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http://virome.diagcomputing.org/#view=home

A web-application designed for scientific exploration of metagenome sequence data collected from viral assemblages occurring within a number of different environmental contexts. The VIROME informatics pipeline focuses on the classification of predicted open-reading frames (ORFs) from viral metagenomes. The portal allows you to submit your viral metagenome to be processed through the VIROME analysis pipeline, and enable you to investigate your data via the VIROME user interface., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VIROME (RRID:SCR_004362) Copy   

•   Source: SciCrunch Registry

http://caintegrator-info.nci.nih.gov/rembrandt

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 28,2023. REMBRANDT is a data repository containing diverse types of molecular research and clinical trials data related to brain cancers, including gliomas, along with a wide variety of web-based analysis tools that readily facilitate the understanding of critical correlations among the different data types. REMBRANDT aims to be the access portal for a national molecular, genetic, and clinical database of several thousand primary brain tumors that is fully open and accessible to all investigators (including intramural and extramural researchers), as well as the public at-large. The main focus is to molecularly characterize a large number of adult and pediatric primary brain tumors and to correlate those data with extensive retrospective and prospective clinical data. Specific data types hosted here are gene expression profiles, real time PCR assays, CGH and SNP array information, sequencing data, tissue array results and images, proteomic profiles, and patients'''' response to various treatments. Clinical trials'''' information and protocols are also accessible. The data can be downloaded as raw files containing all the information gathered through the primary experiments or can be mined using the informatics support provided. This comprehensive brain tumor data portal will allow for easy ad hoc querying across multiple domains, thus allowing physician-scientists to make the right decisions during patient treatments., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Repository of molecular brain neoplasia data (RRID:SCR_004704) Copy   

•   Source: SciCrunch Registry

http://smart.embl.de/

Software tool for identification and annotation of genetically mobile domains and analysis of domain architectures.

Proper citation: SMART (RRID:SCR_005026) Copy   

•   Source: SciCrunch Registry

http://www.mknt.hu/sites/default/files/NEPSYBANK_0.doc

The Hungarian Society of Clinical Neurgenetics established a nationwide collaboration for prospective collection of human biological materials and databases from patient with neurological and psychiatric diseases. The basic triangle of the NEPSYBANK is the sample, the information and the study management. The present participants of the NEPSYBANK are the Department of Neurology and Psychiatry of the four Medical Universities (in Budapest, Debrecen, Pecs, Szeged) and the National Institute of Psychiatry and Neurology in Budapest. The NEPSYBANK is a disease based biobank collecting both phenotypical and environmental data and biological materials such as DNA/RNA, whole blood, plasma, cerebral spinal fluid, muscle / nerve / skin biopsy, brain, and fibroblast. The target of the diseases is presently (Phase I): stroke syndromes, dementias, movement disorders, motoneuron diseases, epilepsy, multiple sclerosis, schizophrenia, alcohol addiction. In the near future (Phase II.) it is planned to enlarge the scale with headaches, disorders of the peripheral nerves, disorders of neuromuscular transmission, disorders of skeletal muscle, depression, anxiety. DNA/RNA is usually extracted from whole blood, but occasionally different tissues such as muscle, brain etc. can be used as well. The extracting procedures differ among the institutes, but in all cases the concentration and the quality of the DNA/RNA must be registered in the database. Participating institutional biobanks have committed themselves to follow common quality standards, which provide access to samples after prioritization on scientific grounds only. In every case the following data are registered. 1. General data: main bank categories, age, sex, ethnicity, body height, body weight, economic stats, education, type of place of living, marital status, birth complications, alcohol, drugs, smoking. 2. Sample properties (sample ID, type of sample, date of extraction, concentration, and level of purity). General patient data as blood pressure, heart rate, internal medical status, ECG, additional diseases. Disease specific question e.g. in schizophrenia the diagnosis after DSMIV and ICD 10, detailed diagnostic questions after both classification, detailed psychiatric and neurological status, laboratory findings, rating scales, data of neuroimaging, genetic tests, applied medication (with generic name, dose, duration), adverse drug effects and other treatments. The Biobank Information Management System (BIMS) is responsible for linkage of databases containing information on the individual sample donors. If you want to have samples from the NEPSYBANK an application must be submitted containing the following information: short research plan including aims and study design, ethic application with a positive decision, specific demands regarding the right of disposition, agreements with grant organizations which regulate immaterial property, information about financing (academic grants, support from industry). All participants have the right to withdraw their samples through a simple order.

