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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://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

https://cloudreg.neurodata.io/

Software automated, terascale, cloud based image analysis pipeline for preprocessing and cross modal, nonlinear registration between volumetric datasets with artifacts. Automatic terabyte scale cross modal brain volume registration.

Proper citation: CloudReg (RRID:SCR_022795) Copy   

•   Source: SciCrunch Registry

https://github.com/Kingsford-Group/kourami

Software graph guided assembly for novel human leukocyte antigen allele discovery. Graph guided assembly for HLA haplotypes covering typing exons using high coverage whole genome sequencing data.Implemented in Java and supported on Linux and Mac OS X.

Proper citation: Kourami (RRID:SCR_022280) Copy   

•   Source: SciCrunch Registry

https://isamplesorg.github.io/home/

Project to align physical sample identifiers. Used to design, develop, and promote service infrastructure to uniquely, consistently, and conveniently identify material samples, record metadata about them, and persistently link them to other samples and derived digital content, including images, data, and publications.

Proper citation: iSamples (RRID:SCR_021750) Copy   

•   Source: SciCrunch Registry

https://github.com/mourisl/Rascaf

Software tool for scaffolding with RNA-seq read alignments. Used for improving genome assembly with RNA sequencing data.

Proper citation: Rascaf (RRID:SCR_022014) Copy   

•   Source: SciCrunch Registry

https://github.com/danbider/lightning-pose

Software video centric package for direct video manipulation. Semi supervised animal pose estimation algorithm, Bayesian post processing approach and deep learning package. Improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools.

Proper citation: Lightning Pose (RRID:SCR_024480) Copy   

•   Source: SciCrunch Registry

http://magi.cs.brown.edu/

A tool for annotating, exploring, and analyzing gene sets that may be associated with cancer.

Proper citation: Mutation Annotation and Genomic Interpretation (RRID:SCR_002800) Copy   

•   Source: SciCrunch Registry

http://avis.princeton.edu/pixie/index.php

bioPIXIE is a general system for discovery of biological networks through integration of diverse genome-wide functional data. This novel system for biological data integration and visualization, allows you to discover interaction networks and pathways in which your gene(s) (e.g. BNI1, YFL039C) of interest participate. The system is based on a Bayesian algorithm for identification of biological networks based on integrated diverse genomic data. To start using bioPIXIE, enter your genes of interest into the search box. You can use ORF names or aliases. If you enter multiple genes, they can be separated by commas or returns. Press ''submit''. bioPIXIE uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the bioPIXIE algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. As you move the mouse over genes in the network, interactions involving these genes are highlighted. If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed. You may need to download the Adobe Scalable Vector Graphic (SVG) plugin to utilize the visualization tool (you will be prompted if you need it).

Proper citation: bioPIXIE (RRID:SCR_004182) Copy   

•   Source: SciCrunch Registry

http://www.ambystoma.org/

Portal that supports Ambystoma-related research and educational efforts. It is composed of several resources: Salamander Genome Project, Ambystoma EST Database, Ambystoma Gene Collection, Ambystoma Map and Marker Collection, Ambystoma Genetic Stock Center, and Ambystoma Research Coordination Network.

Proper citation: Sal-Site (RRID:SCR_002850) Copy   

•   Source: SciCrunch Registry

http://sumsdb.wustl.edu/sums/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on May 11, 2016. Repository of brain-mapping data (surfaces and volumes; structural and functional data) derived from studies including fMRI and MRI from many laboratories, providing convenient access to a growing body of neuroimaging and related data. WebCaret is an online visualization tool for viewing SumsDB datasets. SumsDB includes: * data on cerebral cortex and cerebellar cortex * individual subject data and population data mapped to atlases * data from FreeSurfer and other brainmapping software besides Caret SumsDB provides multiple levels of data access and security: * Free (public) access (e.g., for data associated with published studies) * Data access restricted to collaborators in different laboratories * Owner-only access for work in progress Data can be downloaded from SumsDB as individual files or as bundles archived for offline visualization and analysis in Caret WebCaret provides online Caret-style visualization while circumventing software and data downloads. It is a server-side application running on a linux cluster at Washington University. WebCaret "scenes" facilitate rapid visualization of complex combinations of data Bi-directional links between online publications and WebCaret/SumsDB provide: * Links from figures in online journal article to corresponding scenes in WebCaret * Links from metadata in WebCaret directly to relevant online publications and figures

