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https://github.com/plaisier-lab/sygnal

Software pipeline to integrate correlative, causal and mechanistic inference approaches into unified framework that systematically infers causal flow of information from mutations to TFs and miRNAs to perturbed gene expression patterns across patients. Used to decipher transcriptional regulatory networks from multi-omic and clinical patient data. Applicable for integrating genomic and transcriptomic measurements from human cohorts.

Proper citation: SYGNAL (RRID:SCR_023080) Copy   

•   Source: SciCrunch Registry

https://CRAN.R-project.org/package=simplePHENOTYPES

Software R package that simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in wide range of genetic architectures, including additive, dominance and epistatic models. Used to simulate multiple traits controlled by loci with varying degrees of pleiotropy.

Proper citation: simplePHENOTYPES (RRID:SCR_022523) Copy   

•   Source: SciCrunch Registry

https://github.com/virajbdeshpande/AmpliconArchitect

Software package designed to call circular DNA from short read WGS data.Used to identify one or more connected genomic regions which have simultaneous copy number amplification and elucidates architecture of amplicon.Used to reconstruct structure of focally amplified regions using whole genome sequencing and validate it extensively on multiple simulated and real datasets, across wide range of coverage and copy numbers.

Proper citation: AmpliconArchitect (RRID:SCR_023150) Copy   

•   Source: SciCrunch Registry

https://yeatmanlab.github.io/pyAFQ/

Software package focused on automated delineation of major fiber tracts in individual human brains, and quantification of tissue properties within the tracts.Software for automated processing and analysis of diffusion MRI data. Automates tractometry.

Proper citation: Automated Fiber Quantification in Python (RRID:SCR_023366) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/Maaslin2.html

SoftwareR package that identifies microbial taxa correlated with factors of interest using generalized linear models and mixed models.Used for efficiently determining multivariable association between clinical metadata and microbial meta'omic features.

Proper citation: MaAsLin2 (RRID:SCR_023241) Copy   

•   Source: SciCrunch Registry

https://rockd.org/

Map database allows to record your geological observations and uses your location to provide spatially informed suggestions for nearby geologic units, time intervals, and fossils.

Proper citation: rockd (RRID:SCR_024431) Copy   

•   Source: SciCrunch Registry

http://virusdetect.feilab.net/cgi-bin/virusdetect/index.cgi

Software package to efficiently and exhaustively analyze large scale sRNA datasets for virus identification. Automated pipeline for virus discovery using deep sequencing of small RNAs.

Proper citation: VirusDetect (RRID:SCR_023669) Copy   

•   Source: SciCrunch Registry

http://www.ldeo.columbia.edu/core-repository

Core repository and one of the world's most unique and important collections of scientific samples from the deep sea. Sediment cores from every major ocean and sea are archived at the Core Repository. The collection contains approximately 72,000 meters of core composed of 9,700 piston cores; 7,000 trigger weight cores; and 2,000 other cores such as box, kasten, and large diameter gravity cores. They also hold 4,000 dredge and grab samples, including a large collection of manganese nodules, many of which were recovered by submersibles. Over 100,000 residues are stored and are available for sampling where core material is expended. In addition to physical samples, a database of the Lamont core collection has been maintained for nearly 50 years and contains information on the geographic location of each collection site, core length, mineralogy and paleontology, lithology, and structure, and more recently, the full text of megascopic descriptions. Samples from cores and dredges, as well as descriptions of cores and dredges (including digital images and other cruise information), are provided to scientific investigators upon request. Materials for educational purposes and museum displays may also be made available in limited quantities when requests are adequately justified. Various services and data analyses, including core archiving, carbonate analyses, grain size analyses, and RGB line scan imaging, GRAPE, P-wave velocity and magnetic susceptibility runs, can also be provided at cost. The Repository operates a number of labs and instruments dedicated to making fundamental measurements on material entering the repository including several non-destructive methods. Instruments for conducting and/or assisting with analyses of deep-sea sediments include a GeoTek Multi-Sensor Core Logger, a UIC coulometer, a Micromeritics sedigraph, Vane Shear, X-radiograph, Sonic Sifter, freeze dryer, as well as a variety of microscopes, sieves, and sampling tools. They also make these instruments available to the scientific community for conducting analyses of deep-sea sediments. If you are interested in borrowing any field equipment, please contact the Repository Curator.

