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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
http://ccb.jhu.edu/software/ASprofile/
A suite of programs for extracting, quantifying and comparing alternative splicing (AS) events from RNA-seq data.
Proper citation: ASprofile (RRID:SCR_001833) Copy
http://www.genome.duke.edu/labs/ohler/research/NASTIseq/
Software for integrated detection of natural antisense transcripts using strand-specific RNA sequencing data.
Proper citation: NASTIseq (RRID:SCR_001797) Copy
https://www.bu.edu/tech/support/research/whats-happening/highlights/earlab/
Freely-accessible auditory databases as well as custom designed modeling and data analysis software tools. A fully functional online auditory modeling environment is also available, as well as downloadable models in several languages. The models cover many aspects of auditory function and at many different levels of detail ranging from multi-compartment celluar models to high-level abstractions of large portions of the auditory pathway. Currently a few models are available that can be run online and others are available for downloading. EarLab also provides custom cross-platform software for creating your own distributed auditory modeling environment, as well as software for analyzing the results from experimentation. A database of auditory modules is available for online use or download for the distributed auditory modeling environment, as well as instructions and specifications for creating your own modules. All these databases and custom software tools can be used in a wide variety of hearing research applications. This unique resource provides a wealth of information on auditory processing in humans and other animals. Mathematical models are also provided.
Proper citation: EarLab (RRID:SCR_001798) Copy
https://github.com/cengique/pandora-matlab
Matlab toolbox for analyzing neuronal electrophysiology data and constructing databases.
Proper citation: PANDORA Matlab Toolbox (RRID:SCR_001831) Copy
A freely accessible on-line systems biology resource devoted to all aspects of protein modification, as well as other post-translational modifications. It provides valuable and unique tools for both cell biologists and mass spectroscopists. PhosphoSite is a human- and mouse-centric database. It includes features such as: viewing the locations of modified residues on molecular models; browsing and searching MS2 records by disease, tissue, and cell line; submitting lists of peptides to identify previously reported genes; searching by sub-cellular localization, treatment, tissues, cell types, cell lines and diseases, and protein types and protein domains; searching for experimentally-verified kinase substrates and viewing preferred substrate motifs; and viewing MS2 spectra for peptides and sites not previously published.
Proper citation: PhosphoSitePlus: Protein Modification Site (RRID:SCR_001837) Copy
http://www.bioconductor.org/packages/release/bioc/html/OrderedList.html
An R / bioconductor package for detecting similarity in ordered gene lists. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively.
Proper citation: OrderedList (RRID:SCR_001834) Copy
https://gene.sfari.org/database/human-gene/
Curated public database for autism research built on information extracted from the studies on molecular genetics and biology of Autism Spectrum Disorders (ASD). The genetic information includes data from linkage and association studies, cytogenetic abnormalities, and specific mutations associated with ASD. New gene submissions are welcome. Modules: * Human Gene: thoroughly annotated list of genes that have been studied in the context of autism, with information on the genes themselves, relevant references from the literature, and the nature of the evidence. Uniquely, SFARI Gene incorporates information on both common and rare variants. * Animal Model: information about lines of genetically modified mice that represent potential models of autism. This information includes the nature of the targeting construct, the background strain and, most importantly, a thorough summary of the phenotypic features of the mice that are most relevant to autism. * Protein Interaction (PIN): compilation of all known direct protein interactions for those gene products implicated in autism. It presents both graphical and tabular views of interactomes, highlighting connections between autism candidate genes. Each protein interaction is manually verified by consultation with the primary reference. * Copy Number Variant (CNV): a parallel resource providing genetic information about all known copy number variants linked to autism. * Gene Scoring: includes a "score" for each autism candidate gene, based on an assessment of the strength of human genetic evidence.
Proper citation: AutDB (RRID:SCR_001872) Copy
http://www.public.asu.edu/~jye02/Software/SLEP/
Software package that provides functions for solving a family of sparse learning algorithms. The functions implemented enjoy the convergence rate of O(1/k^2), although the objective function is non-smooth. Main features: * First-Order Method. At each iteration, they only need to evaluate the function value and the gradient; and thus the algorithms can handle large-scale sparse data. * Optimal Convergence Rate. The convergence rate O(1/k^2) is optimal for smooth convex optimization via the first-order black-box methods. * Efficient Projection. The projection problem (proximal operator) can be solved efficiently. * Pathwise Solutions. The SLEP package provides functions that efficiently compute the pathwise solutions corresponding to a series of regularization parameters by the warm-start technique.
Proper citation: Sparse Learning with Efficient Projections (RRID:SCR_001870) Copy
http://www.scripps.edu/research/
Nonprofit American medical research facility that focuses on research and education in the biomedical sciences. Headquartered in San Diego, California with a sister facility in Jupiter, Florida, the institute has laboratories employing scientists, technicians, graduate students, and administrative and other staff, making it the largest private, non-profit biomedical research organization in the United States and among the largest in the world.
Proper citation: Scripps Research Institute (RRID:SCR_001907) Copy
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