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http://www.blueprint-epigenome.eu/

Consortium to further the understanding of how genes are activated or repressed in both healthy and diseased human cells with a focus on distinct types of haematopoietic cells from healthy individuals and on their malignant leukemic counterparts. They will generate at least 100 reference epigenomes and study them to advance and exploit knowledge of the underlying biological processes and mechanisms in health and disease. Reference epigenomes will be generated by state-of-the-art technologies from highly purified cells for a comprehensive set of epigenetic marks in accordance with quality standards set by International Human Epigenome Consortium (IHEC). Access to the data is provided as well as the protocols used to collect the different blood cell types, to perform the different types of epigenomic analyses, etc.). This resource-generating activity will be complemented by hypothesis-driven research into blood-based diseases, including common leukemias and autoimmune disease (Type 1 Diabetes), by discovery and validation of epigenetic markers for diagnostic use and by epigenetic target identification. Since epigenetic changes are reversible, they can be targets for the development of novel and more individualized medical treatments. The involvement of companies will energize epigenomic research in the private sector by the development of smart technologies for better diagnostic tests and by identifying new targets for compounds. Thus the results of the project may lead to targeted diagnostics, new treatments and preventive measures for specific diseases in individual patients, an approach known as "personalized medicine". The Blueprint Data Access Committee will consider applications for access to data sets stored in the European Genome-phenome Archive (EGA) when authorized to do so by the Blueprint consortium and the holders of the original consent documents. Access is conditional upon availability of samples and/or data and signed agreement by the researcher(s) and the responsible employing Institution to abide by policies related to publication, data disposal, ethical approval and confidentiality. At EBI, the ftp site with the data can be found. You can either opt to link to the track hubs yourself or you can add the track hub to a genome browser - UCSC or ENSEMBL. Also Meta Data files and README are available. The data can also be accessed via the BIOMART system.

Proper citation: Blueprint Epigenome (RRID:SCR_003844) Copy   

•   Source: SciCrunch Registry

http://www.wholecellkb.org/

A collection of free, open-source model organism databases designed specifically to enable comprehensive, dynamic simulations of entire cells and organisms. WholeCellKB provides comprehensive, quantitative descriptions of individual species including: * Their subcellular organization, * Their chromosome sequences, * The essentiality, location, length, direction, and homologs of each gene, * The organization and promoter of each transcription unit, * The expression and degradation rate of each RNA gene product, * The specific folding and maturation pathway of each RNA and protein species including the localization, N-terminal cleavage, signal sequence, prosthetic groups, disulfide bonds, and chaperone interactions of each protein species, * The subunit composition of each macromolecular complex, * Their genetic code, * The binding sites and footprint of every DNA-binding protein, * The structure, charge, and hydrophobicity of every metabolite, * The stoichiometry, catalysis, coenzymes, energetics, and kinetics of every chemical reaction, * The regulatory strength of each transcription factor on each promoter, * Their chemical composition, and * The composition of its typical SP-4 laboratory growth medium. WholeCellKB currently contains a single database of Mycoplasma genitalium, an extremely small gram-positive bacterium and common human pathogen. This database is the most comprehensive description of any single organism to date, and was used to develop the first whole-cell computational model. Users can download the WholeCellKB source code and content to create and customize - including the content, data model, and user interface - their own model organism database.

Proper citation: WholeCellKB (RRID:SCR_004104) Copy   

•   Source: SciCrunch Registry

http://www.adgenetics.org/

Consortium to conduct genome-wide association studies (GWAS) to identify genes associated with an increased risk of developing late-onset Alzheimer''''s disease (LOAD). The goal of the ADGC is to identify genetic variants associated with risk for AD. It plans to do this through the following collaborative goals: # Identify genes responsible for AD susceptibility # Identify AD sub-phenotype genes rate-of-progression plaque / tangle load / distribution biomarker variability # Generate a genetic data resource for the AD research community Data generated by ADGC is available at the following website: https://www.niagads.org/content/alzheimers-disease-genetics-consortium-adgc-collection

Proper citation: Alzheimers Disease Genetics Consortium (RRID:SCR_004004) Copy   

•   Source: SciCrunch Registry

http://www.wikigenes.org/e/art/e/258.html

Consortium to discover and map the genes that contribute to Alzheimer's disease and completely understand the role inheritance plays. To achieve this goal, they will work to identify all the genes that contribute to the risk of developing this disease. Investigators will have access to combined genetic data from a large number of Alzheimer's disease subjects and compare it to genetic data from an equally large number of elderly people who do not have Alzheimer's. In the initial phase of the work, more than 20,000 people with Alzheimer's and about 20,000 healthy elderly subjects will be compared. As the study progresses, 10,000 additional people with Alzheimer's and the same number of healthy elderly subjects will be added to the study. The subjects for these studies come from different Alzheimer research project locations across Europe, the UK, the US, and Canada. Data is available from their 2014 publication in Translational Psychiatry at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944635/ (http://www.ncbi.nlm.nih.gov/pubmed/24495969) Currently, there is no public access to the raw individual level genetic data because of privacy considerations. Researchers working with US cohorts deposit data in the database of genotypes and phenotypes (dbGaP), where it is available to all researchers who can show that they are able to guarantee the security of the data. After scanning the DNA of over 74,000 patients and controls from 15 countries, the IGAP consortium reported 11 new regions of the genome involved in late-onset Alzheimer's disease. IGAP published its results in Nature Genetics on October 27, http://www.ncbi.nlm.nih.gov/pubmed/24162737

