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https://code.google.com/p/gutentag/

An interactive, user-editable genetic sequence database tool, targeted at molecular biology research groups that can be browsed using tags. The tool is Web 2.0-flavoured, allowing users to do more than just retrieve information. Its focus on user-editability is supported by the use of tags (metadata) associated with genetic sequences. Several methods of retrieving stored data are available including tag-clouds, BLAST and keyword searches. Also, sequence tags related to HGNC gene names, conserved domains (CDD) and GO terms can be automatically generated given sequence data. The tool is constructed using the high-level Python web framework, Django, with a SQLite3 backend.

Proper citation: Gutentag (RRID:SCR_003051) Copy   

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http://bodb.usc.edu/bodb/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 4, 2023. BODB offers a way to document computational models of brain function by linking each model to Brain Operating Principles (BOPs), related brain regions, Summaries of Simulation Results (SSRs)and Summaries of Experimental Data (SEDs) used either to design or to test the model. Tools are provided to search for related models and to compare their coverage of SEDs. This allows automatic benchmarking of a model against a cluster of models addressing similar BOPs or SEDs or brain regions. Tools allow display of brain imaging results against a human brain applet; a new tool will link data to a macaque brain applet.

Proper citation: Brain Operation Database (RRID:SCR_003050) Copy   

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https://github.com/brunonevado/Pipeliner

Software for evaluating the performance of bioinformatics pipelines for Next Generation re-Sequencing.

Proper citation: Pipeliner (RRID:SCR_003171) Copy   

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http://briansimulator.org/

Software Python package for simulating spiking neural networks. Useful for neuroscientific modelling at systems level, and for teaching computational neuroscience. Intuitive and efficient neural simulator.

Proper citation: Brian Simulator (RRID:SCR_002998) Copy   

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http://dip.doe-mbi.ucla.edu/

Database to catalog experimentally determined interactions between proteins combining information from a variety of sources to create a single, consistent set of protein-protein interactions that can be downloaded in a variety of formats. The data were curated, both, manually and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks. Tools are available to analyze, visualize and integrate user's own experimental data with the information about protein-protein interactions available in the DIP database. The DIP database lists protein pairs that are known to interact with each other. By interact they mean that two amino acid chains were experimentally identified to bind to each other. The database lists such pairs to aid those studying a particular protein-protein interaction but also those investigating entire regulatory and signaling pathways as well as those studying the organization and complexity of the protein interaction network at the cellular level. Registration is required to gain access to most of the DIP features. Registration is free to the members of the academic community. Trial accounts for the commercial users are also available.

Proper citation: Database of Interacting Proteins (DIP) (RRID:SCR_003167) Copy   

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http://developingmouse.brain-map.org/

Map of gene expression in developing mouse brain revealing gene expression patterns from embryonic through postnatal stages. Provides information about spatial and temporal regulation of gene expression with database. Feature include seven sagittal reference atlases created with a developmental ontology. These anatomic atlases may be viewed alongside in situ hybridization (ISH) data as well as by itself.

Proper citation: Allen Developing Mouse Brain Atlas (RRID:SCR_002990) Copy   

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https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/pacBioToCA

A module in the Celera Assembler software package that performs error correction on PacBio long reads by mapping shorter, high accuracy reads onto the long reads.

Proper citation: PacBioToCA (RRID:SCR_003044) Copy   

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http://www.allelebiotech.com/

A private company offering a wide variety of Molecular Biology reagents, fluorescent proteins, luciferase assay substrates, genotyping kits, and various custom services. The company isalso in the RNAi field with its recent patents in Pol III promoter-driven siRNA, shRNA, and miRNA. They introduced high titer lentivirus, camelid antibodies, and cell based assays and also offer Baculovirus protein expression and Gryphon retrovirus systems.

Proper citation: Allele Biotechnology (RRID:SCR_003164) Copy   

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https://github.com/PacificBiosciences/R-pbutils

An R software package providing plotting and convenience functions.

Proper citation: R-pbutils (RRID:SCR_002995) Copy   

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http://cmb.molgen.mpg.de/2ndGenerationSequencing/Solas/

Software package for the statistical language R, devoted to the analysis of next generation short read data of RNA-seq transcripts. It provides predictions of alternative exons in a single condition/cell sample, predictions of differential alternative exons between two conditions/cell samples, and quantification of alternative splice forms in a single condition/cell sample.