Proper citation: Lamont-Doherty Core Repository (RRID:SCR_002216) Copy   

•   Source: SciCrunch Registry

http://lrc.geo.umn.edu/laccore/

Archive of almost 20,000 meters of high quality sediment cores from large and small expeditions to lakes all around the world. LacCore advocates for, coordinates, and facilitates core-based research on Earth's continents through collaborative support for logistics, field and laboratory, and data and sample curation and dissemination. They provide a wide variety of fee-based analytical services, as well as offer training and instrument time to lab visitors. They also develop Standard Operating Procedures (SOPs) for local training and adoption by individuals at other labs.

Proper citation: National Lacustrine Core Facility (RRID:SCR_002215) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/annotation/fungi/fgi/

Produces and analyzes sequence data from fungal organisms that are important to medicine, agriculture and industry. The FGI is a partnership between the Broad Institute and the wider fungal research community, with the selection of target genomes governed by a steering committee of fungal scientists. Organisms are selected for sequencing as part of a cohesive strategy that considers the value of data from each organism, given their role in basic research, health, agriculture and industry, as well as their value in comparative genomics.

Proper citation: Fungal Genome Initiative (RRID:SCR_003169) Copy   

•   Source: SciCrunch Registry

http://cisbp.ccbr.utoronto.ca

Software tool as catalog of inferred sequence binding preferences. Online library of transcription factors and their DNA binding motifs.

Proper citation: CIS-BP (RRID:SCR_017236) Copy   

•   Source: SciCrunch Registry

http://www.ig.utexas.edu/sdc/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 1, 2023. Database of processed seismic reflection / refraction data providing access to metadata, SEG-Y files, navigation files, seismic profile images, processing histories and more. The main features of the web site include a geographic search engine using Google Plugins, a metadata search engine, and metadata pages for the various seismic programs. Metadata are uploaded into mySQL, a public-domain SQL server, and then PHP scripts query the metadata and directories, creating web pages, displaying images, and providing ftp links.

Proper citation: Academic Seismic Portal at UTIG (RRID:SCR_000403) Copy   

•   Source: SciCrunch Registry

http://interactome.baderlab.org/

Project portal for the Human Reference Protein Interactome Project, which aims generate a first reference map of the human protein-protein interactome network by identifying binary protein-protein interactions (PPIs). It achieves this by systematically interrogating all pairwise combinations of predicted human protein-coding genes using proteome-scale technologies.

Proper citation: Human Reference Protein Interactome Project (RRID:SCR_015670) Copy   

•   Source: SciCrunch Registry

https://gitlab.com/gernerlab/cytomap/-/wikis/home

Software tool as spatial analysis software for whole tissue sections.Utilizes information on cell type and position to phenotype local neighborhoods and reveal how their spatial distribution leads to generation of global tissue architecture.Used to make advanced data analytic techniques accessible for single cell data with position information.

Proper citation: CytoMAP (RRID:SCR_021227) Copy   

•   Source: SciCrunch Registry

http://mtshasta.phys.washington.edu/website/SuperSegger.php

Software package as automated MATLAB based trainable image cell segmentation, fluorescence quantification and analysis suite. Used for high throughput time lapse fluorescence microscopy of in vivo bacterial cells. Robust image segmentation, analysis and lineage tracking of bacterial cells.

Proper citation: SuperSegger (RRID:SCR_018532) Copy   

•   Source: SciCrunch Registry

http://virtualplant.bio.nyu.edu/cgi-bin/vpweb/

Software platform to support systems biology research. Integrates genomic data and provides visualization and analysis tools for exploration of genomic data. Provides tools to generate biological hypotheses.

