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http://www.lamhdi.org/

THIS RESOURCE IS NO LONGER IN SERVICE, it has been replaced by Monarch Initiative. LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal model for their research. LAMDHI is a free, Web-based, resource to help researchers bridge the gap between bench testing and human trials. It provides a free, unbiased resource that enables scientists to quickly find the best animal models for their research studies. LAMHDI includes mouse data from MGI, the Mouse Genome Informatics website; zebrafish data from ZFIN, the Zebrafish Model Organism Database; rat data from RGD, the Rat Genome Database; yeast data from SGD, the Saccharomyces Genome Database; and fly data from FlyBase. LAMHDI.org is operational today, and data is added regularly. Enhancements are planned to let researchers contribute their knowledge of the animal models available through LAMHDI. The LAMHDI goal is to allow researchers to share information about and access to animal models so they can refine research and testing, and reduce or replace the use of animal models where possible. LAMHDI Database Search: LAMHDI brings together scientifically validated information from various sources to create a composite multi-species database of animal models of human disease. To do this, the LAMHDI database is prepared from a variety of sources. The LAMHDI team takes publicly available data from OMIM, NCBI''s Entrez Gene database, Homologene, and WikiPathways, and builds a mathematical graph (think of it as a map or a web) that links these data together. OMIM is used to link human diseases with specific human genes, and Entrez provides universal identifiers for each of those genes. Human genes are linked to their counterpart genes in other species with Homologene, and those genes are linked to other genes tentatively or authoritatively using the data in WikiPathways. This preparatory work gives LAMHDI a web of human diseases linked to specific human genes, orthologous human genes, homologous genes in other species, and both human and non-human genes involved in specific metabolic pathways associated with those diseases. LAMHDI includes model data that partners provide directly from their data structures. For instance, MGI provides information about mouse models, including a disease for each model, as well as some genetic information (the ID of the model, in fact, identifies one or more genes). ZFIN provides genetic information for each zebrafish model, but no diseases, so zebrafish models are integrated by using the genes as the glue. For instance, a zebrafish model built to feature the zebrafish PKD2 gene would plug into the larger disease-gene map at the node representing the zebrafish PKD2 gene, which is connected to the node representing the human PKD2 gene, which in turn is connected to the node representing the human disease known as polycystic kidney disease. (Some of the partner data LAMHDI receives can even extend the base map. MGI provides a disease for every model, and in some cases this allows the creation of a disease-to-gene relationship in the LAMHDI database that might not already be documented in the OMIM dataset.) With curatorial and model information in hand, LAMHDI runs a lengthy automated process that exhaustively searches for every possible path between each model and each disease in the data, up to a set number of hops, producing for each disease-to-model pair a set of links from the disease to the model. The algorithm avoids circular paths and paths that include more than one disease anywhere in the middle of the path. At the end of this phase, LAMHDI has a comprehensive set of paths representing all the disease-to-model relationships in the data, varying in length from one hop to many hops. Each disease-to-model path is essentially a string of nodes in the data, where each node represents a disease, a gene, a linkage between genes (an orthologue, a homologue, or a pathway connection, referred to as a gene cluster or association), or a model. Each node has a human-friendly label, a set of terms and keywords, and - in most cases - a URL linking the node to the data source where it originated. When a researcher submits a search on the LAMHDI website, LAMHDI searches for the user''s search terms in its precomputed list of all known disease-to-model paths. It looks for the terms not only in the disease and model nodes, but also in every node along each path. The complete set of hits may include multiple paths between any given disease-to-model pair of endpoints. Each of these disease-to-model pair sets is ordered by the number of hops it involves, and the one involving the fewest hops is chosen to represent its respective disease-to-model pair in the search results presented to the user. Results are sorted by scores that represent their matches. The number of hops is one barometer of the strength of the evidence linking the model and the disease; fewer hops indicates the relationship is stronger, more hops indicates it may be weaker. This indicator works best for comparing models from a single partner dataset: MGI explicitly identifies a disease for each mouse model, so there can be disease-to-model hits for mice that involve just one hop. Because ZFIN does not explicitly identify a disease for each model, no zebrafish model will involve fewer than four hops to the nearest disease, from the zebrafish model to a zebrafish gene to a gene cluster to a human gene to a human disease.

