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http://sourceforge.net/projects/pennseq/
Software for isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution. Instead of making parametric assumptions, they give adequate weight to the underlying data by the use of a non-parametric approach. The rationale is that regardless what factors lead to non-uniformity, whether it is due to hexamer priming bias, local sequence bias, positional bias, RNA degradation, mapping bias or other unknown reasons, the probability that a fragment is sampled from a particular region will be reflected in the aligned data. This empirical approach thus maximally reflects the true underlying non-uniform read distribution.
Proper citation: PennSeq (RRID:SCR_001763) Copy
The Physiome Project is a worldwide public domain effort to provide a computational framework for understanding human and other eukaryotic physiology. It aims to develop integrative models at all levels of biological organization, from genes to the whole organism via gene regulatory networks, protein pathways, integrative cell function, and tissue and whole organ structure/function relations. Additionally, an important goal of the project is to develop applications for teaching physiology. Current projects include the development of: - ontologies to organize biological knowledge and access to databases - markup languages to encode models of biological structure and function in a standard format for sharing between different application programs and for re-use as components of more comprehensive models - databases of structure at the cell, tissue and organ levels - software to render computational models of cell function such as ion channel electrophysiology, cell signaling and metabolic pathways, transport, motility, the cell cycle, etc. in 2 & 3D graphical form - software for displaying and interacting with the organ models which will allow the user to move across all spatial scales Sponsors: This project is supported by the International Union of Physiological Sciences (IUPS), the IEEE Engineering. in Medicine and Biology (EMBS), and the International Federation for Medical and Biological Engineering (IFMBE)
Proper citation: International Union of Physiological Sciences: Physiome Project (RRID:SCR_001760) Copy
http://neuroimage.usc.edu/brainstorm/
Software as collaborative, open source application dedicated to analysis of brain recordings: MEG, EEG, fNIRS, ECoG, depth electrodes and animal invasive neurophysiology. User-Friendly Application for MEG/EEG Analysis.
Proper citation: Brainstorm (RRID:SCR_001761) Copy
Software that allows large scale neuron simulators to communicate during runtime. It allows exchange of data among parallel applications in a cluster environment, interconnects large-scale neuronal network simulators with each other or with other tools, participates in multi-simulations, and is continuously developed and extended. Three simulators currently have MUSIC interfaces: Moose, NEURON and NEST. Three applications execute in parallel while exchanging data via MUSIC. The software interface promotes interoperability by allowing models written for different simulators to be simulated together in a larger system. It enables re-usability of models or tools by providing a standard interface. As data are distributed over a number of processors, it is non-trivial to coordinate data transfer so that it reaches the correct destination at the correct time. Current and future simulators can make use of MUSIC - compliant general purpose tools and participate in multi-simulations, for example when: * Different parts of a complex nervous system model are optimally implemented in different simulators, and need to communicate with each other. * Post-processing of generated data is needed, where the amounts of data are too large for intermediate storage, and requires the simulator to pass the data directly to the post-processing module. A standard interface enables straight-forward independent third-party development and community sharing of interoperable software tools for parallel processing. * Library and utilities are written in C++, uses MPI. * It is possible to add a MUSIC interface to existing simulators. * Works independently, no assumptions are made about other applications to facilitate development of general purpose tools. * Performance Data transport with high bandwidth and low latency.
Proper citation: MUlti SImulation Coordinator (RRID:SCR_001756) Copy
http://dynamicbrain.neuroinf.jp/
THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 19. 2022. Platform to promote studies on dynamic principles of brain functions through unifying experimental and computational approaches in cellular, local circuit, global network and behavioral levels. Provides services such as data sets, popular research findings and articles and current developments in field. This site has been archived since FY2019 and is no longer updated.
Proper citation: Dynamic Brain Platform (RRID:SCR_001754) Copy
http://cmic.cs.ucl.ac.uk/camino/
Free, open-source, object-oriented software package for analysis and reconstruction of Diffusion MRI data, tractography and connectivity mapping. The toolkit implements standard techniques, such as diffusion tensor fitting, mapping fractional anisotropy and mean diffusivity, deterministic and probabilistic tractography. It also contains more specialized and cutting-edge techniques, such as Monte-Carlo diffusion simulation, multi-fibre and HARDI reconstruction techniques, multi-fibre PICo, compartment models, and axon density and diameter estimation. Camino has a modular design to enable construction of processing pipelines that include modules from other software packages. The toolkit is primarily designed for unix platforms and structured to enable simple scripting of processing pipelines for batch processing. Most users use linux, MacOS or a unix emulator like cygwin running under windows. However, the core code is written in Java and thus is simple to call from other platforms and programming environments, such as matlab running under unix or windows.