Proper citation: SumsDB (RRID:SCR_002759) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/annotation/genome/magnaporthe_comparative/MultiHome.html

The Magnaporthe comparative genomics database provides accesses to multiple fungal genomes from the Magnaporthaceae family to facilitate the comparative analysis. As part of the Broad Fungal Genome Initiative, the Magnaporthe comparative project includes the finished M. oryzae (formerly M. grisea) genome, as well as the draft assemblies of Gaeumannomyces graminis var. tritici and M. poae. It provides users the tools to BLAST search, browse genome regions (to retrieve DNA, find clones, and graphically view sequence regions), and provides gene indexes and genome statistics. We were funded to attempt 7x sequence coverage comprising paired end reads from plasmids, Fosmids and BACs. Our strategy involves Whole Genome Shotgun (WGS) sequencing, in which sequence from the entire genome is generated and reassembled. Our specific aims are as follows: 1. Generate and assemble sequence reads yielding 7X coverage of the Magnaporthe oryzae genome through whole genome shotgun sequencing. 2. Generate and incorporate BAC and Fosmid end sequences into the genome assembly to provide a paired-end of average every 2 kb. 3. Integrate the genome sequence with existing physical and genetic map information. 4. Perform automated annotation of the sequence assembly. 5. Distribute the sequence assembly and results of our annotation and analysis through a freely accessible, public web server and by deposition of the sequence assembly in GenBank.

Proper citation: Magnaporthe comparative Database (RRID:SCR_003079) Copy   

•   Source: SciCrunch Registry

http://studyforrest.org

An MRI data repository that holds a set of 7 Tesla images and behavioral metadata. Multi-faceted brain image archive with behavioral measurements. For each participant a number of different scans and auxiliary recordings have been obtained. In addition, several types of minimally preprocessed data are also provided. The full description of the data release is available in a dedicated publication. This project invites anyone to participate in a decentralized effort to explore the opportunities of open science in neuroimaging by documenting how much (scientific) value can be generated out of a single data release by publication of scientific findings derived from a dataset, algorithms and methods evaluated on this dataset, and/or extensions of this dataset by acquisition and integration of new data.

Proper citation: studyforrest.org (RRID:SCR_003112) 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://pir.georgetown.edu/pirwww/dbinfo/pirsf.shtml

A SuperFamily classification system, with rules for functional site and protein name, to facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors. The PIRSF concept is being used as a guiding principle to provide comprehensive and non-overlapping clustering of UniProtKB sequences into a hierarchical order to reflect their evolutionary relationships. The PIRSF classification system is based on whole proteins rather than on the component domains; therefore, it allows annotation of generic biochemical and specific biological functions, as well as classification of proteins without well-defined domains. There are different PIRSF classification levels. The primary level is the homeomorphic family, whose members are both homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). At a lower level are the subfamilies which are clusters representing functional specialization and/or domain architecture variation within the family. Above the homeomorphic level there may be parent superfamilies that connect distantly related families and orphan proteins based on common domains. Because proteins can belong to more than one domain superfamily, the PIRSF structure is formally a network. The FTP site provides free download for PIRSF.

Proper citation: PIRSF (RRID:SCR_003352) Copy   

•   Source: SciCrunch Registry

http://mouseNET.princeton.edu

A functional network for laboratory mouse based on integration of diverse genetic and genomic data. It allows the users to accurately predict novel functional assignments and network components. MouseNET uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the mouseNET algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. The graph may be explored further. As you move the mouse over genes in the network, interactions involving these genes are highlighted.If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed.