Proper citation: LAMHDI: The Initiative to Link Animal Models to Human DIsease (RRID:SCR_008643) Copy   

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http://alizadehlab.stanford.edu/

This is an open-source Mouse Exonic Evidence-Based Oligonucleotide Chip (MEEBOChip), and are in the process of building the human counterpart, HEEBOChip. The set of 70mers for MEEBOChip is already available from Illumina, Inc., with synthesis of HEEBOChip 70mers in progress. Both arrays are based on a novel selection of exonic long-oligonucleotides (70-mers) from a genomic annotation of the corresponding complete genome sequences, using a transcriptome-based annotation of exon structure for each genomic locus. Using a combination of existing and custom-tailored tools and datasets (including millions of mRNA and EST sequences), we built and performed a systematic examination of transcript-supported exon structure for each genomic locus at the base-pair level (i.e., exonic evidence). This strategy allowed them to select both constitutive and in many cases alternative exons for nearly every gene in the corresponding genome (e.g., protocadherin locus), allowing an unprecedented exploration of human and mouse biology. Furthermore, they used experimentally derived data to hone the selection of these 70mers, helping maximize their performance under typical fluorescent labeling and hybridization conditions. Specifically, they applied and refined the ArrayOligoSelector algorithm from Joe DeRisis laboratory to select 70mers, considering not only their uniqueness (i.e., hybridization specificity) within the content of the entire genome, but also to overcome the known biases of labeling and hybridization methods (e.g., 3-biased reverse transcription and in vitro transcription reactions).

Proper citation: Alizadehlab: MeeboChip and HeeboChip Open Source Project (RRID:SCR_008384) Copy   

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http://www.molecularbrain.org/

MolecularBrain is an attempt to collect, collates, analyze and present the microarray derived gene expression data from various brain regions side by side. Transcription Profile of any gene in Mouse (online) and Human Brain (not yet) can be accessed as a histogram along with links to access various aspects of that gene. The expression levels were calculated from microarray data deposited at GEO (Gene expression omnibus). The molecular brain database could be searched using the built in search tool with the terms Entrez GeneID, gene symbol, synonym or description. Gene information along with their expression values can be also accessed from the alphabetical list of gene symbols on the footer. The protocol and GEO sample information is available.

Proper citation: Molecular Brain: Transcription Profiles of Mouse and Human Brains (RRID:SCR_008689) Copy   

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http://www.cmbi.ru.nl/GeneSeeker/

The GeneSeeker allows you to search across different databases simultaneously, given a known human genetic location and expression/phenotypic pattern. The GeneSeeker returns any found gene names which are located on the specified location and expressed in the specified tissue. To search for more expression location in one search, just enter them in the textbox for the expression location and separate them with logical operators (and, or, not). You can specify as many tissues as you want, the program starts 20 queries simultaneously, and then waits for a query to finish before starting another query, to keep server loads to a minimum. You can also search only for expression, just leave the cytogenetic location fields blank, and do the query. If you only want to look for one cytogenetic location, only fill in the first location field, and the GeneSeeker will search with only this one. Housekeeping genes , found in Swissprot can be excluded, or genes that are to be excluded can be specified. Human chromosome localizations are translated with an oxford-grid to mouse chromosome localizations, and then submitted to the Mgd. Sponsors: GeneSeeker is a service provided by the Centre for Molecular and Biomolecular Informatics (CMBI).

Proper citation: GeneSeeker (RRID:SCR_008347) Copy   

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http://www.phac-aspc.gc.ca/msds-ftss/

