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http://www.ebi.ac.uk/Tools/pfa/iprscan/

Software package for functional analysis of sequences by classifying them into families and predicting presence of domains and sites. Scans sequences against InterPro's signatures. Characterizes nucleotide or protein function by matching it with models from several different databases. Used in large scale analysis of whole proteomes, genomes and metagenomes. Available as Web based version and standalone Perl version and SOAP Web Service.

Proper citation: InterProScan (RRID:SCR_005829) Copy   

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http://genemania.org/

Data analysis service to predict the function of your favorite genes and gene sets. Indexing 1,421 association networks containing 266,984,699 interactions mapped to 155,238 genes from 7 organisms. GeneMANIA interaction networks are available for download in plain text format. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets. GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest. GeneMANIA is also a gene recommendation system. GeneMANIA is also accessible via a Cytoscape plugin, designed for power users. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GeneMANIA (RRID:SCR_005709) Copy   

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http://estbioinfo.stat.ub.es/?page_id=2

The Statistics and Bioinformatics research group has as its main objectives the development of methods and tools to deal with problems appearing in the interface between Statistics and Bioinformatics. We started focusing in DNA microarrays but we are also interested in statistical methods for ''omics'' data integration and next generation sequencing (NGS). Our group collaborates with different research groups in the fields of biology and biomedicine, to whom it offers statistical support for problems which are specifically statistic in nature, such as experimental design or microarray data analysis, and also in more general aspects, such as modeling, analysis or data mining. After a first period of collaboration agreements with the Fundaci�� Vall d''Hebr��n Institut de Recerca we contributed to the creation of the Statistics and Bioinformatics Unit (UEB) which provides statistical and bioinformatical support to VHIR researchers.

Proper citation: University of Barcelona Statistics and Bioinformatics Research Group (RRID:SCR_005704) Copy   

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http://geneontology.svn.sourceforge.net/viewvc/geneontology/go-moose/

go-moose is intended as a replacement for the aging go-perl and go-db-perl Perl libraries. It is written using the object oriented Moose libraries. It can be used for performing a number of analyses on GO data, including the remapping of GO annotations to a selected subset of GO terms. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: go-moose (RRID:SCR_005666) Copy   

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http://vortex.cs.wayne.edu/projects.htm#Onto-Translate

In the annotation world, the same piece of information can be stored and viewed differently across different databases. For instance, more than one Affymetrix probe ID can refer to the same GenBank sequence (accession number) and more than one nucleotide sequence from GenBank can be grouped in a single UniGene cluster. The result of Onto-Express depends on whether the input list contains Affymetrix probe IDs, GenBank accession numbers or UniGene cluster IDs. The user has to be aware of relations between the different forms of the data in order to interpret correctly the results. Even if the user is aware of the relationships and knows how to convert them, most existing tools allow conversions of individual genes. Onto-Translate is a tool that allows the user to perform easily such translations. Affymetrix probe IDs, etc., translate GO terms into other identifiers like GenBank accession number, Uniprot IDs. User account required. Platform: Online tool

Proper citation: Onto-Translate (RRID:SCR_005725) Copy   

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http://www.gotaxexplorer.de/

GOTaxExplorer presents a new approach to comparative genomics that integrates functional information and families with the taxonomic classification. It integrates UniProt, Gene Ontology, NCBI Taxonomy, Pfam and SMART in one database. GOTaxExplorer provides four different query types: selection of entity sets, comparison of sets of Pfam families, semantic comparison of sets of GO terms, functional comparison of sets of gene products. This permits to select custom sets of GO terms, families or taxonomic groups. For example, it is possible to compare arbitrarily selected organisms or groups of organisms from the taxonomic tree on the basis of the functionality of their genes. Furthermore, it enables to determine the distribution of specific molecular functions or protein families in the taxonomy. The comparison of sets of GO terms allows to assess the semantic similarity of two different GO terms. The functional comparison of gene products makes it possible to identify functionally equivalent and functionally related gene products from two organisms on the basis of GO annotations and a semantic similarity measure for GO. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GOTaxExplorer (RRID:SCR_005720) Copy   

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http://vortex.cs.wayne.edu/projects.htm#Onto-Miner

Onto-Miner (OM) provides a single and convenient interface that allows the user to interrogate our databases regarding annotations of known genes. OM will return all known information about a given list of genes. Advantages of OM include the fact it allows queries with multiple genes and allows for scripting. This is unlike GenBank which uses a single gene navigation process. Scripted search of the Onto-Tools database for gene annotations. User account required. Platform: Online tool

Proper citation: Onto-Miner (RRID:SCR_005722) Copy   

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http://neuro.imm.dtu.dk/software/brede/

A package for neuroinformatics and neuroimaging analysis mostly programmed in Matlab with a few additional programs in Python and Perl. It allows coordinate-based meta-analysis and visualization, neuroimaging analysis of voxel or regional data - not the original data but rather the summary images (e.g., statistical parametric images) and location data in stereotactic space. Among the algorithms implemented are kernel density estimation (for coordinate-based meta-analysis), independent component analysis, non-negative matrix factorization, k-means clustering, singular value decomposition, partial correlation analysis with permutation testing and partial canonical correlation analysis. Visualization of coordinate, surfaces and volumes are possible in 2D and 3D. Generation of HTML for results are possible and algorithms can be accessed from the command line or via a flexible graphical interface. With the Brede Toolbox comes the Brede Database with a small coordinate database from published neuroimaging studies, and ontologies for, e.g., brain function and brain regions.

