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https://www.sourcebioscience.com/products/life-sciences-research/clones/rnai-resources/c-elegans-rnai-collection-ahringer/

C. elegans RNAi feeding library distributed by Source BioScience Ltd. Designed for genome wide study of gene function in C. elegans through loss of function studies.

Proper citation: C. elegans RNAi Collection (Ahringer) (RRID:SCR_017064) Copy   

•   Source: SciCrunch Registry

http://www.mqtldb.org/

Data collection of large scale genome wide DNA methylation analysis of 1,000 mother-child pairs at serial time points across life course (ARIES).

Proper citation: mqtldb (RRID:SCR_018002) Copy   

•   Source: SciCrunch Registry

https://bpcinstruments.com/bpc_products/ampts3/?gad_source=5&gclid=CjwKCAiApaarBhB7EiwAYiMwqs8UHkyeggWkwFZPlUI3pLd9rJBWQ3bJ9ixnkNuq3dw4Fay4W6lwnxoC1RIQAvD_BwE

Automatic Methane Potential Test System (AMPTS) II is analytical tool for conducting various anaerobic batch fermentation tests. This includes performing, with up to 15 test vials, biochemical methane potential (BMP) tests, anaerobic biodegradability studies, specific methanogenic activity (SMA) assays, as well as conducting residual gas potential (RGP) analyses on digested slurry.

Proper citation: Biomethane Potential Test (RRID:SCR_017319) Copy   

•   Source: SciCrunch Registry

https://brainreader.net

Developer of medical software to offer image analysis technologies. Company in Denmark that provides medical image processing software to get quantifiable and accurate insight into brain.

Proper citation: Brainreader (RRID:SCR_017308) Copy   

•   Source: SciCrunch Registry

https://www.labx.com/item/molecular-devices-molecular-dynamics-storm-860-molecular/3896212

Multifunction phosphoimager fluorimager laser scanner that collects fluorescent or storage phosphor images from gels, membranes and phosphor screens. It is equipped with blue (450 nm) and red (635 nm) lasers, can scan at resolutions down to 50 microns. It is able to quantify radioactive gels for commonly used isotopes such as 32P, 33P, 125I, 35S, and 14C. There are three detection modes: phosphor screen mode, red/blue fluorescence, chemifluorescence.

Proper citation: Molecular Dynamics Storm 860 Molecular Imager (RRID:SCR_018032) Copy   

•   Source: SciCrunch Registry

https://www.rcon-ndt.com/wp-content/uploads/Nanotom.pdf

System for scientific and industrial nanofocus computed tomography nanoCT and 3D metrology. Realizes unique spatial and contrast resolution on wide sample and application range. Used in material science, precision injection moulding, micromechanics, electronics geology and biology. Used in 3D CT applications. Allows many analysis, e.g. non-destructive visualization of slices, arbitrary sectional views, or automatic pore analysis.

Proper citation: Phoenix Nanotom S (RRID:SCR_017995) Copy   

•   Source: SciCrunch Registry

https://www.nzytech.com/

Commercially provides services and products for research in the fields of molecular biology, diagnostics, enzymes and proteins.

Proper citation: NZYTech (RRID:SCR_016772) Copy   

•   Source: SciCrunch Registry

https://bionanogenomics.com/wp-content/uploads/2017/01/30047-Irys-User-Guide.pdf

System by BioNano Genomics ( formerly BioNanomatrix) which provides optical next generation mapping (NGM). Used for sequence assembly and structural variation analysis. Provides Scaffold Bionano genome mapping data with sequencing data to improve assembly contiguity, reduce sequencing coverage needed, and automatically correct errors in sequencing based assemblies.

Proper citation: BioNano Irys system (RRID:SCR_016754) Copy   

•   Source: SciCrunch Registry

https://www.scanco.ch/microct100.html

Micro Computed Tomography 100 scanner for 3D imaging of specimens in vitro supplied with software for scanning, 3D analysis, visualization, image management and data import and export by SCANCO Medical AG.

Proper citation: SCANCO Medical microCT 100 system (RRID:SCR_017119) Copy   

•   Source: SciCrunch Registry

https://www.agilent.com/cs/library/usermanuals/Public/G2505-90019_ScannerC_User.pdf

Microarray Scanner is part of microarray analysis solution from Agilent Technologies. Laser induced fluorescence scanner designed to read microarrays printed on standard slides. Measures fluorescence intensity of labeled sample nucleic acid bound to microarrays.