Material Safety Data Sheets for chemical products are available to laboratory workers for most chemicals and reagents. However because many laboratory workers, whether in research, public health, teaching, etc., are exposed to not only chemicals but infectious substances as well, there was a large gap in the readily available safety literature for employees. These MSDS are produced for personnel working in the life sciences as quick safety reference material relating to infectious micro-organisms. The MSDS are organized to contain health hazard information such as infectious dose, viability (including decontamination), medical information, laboratory hazard, recommended precautions, handling information and spill procedures. The intent of these documents is to provide a safety resource for laboratory personnel working with these infectious substances. Because these workers are usually working in a scientific setting and are potentially exposed to much higher concentrations of these human pathogens than the general public, the terminology in these MSDS is technical and detailed, containing information that is relevant specifically to the laboratory setting. It is hoped along with good laboratory practices, these MSDS will help provide a safer, healthier environment for everyone working with infectious substances. The MSDS is ran by the Public Health Agency of Canada. The Public Health Agency of Canada (PHAC) is the main Government of Canada agency responsible for public health in Canada. PHACs primary goal is to strengthen Canadas capacity to protect and improve the health of Canadians and to help reduce pressures on the health-care system. To do this, the Agency is working to build an effective public health system that enables Canadians to achieve better health and well-being in their daily lives by promoting good health, helping prevent and control chronic diseases and injury, and protecting Canadians from infectious diseases and other threats to their health. PHAC is also committed to reducing health disparities between the most advantaged and disadvantaged Canadians. Because public health is a shared responsibility, the Public Health Agency of Canada works in close collaboration with all levels of government (provincial, territorial and municipal) to build on each others skills and strengths. The Agency also works closely with non-government organizations, including civil society and business, and other countries and international organizations like the World Health Organization (WHO) to share knowledge, expertise and experiences.

Proper citation: Material Safety Data Sheets for Infectious Substances of Canada (RRID:SCR_013003) Copy   

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http://www.fugu-sg.org/

THIS RESOURCE IS NO LONGER IN SERVICE,documented on August 16, 2019. Fugu genome is among the smallest vertebrate genomes and has proved to be a valuable reference genome for identifying genes and other functional elements such as regulatory elements in the human and other vertebrate genomes, and for understanding the structure and evolution of vertebrate genomes. This site presents version 4 of the Fugu genome, released in October 2004 by the International Fugu Genome Consortium. Fugu rubripes has a very compact genome, with less than 15 consisting of dispersed repetitive sequence, which makes it ideal for gene discovery. A draft sequence of the fugu genome was determined by the International Fugu Genome Consortium in 2002 using the ''whole-genome shotgun'' sequencing strategy. Fugu is the second vertebrate genome to be sequenced, the first being the human genome. This webpage presents the annotation made on the fourth assembly by the IMCB team using the Ensembl annotation pipeline. We are continuing with the gap filling work and linking of the scaffolds to obtain super-contigs.

Proper citation: Fugu Genome Project (RRID:SCR_013014) Copy   

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http://tubic.tju.edu.cn/deg

THIS RESOURCE IS NO LONGER IN SEVICE. Documented on August 19,2019.It hosts records of currently available essential genes among a wide range of organisms. For prokaryotes, DEG contains essential genes in more than 10 bacteria, such as E. coli, B. subtilis, H. pylori, S. pneumoniae, M. genitalium and H. influenzae, whereas for eukaryotes, DEG contains those in yeast, humans, mice, worms, fruit flies, zebra fish and the plant A. thaliana. Users can Blast query sequences against DEG, and can also search for essential genes by their functions and names. Essential gene products comprise excellent targets for antibacterial drugs. Essential genes in a bacterium constitute a minimal genome, forming a set of functional modules, which play key roles in the emerging field, synthetic biology.

Proper citation: DEG - Database of Essential Genes (RRID:SCR_012929) Copy   

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http://www.phenomicdb.de/

PhenomicDB is a multi-organism phenotype-genotype database including human, mouse, fruit fly, C.elegans, and other model organisms. The inclusion of gene indices (NCBI Gene) and orthologs (same gene in different organisms) from HomoloGene allows to compare phenotypes of a given gene over many organisms simultaneously. PhenomicDB contains data from publicly available primary databases: FlyBase, Flyrnai.org, WormBase, Phenobank, CYGD, MatDB, OMIM, MGI, ZFIN, SGD, DictyBase, NCBI Gene, and HomoloGene. We brought this wealth of data into a single integrated resource by coarse-grained semantic mapping of the phenotypic data fields, by including common gene indexes (NCBI Gene), and by the use of associated orthology relationships (HomoloGene). PhenomicDB is thought as a first step towards comparative phenomics and will improve the understanding of the gene functions by combining the knowledge about phenotypes from several organisms. It is not intended to compete with the much more dedicated primary source databases but tries to compensate its partial loss of depth by linking back to the primary sources. The basic functional concept of PhenomicDB is an integrated meta-search-engine for phenotypes. Users should be aware that comparison of genotypes or even phenotypes between organisms as different as yeast and man can have serious scientific hurdles. Nevertheless finding that the phenotype of a given mouse gene is described as ��similar to psoriasis�� and at the same time that the human ortholog has been described as a gene causing skin defects can lead to novelty and interesting hypotheses. Similarly, a gene involved in cancer in mammalian organisms could show a proliferation phenotype in a lower organism such as yeast and thus, give further insights to a researcher.