Proper citation: International Genomics of Alzheimers Project (RRID:SCR_004029) Copy   

•   Source: SciCrunch Registry

http://eaglep.case.edu/iamdgc_web/

Consortium aiming to identify the remaining genetic risk variants for Age-related Macular Degeneration (AMD). To increase the statistical power needed to identify genes that have small, yet significant contributions to AMD, the consortium is conducting a meta-analysis on 15 Genome Wide Association Studies (GWAS) pooled from consortium members representing over 8,000 patients with advanced AMD (dry type, neovascular, or both) and 50,000 controls. In addition to verifying known genes, the consortium identified 19 new gene variants. The genes identified in these studies function in the immune system, cholesterol transport and metabolism, and formation and maintenance of connective tissue. This study provides a nearly complete picture of genetic heritability for AMD.

Proper citation: International AMD Genetics Consortium (RRID:SCR_004009) Copy   

•   Source: SciCrunch Registry

http://discovery.hsci.harvard.edu/

An online database of curated cancer stem cell (CSC) experiments coupled to the Galaxy analytical framework. Driven by a need to improve our understanding of molecular processes that are common and unique across cancer stem cells (CSCs), the SCDE allows users to consistently describe, share and compare CSC data at the gene and pathway level. The initial focus has been on carefully curating tissue and cancer stem cell-related experiments from blood, intestine and brain to create a high quality resource containing 53 public studies and 1098 assays. The experimental information is captured and stored in the multi-omics Investigation/Study/Assay (ISA-Tab) format and can be queried in the data repository. A linked Galaxy framework provides a comprehensive, flexible environment populated with novel tools for gene list comparisons against molecular signatures in GeneSigDB and MSigDB, curated experiments in the SCDE and pathways in WikiPathways. Investigation/Study/Assay (ISA) infrastructure is the first general-purpose format and freely available desktop software suite targeted to experimentalists, curators and developers and that: * assists in the reporting and local management of experimental metadata (i.e. sample characteristics, technology and measurement types, sample-to-data relationships) from studies employing one or a combination of technologies; * empowers users to uptake community-defined minimum information checklists and ontologies, where required; * formats studies for submission to a growing number of international public repositories endorsing the tools, currently ENA (genomics), PRIDE (proteomics) and ArrayExpress (transcriptomics). Galaxy allows you to do analyses you cannot do anywhere else without the need to install or download anything. You can analyze multiple alignments, compare genomic annotations, profile metagenomic samples and much much more. Best of all, Galaxy''''s history system provides a complete analyses record that can be shared. Every history is an analysis workflow, which can be used to reproduce the entire experiment. The code for this Galaxy instance is available for download from BitBucket.

Proper citation: Stem Cell Discovery Engine (RRID:SCR_004453) Copy   

•   Source: SciCrunch Registry

https://4dgenome.research.chop.edu/

Repository for chromatin interaction data. Records can be queried by genomic regions, gene names, organism, and detection technology. Database is continuously updated by curators. Contributions from scientific community.

Proper citation: 4D Genome (RRID:SCR_017489) Copy   

•   Source: SciCrunch Registry

https://github.com/YosefLab/FastProject

Software Python tool for low dimensional analysis of single-cell RNA-Seq data. Software package for two dimensional visualization of single cell data. Analyzes gene expression matrix and produces output report in which two-dimensional of data can be explored.

Proper citation: FastProject (RRID:SCR_017462) Copy   

•   Source: SciCrunch Registry

https://amp.pharm.mssm.edu/geneshot/

Software tool as search engine for ranking genes from arbitrary text queries. Enables to enter arbitrary search terms, to receive ranked lists of genes relevant to search terms. Returned ranked gene lists contain genes that were previously published in association with search terms, as well as genes predicted to be associated with terms based on data integration from multiple sources. Search results are presented with interactive visualizations.

Proper citation: Geneshot (RRID:SCR_017582) Copy   

•   Source: SciCrunch Registry

http://geneontology.org/docs/go-consortium/

Consortium integrates resources from variety of research groups, from model organisms to protein databases to biological research communities actively involved in development and implementation of Gene Ontology. Mission to develop up to date, comprehensive, computational model of biological systems, from molecular level to larger pathways, cellular and organism level systems.