Proper citation: Solas (RRID:SCR_003168) Copy   

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http://bibiserv.techfak.uni-bielefeld.de/dialign/

Tool for multiple sequence alignment using various sources of external information that is particularly useful to detect local homologies in sequences with low overall similarity. While standard alignment methods rely on comparing single residues and imposing gap penalties, DIALIGN constructs pairwise and multiple alignments by comparing entire segments of the sequences. No gap penalty is used. This approach can be used for both global and local alignment, but it is particularly successful in situations where sequences share only local homologies. Several versions of DIALIGN are available online at GOBICS, http://dialign.gobics.de/

Proper citation: DIALIGN (RRID:SCR_003041) Copy   

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http://cbio.mskcc.org/public/raetschlab/user/drewe/rdiff/

Software tool for detecting differential RNA processing from RNA-Seq data. It implements two statistical tests, rDiff.parametric and rDiff.nonparametric, to detect changes of the RNA processing between two samples.

Proper citation: rDiff (RRID:SCR_003162) Copy   

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http://www.dana-farber.org/

Cancer institute that provides expert, compassionate care to children and adults with cancer while advancing the understanding, diagnosis, treatment, cure, and prevention of cancer and related diseases. As an affiliate of Harvard Medical School and a Comprehensive Cancer Center designated by the National Cancer Institute, the Institute also provides training for new generations of physicians and scientists, designs programs that promote public health particularly among high-risk and underserved populations, and disseminates innovative patient therapies and scientific discoveries to their target community across the United States and throughout the world.

Proper citation: Dana-Farber Cancer Institute (RRID:SCR_003040) Copy   

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http://www.histmed.org/

A professional association of historians, physicians, nurses, archivists, curators, librarians, and others that promotes and encourages research, study, writing, and interest in the history of medicine and allied fields.

Proper citation: American Association for the History of Medicine (RRID:SCR_003161) Copy   

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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   

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http://chiulab.ucsf.edu/surpi/

Software providing a computational pipeline for pathogen identification from complex metagenomic next-generation sequencing (NGS) data generated from clinical samples.

Proper citation: SURPI (RRID:SCR_003071) Copy   

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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   

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http://www.fly-trap.org/

Flytrap is an interactive database for displaying gene expression patterns, in particular P(GAL4) patterns, via an intuitive WWW based interface. This development consists of two components, the first being the HTML interface to the database and the second, a tool-kit for constructing and maintaining the database. The browser component of the project is entirely platform independent; based on javascript and HTML and therefore only requires a "standard" browser. This is to facilitate CD-ROM distribution and off-line browsing. Whether on-line or on CD, the basic browser structure does not reply on any server based scripts. Basic searching is now available. The search page uses javascript and will work off-line (i.e. from a CD-ROM copy). The construction tool-kit is UNIX based and requires an on-line web server. The tool-kit is used to compile the HTML browser interface from a simple database. The tool-kit part comprises a forms based HTML interface to the datasets allowing new information to b e added and updated very simply. We are also developing a java interface for the tool-kit that will enable us to edit and annotate images on-line. The basic browser interface is complete and a demonstration version can be accessed via the website. The first working version of the tool-kit is now on-line and is available for use.

Proper citation: flytrap (RRID:SCR_003075) Copy   

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http://www.vision.edu.au/

Centre of Excellence in Vision Science that brings together major vision research programs at the The Australian National University with cognate programs at the Universities of Queensland, Sydney and Western Australia. The research is focused on unravelling the cellular basis of visual sensing and processing; on revealing the algorithms that underlie the visual control of behavior and perception; and on discovering the cellular mechanisms that make the eye and retina stable, and whose breakdown causes blindness.

Proper citation: ARC Centre of Excellence in Vision Science (RRID:SCR_003196) Copy   

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http://www2.niddk.nih.gov/Research/Resources/ObesityResources.htm

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 23, 2017. This website contains resources for obesity researchers including: Obesity Databases, Registries and Information; Obesity Multicenter Clinical Research; Obesity Basic Research Networks; Obesity Reagents; Obesity Services; Obesity Standardization Programs; Obesity Tissues, Cells, Animals; Obesity Useful Tools.

Proper citation: NIDDK- National Institute of Diabetes and Digestive and Kidney Diseases Obesity Resources (RRID:SCR_003074) Copy   

•   Source: SciCrunch Registry