Proper citation: PhenomicDB (RRID:SCR_013051) Copy   

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http://www.treefam.org

A database of phylogenetic trees of animal genes. It aims at developing a curated resource that gives reliable information about ortholog and paralog assignments, and evolutionary history of various gene families. TreeFam defines a gene family as a group of genes that evolved after the speciation of single-metazoan animals. It also tries to include outgroup genes like yeast (S. cerevisiae and S. pombe) and plant (A. thaliana) to reveal these distant members.TreeFam is also an ortholog database. Unlike other pairwise alignment based ones, TreeFam infers orthologs by means of gene trees. It fits a gene tree into the universal species tree and finds historical duplications, speciations and losses events. TreeFam uses this information to evaluate tree building, guide manual curation, and infer complex ortholog and paralog relations.The basic elements of TreeFam are gene families that can be divided into two parts: TreeFam-A and TreeFam-B families. TreeFam-B families are automatically created. They might contain errors given complex phylogenies. TreeFam-A families are manually curated from TreeFam-B ones. Family names and node names are assigned at the same time. The ultimate goal of TreeFam is to present a curated resource for all the families. phylogenetic tree, animal, vertebrate, invertebrate, gene, ortholog, paralog, evolutionary history, gene families, single-metazoan animals, outgroup genes like yeast (S. cerevisiae and S. pombe), plant (A. thaliana), historical duplications, speciations, losses, Human, Genome, comparative genomics

Proper citation: Tree families database (RRID:SCR_013401) Copy   

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http://www.informatics.jax.org/phenotypes.shtml

Enables comparative phenotype analysis, searches for human disease models, and hypothesis generation by providing access to spontaneous, induced, and genetically engineered mutations and their strain-specific phenotypes.

Proper citation: Phenotypes and Mutant Alleles (RRID:SCR_017523) Copy   

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https://www.hmtvar.uniba.it

Manually curated database offering variability and pathogenicity information about mtDNA variants. Human mitochondrial variants data of healthy and diseased subjects.Data and text mining pipeline to annotate human mitochondrial variants with functional and clinical information.

Proper citation: HmtVar (RRID:SCR_017288) Copy   

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http://www.humphreyslab.com/SingleCell/

Software tool as analyzer for kidney single cell datasets. Allows users to query gene expression from mouse or human kidney and human kidney organoid single cell datasets. For details about datasets visit ReBuilding a Kidney website.

Proper citation: Kidney Interactive Transcriptomics (RRID:SCR_017209) Copy   

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https://brads.nichd.nih.gov/Home/

Access to data from the Division of Intramural Population Health Research (DIPHR) of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) from completed studies, including biospecimens and ancillary data.

Proper citation: Biospecimen Repository Access and Data Sharing (RRID:SCR_017383) Copy   

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http://10kimmunomes.org

Collection of reference datasets for human immunology, derived from control subjects in the NIAID ImmPort database . Available data include flow cytometry, CyTOF, multiplex ELISA, gene expression, HAI titers, clinical lab tests, HLA type, and others.

Proper citation: The 10000 Immunomes (RRID:SCR_016624) Copy   

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https://www.grnpedia.org/trrust/

TRUSST is reference database of human transcriptional regulatory interactions.TRRUST v2 is manually curated expanded reference database of human and mouse transcriptional regulatory interactions.

Proper citation: Transcriptional Regulatory Relationships Unrevealed by Sentence based Text mining database (RRID:SCR_022554) Copy   

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https://gitlab.com/rosen-lab/white-adipose-atlas

Single cell atlas of human and mouse white adipose tissue.