Proper citation: Camino (RRID:SCR_001638) Copy
http://csg.sph.umich.edu//abecasis/MACH/index.html
A Markov Chain based software tool for haplotyping, genotype imputation and disease association analysis that can resolve long haplotypes or infer missing genotypes in samples of unrelated individuals.
Proper citation: MACH 1.0 (RRID:SCR_001759) Copy
Database of raw data from people who have shared their direct-to-customer (DTC) genetic results from 23andMe, deCODEme or FamilyTreeDNA. Logged-In users can search the database for users with specific phenotypes and mass-download all corresponding SNP-datasets. This allows you to get datasets like All genotyping files of openSNP-users that have Alzheimer and the corresponding control group. They are currently working on providing API-access. You can also use JSON to get access to openSNP-data and some other ways: If you want to automate the file-downloads for a given phenotype the RSS-feeds could help you. Inside the RSS-XML there are 2 flags you could use to automatically create correct genotype-groups: gives you the variation of this user at the phenotype you are looking at and gives you the download link. If you were genotyped by 23andMe, deCODEme or FamilyTreeDNA (contact them regarding others) you can upload the raw genotype data which you can download from your DTC test provider. The data will then be openly available for the world to see and download. They also parse these SNPs and annotate them. For annotation they include the manually curated SNPedia and find Open Access primary publications which appear in the journals of The Public Library of Science (PLoS), an Open Access publishing group. Additionally they screen Mendeley, a crowd-sourced repository of scientific publications. You can also publish some of your phenotypes so some day it might get possible to associate some SNPs with phenotypes. You can also share your knowledge about SNPs and phenotypes with other users and can socialize.
Proper citation: openSNP (RRID:SCR_001636) Copy
Private research university in Thuwal, Saudi Arabia that offers undergraduate and graduate degree programs in Biological and Environmental Science and Engineering (BESE), Computer, Electrical, and Mathematical Science and Engineering (CEMSE), and Physical Science and Engineering (PSE).
Proper citation: King Abdullah University of Science and Technology; Makkah Province; Saudi Arabia (RRID:SCR_001758) Copy
Not-for-profit association representing all 141 accredited U.S. and 17 accredited Canadian medical schools; nearly 400 major teaching hospitals and health systems, including 51 Department of Veterans Affairs medical centers; and 90 academic and scientific societies. Through these institutions and organizations, the AAMC represents 128,000 faculty members, 75,000 medical students, and 110,000 resident physicians. Through its programs and services, it strengthens the world's most advanced medical care by supporting the entire spectrum of education, research, and patient care activities conducted by member institutions. The AAMC and its members are dedicated to the communities they serve and steadfast in their desire to earn and keep the public's trust for the role they play in improving the nation's health. The vision of the AAMC and its members is a healthy nation and world in which: - America's system of medical education, through continual renewal and innovation, prepares physicians and scientists to meet the nation's evolving health needs. - The nation's medical students, biomedical graduate students, residents, fellows, faculty, and the health care workforce are diverse and culturally competent. - Advances in medical knowledge, therapies, and technologies prevent disease, alleviate suffering, and improve quality of life. - The nation's health system meets the needs of all. - Concern for compassion, quality, safety, efficacy, accountability, affordability, professionalism, and the public good guide the health care community.
Proper citation: Association of American Medical Colleges (RRID:SCR_001670) Copy
http://www.genes2cognition.org/resources/
Biological resources, including gene-targeting vectors, ES cell lines, antibodies, and transgenic mice, generated for its phenotyping pipeline as part of the Genes to Cognition research program are freely-available to interested researchers. Available Transgenic Mouse Lines: *Hras1 (H-ras) knockout,C57BL/6J *Dlg4 (PSD-95) knockout,129S5 *Dlg4 (PSD-95) knockout,C57BL/6J *Dlg3 (SAP102) knockout with hprt mutation,129S5 *Dlg3 (SAP102) knockout (wild-type for hprt,C57BL/6J *Syngap1 (SynGAP) knockout (from 8.24 clone), C57BL/6J *Dlg4 (PSD-95) guanylate kinase domain deletion, C57BL/6J *Ptk2 (FAK) knockout,C57BL/6J
Proper citation: Genes to Cognition - Biological Resources (RRID:SCR_001675) Copy
The Phelan-McDermid Syndrome Foundation, established in 2002, is a 501(c)3 nonprofit group that provides support services for those who have family members affected by 22q13 Deletion Syndrome / Phelan-McDermid Syndrome. It also raises money to further awareness of the syndrome through research and sponsoring an international conference every two years that brings together families, researchers and therapists. The Foundation facilitates connections between families through networking, communications and support services. We also build alliances with other rare diseases groups to expand our reach and exposure. The syndrome, which affects families worldwide, is a rare genetic occurrence and is the result of a damaged or missing protein on the 22nd chromosome. Our Foundation works with researchers who are looking into the cause and possible cure for the syndrome. PMSF's grants and fellowships program is intended to encourage research projects that will advance the development of treatments and cures for PMS. Our mission is to bring together everyone affected by 22q13 Deletion Syndrome/Phelan-McDermid Syndrome to help them through the challenges they face every day and to raise awareness in the medical and research communities.
