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https://github.com/nicolazzie/AffyPipe
An open-source software pipeline for Affymetrix Axiom genotyping workflow.
Proper citation: AffyPipe (RRID:SCR_002032) Copy
Non-profit plasmid repository dedicated to helping scientists around the world share high-quality plasmids. Facilitates archiving and distributing DNA-based research reagents and associated data to scientists worldwide. Repository contains over 65,000 plasmids, including special collections on CRISPR, fluorescent proteins, and ready-to-use viral preparations. There is no cost for scientists to deposit plasmids, which saves time and money associated with shipping plasmids themselves. All plasmids are fully sequenced for validation and sequencing data is openly available. We handle the appropriate Material Transfer Agreements (MTA) with institutions, facilitating open exchange and offering intellectual property and liability protection for depositing scientists. Furthermore, we curate free educational resources for the scientific community including a blog, eBooks, video protocols, and detailed molecular biology resources.
Proper citation: Addgene (RRID:SCR_002037) Copy
https://github.com/adrlar/CanSNPer
Software that is a hierarchical genotype classifier of clonal pathogens.
Proper citation: CanSNPer (RRID:SCR_001980) Copy
Database of genetic and molecular biological information about Candida albicans. Contains information about genes and proteins, descriptions and classifications of their biological roles, molecular functions, and subcellular localizations, gene, protein, and chromosome sequence information, tools for analysis and comparison of sequences and links to literature information. Each CGD gene or open reading frame has an individual Locus Page. Genetic loci that are not tied to DNA sequence also have Locus Pages. Provides Gene Ontology, GO, to all its users. Three ontologies that comprise GO (Molecular Function, Cellular Component, and Biological Process) are used by multiple databases to annotate gene products, so that this common vocabulary can be used to compare gene products across species. Development of ontologies is ongoing in order to incorporate new information. Data submissions are welcome.
Proper citation: Candida Genome Database (RRID:SCR_002036) Copy
http://sourceforge.net/projects/dmetanalyzer/
Software tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The system allows: (i) to automatize the workflow of analysis of DMET (drug metabolism enzymes and transporters)-SNP (Single Nucleotide Polymorphism) data avoiding the use of multiple tools; (ii) the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP), (iii) the association of SNP with pathway through the search in PharmaKGB, a major knowledge base for pharmacogenomic studies. It has a simple graphical user interface that allows users (doctors/biologists) to upload and analyze DMET files produced by Affymetrix DMET-Console in an interactive way.
Proper citation: DMET-Analyzer (RRID:SCR_002030) Copy
http://biodev.extra.cea.fr/interoporc/
Automatic prediction tool to infer protein-protein interaction networks, it is applicable for lots of species using orthology and known interactions. The interoPORC method is based on the interolog concept and combines source interaction datasets from public databases as well as clusters of orthologous proteins (PORC) available on Integr8. Users can use this page to ask InteroPorc for all species present in Integr8. Some results are already computed and users can run InteroPorc to investigate any other species. Currently, the following databases are processed and merged (with datetime of the last available public release for each database used): IntAct, MINT, DIP, and Integr8.
Proper citation: InteroPorc (RRID:SCR_002067) Copy
Statistical software tool for calling common and rare variants in analysis of pool or individual next-generation sequencing data. This software is optimized for analysis of whole-exome sequencing data and whole-genome sequencing data.
Proper citation: SNVer (RRID:SCR_002061) Copy
http://neuralprediction.berkeley.edu/
The aim of the Neural Prediction Challenge is to accelerate the development of predictive models and to provide computational neuroscientists an opportunity to test their models objectively. The challenge is really quite simple: you will be given some (visual and/or auditory) stimuli and corresponding neural responses, and you must try to predict responses to other stimuli. Each data set will be divided in to two subsets: a fit set (90% of the data) that includes both the stimuli and the corresponding neuronal responses; and a validation set (10% of the data) that includes only stimuli (no responses). Your job is to use the fit set to fit your model and then to generate predicted responses based on the stimuli provided in the validation set. Once you have the predictions you should return them to us. We will compare your predicted responses to the responses actually observed in the validation set. Current data consist of recordings from visual and auditory neurons during naturalistic stimulation. Data are provided in simple ascii files that are easily readable in Matlab (or by any other modern programming language). Details on data formatting are provided with each data set. Predictions will be evaluated continuously as they are received and results will be posted in aggregate form. Individuals' names, prediction scores and models will not be posted without prior permission (though we may contact participants directly, see official rules). Please note that this is an academic research project, it is not a traditional contest. There is no real ending date, and there is nothing to win. Sponsors: This project is supported by the NIH Human Brain Project.
Proper citation: University of California at Berkeley: The Neural Prediction Challenge (RRID:SCR_001920) Copy
http://www.bioinf.manchester.ac.uk/resources/puma/
Software program for developing probabilistic models for the analysis of microarray data.
Proper citation: PUMA (RRID:SCR_002057) Copy
http://sourceforge.net/projects/denovoassembler/files/
Software that assembles reads obtained with new sequencing technologies (Illumina, 454, SOLiD) using MPI 2.2.
Proper citation: Ray (RRID:SCR_001916) Copy
http://www.geneticseducation.nhs.uk/
The NHS National Genetics Education and Development Centre is working with a range of groups throughout the UK to facilitate the integration of genetics education into all levels of education and training for all NHS health professionals. The key aims of this center: 1. Providing leadership in genetics education 2. Helping to raise awareness 3. Involving patients and their families in informing all aspects of our work 4. Identifying the genetics knowledge, skills and attitudes which are useful for clinical role 5. Developing a framework for competences in genetics 6. Facilitating the integration of genetics into curricula and courses 7. Identifying and developing resources appropriate to the needs of health professionals (and their trainers) 8. Supporting and disseminating learning from service development initiatives in genetics Sponsors: The Centre is funded by the Department of Health as one of the major initiatives of the 2003 Genetics White Paper, Our Inheritance, Our Future - Realising the potential of genetics in the NHS which set out the Governments strategy for ensuring that the potential benefits of genetics are realised by the NHS.