Proper citation: Hungarian Neurological-Psychiatric Biobank (RRID:SCR_003715) Copy   

•   Source: SciCrunch Registry

http://www.type2diabetesgenetics.org/

Portal and database of DNA sequence, functional and epigenomic information, and clinical data from studies on type 2 diabetes and analytic tools to analyze these data. .Provides data and tools to promote understanding and treatment of type 2 diabetes and its complications. Used for identifying genetic biomarkers correlated to Type 2 diabetes and development of novel drugs for this disease.

Proper citation: Accelerating Medicines Partnership Type 2 Diabetes Knowledge Portal (AMP-T2D) (RRID:SCR_003743) Copy   

•   Source: SciCrunch Registry

http://coot.embl.de/g2d/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of candidate genes for mapped inherited human diseases. Candidate priorities are automatically established by a data mining algorithm that extracts putative genes in the chromosomal region where the disease is mapped, and evaluates their possible relation to the disease based on the phenotype of the disorder. Data analysis uses a scoring system developed for the possible functional relations of human genes to genetically inherited diseases that have been mapped onto chromosomal regions without assignment of a particular gene. Methodology can be divided in two parts: the association of genes to phenotypic features, and the identification of candidate genes on a chromosonal region by homology. This is an analysis of relations between phenotypic features and chemical objects, and from chemical objects to protein function terms, based on the whole MEDLINE and RefSeq databases.

Proper citation: Candidate Genes to Inherited Diseases (RRID:SCR_008190) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/enhanced/

Dataset of 1000 characterized community-ascertained participants using state-of-the-art multiband imaging-based resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI), genetics, and a deep phenotyping protocol from a large cross-sectional sample of brain development, maturation and aging (ages 6 - 85 yrs). The Center for Magnetic Resonance Research (CMRR), University of Minnesota, provided the NKI-RS effort with the latest version of the Multiband EPI sequence (Xu et al. 2012) and associated image reconstruction algorithms, enabling the acquisition of state-of-the-art imaging datasets for this large-scale imaging effort. The enhanced NKI-RS expands upon the phenotypic protocol of the original NKI-RS and captures a broad range of behavioral and cognitive phenomenology relevant to psychiatric health and illness. The validity and value of assessments were evaluated by consulting leaders in the field of psychiatric phenotyping.

Proper citation: NKI-RS Enhanced Sample (RRID:SCR_010461) Copy   

•   Source: SciCrunch Registry

http://upr.eagle-i.net/i/0000012c-9dd1-fddf-95a5-371e80000000

Core facility that provides the following services: Necropsy. The Sabana Seca Field Station (SSFS) is the administrative headquarters for the Caribbean Primate Research Center, or CPRC. This area contains offices, a clinic, and laboratories for reproductive biology and bone densitometry, necropsy and maintenance buildings. It houses rhesus monkeys from the CS colony or from the CS genetic line.

Proper citation: UPR Sabana Seca Field Station Caribbean Primate Research Center (RRID:SCR_010134) Copy   

•   Source: SciCrunch Registry

http://www.seattle.eric.research.va.gov/VETR/Home.asp

The Vietnam Era Twin (VET) Registry is a closed cohort composed of approximately 7,000 middle-aged male-male twin pairs both of whom served in the military during the time of the Vietnam conflict (1964-1975). The Registry is a United States Department of Veterans Affairs (VA) resource that was originally constructed from military records; the Registry has been in existence for almost 20 years. It is one of the largest national twin registries in the US and currently has members living in all 50 states. Initially formed to address questions about the long-term health effects of service in Vietnam, the Registry has evolved into a resource for genetic epidemiological studies of mental and physical health conditions. Several waves of mail and telephone surveys have collected a wealth of health-related information on Registry twins, referred to as members. In addition to twins, selected adult offspring of twins and the mothers of those offspring are also VET Registry members. More recent data collection efforts have focused on specific sets of twin pairs and have conducted detailed clinical or laboratory testing. Selected Vietnam Era Registry Research Studies: * Veteran Health Study * VETSA 2: A Longitudinal Study of Cognitive Aging * Alcoholism Course thought Midlife: A Twin Family Study and Offspring of Twins: G, E and GxE Risk for Alcoholism * GE: Offspring of Twins with Substance Use Disorder * Mechanisms Linking Depression to Cardiovascular Risk (Twins Heart Study 2) * Post-traumatic Stress Disorder and Cardiovascular Disease * Biological Markers for Post-traumatic Stress Disorder (T3) * Memory and the Hippocampus in Vietnam-era Twins with PTSD (Time 3)