Proper citation: Phelan-McDermid Syndrome Foundation (RRID:SCR_001707) Copy
http://www.ncbi.nlm.nih.gov/projects/homology/maps/
This page provides quick access to the Comparative mapping functions available in the Map Viewer. Currently, comparative maps are calculated using HomoloGene orthology predictions. Once the gene pairs have been established, blocks of conserved syteny can be established using the positions of each gene object in their respective builds. Sponsors: This resource is supported by NCBI.
Proper citation: Homology Maps Page (RRID:SCR_001666) Copy
Data analysis service that searches PubMed literature database (abstracts) about specific relationships between proteins, genes, or keywords using a NLP-based text-mining approach. The results are returned as a graph. The synonym database used in Chilibot is available, without fee, for academic use only. Several different search methods are supported including: * searching for relationship between two genes, proteins or keywords * searching for relationships between many genes, proteins, or keywords * searching for relationships between two lists of genes, proteins, or keywords Advanced options include: * Automated hypothesis generation (graph) * Restricting context using keywords * Providing your own synonyms * Modifying synonyms provided by Chilibot * Color coding nodes with gene expression values * Special search: modulation
Proper citation: Chilibot: Gene and Protein relationships from MEDLINE (RRID:SCR_001705) Copy
http://www.bioconductor.org/packages/release/bioc/html/SLqPCR.html
Software functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH.
Proper citation: SLqPCR (RRID:SCR_001669) Copy
http://www.emory.edu/LIVING_LINKS/
The primary mission of the Living Links Center is to study human evolution by investigating our close genetic, anatomical, cognitive, and behavioral similarities with great apes. The Living Links Center was established for primate studies that shed light on human behavioral evolution. It is an integrated part of the Yerkes National Primate Research Center, which is the nation's oldest and largest primate center. The Living Links Center is home to two socially housed groups of chimpanzees and two socially housed groups of capuchin monkeys. The research conducted in this center is broken down into four categories: - Chimpanzees: Chimpanzee research at the Living Links Center is conducted at the Yerkes Field Station, which is home to two socially housed chimpanzee groups known as FS1 and FS2. Each mixed gender group of 12 individuals lives in a large outdoor enclosure with wooden climbing structures and play objects attached to an indoor sleeping area. FS1 and FS2 can hear, but not see each other because their enclosures are ~200m apart and separated by a small hill. Chimpanzee research is conducted on a volunteer basis with members of each group. - Elephants: This newly found presence of mirror self-recognition in elephants, previously predicted due to their well-known social complexity, is thought to relate to empathetic tendencies and the ability to distinguish oneself from others. As a result of this study, the elephant now joins a cognitive elite among animals commensurate with its well-known complex social life and high level of intelligence. Although elephants are far more distantly related to us than the great apes, they seem to have evolved similar social and cognitive capacities making complex social systems and intelligence part of this picture. These parallels between humans and elephants suggest a convergent cognitive evolution possibly related to complex sociality and cooperation. - Capuchin Monkeys: Though there are several different species of capuchin monkey, the one most widely studied in captivity by Living Links, is the brown, or tufted, capuchin (Cebus apella). - Collaborative Projects: projects with collaborators around the world. Sponsors: This center is supported by the Yerkes National Primate Research Center.
Proper citation: Living Links: Center for the Advanced Study of Ape and Human Evolution (RRID:SCR_001776) Copy
A web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document. It offers a comprehensive library on top of which more sophisticated systems can be built. The project provides an enhanced interactive environment that includes support for data visualization and facilities for distributed and parallel computation.
