SCR_001710) Copy   

•   Source: SciCrunch Registry

http://www.rightfield.org.uk/

An open-source tool for adding ontology term selection to Excel spreadsheets. It is used by a "Template Creator" to create semantically aware Excel spreadsheet templates. The Excel templates are then reused by Scientists to collect and annotate their data; without any need to understand, or even be aware of, RightField or the ontologies used. For each annotation field, RightField can specify a range of allowed terms from a chosen ontology (subclasses, individuals or combinations). The resulting spreadsheet presents these terms to the users as a simple drop-down list. This reduces the adoption barrier for using community ontologies as the annotation is made by the scientist that generated the data rather than a third party, and the annotation is collected at the time of data collection. RightField is a standalone Java application which uses Apache-POI for interacting with Microsoft documents. It enables users to import Excel spreadsheets, or generate new ones from scratch. Ontologies can either be imported from their local file systems, or from the BioPortal ontology repository. Individual cells, or whole columns or rows can be marked with the required ranges of ontology terms and an individual spreadsheet can be annotated with terms from multiple ontologies.

Proper citation: RightField (RRID:SCR_002649) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/genome-smasher/

Software repository for tools used to create diploid FASTA files with containing snps, indels, duplications, deletions, and translocations. They can be used to create artificial genomes for next-gen sequencing simulations.

Proper citation: GenomeSmasher (RRID:SCR_002406) Copy   

•   Source: SciCrunch Registry

http://lgsun.grc.nia.nih.gov/cDNA/cDNA.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Project portal housing NIA Mouse EST Project, NIA Mouse cDNA Clone Sets, a NIA Mouse Gene Index, NIA Mouse cDNA Database, and NIA Mouse Microarrays. Characteristics of NIA 15K Mouse cDNA Clone Set * ~15,000 unique cDNA clones were rearrayed among 52,374 ESTs from pre- and periimplantation embryos, E12.5 female gonad/mesonephros, and newborn ovary. * Up to 50% are derived from novel genes. * ~1.5 kb average insert size. * Clones were sequenced from 5' and 3' termini to obtain longer reads and verify sequence. Sequence information is available at this Web Site. Clone names are from H3001A01 to H3159G07. * Handling of NIA 15k cDNA Clone Set(June3, 2000) Characteristics of NIA mouse 7.4K cDNA Clone Set * ~7407 cDNA clones with no redundancy within the set or with NIA Mouse 15K. * ~1.5 kb average insert size for short insert clones and ~2.5-3.0 kb average insert size for long-insert enriched clones.. * Clones were sequenced from 5' and 3' termini to obtain longer reads and verify sequence. Sequence information is available at this Web Site. Clone names are from H4001A01 to H4079G07. * Handling of NIA mouse 7.4k cDNA Clone Set (similar to handling of NIA mouse 15K, to be updated) Individual Clones are available from ATCC and MRC geneservice, UK. To obtain Clone, search the database using either the rearrayed clone name or GenBank accession number at the Key Word Search page. Follow the link to the sequence information page for the rearrayed clone to obtain source clone ATCC number. Clicking the ATCC number will bring up the ATCC ordering page for the source clone. There is essentially no overlap between the two clone sets (7.4K and 15K) said Minoru S.H. Ko, M.D., Ph.D., head of the Developmental Genomics and Aging Section in the NIA's Laboratory of Genetics. In addition, all cDNA clones in the NIA 7.4K set were purified by single colony isolation and sequence-verified, and more than half were prepared by a new procedure that yields long full-length cDNAs (average size 3-4 kb). The NIA Mouse 15k and 7.4k Clone Set Data and Published Microarray Data are available for download. NIA Mouse Microarrays *Microarray Data Download * 60-mer Oligo Array Platform ** (A) NIA 22k Oligo Microarray Gene List (21939 gene features) ( Carter et al 2003 ) ** (B) Agilent Mouse Development Oligo Microarray Gene List ** ( Subset of Microarray (A): 20,280 gene features ) * Data Analysis Tools