Proper citation: Vietnam Era Twin Registry (RRID:SCR_008807) Copy   

•   Source: SciCrunch Registry

http://www.cdc.gov/genomics/hugenet/default.htm

Human Genome Epidemiology Network, or HuGENet, is a global collaboration of individuals and organizations committed to the assessment of the impact of human genome variation on population health and how genetic information can be used to improve health and prevent disease. Its goals include: establishing an information exchange that promotes global collaboration in developing peer-reviewed information on the relationship between human genomic variation and health and on the quality of genetic tests for screening and prevention; providing training and technical assistance to researchers and practitioners interested in assessing the role of human genomic variation on population health and how such information can be used in practice; developing an updated and accessible knowledge base on the World Wide Web; and promoting the use of this knowledge base by health care providers, researchers, industry, government, and the public for making decisions involving the use of genetic information for disease prevention and health promotion. HuGENet collaborators come from multiple disciplines such as epidemiology, genetics, clinical medicine, policy, public health, education, and biomedical sciences. Currently, there are 4 HuGENet Coordinating Centers for the implementation of HuGENet activities: CDC''s Office of Public Health Genomics, Atlanta, Georgia; HuGENet UK Coordinating Center, Cambridge, UK; University of Ioannina, Greece; University of Ottawa , Ottawa, Canada. HuGENet includes: HuGE e-Journal Club: The HuGE e-Journal Club is an electronic discussion forum where new human genome epidemiologic (HuGE) findings, published in the scientific literature in the CDC''s Office of Public Health Genomics Weekly Update, will be abstracted, summarized, presented, and discussed via a newly created HuGENet listserv. HuGE Reviews: A HuGE Review identifies human genetic variations at one or more loci, and describes what is known about the frequency of these variants in different populations, identifies diseases that these variants are associated with and summarizes the magnitude of risks and associated risk factors, and evaluates associated genetic tests. Reviews point to gaps in existing epidemiologic and clinical knowledge, thus stimulating further research in these areas. HuGE Fact Sheets: HuGE Fact Sheets summarize information about a particular gene, its variants, and associated diseases. HuGE Case Studies: An on-line presentation designed to sharpen your epidemiological skills and enhance your knowledge on genomic variation and human diseases. Its purpose is to train health professionals in the practical application of human genome epidemiology (HuGE), which translates gene discoveries to disease prevention by integrating population-based data on gene-disease relationships and interventions. Students will acquire conceptual and practical tools for critically evaluating the growing scientific literature in specific disease areas. HUGENet Publications: Articles related to the HuGENet movement written by our HuGENet collaborators. HuGE Navigator: An integrated, searchable knowledge base of genetic associations and human genome epidemiology, including information on population prevalence of genetic variants, gene-disease associations, gene-gene and gene- environment interactions, and evaluation of genetic tests. HuGE Workshops: HuGENet has sponsored meetings and workshops with national and international partners since 2001. Available are detailed summaries, agendas or the ability to download speaker slides. HuGE Book: Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. (The findings and conclusions in this book are those of the author(s) and do not necessarily represent the views of the funding agency.) HuGENet Collaborators: HuGENet is interested in establishing collaborations with individuals and organizations working on population based research involving genetic information. HuGE Funding: Funding opportunities for specific population-based genetic epidemiology research projects are available. Research initiatives whose aims include assessing the prevalence of human genetic variation, the association between genetic variants and human diseases, the measurement of gene-gene or gene-environment interaction, and the evaluation of genetic tests for screening and prevention are compiled to create a posted listing. Additional information and application details can be found by clicking on the respective links.

Proper citation: Human Genome Epidemiology Network (RRID:SCR_013117) Copy   

•   Source: SciCrunch Registry

http://www.omicsexpress.com/sva.php

Software package to annotate, visualize, and analyze the genetic variants identified through next-generation sequencing studies, including whole-genome sequencing (WGS) and exome sequencing studies. SVA aims to provide the research community with a user-friendly and efficient tool to analyze large amount of genetic variants, and to facilitate the identification of the genetic causes of human diseases and related traits.