Proper citation: IPython (RRID:SCR_001658) Copy
https://www.genome.wisc.edu/tools/asap.htm
Database and web interface developed to store, update and distribute genome sequence data and gene expression data. ASAP was designed to facilitate ongoing community annotation of genomes and to grow with genome projects as they move from the preliminary data stage through post-sequencing functional analysis. The ASAP database includes multiple genome sequences at various stages of analysis, and gene expression data from preliminary experiments. Use of some of this preliminary data is conditional, and it is the users responsibility to read the data release policy and to verify that any use of specific data obtained through ASAP is consistent with this policy. There are four main routes to viewing the information in ASAP: # a summary page, # a form to query the genome annotations, # a form to query strain collections, and # a form to query the experimental data. Navigational buttons appear on every page allowing users to jump to any of these four points., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: ASAP (RRID:SCR_001849) Copy
MedMOLE improves the comprehension of microarray experimental results by grouping co-regulated genes on the basis of the informational content of MEDLINE documents. The tool relies on two components: a gene name extractor and a mining algorithm. The name extractor is based on existing dictionaries of gene names and aliases. The mining algorithm analyses the co-occurrences of words in the selected documents in order to automatically interpret the context, identify where the gene names appear, and map documents/genes into functional classes. DNA microarray technology is a high throughput method for gaining information on gene function. This large amount of data can be analyzed to identify groups of genes that share common expression characteristics, but the obtained results provide little information regarding the presence of functional biological correlations of genes within clusters. The published literature, on the other hand, provides a potential source of information to assist in interpretation of clustering results. We have developed a tool (MedMOLE) that improves the comprehension of microarray experimental results by grouping co-regulated genes on the basis of the informational content of MEDLINE documents. The tool relies on two components: a gene name extractor and a mining algorithm. The name extractor is based on existing dictionaries of gene names and aliases. The mining algorithm analyses the co-occurrences of words in the selected documents in order to automatically interpret the context, identify where the gene names appear, and map documents/genes into functional classes. Microarray transcriptional profiling is a powerful tool used in the study of transcriptional control mechanisms. An important point in the analysis of microarray data is the identification of hidden correlations between the differentially expressed genes generated upon some kind of cell stimulus. Functional annotation is an important topic for microarray data mining, however this is quite limited for complex organisms (e.g. H. sapiens, M. musculus) where a limited number of genes are well characterized and annotated. However, functional data are rapidly accumulating in the scientific literature and most of them are collected by MEDLINE, a database that contains over 11,000,000 biomedical journal citations. A microarray analysis usually generates few hundred of differentially expressed genes and, after statistical validation of the data and transcription profiles clustering, biologists try to identify genes functionally correlated by scientific literature analysis. Even if some tools have been recently developed to simplify information extraction on the MEDLINE database, reading every article requires too much time and labor. Therefore, it is necessary to have some kind of intelligent information extracting system that recognizes gene names inside the texts. The analysis of text documents (e.g. MEDLINE abstracts) can be approached by two different points of view: text mining and information extraction (I.E.). The former aims at the automatic identification of groups of documents that share the same patterns of words, and thus refer to the same topic or theme. The latter aims at providing a structured representation of the textual information and requires a pre-definition of entities and relationships to be looked for inside texts. Thus while the text mining algorithms are general purpose, the information extraction algorithms are specific to the application. Furthermore, the text mining approach is explorative and enables the discovery of new concepts and relations while information extraction only extracts those elements that have already been defined. These two approaches can be integrated: information extraction tools generate databases that can be analyzed using data mining techniques, and, on the other side, text mining tools might take advantage of specific domain information extracted using I.E. techniques. MedMOLE takes advantage of text mining techniques, and simplifies the extraction of functional knowledge by literature abstracts directly/indirectly related to differentially expressed genes identified by microarray technology. Sponsors: This work was partially supported by PRIN 2001 and FIRB 2002 grants.
Proper citation: Mining On-Line Expert on MedLine (RRID:SCR_001848) Copy
Service and training support for academic, government, and private sector scientists worldwide in genomics, including laboratory experimentation, statistical analysis, and comprehensive bioinformatics support, including large-scale genome comparisons, algorithm and tools development, and database curation, annotation and hosting. The Centre for Applied Genomics hosts a variety of databases related to ongoing supported projects: *Autism Chromosome Rearrangement Database *Cystic Fibrosis Mutation Database *The Lafora Progressive Myoclonus Epilepsy Mutation and Polymorphism Database *Database of Genomic Variants *The Chromosome 7 Annotation Project *Human Genome Segmental Duplication Database *Non-Human Segmental Duplication Database Healthy control DNA samples from the Ontario Population Genomics Platform are available. The Biobanking and Databasing Facility provides DNA extraction from lymphoblasts, fibroblasts and other cell types, archiving of white cell pellets, preparation and immortalization of cell lines, and comprehensive databasing and tracking of samples and/or cell lines within the facility.
Proper citation: TCAG (RRID:SCR_001840) Copy
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