Proper citation: NIA Mouse cDNA Project Home Page (RRID:SCR_001472) Copy   

•   Source: SciCrunch Registry

http://www.diabetestrialnet.org/biobank/

Provides investigators with the opportunity to obtain on-demand biological samples from selected individuals that TrialNet has developed through the longitudinal monitoring of individuals at risk for the development of type 1 diabetes within the Natural History study. Exploratory research is encouraged under this initiative. Studies must use TrialNet screened subjects, and cannot interfere with ongoing clinical trials or studies. Investigators can select the clinical characteristics needed for their study as well as the sample type and collection frequency. Although many Living Biobank studies may be implemented through cost-sharing with the TrialNet network, special sample collections and visits outside of the normal visit schedules will incur additional costs which should be covered by the approved applicant. In addition, some studies may require effort from the TrialNet coordinating center, with costs covered by the approved ancillary study. Living biobank studies will be evaluated with careful consideration for their potential impact on the objectives and performance of the TrialNet Natural History study. To protect the interests of TrialNet, each living biobank study must be reviewed and approved by the Ancillary Studies Committee before its initiation. All approved living biobank studies will be reviewed yearly to evaluate their progress, and impact on TrialNet as a whole. TrialNet welcomes the submission of living biobank studies as an adjunct to ongoing protocols.

Proper citation: Living Biobank (RRID:SCR_001510) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/slicer3examples/

Example Slicer3 plugins that can be built against a Slicer3 build or a Slicer3 installation. Note: these are for 3D Slicer version 3. There is now a version 4 of 3D Slicer available. Information about extensions for version 4 can be found at the following links: http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/SlicerApplication/ExtensionsManager http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Developers/Tutorials/BuildTestPackageDistributeExtensions

Proper citation: Slicer3 Example Modules (RRID:SCR_002559) Copy   

•   Source: SciCrunch Registry

http://sig.biostr.washington.edu/projects/wirm/

WIRM is an innovative software toolkit that allows the creation of web applications that facilitate the acquisition, integration, and dissemination of multimedia biomedical data over the web, thereby reducing the cost of knowledge sharing. WIRM reduces the complexity of building custom biomedical web applications and it's visual modeling tools enable domain experts to describe the structure of their knowledge, from which WIRM automatically generates full-featured, customizable content management systems. WIRM is a Perl-based application server that provides a high-level programming environment for developing web information systems. WIRM consists of an object-relational database and a suite of Perl interfaces for visualizing, integrating and analyzing heterogeneous multimedia data. WIRM provides facilities for creating context-sensitive views over a multimedia database, allowing developers to rapidly build dynamic web sites that adapt their content and presentation to multiple classes of end-users. WIRM was developed by Rex Jakobovits as part of his PhD dissertation at the University of Washington Computer Science department, under the guidance of Dr. James F. Brinkley of the Department of Biological Structure. The system was generalized from work funded by NIH SBIR grant R43-MH61277-01 and Human Brain Project grant DC/LM02310.

Proper citation: WIRM - Web Interfacing Repository Manager (RRID:SCR_002039) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/multimpute/

A software toolkit that performs multiple imputation for group level, single sample t-tests. Whole brain group level statistic maps from fMRI rarely cover the entire brain as a result of missing data. Missingness between subjects in fMRI datasets can result from susceptibility artifacts, bounding box (acquisition parameters), and small differences in post-normalized morphology. The toolkit consists of several interactive command line scripts that guide the user to map the spatial distribution of missing data across contrast images, calculate spatial neighborhood averages that help impute values, perform conventional and multiple imputed t-statistics, save the results to brain maps, and create result tables. The toolkit contains an instruction manual (pdf), two Matlab scripts and one R-Statistics script, which depend on functions defined in the popular SPM toolbox and functions defined in the MICE package for (R).