Proper citation: United Kingdom National Health Service: National Genetics Education and Development Centre (RRID:SCR_001919) Copy
The Neural Information Processing Systems (NIPS) Foundation is a non-profit corporation whose purpose is to foster the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. Neural information processing is a field which benefits from a combined view of biological, physical, mathematical, and computational sciences. The primary focus of the NIPS Foundation is the presentation of a continuing series of professional meetings known as the Neural Information Processing Systems Conference, held over the years at various locations in the United States and Canada.
Proper citation: NIPS - Neural Information Processing Systems Conference (RRID:SCR_001998) Copy
https://www.bioinformatics.babraham.ac.uk/projects/seqmonk/
Software tool to visualize and analyse high throughput mapped sequence data.
Proper citation: SeqMonk (RRID:SCR_001913) Copy
Center for advancing scientific understanding and improving the health and well-being of humans and nonhuman primates. The Center conducts research in microbiology and immunology, neurologic diseases, neuropharmacology, behavioral, cognitive and developmental neuroscience, and psychiatric disorders.
Proper citation: Yerkes National Primate Research Center (RRID:SCR_001914) Copy
https://datashare.nida.nih.gov
Website which allows data from completed clinical trials to be distributed to investigators and public. Researchers can download de-identified data from completed NIDA clinical trial studies to conduct analyses that improve quality of drug abuse treatment. Incorporates data from Division of Therapeutics and Medical Consequences and Center for Clinical Trials Network.
Proper citation: NIDA Data Share (RRID:SCR_002002) Copy
http://cmb.gis.a-star.edu.sg/ChIPSeq/paperChIPSeq.htm
THIS RESOURCE IS NO LONGER IN SERVICE, documented on April 12, 2017. A software tool to find peaks from ChIPSeq data generated from the Solexa/Illumina platform., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: ChIPSeq Peak Finder (RRID:SCR_002081) Copy
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. INOH (Integrating Network Objects with Hierarchies) is a pathway database of model organisms including human, mouse, rat and others. In INOH, the term pathway refers to higher order functional knowledge such as relationships among multiple bio-molecules that constitute signal transduction pathways or biological events in general. As most part of this knowledge resides in scientific articles, the database focuses on curating and encoding textual knowledge into a machine-processable form. The system provides pathway information as a composite of biological events, since functional knowledge is usually described as a set of fragmented processes. Each event is annotated with entries of a event ontology, which also has links to GO.
Proper citation: Integrating Network Objects with Hierarchies (RRID:SCR_002084) Copy
The Center for Bio-Image Informatics is an interdisciplinary research effort between Biology, Computer Science, Statistics, Multimedia and Engineering. The overarching goal of the center is the advancement of human knowledge of the complex biological processes which occur at both cellular and sub-cellular levels. the center employs and develops cutting edge techniques in the fields of imaging, pattern recognition and data mining. Research also focuses on development of new information processing techniques which can afford us a better understanding of biological processes depicted in microscopy images of cells and tissues, specifically on the distributions of biological molecules within these samples. This is achieved by borrowing methods for information processing at the sensor level to enable high speed and super-resolution imaging. By applying pattern recognition and data mining methods to bio-molecular images, full automation of both the extraction of information and the construction of statistically-sound models of the processes depicted in those images was possible. At the heart of the center's reseach is the BISQUE system, an online repository for multidimensional bio-images, and testbed for new research techniques and methods. BISQUE: Online Semantic Query User Environment is an online database for managing up to 5 dimensional scientific images with associated metadata and a flexible, collaborative tagging system. Currently the system has more than 85,000 user-provided tags and 128006 2-D planes from over 6,000 biological images. BISQUE is much more than just a repository for scientific images- the system provides resources for complex scientific analysis over images, result visualization, user-extensible modules, customized organization of images, advanced search features, graphical annotations, textual annotations and compatible client-side applications. Sponsors: This work is supported in part by an NSF infrastructure award No. EIA-0080134 and IIS-0808772.
Proper citation: Center for Bio-Image Informatics (RRID:SCR_001949) Copy
https://meded.ucsd.edu/index.cfm/asa/dcp/goddp/
The UCSD School of Medicine are dedicated to producing future leaders in all areas of medicine. As such, the School promotes the pursuit of dual degrees, either in the Medical School's own graduate degree programs or programs offered in other disciplines in the institution. In addition to the study of medicine, the School of Medicine actively encourages its student body to explore broadly in various scholarly areas related to the biomedical sciences. The goal of this additional training is to produce graduates who will bring fresh, innovative ideas to the research laboratory, the public health sector, the humanities and the social sciences, and the business environment. Students may elect to obtain advanced degrees in the following areas: * Biomedical Sciences * Masters in Bioengineering * Masters in Public Health * Masters in Leadership of Health Care Organizations * Masters of Advanced Studies in Clinical Research * Ph.D. Program in the Humanities and Social Sciences * Independent Ph.D. programs Opportunities for enrollment in research or degree programs outside of UCSD are also available, and students are encouraged to investigate these, if interested. Sponsors: This program is supported by the University of California at San Diego.
Proper citation: University of California at San Diego, School of Medicine: Graduate Opportunities & Dual Degree Programs (RRID:SCR_001942) Copy
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