Proper citation: SVA (RRID:SCR_002155) Copy   

•   Source: SciCrunch Registry

https://www.sfari.org/funding-opportunities/

The mission of SFARI is to improve the diagnosis, treatment, and prevention of autism and related developmental disorders. SFARI explores neuroscience from multiple directions, including molecular, cellular, systems, immunological, cognitive, behavioral, genetic, theoretical and computational perspectives. Funding for innovative scientific research is available through a peer-reviewed proposal process at regular intervals. Research projects are reviewed by a scientific advisory board and managed by the scientific director and a highly qualified staff. Proposals in multiple research areas are sought, to reflect the complex nature of autism. The Foundation supports innovative scientific projects where our involvement will play an essential role. In the course of this support, The Foundation is interested in partnering with other entities, or providing matching support where appropriate. The Simons Foundation has historically accepted only solicited grant proposals. These grant decisions are made by the Trustees of The Simons Foundation, who review applications on an ongoing basis. In the area of autism research, requests for proposals are issued on an annual basis. The Simons Foundation does not give grants to individuals, except through their institutions.

Proper citation: Simons Foundation Autism Research Initiative: Grant Resource (RRID:SCR_001862) Copy   

•   Source: SciCrunch Registry

http://www.stats.ox.ac.uk/%7Emarchini/software.html

An R package that specifically focuses on statistical and population genetics methods. The motivation behind the package is to produce an easy to use interface to many of the commonly used methods and models used in statistical and population genetics and an alternative interface for some of the methodology produced by our group. (entry from Genetic Analysis Software)

Proper citation: POPGEN (RRID:SCR_007315) Copy   

•   Source: SciCrunch Registry

http://gaow.github.io/genetic-analysis-software/e-1.html#ehp

Software application that provides variance estimates for haplotype frequency estimates, it allows several kinds of missing information in the genotype data, it also allows for combined genotype data of different pool sizes. This program can be used for testing haplotype-disease associations in case control studies by calculating the likelihood ratio test: 2 log(likelihood for cases) + 2 log(likelihood for controls) - 2 log(likelihood for case+controls). (entry from Genetic Analysis Software)

Proper citation: EHP (RRID:SCR_009170) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/onemap/index.html

Software environment for constructing linkage maps in outcrossing plant species, using full-sib families derived from two outbreed (non-inbreeding) parent plants. (entry from Genetic Analysis Software)

Proper citation: R/ONEMAP (RRID:SCR_009371) Copy   

•   Source: SciCrunch Registry

http://courses.jax.org/2012/addiction.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. This course emphasizes genetic applications and approaches to drug addiction research through methodological instruction based on literature, data sets and informatics resources drawn from studies of addiction related phenotypes. The course includes plenary sessions on major progress in addiction genetics, and discussion sessions in which students present their work for discussion on applications of genetic methods. Students will leave the course able to design and interpret genetic and genomic studies of addiction as they relate to their specific research question, and will be able to make use of current bioinformatics resources to identify research resources and make use of public data sources in their own research.

Proper citation: Short Course on the Genetics of Addiction (RRID:SCR_005560) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/hapassoc/index.html

Software application using a likelihood approach to inference of haplotype and nongenetic effects and their interactions in generalized linear models of disease penetrance, when haplotype phase is unknown for some subjects. Parameter estimates are obtained by use of an expectation-maximization (EM) algorithm and standard errors are calculated using Louis'' formula. (entry from Genetic Analysis Software)

Proper citation: R/HAPASSOC (RRID:SCR_009365) Copy   

•   Source: SciCrunch Registry

http://www.cs.cmu.edu/~genome/FAST-MAP.html

Fluorescent allele-calling software toolkit: a computer software for fully automated microsatellite genotyping. (entry from Genetic Analysis Software)

Proper citation: FASTMAP (1) (RRID:SCR_008346) Copy   

•   Source: SciCrunch Registry

http://automl.info/tpot/

Software Python package to automate building of ML pipelines by combining flexible expression tree representation of pipelines with stochastic search algorithms such as genetic programming.

Proper citation: Tree-Based Pipeline Optimization Tool (RRID:SCR_017531) Copy   

•   Source: SciCrunch Registry

http://wiringthebrain.blogspot.com/

This blog highlights and comments on current research and hypotheses relating to how the brain wires itself up during development, how the end result can vary in different people and what happens when it goes wrong. It includes discussions of the genetic and neurodevelopmental bases of traits such as intelligence and personality characteristics, as well as of conditions such as schizophrenia, autism, dyslexia, epilepsy, synaesthesia and others.

Proper citation: Wiring the Brain (RRID:SCR_005528) Copy   

•   Source: SciCrunch Registry