Proper citation: Group Level Imputation of Statistic Maps (RRID:SCR_002397) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/CBBresearch/Schaffer/pedhunter.html

Software package that facilitates creation and verification of pedigrees within large genealogies. The pedigrees are produced as files in LINKAGE format ready for linkage analysis and for drawing with a variety of drawing programs, such as PEDDRAW and cranefoot.

Proper citation: PEDHUNTER (RRID:SCR_002031) Copy   

•   Source: SciCrunch Registry

http://www.sph.umich.edu/csg/abecasis/LAMP/

Software for linkage and association modeling in pedigrees that uses a maximum likelihood model to extract information on genetic linkage and association from samples of unrelated individuals, sib pairs, trios and larger pedigrees (Li et al, 2005; Li et al, 2006). It provides estimates of genetic model parameters and powerful tests of association in settings where population stratification is not a concern.

Proper citation: LAMP (RRID:SCR_001740) Copy   

•   Source: SciCrunch Registry

http://www.utsouthwestern.edu/labs/acute-liver/

Clinical research network for gathering prospective data and bio-samples on acute liver failure in adults since 1998. Clinical histories and laboratory and outcome data are available. Sample types include serum, plasma, urine, DNA, and liver tissue.

Proper citation: Acute Liver Failure Study Group (RRID:SCR_001463) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/software/syzygy/

A targeted sequencing post processing analysis software tool that allows: 1. SNP and indel detection; 2. Allele frequency estimation; 3. Single-marker association test; 4. Group-wise marker test association; 5. Experimental QC summary (%dbSNP, Ts/Tv, Ns/S); 6. Power to detect variant. (entry from Genetic Analysis Software)

Proper citation: SYZYGY (RRID:SCR_002157) Copy   

•   Source: SciCrunch Registry

http://www.ncbcs.org/biositemaps/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 27,2023. Biositemaps represent a mechanism for computational biologists and bio-informaticians to openly broadcast and retrieve meta-data about biomedical data, tools and services (i.e., biomedical resources) over the Internet. All Institutions with an interest in biomedical research can publish a biositemap.rdf file on their Internet site. The technology, developed by the Biositemaps Working Group of the NIH Roadmap National Centers of Biomedical Computing (NCBC), addresses (i) locating, (ii) querying, (iii) composing or combining, and (iv) mining biomedical resources. Each site which intends to contribute to the inventory instantiates a file on its Internet site biositemap.rdf which conforms to a defined RDF schema and uses concepts from the Biomedical Resource Ontology to describe the resources. Each biositemap.rdf file is simply a list of controlled metadata about resources (software tools, databases, material resources) that your organization uses or believes are important to biomedical research. The key enabling technologies are the Information Model (IM) which is the list of metadata fields about each resource (resource_name, description, contact_person, resource_type,...) and the Biomedical Resource Ontology (BRO) which is a controlled terminology for the resource_typeand which is used to improve the sensitivity and specificity of web searches. Biositemaps blend the features of Sitemaps (enabling efficient web-content exploration) and RSS Feeds (a mechanism for wide and effective news dissemination). As a hybrid between Sitemaps and RSS feeds, the Biositemap infrastructure facilitates a decentralized, portable, extensible and computationally tractable generation and consumption of meta-data about existent, revised and new resources for biomedical computation. Web browsers, crawlers and robots can discover, accumulate, process, integrate and deliver Biositemaps content to (human or machine) users in a variety of graphical, tabular, computational formats. Biositemaps content allows such web browsers to pool resource-associated metadata from disparate and diverse sites and present it to the user in an integrated fashion. The Biositemaps protocol provides clues, information and directives for all Biositemap web harvesters that point to the existence and content of such biomedical resources at different sites.

Proper citation: Biositemaps (RRID:SCR_001976) Copy   

•   Source: SciCrunch Registry