Proper citation: MouseNET (RRID:SCR_003357) Copy   

•   Source: SciCrunch Registry

http://www.socr.ucla.edu/

A hierarchy of portable online interactive aids for motivating, modernizing probability and statistics applications. The tools and resources include a repository of interactive applets, computational and graphing tools, instructional and course materials. The core SOCR educational and computational components include the following suite of web-based Java applets: * Distributions (interactive graphs and calculators) * Experiments (virtual computer-generated games and processes) * Analyses (collection of common web-accessible tools for statistical data analysis) * Games (interfaces and simulations to real-life processes) * Modeler (tools for distribution, polynomial and spectral model-fitting and simulation) * Graphs, Plots and Charts (comprehensive web-based tools for exploratory data analysis), * Additional Tools (other statistical tools and resources) * SOCR Java-based Statistical Computing Libraries * SOCR Wiki (collaborative Wiki resource) * Educational Materials and Hands-on Activities (varieties of SOCR educational materials), * SOCR Statistical Consulting In addition, SOCR provides a suite of tools for volume-based statistical mapping (http://wiki.stat.ucla.edu/socr/index.php/SOCR_EduMaterials_AnalysesCommandLine) via command-line execution and via the LONI Pipeline workflows (http://www.nitrc.org/projects/pipeline). Course instructors and teachers will find the SOCR class notes and interactive tools useful for student motivation, concept demonstrations and for enhancing their technology based pedagogical approaches to any study of variation and uncertainty. Students and trainees may find the SOCR class notes, analyses, computational and graphing tools extremely useful in their learning/practicing pursuits. Model developers, software programmers and other engineering, biomedical and applied researchers may find the light-weight plug-in oriented SOCR computational libraries and infrastructure useful in their algorithm designs and research efforts. The three types of SOCR resources are: * Interactive Java applets: these include a number of different applets, simulations, demonstrations, virtual experiments, tools for data visualization and analysis, etc. All applets require a Java-enabled browser (if you see a blank screen, see the SOCR Feedback to find out how to configure your browser). * Instructional Resources: these include data, electronic textbooks, tutorials, etc. * Learning Activities: these include various interactive hands-on activities. * SOCR Video Tutorials (including general and tool-specific screencasts).

Proper citation: Statistics Online Computational Resource (RRID:SCR_003378) 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

http://protege.stanford.edu

Protege is a free, open-source platform that provides a growing user community with a suite of tools to construct domain models and knowledge-based applications with ontologies. At its core, Protege implements a rich set of knowledge-modeling structures and actions that support the creation, visualization, and manipulation of ontologies in various representation formats. Protege can be customized to provide domain-friendly support for creating knowledge models and entering data. Further, Protege can be extended by way of a plug-in architecture and a Java-based Application Programming Interface (API) for building knowledge-based tools and applications. An ontology describes the concepts and relationships that are important in a particular domain, providing a vocabulary for that domain as well as a computerized specification of the meaning of terms used in the vocabulary. Ontologies range from taxonomies and classifications, database schemas, to fully axiomatized theories. In recent years, ontologies have been adopted in many business and scientific communities as a way to share, reuse and process domain knowledge. Ontologies are now central to many applications such as scientific knowledge portals, information management and integration systems, electronic commerce, and semantic web services. The Protege platform supports two main ways of modeling ontologies: * The Protege-Frames editor enables users to build and populate ontologies that are frame-based, in accordance with the Open Knowledge Base Connectivity protocol (OKBC). In this model, an ontology consists of a set of classes organized in a subsumption hierarchy to represent a domain's salient concepts, a set of slots associated to classes to describe their properties and relationships, and a set of instances of those classes - individual exemplars of the concepts that hold specific values for their properties. * The Protege-OWL editor enables users to build ontologies for the Semantic Web, in particular in the W3C's Web Ontology Language (OWL). An OWL ontology may include descriptions of classes, properties and their instances. Given such an ontology, the OWL formal semantics specifies how to derive its logical consequences, i.e. facts not literally present in the ontology, but entailed by the semantics. These entailments may be based on a single document or multiple distributed documents that have been combined using defined OWL mechanisms (see the OWL Web Ontology Language Guide). Protege is based on Java, is extensible, and provides a plug-and-play environment that makes it a flexible base for rapid prototyping and application development.

Proper citation: Protege (RRID:SCR_003299) Copy   

•   Source: SciCrunch Registry