Proper citation: VirtualPlant (RRID:SCR_022576) Copy   

•   Source: SciCrunch Registry

http://trex.biohpc.org/

T-REX is a free, platform-independent online tool that allows for an integrated, rapid, and more robust analysis of T-RFLP data. Despite increasing popularity and improvements in terminal restriction fragment length polymorphism (T-RFLP) and other microbial community fingerprinting techniques, there are still numerous obstacles that hamper the analysis of these datasets. Many steps are required to process raw data into a format ready for analysis and interpretation. These steps can be time-intensive, error-prone, and can introduce unwanted variability into the analysis. Accordingly, we developed T-REX, free, online software for the processing and analysis of T-RFLP data. Analysis of T-RFLP data generated from a multiple-factorial study was performed with T-REX. With this software, we were able to i) label raw data with attributes related to the experimental design of the samples, ii) determine a baseline threshold for identification of true peaks over noise, iii) align terminal restriction fragments (T-RFs) in all samples (i.e., bin T-RFs), iv) construct a two-way data matrix from labeled data and process the matrix in a variety of ways, v) produce several measures of data matrix complexity, including the distribution of variance between main and interaction effects and sample heterogeneity, and vi) analyze a data matrix with the additive main effects and multiplicative interaction (AMMI) model.

Proper citation: T-REX (RRID:SCR_010715) Copy   

•   Source: SciCrunch Registry

http://www.cravat.us/

A web-based application designed with an easy-to-use interface to facilitate the high-throughput assessment and prioritization of genes and missense alterations important for cancer tumorigenesis.

Proper citation: CRAVAT (RRID:SCR_012776) Copy   

•   Source: SciCrunch Registry

http://function.princeton.edu/GOLEM/index.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 7, 2017. Welcome to the home of GOLEM: An interactive, graphical gene-ontology visualization, navigation,and analysis tool on the web. GOLEM is a useful tool which allows the viewer to navigate and explore a local portion of the Gene Ontology (GO) hierarchy. Users can also load annotations for various organisms into the ontology in order to search for particular genes, or to limit the display to show only GO terms relevant to a particular organism, or to quickly search for GO terms enriched in a set of query genes. GOLEM is implemented in Java, and is available both for use on the web as an applet, and for download as a JAR package. A brief tutorial on how to use GOLEM is available both online and in the instructions included in the program. We also have a list of links to libraries used to make GOLEM, as well as the various organizations that curate organism annotations to the ontology. GOLEM is available as a .jar package and a macintosh .app for use on- or off- line as a stand-alone package. You will need to have Java (v.1.5 or greater) installed on your system to run GOLEM. Source code (including Eclipse project files) are also available. GOLEM (Gene Ontology Local Exploration Map)is a visualization and analysis tool for focused exploration of the gene ontology graph. GOLEM allows the user to dynamically expand and focus the local graph structure of the gene ontology hierarchy in the neighborhood of any chosen term. It also supports rapid analysis of an input list of genes to find enriched gene ontology terms. The GOLEM application permits the user either to utilize local gene ontology and annotations files in the absence of an Internet connection, or to access the most recent ontology and annotation information from the gene ontology webpage. GOLEM supports global and organism-specific searches by gene ontology term name, gene ontology id and gene name. CONCLUSION: GOLEM is a useful software tool for biologists interested in visualizing the local directed acyclic graph structure of the gene ontology hierarchy and searching for gene ontology terms enriched in genes of interest. It is freely available both as an application and as an applet.

Proper citation: GOLEM An interactive, graphical gene-ontology visualization, navigation, and analysis tool (RRID:SCR_003191) Copy   

•   Source: SciCrunch Registry

http://rostlab.org/services/nlsdb/

A database of nuclear localization signals (NLSs) and of nuclear proteins targeted to the nucleus by NLS motifs. NLSs are short stretches of residues mediating transport of nuclear proteins into the nucleus. The database contains 114 experimentally determined NLSs that were obtained through an extensive literature search. Using "in silico mutagenesis" this set was extended to 308 experimental and potential NLSs. This final set matched over 43% of all known nuclear proteins and matches no currently known non-nuclear protein. NLSdb contains over 6000 predicted nuclear proteins and their targeting signals from the PDB and SWISS-PROT/TrEMBL databases. The database also contains over 12 500 predicted nuclear proteins from six entirely sequenced eukaryotic proteomes (Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana and Saccharomyces cerevisiae). NLS motifs often co-localize with DNA-binding regions. This observation was used to also annotate over 1500 DNA-binding proteins. From this site you can: * Query NLSdb * Find out how to use NLSdb * Browse the entries in NLSdb * Find out if your protein has an NLS using PredictNLS * Predict subcellular localization of your protein using LOCtree

Proper citation: NLSdb: a database of nuclear localization signals (RRID:SCR_003273) Copy   

•   Source: SciCrunch Registry