Proper citation: White Adipose Atlas (RRID:SCR_023625) Copy   

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https://www.broadinstitute.org/ccle/

A collaborative project between the Broad Institute and the Novartis Institutes for Biomedical Research and its Genomics Institute of the Novartis Research Foundation, with the goal of conducting a detailed genetic and pharmacologic characterization of a large panel of human cancer models. The CCLE also works to develop integrated computational analyses that link distinct pharmacologic vulnerabilities to genomic patterns and to translate cell line integrative genomics into cancer patient stratification. The CCLE provides public access to genomic data, analysis and visualization for about 1000 cell lines.

Proper citation: Cancer Cell Line Encyclopedia (RRID:SCR_013836) Copy   

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http://icmic.rad.jhmi.edu/

The vision of the JHU ICMIC is to combine state-of-the-art imaging capabilities with powerful molecular biology techniques to define strategies with intent to cure. It has drawn upon its human resources at JHU to create a center consisting of a multidisciplinary group of premier individuals with diverse skills focused on translating molecular capabilities into imaging possibilities with the single purpose of understanding and curing cancer. Nearly all of the investigators participating in this ICMIC have interactive collaborative projects with one or more of the other investigators. The synergism generated by the collective skills of this unique group of individuals will lead to significant advances in the understanding of cancer and its treatment. The JHU ICMIC structure consists of four interactive and closely related research components focused on hypoxia, HIF-1, and exploiting the hypoxia response element to target cancer cells through choline kinase inhibition. These research components are anchored by the participation of world renowned expertise in HIF-1. The research components utilize MR, PET and Optical Imaging technology to understand cancer vascularization, invasion and metastasis, to achieve effective cancer therapy. The center has selected developmental projects which are highly relevant to the goals of the ICMIC and interactive with the research components. Five resources devoted to adminstration, molecular biology, imaging, probes, and translational application provide the infrastructure to support the research activities of the ICMIC. Research Components in the JHU ICMIC: - Combining Anti-angiogenic therapy with siRNA targeting of choline kinase. - Imaging the Role of HIF-1 in Breast Cancer Progression - Imaging and Targeting Hypoxia in Solid Tumors - Molecular and Functional Imaging of the HER-2/neu Receptor The following are developmental projects currently taking place in ICMIC 1. Receptor imaging using nonparamagnetic MRI contrast agents (2003) 2. New imaging agents for prostate cancer (2003) 3. Non-invasive monitoring of therapeutic effect of siRNA-mediated radiation sensitization in human prostate cancer xenografts (2003) 4. Imaging of the endothelin receptor in cancer (2003) 5. Imaging studies of c-myc regulation of tumor metabolism (2003) 6. Imaging studies of anti-tumorigenic effects of anti-oxidants in vivo (2005) 7. Molecular Imaging with Magnetic Resonance Microsystems (2005) 8. Endogenous angiogenesis inhibitors (2005) 9. MR imaging and spectroscopy in detection and localization of prostate cancer: a prospective trial in patients undergoing cystoprostatectomy and radical prostatectomy. (2005) 10. A versatile visualization system for the analysis of multi-modality and multidimensional cancer imaging (2007) 11. Non-invasive imaging of CXCR4 expression in breast cancer (2007)

Proper citation: John Hopkins University, In-Vivo Cellular Molecular Imaging Center (RRID:SCR_013198) Copy   

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http://lincs.hms.harvard.edu/

Center that is part of the NIH Library of Integrated Network-based Cellular Signatures (LINCS) Program. Its goals are to collect and disseminate data and analytical tools needed to understand how human cells respond to perturbation by drugs, the environment, and mutation.

Proper citation: HMS LINCS Center (RRID:SCR_016370) Copy   

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https://community.brain-map.org/t/allen-human-reference-atlas-3d-2020-new/405

Parcellation of adult human brain in 3D, labeling every voxel with brain structure spanning 141 structures. These parcellations were drawn and adapted from prior 2D version of adult human brain atlas.

Proper citation: Allen Human Reference Atlas, 3D, 2020 (RRID:SCR_017764) Copy   

•   Source: SciCrunch Registry