Proper citation: Agilent G2565CA Microarray Scanner (RRID:SCR_017206) Copy   

•   Source: SciCrunch Registry

https://www.thermofisher.com/order/catalog/product/00-0210

Scanner for microarray analysis to scan next-generation higher-density arrays, including SNP arrays, tiling arrays for transcription and all-exon arrays for whole-genome analysis.

Proper citation: GeneChip™ Scanner 3000 7G (RRID:SCR_016522) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://github.com/mikelove/tximport

Software R package for importing pseudoaligned reads into R for use with downstream differential expression analysis. Used for import and summarize transcript level estimates for transcript and gene level analysis.

Proper citation: tximport (RRID:SCR_016752) Copy   

•   Source: SciCrunch Registry

https://github.com/zburkett/VoICE

Software that groups vocal elements of birdsong by creating a high dimensionality dataset through scoring spectral similarity between vocalizations.

Proper citation: Vocal Inventory Clustering Engine (VoICE) (RRID:SCR_016004) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://biocodecommons.org/

Platform provides resources for genomic observations from collection to analysis and publication. Works with standards community to ensure clear vocabularies and useful ontologies for biological resources and related assets. Biocode Commons is also collaborating on development of Biological Collections Ontology, working to better integrate ontologies, vocabularies, and relevant standards that are related to BCO.

Proper citation: Biocode Commons (RRID:SCR_024553) Copy   

•   Source: SciCrunch Registry

http://bmsr.usc.edu/software/targetgene/

MATLAB tool to effectively identify potential therapeutic targets and drugs in cancer using genetic network-based approaches. It can rapidly extract genetic interactions from a precompiled database stored as a MATLAB MAT-file without the need to interrogate remote SQL databases. Millions of interactions involving thousands of candidate genes can be mapped to the genetic network within minutes. While TARGETgene is currently based on the gene network reported in (Wu et al.,Bioinformatics 26:807-813, 2010), it can be easily extended to allow the optional use of other developed gene networks. The simple graphical user interface also enables rapid, intuitive mapping and analysis of therapeutic targets at the systems level. By mapping predictions to drug-target information, TARGETgene may be used as an initial drug screening tool that identifies compounds for further evaluation. In addition, TARGETgene is expected to be applicable to identify potential therapeutic targets for any type or subtype of cancers, even those rare cancers that are not genetically recognized. Identification of Potential Therapeutic Targets * Prioritize potential therapeutic targets from thousands of candidate genes generated from high-throughput experiments using network-based metrics * Validate predictions (prioritization) using user-defined benchmark genes and curated cancer genes * Explore biologic information of selected targets through external databases (e.g., NCBI Entrez Gene) and gene function enrichment analysis Initial Drug Screening * Identify for further evaluation existing drugs and compounds that may act on the potential therapeutic targets identified by TARGETgene * Explore general information on identified drugs of interest through several external links Operating System: Windows XP / Vista / 7

Proper citation: TARGETgene (RRID:SCR_001392) Copy   

•   Source: SciCrunch Registry

http://www.preger.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Sample collection of oocytes obtained from various sized antral follicles, and embryos obtained through a variety of different protocols. The PREGER makes it possible to undertake quantitative gene-expression studies in rhesus monkey oocytes and embryos through simple and cost-effective hybridization-based methods.

Proper citation: Primate Embryo Gene Expression Resource (RRID:SCR_002765) Copy   

•   Source: SciCrunch Registry

http://www.altanalyze.org/

Software application for microarry, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), it will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, it provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichement and more). AltAnalyze can be run through an intuitive graphical user interface or command-line and requires no advanced knowledge of bioinformatics programs or scripting. Alternative regulated exons can be subsequently visualized in the context of proteins, domains and microRNA binding sites with the Cytoscape Plugin DomainGraph.

Proper citation: AltAnalyze - Alternative Splicing Analysis Tool (RRID:SCR_002951) Copy   

•   Source: SciCrunch Registry

http://nipy.org/nipype/

A package for writing fMRI analysis pipelines and interfacing with external analysis packages (SPM, FSL, AFNI). Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface. Nipype, an open-source, community-developed initiative under the umbrella of Nipy, is a Python project that solves these issues by providing a uniform interface to existing neuroimaging software and by facilitating interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., SPM, FSL), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.

Proper citation: Nipype (RRID:SCR_002502) Copy   

•   Source: SciCrunch Registry