Proper citation: Brede Toolbox (RRID:SCR_006204) Copy   

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http://knime.org/

KNIME (Konstanz Information Miner) is a user-friendly and comprehensive Open-Source data integration, processing, analysis, and exploration platform. KNIME (naim) is a user-friendly graphical workbench for the entire analysis process: data access, data transformation, initial investigation, powerful predictive analytics, visualization and reporting. The open integration platform provides over 1000 modules (nodes), including those of the KNIME community and its extensive partner network. KNIME can be downloaded onto the desktop and used free of charge. KNIME products include additional functionalities such as shared repositories, authentication, remote execution, scheduling, SOA integration and a web user interface as well as world-class support. Robust big data extensions are available for distributed frameworks such as Hadoop. KNIME is used by over 3000 organizations in more than 60 countries. The modular data exploration platform, initially developed at the University of Konstanz, Germany, enables the user to visually create data flows, execute selected analysis steps, and later investigate the results through interactive views on data and models. KNIME is a proven integration platform for tools of numerous vendors due to its open and modular API. The KNIME.com product pipeline includes an Enterprise Server, Cluster Execution, Reporting solutions, and professional KNIME support subscriptions. KNIME.com also offer services such as data analysis, hands-on training and the development of customized components for KNIME.

Proper citation: Knime (RRID:SCR_006164) Copy   

•   Source: SciCrunch Registry

https://www.biotech.cornell.edu/core-facilities-brc/facilities/bioinformatics-facility

Facility provides access to high performance computing environment, BioHPC, which includes both hosted hardware and shared machines. Provides consulting and collaborations for Bioinformatics analysis and workflows.

Proper citation: Cornell University BRC Bioinformatics Core Facility (RRID:SCR_021757) Copy   

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http://www.gaworkshop.org/

The Genetic Analysis Workshops (GAWs) are a collaborative effort among genetic epidemiologists to evaluate and compare statistical genetic methods. For each GAW, topics are chosen that are relevant to current analytical problems in genetic epidemiology, and sets of real or computer-simulated data are distributed to investigators worldwide. Results of analyses are discussed and compared at meetings held in even-numbered years. The GAWs began in 1982 were initially motivated by the development and publication of several new algorithms for statistical genetic analysis, as well as by reports in the literature in which different investigators, using different methods of analysis, had reached contradictory conclusions. The impetus was initially to determine the numerical accuracy of the algorithms, to examine the robustness of the methodologies to violations of assumptions, and finally, to compare the range of conclusions that could be drawn from a single set of data. The Workshops have evolved to include consideration of problems related to analyses of specific complex traits, but the focus has always been on analytical methods. The Workshops provide an opportunity for participants to interact in addressing methodological issues, to test novel methods on the same well-characterized data sets, to compare results and interpretations, and to discuss current problems in genetic analysis. The Workshop discussions are a forum for investigators who are evolving new methods of analysis as well as for those who wish to gain further experience with existing methods. The success of the Workshops is due at least in part to the focus on specific problems and data sets, the informality of sessions, and the requirement that everyone who attends must have made a contribution. Topics are chosen and a small group of organizers is selected by the GAW Advisory Committee. Data sets are assembled, and six or seven months before each GAW, a memo is sent to individuals on the GAW mailing list announcing the availability of the GAW data. Included with the memo is a short description of the data sets and a form for requesting data. The form contains a statement to be signed by any investigator requesting the data, acknowledging that the data are confidential and agreeing not to use them for any purpose other than the Genetic Analysis Workshop without written permission from the data provider(s). Data are distributed by the ftp or CD-ROM or, most recently, on the web, together with a more complete written description of the data sets. Investigators who wish to participate in GAW submit written contributions approximately 6-8 weeks before the Workshop. The GAW Advisory Committee reviews contributions for relevance to the GAW topics. Contributions are assembled and distributed to all participants approximately two weeks before the Workshop. Participation in the GAWs is limited to investigators who (1) submit results of their analyses for presentation at the Workshop, or (2) are data providers, invited speakers or discussants, or Workshop organizers. GAWs are held just before the meetings of the American Society of Human Genetics or the International Genetic Epidemiology Society, at a meeting site nearby. We choose a location that will encourage interaction among participants and permit an intense period of concentrated work. The proceedings of each GAW are published. Proceedings from GAW16 were published in part by Genetic Epidemiology 33(Suppl 1), S1-S110 (2009) and in part by Biomed Central (BMC Proceedings, Volume 3, Supplement 7, 2009). Sponsors: GAW is funded by the Southwest Foundation for Biomedical Research.

Proper citation: Genetic Analysis Workshop (RRID:SCR_008350) Copy   

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http://www.bioconductor.org/

Software repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.