Proper citation: GO Gene Ontology Consortium and Knowledgebase (RRID:SCR_017505) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/mgihome/nomen/index.shtml

Authoritative source of official names for mouse genes, alleles, and strains. Nomenclature follows rules and guidelines established by International Committee on Standardized Genetic Nomenclature for Mice.

Proper citation: Nomenclature (RRID:SCR_017511) Copy   

•   Source: SciCrunch Registry

https://github.com/epurdom/clusterExperiment

Software open source R package for executing, evaluating and visualizing different clusterings of experimental data, including data from single cell RNA-Seq studies. Software for running and comparing different clusterings of single cell sequencing data.

Proper citation: clusterExperiment (RRID:SCR_017439) Copy   

•   Source: SciCrunch Registry

http://sydney.edu.au/medicine/bosch/facilities/molecular-biology/nucleic-acid/corbett-rotor-gene.php

Rotor-Gene real-time analysis software system for ROTOR GENE 6000 REAL-TIME PCR machine. Software has been refined to provide an intuitive, Wizard driven interface, enabling flexibility and automation. Build-in extensive analysis, graphing and statistical functions. Unlimited use software license for Windows XP, Pentium IV (2GHz) or higher PC.

Proper citation: Rotor-Gene 6000 series software (RRID:SCR_017552) Copy   

•   Source: SciCrunch Registry

http://topaz.gatech.edu/GeneMark/

Software package for ab initio identification of protein coding regions in RNA transcripts. Algorithm parameters are estimated by unsupervised training which makes unnecessary manually curated preparation of training sets. Sets of assembled eukaryotic transcripts can be analyzed by modified GeneMarkS-T algorithm which part of gene prediction programs GeneMark.

Proper citation: GeneMarkS-T (RRID:SCR_017648) Copy   

•   Source: SciCrunch Registry

https://nyumc.ilab.agilent.com/service_center/4273

Core offers services for researchers who want to apply advanced molecular genetic techniques in rodent models of physiology and disease. Provides expertise in generating novel mutant and transgenic mouse strains using genome engineering in mouse embryos and in embryonic stem cells (ESCs). Available technologies include:Generation of genome-edited mice by embryo pronuclear microinjection of DNA and genome editors (e.g., CRISPR/Cas9, site-specific recombinases) or traditional BAC transgenesis;Generation of genome-edited mice from mouse embryonic stem cells (mESCs) by chimeric blastocyst injection;Generation of genome-edited mice from mESCs by tetraploid blastocyst injection; Generation of mice from induced pluripotent stem cells;Assisted reproductive technologies; Sperm and embryo cryopreservation, storage and import/export;in vitro fertilization (IVF); Embryo rederivation technologies for animal import into barrier vivaria through quarantine.

Proper citation: NYU Langone’s Advanced Rodent Transgenics Laboratory ART-Lab Core Facility (RRID:SCR_017692) Copy   

•   Source: SciCrunch Registry

https://github.com/hariszaf/pema

Software as flexible pipeline for environmental DNA metabarcoding analysis of 16S/18S rRNA, ITS and COI marker genes. Performs reads’ pre-processing, clustering to (M)OTUs and taxonomy assignment for 16S rRNA and COI marker gene data. Allows users to explore alternative algorithms for specific steps of pipeline without need of complete re-execution.

Proper citation: PEMA (RRID:SCR_017676) Copy   

•   Source: SciCrunch Registry

https://github.com/shanglicheng/BootstrappingWithoutReplacement

Software tool to dig out more robust and reliable differentially expressed genes between two groups. Samples from different groups will be re-sampled randomly associated with total number of samples.

Proper citation: BootstrappingWithoutReplacement (RRID:SCR_017673) Copy   

•   Source: SciCrunch Registry

http://lrpath.ncibi.org/

Web tool to perform gene set enrichment testing. Used to test for predefined biologically relevant gene sets that contain more significant genes from experimental dataset than expected by chance. Logistic regression approach for identifying enriched biological groups in gene expression data.

Proper citation: LRPath (RRID:SCR_018572) Copy   

•   Source: SciCrunch Registry

http://theparkerlab.org/tools/isleteqtl/

Web tool for exploring variants in islet expression quantitative trait loci. Data is result of collaboration between Michigan University Parker lab, Department of Biostatistics and Center for Statistical Genetics at University of Michigan, National Human Genome Research Institute, Jackson Laboratory for Genomic Medicine, Department of Genetics at University of North Carolina, European Bioinformatics Institute, Department of Preventive Medicine at University of Southern California, and Department of Physiology and Biophysics at University of Southern California.

Proper citation: Islet eQTL Explorer (RRID:SCR_018692) Copy   

•   Source: SciCrunch Registry

https://kmplot.com/analysis/

Web tool for meta analysis based discovery and validation of survival biomarkers. Assesses effect of genes on survival using cancer samples including breast, ovarian, lung, and gastric cancer. Sources for databases include GEO, EGA, and TCGA.

Proper citation: Kaplan Meier Plotter (RRID:SCR_018753) Copy   

•   Source: SciCrunch Registry