Proper citation: Bioconductor (RRID:SCR_006442) Copy   

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https://edspace.american.edu/openbehavior/

Repository of open source tools for behavioral neuroscience research. OpenBehavior features hardware (tools, devices, apparatuses), as well as software for data acquisition and analysis and for the investigation of animal behavior and cognition. Dedicated to accelerating research through promotion of collaboration and open source projects.

Proper citation: OpenBehavior (RRID:SCR_015938) Copy   

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http://www.sbpdiscovery.org/technology/sr/Pages/LaJolla_StemCells.aspx

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The former functions of this facility are split into two separate operations. The first is the generation and characterization of induced Pluripotent Stem Cells (iPSCs) is now being performed on a collaborative basis for both internal and external investigators with the Snyder lab. The second is a shared laboratory dedicated to the culture and analysis of stem cells that is available to SBP investigators.

Proper citation: Sanford Burnham Prebys Medical Discovery Institute Stem Cell Core (RRID:SCR_014856) Copy   

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http://montana.eagle-i.net/i/0000012b-00be-4e65-df3b-3fdc80000000

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 27, 2023. Core for Microarray analysis, Database development, Systems biology analysis, Genome assembly, Pathway data analysis, Expression data analysis, Metagenomics analysis. To maintain equipment and software for bioinformatic research, promote bioinformatics education on the MSU campus, and provide training and support to biologists implementing bioinformatics tools in their research.

Proper citation: Montana State University Bioinformatics Core Facility (RRID:SCR_009937) Copy   

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https://dna.uga.edu/

Core laboratory for nucleic acid sequencing and bioinformatics. Used for research support, education, and training. Services include genomic techniques and applications, sequencing technologies, and bioinformatics analyses, writting letters of support for grant applications submitted to funding agencies. GGBC operates multiple platforms for short-, long-, and single-molecule sequencing reads (i.e., Illumina MiSeq and NextSeq, PacBio Sequel, and Oxford Nanopore MinIon).

Proper citation: Georgia Genomics and Bioinformatics Core at the University of Georgia (RRID:SCR_010994) Copy   

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http://www.labmedmolge.unisa.it/inglese/index

Core equipped for structural and functional studies of genomes, includes equipment for next generation sequencing applications (Illumina HiSeq 1500, GAIIx and MiSeq, Life Technologies Ion Torrent PGM), RNA and microRNA expression profiling, array-based DNA methylation analyses and SNP genotyping (Illumina HiScan and Agilent High Resolution microarray scanners), informatics and bioinformatics (a server farm for genomics data computation and a high capacity data storage unit), fluorescence and confocal microscopy, long-term sample storage, cell culture, including a facility for generation and handling of viral vectors for gene transfer and gene therapy, access to a small animal facility for ''in vivo'' experimentations. Trained biotechnologists, molecular biologists and bioinformaticians handle all aspects related to experimental design, technical implementation and data analysis and storage.

Proper citation: University of Salerno Laboratory of Molecular Medicine and Genomics (RRID:SCR_011047) Copy   

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https://pharmacycorefacilities.usc.edu/translational-lab/

Core is equipped with a wide variety of technologically advanced instruments essential for cutting edge biomedical discovery and therapeutic development research. TRLab is composed of two major units. The Computational Bioinformatics Unit houses graphic workstations and modeling programs that enable in silico virtual screening and rational drug design applications. The Therapeutic Screening Unit houses a number of specialized instruments that enable a broad range of automated and multiplexed biological analyses in a throughput manner. The core mission of the TRLab has been to provide investigators with a state-of-the-art technological platform and technical expertise to advance translational research endeavors in the School of Pharmacy and at USC.

Proper citation: University of Southern California School of Pharmacy Translational Research Laboratory (RRID:SCR_012253) Copy   

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http://www.hunter.cuny.edu/chemistry/facilities/nmr/home

A service facility with four main spectrometers. The facility consists of four NMR instruments: a JEOL GX-400, a Varian Inova 500, a Bruker Avance 500 equipped with a 13C-1H cryoprobe, and a Bruker Avance III 600 MHz spectrometer equipped with a cryoprobe. These spectrometers are utilized by scientists from Hunter College, as well as from the entire CUNY community. The large variety of available probes allows detection of virtually any MR-active nuclide. Data analysis is performed either at the spectrometer workstation with vendor software or off-line with third party software packages.

Proper citation: Hunter NMR Spectroscopy Facility (RRID:SCR_000883) Copy   

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http://www.nmrfam.wisc.edu/

Provides access and developes NMR technology to advance range of applications and improves the efficiency, rigor and reproducibility of NMR data acquisition and analysis. Houses NMR spectrometers equipped with state-of-the-art probe technology and protocols to support acquisition of high-quality data. Spectrometers range from 500 MHz to 1100 MHz. Service is tailored to the needs of individual users and projects. Provides training and advice on experimental design, best practices for data acquisition, and data analysis. Experienced staff support users with training opportunities including workshops, video tutorials and protocols.

Proper citation: National Magnetic Resonance Facility at Madison (RRID:SCR_001449) Copy   

•   Source: SciCrunch Registry